CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: Pareto solutions</title> <meta name="description" content="Search results for: Pareto solutions"> <meta name="keywords" content="Pareto solutions"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="Pareto solutions" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Pareto solutions"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3986</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Pareto solutions</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3986</span> Performance Evaluation of Karanja Oil Based Biodiesel Engine Using Modified Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Bhushan">G. Bhushan</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Dhingra"> S. Dhingra</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20K.%20Dubey"> K. K. Dubey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the evaluation of performance (BSFC and BTE), combustion (P<sub>max</sub>) and emission (CO, NO<sub>x</sub>, HC and smoke opacity) parameters of karanja biodiesel in a single cylinder, four stroke, direct injection diesel engine by considering significant engine input parameters (blending ratio, compression ratio and load torque). Multi-objective optimization of performance, combustion and emission parameters is also carried out in a karanja biodiesel engine using hybrid RSM-NSGA-II technique. The pareto optimum solutions are predicted by running the hybrid RSM-NSGA-II technique. Each pareto optimal solution is having its own importance. Confirmation tests are also conducted at randomly selected few pareto solutions to check the authenticity of the results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=rsm" title=" rsm"> rsm</a>, <a href="https://publications.waset.org/abstracts/search?q=biodiesel" title=" biodiesel"> biodiesel</a>, <a href="https://publications.waset.org/abstracts/search?q=karanja" title=" karanja"> karanja</a> </p> <a href="https://publications.waset.org/abstracts/51865/performance-evaluation-of-karanja-oil-based-biodiesel-engine-using-modified-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51865.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">306</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3985</span> A Hybrid Pareto-Based Swarm Optimization Algorithm for the Multi-Objective Flexible Job Shop Scheduling Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new hybrid particle swarm optimization algorithm is proposed for the multi-objective flexible job shop scheduling problem that is very important and hard combinatorial problem. The Pareto approach is used for solving the multi-objective problem. Several new local search heuristics are integrated into an algorithm based on the critical block concept to enhance the performance of the algorithm. The algorithm is compared with the recently published multi-objective algorithms based on benchmarks selected from the literature. Several metrics are used for quantifying performance and comparison of the achieved solutions. The algorithms are also compared based on the Weighting summation of objectives approach. The proposed algorithm can find the Pareto solutions more efficiently than the compared algorithms in less computational time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=swarm-based%20optimization" title="swarm-based optimization">swarm-based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20search" title=" local search"> local search</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20optimality" title=" Pareto optimality"> Pareto optimality</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop%20scheduling" title=" flexible job shop scheduling"> flexible job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/72144/a-hybrid-pareto-based-swarm-optimization-algorithm-for-the-multi-objective-flexible-job-shop-scheduling-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72144.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">369</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3984</span> A Flexible Pareto Distribution Using α-Power Transformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shumaila%20Ehtisham">Shumaila Ehtisham</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Statistical Distribution Theory, considering an additional parameter to classical distributions is a usual practice. In this study, a new distribution referred to as α-Power Pareto distribution is introduced by including an extra parameter. Several properties of the proposed distribution including explicit expressions for the moment generating function, mode, quantiles, entropies and order statistics are obtained. Unknown parameters have been estimated by using maximum likelihood estimation technique. Two real datasets have been considered to examine the usefulness of the proposed distribution. It has been observed that α-Power Pareto distribution outperforms while compared to different variants of Pareto distribution on the basis of model selection criteria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=%CE%B1-power%20transformation" title="α-power transformation">α-power transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=moment%20generating%20function" title=" moment generating function"> moment generating function</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20distribution" title=" Pareto distribution"> Pareto distribution</a> </p> <a href="https://publications.waset.org/abstracts/89859/a-flexible-pareto-distribution-using-a-power-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89859.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3983</span> The Application of Pareto Local Search to the Single-Objective Quadratic Assignment Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Alsheddy">Abdullah Alsheddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the employment of Pareto optimality as a strategy to help (single-objective) local search escaping local optima. Instead of local search, Pareto local search is applied to solve the quadratic assignment problem which is multi-objectivized by adding a helper objective. The additional objective is defined as a function of the primary one with augmented penalties that are dynamically updated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pareto%20optimization" title="Pareto optimization">Pareto optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objectivization" title=" multi-objectivization"> multi-objectivization</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic%20assignment%20problem" title=" quadratic assignment problem"> quadratic assignment problem</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20search" title=" local search"> local search</a> </p> <a href="https://publications.waset.org/abstracts/9877/the-application-of-pareto-local-search-to-the-single-objective-quadratic-assignment-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9877.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">466</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3982</span> Multi-Objective Random Drift Particle Swarm Optimization Algorithm Based on RDPSO and Crowding Distance Sorting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yiqiong%20Yuan">Yiqiong Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun%20Sun"> Jun Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Dongmei%20Zhou"> Dongmei Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianan%20Sun"> Jianan Sun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we presented a Multi-Objective Random Drift Particle Swarm Optimization algorithm (MORDPSO-CD) based on RDPSO and crowding distance sorting to improve the convergence and distribution with less computation cost. MORDPSO-CD makes the most of RDPSO to approach the true Pareto optimal solutions fast. We adopt the crowding distance sorting technique to update and maintain the archived optimal solutions. Introducing the crowding distance technique into MORDPSO can make the leader particles find the true Pareto solution ultimately. The simulation results reveal that the proposed algorithm has better convergence and distribution <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title="multi-objective optimization">multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20drift%20particle%20swarm%20optimization" title=" random drift particle swarm optimization"> random drift particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=crowding%20distance%20sorting" title=" crowding distance sorting"> crowding distance sorting</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto%20optimal%20solution" title=" pareto optimal solution"> pareto optimal solution</a> </p> <a href="https://publications.waset.org/abstracts/44631/multi-objective-random-drift-particle-swarm-optimization-algorithm-based-on-rdpso-and-crowding-distance-sorting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44631.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">255</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3981</span> A Hybrid Tabu Search Algorithm for the Multi-Objective Job Shop Scheduling Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a hybrid Tabu Search (TS) algorithm is suggested for the multi-objective job shop scheduling problems (MO-JSSPs). The algorithm integrates several shifting bottleneck based neighborhood structures with the Giffler & Thompson algorithm, which improve efficiency of the search. Diversification and intensification are provided with local and global left shift algorithms application and also new semi-active, active, and non-delay schedules creation. The suggested algorithm is tested in the MO-JSSPs benchmarks from the literature based on the Pareto optimality concept. Different performances criteria are used for the multi-objective algorithm evaluation. The proposed algorithm is able to find the Pareto solutions of the test problems in shorter time than other algorithm of the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tabu%20search" title="tabu search">tabu search</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=job%20shop%20scheduling" title=" job shop scheduling"> job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20optimality" title=" Pareto optimality"> Pareto optimality</a> </p> <a href="https://publications.waset.org/abstracts/71920/a-hybrid-tabu-search-algorithm-for-the-multi-objective-job-shop-scheduling-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71920.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3980</span> On Multiobjective Optimization to Improve the Scalability of Fog Application Deployments Using Fogtorch</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suleiman%20Aliyu">Suleiman Aliyu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Integrating IoT applications with Fog systems presents challenges in optimization due to diverse environments and conflicting objectives. This study explores achieving Pareto optimal deployments for Fog-based IoT systems to address growing QoS demands. We introduce Pareto optimality to balance competing performance metrics. Using the FogTorch optimization framework, we propose a hybrid approach (Backtracking search with branch and bound) for scalable IoT deployments. Our research highlights the advantages of Pareto optimality over single-objective methods and emphasizes the role of FogTorch in this context. Initial results show improvements in IoT deployment cost in Fog systems, promoting resource-efficient strategies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pareto%20optimality" title="pareto optimality">pareto optimality</a>, <a href="https://publications.waset.org/abstracts/search?q=fog%20application%20deployment" title=" fog application deployment"> fog application deployment</a>, <a href="https://publications.waset.org/abstracts/search?q=resource%20allocation" title=" resource allocation"> resource allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=internet%20of%20things" title=" internet of things"> internet of things</a> </p> <a href="https://publications.waset.org/abstracts/175224/on-multiobjective-optimization-to-improve-the-scalability-of-fog-application-deployments-using-fogtorch" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175224.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">88</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3979</span> Optimal Sliding Mode Controller for Knee Flexion during Walking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20Sitler">Gabriel Sitler</a>, <a href="https://publications.waset.org/abstracts/search?q=Yousef%20Sardahi"> Yousef Sardahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Asad%20Salem"> Asad Salem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an optimal and robust sliding mode controller (SMC) to regulate the position of the knee joint angle for patients suffering from knee injuries. The controller imitates the role of active orthoses that produce the joint torques required to overcome gravity and loading forces and regain natural human movements. To this end, a mathematical model of the shank, the lower part of the leg, is derived first and then used for the control system design and computer simulations. The design of the controller is carried out in optimal and multi-objective settings. Four objectives are considered: minimization of the control effort and tracking error; and maximization of the control signal smoothness and closed-loop system’s speed of response. Optimal solutions in terms of the Pareto set and its image, the Pareto front, are obtained. The results show that there are trade-offs among the design objectives and many optimal solutions from which the decision-maker can choose to implement. Also, computer simulations conducted at different points from the Pareto set and assuming knee squat movement demonstrate competing relationships among the design goals. In addition, the proposed control algorithm shows robustness in tracking a standard gait signal when accounting for uncertainty in the shank’s parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sliding%20mode%20control" title=" sliding mode control"> sliding mode control</a>, <a href="https://publications.waset.org/abstracts/search?q=wearable%20knee%20exoskeletons" title=" wearable knee exoskeletons"> wearable knee exoskeletons</a> </p> <a href="https://publications.waset.org/abstracts/164514/optimal-sliding-mode-controller-for-knee-flexion-during-walking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164514.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3978</span> Multi Objective Simultaneous Assembly Line Balancing and Buffer Sizing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saif%20Ullah">Saif Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Guan%20Zailin"> Guan Zailin</a>, <a href="https://publications.waset.org/abstracts/search?q=Xu%20Xianhao"> Xu Xianhao</a>, <a href="https://publications.waset.org/abstracts/search?q=He%20Zongdong"> He Zongdong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Baoxi"> Wang Baoxi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Assembly line balancing problem is aimed to divide the tasks among the stations in assembly lines and optimize some objectives. In assembly lines the workload on stations is different from each other due to different tasks times and the difference in workloads between stations can cause blockage or starvation in some stations in assembly lines. Buffers are used to store the semi-finished parts between the stations and can help to smooth the assembly production. The assembly line balancing and buffer sizing problem can affect the throughput of the assembly lines. Assembly line balancing and buffer sizing problems have been studied separately in literature and due to their collective contribution in throughput rate of assembly lines, balancing and buffer sizing problem are desired to study simultaneously and therefore they are considered concurrently in current research. Current research is aimed to maximize throughput, minimize total size of buffers in assembly line and minimize workload variations in assembly line simultaneously. A multi objective optimization objective is designed which can give better Pareto solutions from the Pareto front and a simple example problem is solved for assembly line balancing and buffer sizing simultaneously. Current research is significant for assembly line balancing research and it can be significant to introduce optimization approaches which can optimize current multi objective problem in future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=assembly%20line%20balancing" title="assembly line balancing">assembly line balancing</a>, <a href="https://publications.waset.org/abstracts/search?q=buffer%20sizing" title=" buffer sizing"> buffer sizing</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions" title=" Pareto solutions "> Pareto solutions </a> </p> <a href="https://publications.waset.org/abstracts/14084/multi-objective-simultaneous-assembly-line-balancing-and-buffer-sizing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14084.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">491</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3977</span> The Generalized Pareto Distribution as a Model for Sequential Order Statistics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdy%20%E2%80%8EEsmailian">Mahdy ‎Esmailian</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20%E2%80%8EDoostparast"> Mahdi ‎Doostparast</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20%E2%80%8EParsian"> Ahmad ‎Parsian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> ‎In this article‎, ‎sequential order statistics (SOS) censoring type II samples coming from the generalized Pareto distribution are considered‎. ‎Maximum likelihood (ML) estimators of the unknown parameters are derived on the basis of the available multiple SOS data‎. ‎Necessary conditions for existence and uniqueness of the derived ML estimates are given‎. Due to complexity in the proposed likelihood function‎, ‎a useful re-parametrization is suggested‎. ‎For illustrative purposes‎, ‎a Monte Carlo simulation study is conducted and an illustrative example is analysed‎. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20estimation%E2%80%8E" title="bayesian estimation‎">bayesian estimation‎</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20pareto%20distribution%E2%80%8E" title=" generalized pareto distribution‎"> generalized pareto distribution‎</a>, <a href="https://publications.waset.org/abstracts/search?q=%E2%80%8Emaximum%20likelihood%20%20estimation%E2%80%8E" title=" ‎maximum likelihood estimation‎"> ‎maximum likelihood estimation‎</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20order%20statistics" title=" sequential order statistics"> sequential order statistics</a> </p> <a href="https://publications.waset.org/abstracts/26988/the-generalized-pareto-distribution-as-a-model-for-sequential-order-statistics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26988.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">509</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3976</span> A Novel Guided Search Based Multi-Objective Evolutionary Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Baviskar">A. Baviskar</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Sandeep"> C. Sandeep</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Shankar"> K. Shankar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Solving Multi-objective Optimization Problems requires faster convergence and better spread. Though existing Evolutionary Algorithms (EA's) are able to achieve this, the computation effort can further be reduced by hybridizing them with innovative strategies. This study is focuses on converging to the pareto front faster while adapting the advantages of Strength Pareto Evolutionary Algorithm-II (SPEA-II) for a better spread. Two different approaches based on optimizing the objective functions independently are implemented. In the first method, the decision variables corresponding to the optima of individual objective functions are strategically used to guide the search towards the pareto front. In the second method, boundary points of the pareto front are calculated and their decision variables are seeded to the initial population. Both the methods are applied to different constrained and unconstrained multi-objective test functions. It is observed that proposed guided search based algorithm gives better convergence and diversity than several well-known existing algorithms (such as NSGA-II and SPEA-II) in considerably less number of iterations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boundary%20points" title="boundary points">boundary points</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms%20%28EA%27s%29" title=" evolutionary algorithms (EA's)"> evolutionary algorithms (EA's)</a>, <a href="https://publications.waset.org/abstracts/search?q=guided%20search" title=" guided search"> guided search</a>, <a href="https://publications.waset.org/abstracts/search?q=strength%20pareto%20evolutionary%20algorithm-II%20%28SPEA-II%29" title=" strength pareto evolutionary algorithm-II (SPEA-II)"> strength pareto evolutionary algorithm-II (SPEA-II)</a> </p> <a href="https://publications.waset.org/abstracts/40983/a-novel-guided-search-based-multi-objective-evolutionary-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40983.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">277</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3975</span> An Extended Inverse Pareto Distribution, with Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdel%20Hadi%20Ebraheim">Abdel Hadi Ebraheim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a new extension of the Inverse Pareto distribution in the framework of Marshal-Olkin (1997) family of distributions. This model is capable of modeling various shapes of aging and failure data. The statistical properties of the new model are discussed. Several methods are used to estimate the parameters involved. Explicit expressions are derived for different types of moments of value in reliability analysis are obtained. Besides, the order statistics of samples from the new proposed model have been studied. Finally, the usefulness of the new model for modeling reliability data is illustrated using two real data sets with simulation study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pareto%20distribution" title="pareto distribution">pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=marshal-Olkin" title=" marshal-Olkin"> marshal-Olkin</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability" title=" reliability"> reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=hazard%20functions" title=" hazard functions"> hazard functions</a>, <a href="https://publications.waset.org/abstracts/search?q=moments" title=" moments"> moments</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation" title=" estimation"> estimation</a> </p> <a href="https://publications.waset.org/abstracts/169013/an-extended-inverse-pareto-distribution-with-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169013.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3974</span> Improving the Penalty-free Multi-objective Evolutionary Design Optimization of Water Distribution Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emily%20Kambalame">Emily Kambalame</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Water distribution networks necessitate many investments for construction, prompting researchers to seek cost reduction and efficient design solutions. Optimization techniques are employed in this regard to address these challenges. In this context, the penalty-free multi-objective evolutionary algorithm (PFMOEA) coupled with pressure-dependent analysis (PDA) was utilized to develop a multi-objective evolutionary search for the optimization of water distribution systems (WDSs). The aim of this research was to find out if the computational efficiency of the PFMOEA for WDS optimization could be enhanced. This was done by applying real coding representation and retaining different percentages of feasible and infeasible solutions close to the Pareto front in the elitism step of the optimization. Two benchmark network problems, namely the Two-looped and Hanoi networks, were utilized in the study. A comparative analysis was then conducted to assess the performance of the real-coded PFMOEA in relation to other approaches described in the literature. The algorithm demonstrated competitive performance for the two benchmark networks by implementing real coding. The real-coded PFMOEA achieved the novel best-known solutions ($419,000 and $6.081 million) and a zero-pressure deficit for the two networks, requiring fewer function evaluations than the binary-coded PFMOEA. In previous PFMOEA studies, elitism applied a default retention of 30% of the least cost-feasible solutions while excluding all infeasible solutions. It was found in this study that by replacing 10% and 15% of the feasible solutions with infeasible ones that are close to the Pareto front with minimal pressure deficit violations, the computational efficiency of the PFMOEA was significantly enhanced. The configuration of 15% feasible and 15% infeasible solutions outperformed other retention allocations by identifying the optimal solution with the fewest function evaluation <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=design%20optimization" title="design optimization">design optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20evolutionary" title=" multi-objective evolutionary"> multi-objective evolutionary</a>, <a href="https://publications.waset.org/abstracts/search?q=penalty-free" title=" penalty-free"> penalty-free</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20distribution%20systems" title=" water distribution systems"> water distribution systems</a> </p> <a href="https://publications.waset.org/abstracts/178023/improving-the-penalty-free-multi-objective-evolutionary-design-optimization-of-water-distribution-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178023.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">62</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3973</span> Modeling of Maximum Rainfall Using Poisson-Generalized Pareto Distribution in Kigali, Rwanda</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Iyamuremye">Emmanuel Iyamuremye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extreme rainfall events have caused significant damage to agriculture, ecology, and infrastructure, disruption of human activities, injury, and loss of life. They also have significant social, economic, and environmental consequences because they considerably damage urban as well as rural areas. Early detection of extreme maximum rainfall helps to implement strategies and measures, before they occur, hence mitigating the consequences. Extreme value theory has been used widely in modeling extreme rainfall and in various disciplines, such as financial markets, the insurance industry, failure cases. Climatic extremes have been analyzed by using either generalized extreme value (GEV) or generalized Pareto (GP) distributions, which provides evidence of the importance of modeling extreme rainfall from different regions of the world. In this paper, we focused on Peak Over Thresholds approach, where the Poisson-generalized Pareto distribution is considered as the proper distribution for the study of the exceedances. This research also considers the use of the generalized Pareto (GP) distribution with a Poisson model for arrivals to describe peaks over a threshold. The research used statistical techniques to fit models that used to predict extreme rainfall in Kigali. The results indicate that the proposed Poisson-GP distribution provides a better fit to maximum monthly rainfall data. Further, the Poisson-GP models are able to estimate various return levels. The research also found a slow increase in return levels for maximum monthly rainfall for higher return periods, and further, the intervals are increasingly wider as the return period is increasing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exceedances" title="exceedances">exceedances</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title=" extreme value theory"> extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20Pareto%20distribution" title=" generalized Pareto distribution"> generalized Pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20generalized%20Pareto%20distribution" title=" Poisson generalized Pareto distribution"> Poisson generalized Pareto distribution</a> </p> <a href="https://publications.waset.org/abstracts/127379/modeling-of-maximum-rainfall-using-poisson-generalized-pareto-distribution-in-kigali-rwanda" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127379.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">135</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3972</span> Pareto System of Optimal Placement and Sizing of Distributed Generation in Radial Distribution Networks Using Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sani%20M.%20Lawal">Sani M. Lawal</a>, <a href="https://publications.waset.org/abstracts/search?q=Idris%20Musa"> Idris Musa</a>, <a href="https://publications.waset.org/abstracts/search?q=Aliyu%20D.%20Usman"> Aliyu D. Usman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Pareto approach of optimal solutions in a search space that evolved in multi-objective optimization problems is adopted in this paper, which stands for a set of solutions in the search space. This paper aims at presenting an optimal placement of Distributed Generation (DG) in radial distribution networks with an optimal size for minimization of power loss and voltage deviation as well as maximizing voltage profile of the networks. And these problems are formulated using particle swarm optimization (PSO) as a constraint nonlinear optimization problem with both locations and sizes of DG being continuous. The objective functions adopted are the total active power loss function and voltage deviation function. The multiple nature of the problem, made it necessary to form a multi-objective function in search of the solution that consists of both the DG location and size. The proposed PSO algorithm is used to determine optimal placement and size of DG in a distribution network. The output indicates that PSO algorithm technique shows an edge over other types of search methods due to its effectiveness and computational efficiency. The proposed method is tested on the standard IEEE 34-bus and validated with 33-bus test systems distribution networks. Results indicate that the sizing and location of DG are system dependent and should be optimally selected before installing the distributed generators in the system and also an improvement in the voltage profile and power loss reduction have been achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation" title="distributed generation">distributed generation</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto" title=" pareto"> pareto</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20loss" title=" power loss"> power loss</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20deviation" title=" voltage deviation"> voltage deviation</a> </p> <a href="https://publications.waset.org/abstracts/54635/pareto-system-of-optimal-placement-and-sizing-of-distributed-generation-in-radial-distribution-networks-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54635.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">364</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3971</span> Optimal Retrofit Design of Reinforced Concrete Frame with Infill Wall Using Fiber Reinforced Plastic Materials</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sang%20Wook%20Park">Sang Wook Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Se%20Woon%20Choi"> Se Woon Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yousok%20Kim"> Yousok Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung%20Kwan%20Oh"> Byung Kwan Oh</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyo%20Seon%20Park"> Hyo Seon Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Various retrofit techniques for reinforced concrete frame with infill wall have been steadily developed. Among those techniques, strengthening methodology based on diagonal FRP strips (FRP bracings) has numerous advantages such as feasibility of implementing without interrupting the building under operation, reduction of cost and time, and easy application. Considering the safety of structure and retrofit cost, the most appropriate retrofit solution is needed. Thus, the objective of this study is to suggest pareto-optimal solution for existing building using FRP bracings. To find pareto-optimal solution analysis, NSGA-II is applied. Moreover, the seismic performance of retrofit building is evaluated. The example building is 5-storey, 3-bay RC frames with infill wall. Nonlinear static pushover analyses are performed with FEMA 356. The criterion of performance evaluation is inter-story drift ratio at the performance level IO, LS, CP. Optimal retrofit solutions is obtained for 32 individuals and 200 generations. Through the proposed optimal solutions, we confirm the improvement of seismic performance of the example building. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=retrofit" title="retrofit">retrofit</a>, <a href="https://publications.waset.org/abstracts/search?q=FRP%20bracings" title=" FRP bracings"> FRP bracings</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforced%20concrete%20frame%20with%20infill%20wall" title=" reinforced concrete frame with infill wall"> reinforced concrete frame with infill wall</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20performance%20evaluation" title=" seismic performance evaluation"> seismic performance evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=NSGA-II" title=" NSGA-II"> NSGA-II</a> </p> <a href="https://publications.waset.org/abstracts/42927/optimal-retrofit-design-of-reinforced-concrete-frame-with-infill-wall-using-fiber-reinforced-plastic-materials" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42927.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">437</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3970</span> Modelling Water Usage for Farming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ozgu%20Turgut">Ozgu Turgut</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Water scarcity is a problem for many regions which requires immediate action, and solutions cannot be postponed for a long time. It is known that farming consumes a significant portion of usable water. Although in recent years, the efforts to make the transition to dripping or spring watering systems instead of using surface watering started to pay off. It is also known that this transition is not necessarily translated into an increase in the capacity dedicated to other water consumption channels such as city water or power usage. In order to control and allocate the water resource more purposefully, new watering systems have to be used with monitoring abilities that can limit the usage capacity for each farm. In this study, a decision support model which relies on a bi-objective stochastic linear optimization is proposed, which takes crop yield and price volatility into account. The model generates annual planting plans as well as water usage limits for each farmer in the region while taking the total value (i.e., profit) of the overall harvest. The mathematical model is solved using the L-shaped method optimally. The decision support model can be especially useful for regional administrations to plan next year's planting and water incomes and expenses. That is why not only a single optimum but also a set of representative solutions from the Pareto set is generated with the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20support" title="decision support">decision support</a>, <a href="https://publications.waset.org/abstracts/search?q=farming" title=" farming"> farming</a>, <a href="https://publications.waset.org/abstracts/search?q=water" title=" water"> water</a>, <a href="https://publications.waset.org/abstracts/search?q=tactical%20planning" title=" tactical planning"> tactical planning</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic" title=" stochastic"> stochastic</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto" title=" pareto"> pareto</a> </p> <a href="https://publications.waset.org/abstracts/160722/modelling-water-usage-for-farming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160722.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">74</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3969</span> Optimum Stratiﬁcation of a Skewed Population</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20K.%20Rao">D. K. Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20G.%20M.%20Khan"> M. G. M. Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20G.%20Reddy"> K. G. Reddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The focus of this paper is to develop a technique of solving a combined problem of determining Optimum Strata Boundaries (OSB) and Optimum Sample Size (OSS) of each stratum, when the population understudy is skewed and the study variable has a Pareto frequency distribution. The problem of determining the OSB is formulated as a Mathematical Programming Problem (MPP) which is then solved by dynamic programming technique. A numerical example is presented to illustrate the computational details of the proposed method. The proposed technique is useful to obtain OSB and OSS for a Pareto type skewed population, which minimizes the variance of the estimate of population mean. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=strati%EF%AC%81ed%20sampling" title="stratiﬁed sampling">stratiﬁed sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=optimum%20strata%20boundaries" title=" optimum strata boundaries"> optimum strata boundaries</a>, <a href="https://publications.waset.org/abstracts/search?q=optimum%20sample%20size" title=" optimum sample size"> optimum sample size</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto%20distribution" title=" pareto distribution"> pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20programming%20problem" title=" mathematical programming problem"> mathematical programming problem</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20programming%20technique" title=" dynamic programming technique"> dynamic programming technique</a> </p> <a href="https://publications.waset.org/abstracts/5894/optimum-stratification-of-a-skewed-population" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5894.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">455</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3968</span> Supply Chain Network Design for Perishable Products in Developing Countries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abhishek%20Jain">Abhishek Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Kavish%20Kejriwal"> Kavish Kejriwal</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Balaji%20Rao"> V. Balaji Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=Abhigna%20Chavda"> Abhigna Chavda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Increasing environmental and social concerns are forcing companies to take a fresh view of the impact of supply chain operations on environment and society when designing a supply chain. A challenging task in today’s food industry is the distribution of high-quality food items throughout the food supply chain. Improper storage and unwanted transportation are the major hurdles in food supply chain and can be tackled by making dynamic storage facility location decisions with the distribution network. Since food supply chain in India is one of the biggest supply chains in the world, the companies should also consider environmental impact caused by the supply chain. This project proposes a multi-objective optimization model by integrating sustainability in decision-making, on distribution in a food supply chain network (SCN). A Multi-Objective Mixed-Integer Linear Programming (MOMILP) model between overall cost and environmental impact caused by the SCN is formulated for the problem. The goal of MOMILP is to determine the pareto solutions for overall cost and environmental impact caused by the supply chain. This is solved by using GAMS with CPLEX as third party solver. The outcomes of the project are pareto solutions for overall cost and environmental impact, facilities to be operated and the amount to be transferred to each warehouse during the time horizon. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20mixed%20linear%20programming" title="multi-objective mixed linear programming">multi-objective mixed linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=food%20supply%20chain%20network" title=" food supply chain network"> food supply chain network</a>, <a href="https://publications.waset.org/abstracts/search?q=GAMS" title=" GAMS"> GAMS</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-product" title=" multi-product"> multi-product</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-period" title=" multi-period"> multi-period</a>, <a href="https://publications.waset.org/abstracts/search?q=environment" title=" environment"> environment</a> </p> <a href="https://publications.waset.org/abstracts/48287/supply-chain-network-design-for-perishable-products-in-developing-countries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48287.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3967</span> Critical Analysis of Heat Exchanger Cycle for its Maintainability Using Failure Modes and Effect Analysis and Pareto Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sayali%20Vyas">Sayali Vyas</a>, <a href="https://publications.waset.org/abstracts/search?q=Atharva%20Desai"> Atharva Desai</a>, <a href="https://publications.waset.org/abstracts/search?q=Shreyas%20Badave"> Shreyas Badave</a>, <a href="https://publications.waset.org/abstracts/search?q=Apurv%20Kulkarni"> Apurv Kulkarni</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Rajiv"> B. Rajiv</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Failure Modes and Effect Analysis (FMEA) is an efficient evaluation technique to identify potential failures in products, processes, and services. FMEA is designed to identify and prioritize failure modes. It proves to be a useful method for identifying and correcting possible failures at its earliest possible level so that one can avoid consequences of poor performance. In this paper, FMEA tool is used in detection of failures of various components of heat exchanger cycle and to identify critical failures of the components which may hamper the system’s performance. Further, a detailed Pareto analysis is done to find out the most critical components of the cycle, the causes of its failures, and possible recommended actions. This paper can be used as a checklist which will help in maintainability of the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FMEA" title="FMEA">FMEA</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20exchanger%20cycle" title=" heat exchanger cycle"> heat exchanger cycle</a>, <a href="https://publications.waset.org/abstracts/search?q=Ishikawa%20diagram" title=" Ishikawa diagram"> Ishikawa diagram</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto%20analysis" title=" pareto analysis"> pareto analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=RPN%20%28Risk%20Priority%20Number%29" title=" RPN (Risk Priority Number)"> RPN (Risk Priority Number)</a> </p> <a href="https://publications.waset.org/abstracts/70737/critical-analysis-of-heat-exchanger-cycle-for-its-maintainability-using-failure-modes-and-effect-analysis-and-pareto-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70737.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">402</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3966</span> A Bi-Objective Stochastic Mathematical Model for Agricultural Supply Chain Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Mahdi%20Paydar">Mohammad Mahdi Paydar</a>, <a href="https://publications.waset.org/abstracts/search?q=Armin%20Cheraghalipour"> Armin Cheraghalipour</a>, <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Hajiaghaei-Keshteli"> Mostafa Hajiaghaei-Keshteli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, in advanced countries, agriculture as one of the most significant sectors of the economy, plays an important role in its political and economic independence. Due to farmers' lack of information about products' demand and lack of proper planning for harvest time, annually the considerable amount of products is corrupted. Besides, in this paper, we attempt to improve these unfavorable conditions via designing an effective supply chain network that tries to minimize total costs of agricultural products along with minimizing shortage in demand points. To validate the proposed model, a stochastic optimization approach by using a branch and bound solver of the LINGO software is utilized. Furthermore, to accumulate the data of parameters, a case study in Mazandaran province placed in the north of Iran has been applied. Finally, using ɛ-constraint approach, a Pareto front is obtained and one of its Pareto solutions as best solution is selected. Then, related results of this solution are explained. Finally, conclusions and suggestions for the future research are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=perishable%20products" title="perishable products">perishable products</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20optimization" title=" stochastic optimization"> stochastic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=agricultural%20supply%20chain" title=" agricultural supply chain"> agricultural supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=%C9%9B-constraint" title=" ɛ-constraint"> ɛ-constraint</a> </p> <a href="https://publications.waset.org/abstracts/82374/a-bi-objective-stochastic-mathematical-model-for-agricultural-supply-chain-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82374.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">366</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3965</span> Evidence Theory Based Emergency Multi-Attribute Group Decision-Making: Application in Facility Location Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bidzina%20Matsaberidze">Bidzina Matsaberidze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is known that, in emergency situations, multi-attribute group decision-making (MAGDM) models are characterized by insufficient objective data and a lack of time to respond to the task. Evidence theory is an effective tool for describing such incomplete information in decision-making models when the expert and his knowledge are involved in the estimations of the MAGDM parameters. We consider an emergency decision-making model, where expert assessments on humanitarian aid from distribution centers (HADC) are represented in q-rung ortho-pair fuzzy numbers, and the data structure is described within the data body theory. Based on focal probability construction and experts’ evaluations, an objective function-distribution centers’ selection ranking index is constructed. Our approach for solving the constructed bicriteria partitioning problem consists of two phases. In the first phase, based on the covering’s matrix, we generate a matrix, the columns of which allow us to find all possible partitionings of the HADCs with the service centers. Some constraints are also taken into consideration while generating the matrix. In the second phase, based on the matrix and using our exact algorithm, we find the partitionings -allocations of the HADCs to the centers- which correspond to the Pareto-optimal solutions. For an illustration of the obtained results, a numerical example is given for the facility location-selection problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emergency%20MAGDM" title="emergency MAGDM">emergency MAGDM</a>, <a href="https://publications.waset.org/abstracts/search?q=q-rung%20orthopair%20fuzzy%20sets" title=" q-rung orthopair fuzzy sets"> q-rung orthopair fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=evidence%20theory" title=" evidence theory"> evidence theory</a>, <a href="https://publications.waset.org/abstracts/search?q=HADC" title=" HADC"> HADC</a>, <a href="https://publications.waset.org/abstracts/search?q=facility%20location%20problem" title=" facility location problem"> facility location problem</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20combinatorial%20optimization%20problem" title=" multi-objective combinatorial optimization problem"> multi-objective combinatorial optimization problem</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto-optimal%20solutions" title=" Pareto-optimal solutions"> Pareto-optimal solutions</a> </p> <a href="https://publications.waset.org/abstracts/164966/evidence-theory-based-emergency-multi-attribute-group-decision-making-application-in-facility-location-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164966.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">92</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3964</span> Pareto Optimal Material Allocation Mechanism</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peter%20Egri">Peter Egri</a>, <a href="https://publications.waset.org/abstracts/search?q=Tamas%20Kis"> Tamas Kis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Scheduling problems have been studied by the algorithmic mechanism design research from the beginning. This paper is focusing on a practically important, but theoretically rather neglected field: the project scheduling problem where the jobs connected by precedence constraints compete for various nonrenewable resources, such as materials. Although the centralized problem can be solved in polynomial-time by applying the algorithm of Carlier and Rinnooy Kan from the Eighties, obtaining materials in a decentralized environment is usually far from optimal. It can be observed in practical production scheduling situations that project managers tend to cache the required materials as soon as possible in order to avoid later delays due to material shortages. This greedy practice usually leads both to excess stocks for some projects and materials, and simultaneously, to shortages for others. The aim of this study is to develop a model for the material allocation problem of a production plant, where a central decision maker—the inventory—should assign the resources arriving at different points in time to the jobs. Since the actual due dates are not known by the inventory, the mechanism design approach is applied with the projects as the self-interested agents. The goal of the mechanism is to elicit the required information and allocate the available materials such that it minimizes the maximal tardiness among the projects. It is assumed that except the due dates, the inventory is familiar with every other parameters of the problem. A further requirement is that due to practical considerations monetary transfer is not allowed. Therefore a mechanism without money is sought which excludes some widely applied solutions such as the Vickrey–Clarke–Groves scheme. In this work, a type of Serial Dictatorship Mechanism (SDM) is presented for the studied problem, including a polynomial-time algorithm for computing the material allocation. The resulted mechanism is both truthful and Pareto optimal. Thus the randomization over the possible priority orderings of the projects results in a universally truthful and Pareto optimal randomized mechanism. However, it is shown that in contrast to problems like the many-to-many matching market, not every Pareto optimal solution can be generated with an SDM. In addition, no performance guarantee can be given compared to the optimal solution, therefore this approximation characteristic is investigated with experimental study. All in all, the current work studies a practically relevant scheduling problem and presents a novel truthful material allocation mechanism which eliminates the potential benefit of the greedy behavior that negatively influences the outcome. The resulted allocation is also shown to be Pareto optimal, which is the most widely used criteria describing a necessary condition for a reasonable solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=material%20allocation" title="material allocation">material allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=mechanism%20without%20money" title=" mechanism without money"> mechanism without money</a>, <a href="https://publications.waset.org/abstracts/search?q=polynomial-time%20mechanism" title=" polynomial-time mechanism"> polynomial-time mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20scheduling" title=" project scheduling"> project scheduling</a> </p> <a href="https://publications.waset.org/abstracts/68829/pareto-optimal-material-allocation-mechanism" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68829.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">333</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3963</span> Taguchi Approach for the Optimization of the Stitching Defects of Knitted Garments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adel%20El-Hadidy">Adel El-Hadidy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For any industry, the production and quality management or wastages reductions have major impingement on overall factory economy. This work discusses the quality improvement of garment industry by applying Pareto analysis, cause and effect diagram and Taguchi experimental design. The main purpose of the work is to reduce the stitching defects, which will also minimize the rejection and reworks rate. Application of Pareto chart, fish bone diagram and Process Sigma Level/and or Performance Level tools helps solving those problems on priority basis. Among all, only sewing, defects are responsible form 69.3% to 97.3 % of total defects. Process Sigma level has been improved from 0.79 to 1.3 and performance rate improved, from F to D level. The results showed that the new set of sewing parameters was superior to the original one. It can be seen that fabric size has the largest effect on the sewing defects and that needle size has the smallest effect on the stitching defects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=garment" title="garment">garment</a>, <a href="https://publications.waset.org/abstracts/search?q=sewing%20defects" title=" sewing defects"> sewing defects</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20of%20rework" title=" cost of rework"> cost of rework</a>, <a href="https://publications.waset.org/abstracts/search?q=DMAIC" title=" DMAIC"> DMAIC</a>, <a href="https://publications.waset.org/abstracts/search?q=sigma%20level" title=" sigma level"> sigma level</a>, <a href="https://publications.waset.org/abstracts/search?q=cause%20and%20effect%20diagram" title=" cause and effect diagram"> cause and effect diagram</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20analysis" title=" Pareto analysis"> Pareto analysis</a> </p> <a href="https://publications.waset.org/abstracts/95883/taguchi-approach-for-the-optimization-of-the-stitching-defects-of-knitted-garments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95883.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">165</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3962</span> Multi-Objective Optimization in Carbon Abatement Technology Cycles (CAT) and Related Areas: Survey, Developments and Prospects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hameed%20Rukayat%20Opeyemi">Hameed Rukayat Opeyemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Pericles%20Pilidis"> Pericles Pilidis</a>, <a href="https://publications.waset.org/abstracts/search?q=Pagone%20Emanuele"> Pagone Emanuele</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An infinitesimal increase in performance can have immense reduction in operating and capital expenses in a power generation system. Therefore, constant studies are being carried out to improve both conventional and novel power cycles. Globally, power producers are constantly researching on ways to minimize emission and to collectively downsize the total cost rate of power plants. A substantial spurt of developmental technologies of low carbon cycles have been suggested and studied, however they all have their limitations and financial implication. In the area of carbon abatement in power plants, three major objectives conflict: The cost rate of the plant, Power output and Environmental impact. Since, an increase in one of this parameter directly affects the other. This poses a multi-objective problem. It is paramount to be able to discern the point where improving one objective affects the other. Hence, the need for a Pareto-based optimization algorithm. Pareto-based optimization algorithm helps to find those points where improving one objective influences another objective negatively and stops there. The application of Pareto-based optimization algorithm helps the user/operator/designer make an informed decision. This paper sheds more light on areas that multi-objective optimization has been applied in carbon abatement technologies in the last five years, developments and prospects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gas%20turbine" title="gas turbine">gas turbine</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20carbon%20technology" title=" low carbon technology"> low carbon technology</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto%20optimal" title=" pareto optimal"> pareto optimal</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/27738/multi-objective-optimization-in-carbon-abatement-technology-cycles-cat-and-related-areas-survey-developments-and-prospects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27738.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">791</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3961</span> VaR or TCE: Explaining the Preferences of Regulators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Silvia%20Faroni">Silvia Faroni</a>, <a href="https://publications.waset.org/abstracts/search?q=Olivier%20Le%20Courtois"> Olivier Le Courtois</a>, <a href="https://publications.waset.org/abstracts/search?q=Krzysztof%20Ostaszewski"> Krzysztof Ostaszewski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> While a lot of research concentrates on the merits of VaR and TCE, which are the two most classic risk indicators used by financial institutions, little has been written on explaining why regulators favor the choice of VaR or TCE in their set of rules. In this paper, we investigate the preferences of regulators with the aim of understanding why, for instance, a VaR with a given confidence level is ultimately retained. Further, this paper provides equivalence rules that explain how a given choice of VaR can be equivalent to a given choice of TCE. Then, we introduce a new risk indicator that extends TCE by providing a more versatile weighting of the constituents of probability distribution tails. All of our results are illustrated using the generalized Pareto distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20pareto%20distribution" title="generalized pareto distribution">generalized pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20tail%20conditional%20expectation" title=" generalized tail conditional expectation"> generalized tail conditional expectation</a>, <a href="https://publications.waset.org/abstracts/search?q=regulator%20preferences" title=" regulator preferences"> regulator preferences</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20measure" title=" risk measure"> risk measure</a> </p> <a href="https://publications.waset.org/abstracts/146694/var-or-tce-explaining-the-preferences-of-regulators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146694.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">170</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3960</span> Analysis of Labor Effectiveness at Green Tea Dry Sorting Workstation for Increasing Tea Factory Competitiveness</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayu%20Anggara">Bayu Anggara</a>, <a href="https://publications.waset.org/abstracts/search?q=Arita%20Dewi%20Nugrahini"> Arita Dewi Nugrahini</a>, <a href="https://publications.waset.org/abstracts/search?q=Didik%20Purwadi"> Didik Purwadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dry sorting workstation needs labor to produce green tea in Gambung Tea Factory. Observation results show that there is labor who are not working at the moment and doing overtime jobs to meet production targets. The measurement of the level of labor effectiveness has never been done before. The purpose of this study is to determine the level of labor effectiveness and provide recommendations for improvement based on the results of the Pareto diagram and Ishikawa diagram. The method used to measure the level of labor effectiveness is Overall Labor Effectiveness (OLE). OLE had three indicators which are availability, performance, and quality. Recommendations are made based on the results of the Pareto diagram and Ishikawa diagram for indicators that do not meet world standards. Based on the results of the study, the OLE value was 68.19%. Recommendations given to improve labor performance are adding mechanics, rescheduling rest periods, providing special training for labor, and giving rewards to labor. Furthermore, the recommendations for improving the quality of labor are procuring water content measuring devices, create material standard policies, and rescheduling rest periods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ishikawa%20diagram" title="Ishikawa diagram">Ishikawa diagram</a>, <a href="https://publications.waset.org/abstracts/search?q=labor%20effectiveness" title=" labor effectiveness"> labor effectiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=OLE" title=" OLE"> OLE</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20diagram" title=" Pareto diagram"> Pareto diagram</a> </p> <a href="https://publications.waset.org/abstracts/141467/analysis-of-labor-effectiveness-at-green-tea-dry-sorting-workstation-for-increasing-tea-factory-competitiveness" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141467.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">229</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3959</span> A Hybrid Based Algorithm to Solve the Multi-objective Minimum Spanning Tree Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boumesbah%20Asma">Boumesbah Asma</a>, <a href="https://publications.waset.org/abstracts/search?q=Chergui%20Mohamed%20El-amine"> Chergui Mohamed El-amine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since it has been shown that the multi-objective minimum spanning tree problem (MOST) is NP-hard even with two criteria, we propose in this study a hybrid NSGA-II algorithm with an exact mutation operator, which is only used with low probability, to find an approximation to the Pareto front of the problem. In a connected graph G, a spanning tree T of G being a connected and cycle-free graph, if k edges of G\T are added to T, we obtain a partial graph H of G inducing a reduced size multi-objective spanning tree problem compared to the initial one. With a weak probability for the mutation operator, an exact method for solving the reduced MOST problem considering the graph H is then used to give birth to several mutated solutions from a spanning tree T. Then, the selection operator of NSGA-II is activated to obtain the Pareto front approximation. Finally, an adaptation of the VNS metaheuristic is called for further improvements on this front. It allows finding good individuals to counterbalance the diversification and the intensification during the optimization search process. Experimental comparison studies with an exact method show promising results and indicate that the proposed algorithm is efficient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=minimum%20spanning%20tree" title="minimum spanning tree">minimum spanning tree</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20objective%20linear%20optimization" title=" multiple objective linear optimization"> multiple objective linear optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=combinatorial%20optimization" title=" combinatorial optimization"> combinatorial optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=non-sorting%20genetic%20algorithm" title=" non-sorting genetic algorithm"> non-sorting genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20neighborhood%20search" title=" variable neighborhood search"> variable neighborhood search</a> </p> <a href="https://publications.waset.org/abstracts/157395/a-hybrid-based-algorithm-to-solve-the-multi-objective-minimum-spanning-tree-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157395.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">91</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3958</span> Symbolic Computation and Abundant Travelling Wave Solutions to Modified Burgers' Equation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Younis">Muhammad Younis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, the novel (G′/G)-expansion method is successfully applied to construct the abundant travelling wave solutions to the modified Burgers’ equation with the aid of computation. The method is reliable and useful, which gives more general exact travelling wave solutions than the existing methods. These obtained solutions are in the form of hyperbolic, trigonometric and rational functions including solitary, singular and periodic solutions which have many potential applications in physical science and engineering. Some of these solutions are new and some have already been constructed. Additionally, the constraint conditions, for the existence of the solutions are also listed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traveling%20wave%20solutions" title="traveling wave solutions">traveling wave solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=NLPDE" title=" NLPDE"> NLPDE</a>, <a href="https://publications.waset.org/abstracts/search?q=computation" title=" computation"> computation</a>, <a href="https://publications.waset.org/abstracts/search?q=integrability" title=" integrability"> integrability</a> </p> <a href="https://publications.waset.org/abstracts/48762/symbolic-computation-and-abundant-travelling-wave-solutions-to-modified-burgers-equation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48762.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">433</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3957</span> Multi-Objective Optimization of Run-of-River Small-Hydropower Plants Considering Both Investment Cost and Annual Energy Generation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Am%C3%A8d%C3%A9djihund%C3%A9%20H.%20J.%20Hounnou">Amèdédjihundé H. J. Hounnou</a>, <a href="https://publications.waset.org/abstracts/search?q=Fr%C3%A9d%C3%A9ric%20Dubas"> Frédéric Dubas</a>, <a href="https://publications.waset.org/abstracts/search?q=Fran%C3%A7ois-Xavier%20Fifatin"> François-Xavier Fifatin</a>, <a href="https://publications.waset.org/abstracts/search?q=Didier%20Chamagne"> Didier Chamagne</a>, <a href="https://publications.waset.org/abstracts/search?q=Antoine%20Vianou"> Antoine Vianou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the techno-economic evaluation of run-of-river small-hydropower plants. In this regard, a multi-objective optimization procedure is proposed for the optimal sizing of the hydropower plants, and NSGAII is employed as the optimization algorithm. Annual generated energy and investment cost are considered as the objective functions, and number of generator units (<em>n</em>) and nominal turbine flow rate (<em>Q<sub>T</sub></em>) constitute the decision variables. Site of Yeripao in Benin is considered as the case study. We have categorized the river of this site using its environmental characteristics: gross head, and first quartile, median, third quartile and mean of flow. Effects of each decision variable on the objective functions are analysed. The results gave Pareto Front which represents the trade-offs between annual energy generation and the investment cost of hydropower plants, as well as the recommended optimal solutions. We noted that with the increase of the annual energy generation, the investment cost rises. Thus, maximizing energy generation is contradictory with minimizing the investment cost. Moreover, we have noted that the solutions of Pareto Front are grouped according to the number of generator units (<em>n)</em>. The results also illustrate that the costs per kWh are grouped according to the <em>n</em> and rise with the increase of the nominal turbine flow rate. The lowest investment costs per kWh are obtained for <em>n</em> equal to one and are between 0.065 and 0.180 €/kWh. Following the values of n (equal to 1, 2, 3 or 4), the investment cost and investment cost per kWh increase almost linearly with increasing the nominal turbine flowrate while annual generated. Energy increases logarithmically with increasing of the nominal turbine flowrate. This study made for the Yeripao river can be applied to other rivers with their own characteristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hydropower%20plant" title="hydropower plant">hydropower plant</a>, <a href="https://publications.waset.org/abstracts/search?q=investment%20cost" title=" investment cost"> investment cost</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=number%20of%20generator%20units" title=" number of generator units"> number of generator units</a> </p> <a href="https://publications.waset.org/abstracts/103977/multi-objective-optimization-of-run-of-river-small-hydropower-plants-considering-both-investment-cost-and-annual-energy-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103977.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">157</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions&page=132">132</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions&page=133">133</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>