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optimization</title> <meta name="description" content="Search results for: capacity optimization"> <meta name="keywords" content="capacity optimization"> <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 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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="capacity optimization"> <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> 7191</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: capacity optimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7191</span> Capacity Optimization in Cooperative Cognitive Radio Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Pirmoradian">Mahdi Pirmoradian</a>, <a href="https://publications.waset.org/abstracts/search?q=Olayinka%20Adigun"> Olayinka Adigun</a>, <a href="https://publications.waset.org/abstracts/search?q=Christos%20Politis"> Christos Politis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cooperative spectrum sensing is a crucial challenge in cognitive radio networks. Cooperative sensing can increase the reliability of spectrum hole detection, optimize sensing time and reduce delay in cooperative networks. In this paper, an efficient central capacity optimization algorithm is proposed to minimize cooperative sensing time in a homogenous sensor network using OR decision rule subject to the detection and false alarm probabilities constraints. The evaluation results reveal significant improvement in the sensing time and normalized capacity of the cognitive sensors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cooperative%20networks" title="cooperative networks">cooperative networks</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20capacity" title=" normalized capacity"> normalized capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=sensing%20time" title=" sensing time"> sensing time</a> </p> <a href="https://publications.waset.org/abstracts/25670/capacity-optimization-in-cooperative-cognitive-radio-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25670.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">633</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">7190</span> Updating Stochastic Hosting Capacity Algorithm for Voltage Optimization Programs and Interconnect Standards</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nicholas%20Burica">Nicholas Burica</a>, <a href="https://publications.waset.org/abstracts/search?q=Nina%20Selak"> Nina Selak </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ADHCAT (Automated Distribution Hosting Capacity Assessment Tool) was designed to run Hosting Capacity Analysis on the ComEd system via a stochastic DER (Distributed Energy Resource) placement on multiple power flow simulations against a set of violation criteria. The violation criteria in the initial version of the tool captured a limited amount of issues that individual departments design against for DER interconnections. Enhancements were made to the tool to further align with individual department violation and operation criteria, as well as the addition of new modules for use for future load profile analysis. A reporting engine was created for future analytical use based on the simulations and observations in the tool. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20energy%20resources" title="distributed energy resources">distributed energy resources</a>, <a href="https://publications.waset.org/abstracts/search?q=hosting%20capacity" title=" hosting capacity"> hosting capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=interconnect" title=" interconnect"> interconnect</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20optimization" title=" voltage optimization"> voltage optimization</a> </p> <a href="https://publications.waset.org/abstracts/127789/updating-stochastic-hosting-capacity-algorithm-for-voltage-optimization-programs-and-interconnect-standards" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127789.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">190</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">7189</span> Optimization of Hybrid off Grid Energy Station</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yehya%20Abdellatif">Yehya Abdellatif</a>, <a href="https://publications.waset.org/abstracts/search?q=Iyad%20M.%20Muslih"> Iyad M. Muslih</a>, <a href="https://publications.waset.org/abstracts/search?q=Azzah%20Alkhalailah"> Azzah Alkhalailah</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdallah%20Muslih"> Abdallah Muslih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hybrid Optimization Model for Electric Renewable (HOMER) software was utilized to find the optimum design of a hybrid off-Grid system, by choosing the optimal solution depending on the cost analysis of energy based on different capacity shortage percentages. A complete study for the site conditions and load profile was done to optimize the design and implementation of a hybrid off-grid power station. In addition, the solution takes into consecration the ambient temperature effect on the efficiency of the power generation and the economical aspects of selection depending on real market price. From the analysis of the HOMER model results, the optimum hybrid power station was suggested, based on wind speed, and solar conditions. The optimization function objective is to minimize the Net Price Cost (NPC) and the Cost of Energy (COE) with zero and 10 percentage of capacity shortage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20modeling" title="energy modeling">energy modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=HOMER" title=" HOMER"> HOMER</a>, <a href="https://publications.waset.org/abstracts/search?q=off-grid%20system" title=" off-grid system"> off-grid system</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/32205/optimization-of-hybrid-off-grid-energy-station" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32205.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">563</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">7188</span> A New Complex Method for Integrated Warehouse Design in Aspect of Dynamic and Static Capacity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tamas%20Hartvanyi">Tamas Hartvanyi</a>, <a href="https://publications.waset.org/abstracts/search?q=Zoltan%20Andras%20Nagy"> Zoltan Andras Nagy</a>, <a href="https://publications.waset.org/abstracts/search?q=Miklos%20Szabo"> Miklos Szabo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The dynamic and static capacity are two opposing aspect of warehouse design. Static capacity optimization aims to maximize the space-usage for goods storing, while dynamic capacity needs more free place to handling them. They are opposing by the building structure and the area utilization. According to Pareto principle: the 80% of the goods are the 20% of the variety. From the origin of this statement, it worth to store the big amount of same products by fulfill the space with minimal corridors, meanwhile the rest 20% of goods have the 80% variety of the whole range, so there is more important to be fast-reachable instead of the space utilizing, what makes the space fulfillment numbers worse. The warehouse design decisions made in present practice by intuitive and empiric impressions, the planning method is formed to one selected technology, making this way the structure of the warehouse homogeny. Of course the result can’t be optimal for the inhomogeneous demands. A new innovative model based on our research will be introduced in this paper to describe the technic capacities, what makes possible to define optimal cluster of technology. It is able to optimize the space fulfillment and the dynamic operation together with this cluster application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=warehouse" title="warehouse">warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=warehouse%20capacity" title=" warehouse capacity"> warehouse capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=warehouse%20design%20method" title=" warehouse design method"> warehouse design method</a>, <a href="https://publications.waset.org/abstracts/search?q=warehouse%20optimization" title=" warehouse optimization"> warehouse optimization</a> </p> <a href="https://publications.waset.org/abstracts/127517/a-new-complex-method-for-integrated-warehouse-design-in-aspect-of-dynamic-and-static-capacity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127517.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">141</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">7187</span> Hierarchical Optimization of Composite Deployable Bridge Treadway 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=Ashraf%20Osman">Ashraf Osman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Effective deployable bridges that are characterized by an increased capacity to weight ratio are recently needed for post-disaster rapid mobility and military operations. In deployable bridging, replacing metals as the fabricating material with advanced composite laminates as lighter alternatives with higher strength is highly advantageous. This article presents a hierarchical optimization strategy of a composite bridge treadway considering maximum strength design and bridge weight minimization. Shape optimization of a generic deployable bridge beam cross-section is performed to achieve better stress distribution over the bridge treadway hull. The developed cross-section weight is minimized up to reserving the margins of safety of the deployable bridging code provisions. Hence, the strength of composite bridge plates is maximized through varying the plies orientation. Different loading cases are considered of a tracked vehicle patch load. The orthotropic plate properties of a composite sandwich core are used to simulate the bridge deck structural behavior. Whereas, the failure analysis is conducted using Tsai-Wu failure criterion. The naturally inspired particle swarm optimization technique is used in this study. The proposed technique efficiently reduced the weight to capacity ratio of the developed bridge beam. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CFRP%20deployable%20bridges" title="CFRP deployable bridges">CFRP deployable bridges</a>, <a href="https://publications.waset.org/abstracts/search?q=disaster%20relief" title=" disaster relief"> disaster relief</a>, <a href="https://publications.waset.org/abstracts/search?q=military%20bridging" title=" military bridging"> military bridging</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20of%20composites" title=" optimization of composites"> optimization of composites</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/102352/hierarchical-optimization-of-composite-deployable-bridge-treadway-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102352.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">140</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">7186</span> Generalized Limit Equilibrium Solution for the Lateral Pile Capacity Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomer%20Gans-Or">Tomer Gans-Or</a>, <a href="https://publications.waset.org/abstracts/search?q=Shmulik%20Pinkert"> Shmulik Pinkert</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The determination of lateral pile capacity per unit length is a key aspect in geotechnical engineering. Traditional approaches for assessing piles lateral capacity in cohesive soils involve the application of upper-bound and lower-bound plasticity theorems. However, a comprehensive solution encompassing the entire spectrum of soil strength parameters, particularly in frictional soils with or without cohesion, is still lacking. This research introduces an innovative implementation of the slice method limit equilibrium solution for lateral capacity assessment. For any given numerical discretization of the soil's domain around the pile, the lateral capacity evaluation is based on mobilized strength concept. The critical failure geometry is then found by a unique optimization procedure which includes both factor of safety minimization and geometrical optimization. The robustness of this suggested methodology is that the solution is independent of any predefined assumptions. Validation of the solution is accomplished through a comparison with established plasticity solutions for cohesive soils. Furthermore, the study demonstrates the applicability of the limit equilibrium method to address unresolved cases related to frictional and cohesive-frictional soils. Beyond providing capacity values, the method enables the utilization of the mobilized strength concept to generate safety-factor distributions for scenarios representing pre-failure states. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lateral%20pile%20capacity" title="lateral pile capacity">lateral pile capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=slice%20method" title=" slice method"> slice method</a>, <a href="https://publications.waset.org/abstracts/search?q=limit%20equilibrium" title=" limit equilibrium"> limit equilibrium</a>, <a href="https://publications.waset.org/abstracts/search?q=mobilized%20strength" title=" mobilized strength"> mobilized strength</a> </p> <a href="https://publications.waset.org/abstracts/182938/generalized-limit-equilibrium-solution-for-the-lateral-pile-capacity-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182938.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">60</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">7185</span> A Review on Bearing Capacity Factor Nγ of Foundations with Different Shapes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Ziaie%20Moayed">R. Ziaie Moayed</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Taghvamanesh"> S. Taghvamanesh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> So far several methods by different researchers have been developed in order to calculate the bearing capacity factors of foundations and retaining walls. In this paper, the bearing capacity factor Ny (shape factor) for different types of foundation have been investigated. The formula for bearing capacity on c–φ–γ soil can still be expressed by Terzaghi’s equation except that the bearing capacity factor Ny depends on the surcharge ratio, and friction angle φ. Many empirical definitions have been used for measurement of the bearing capacity factors N <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bearing%20capacity" title="bearing capacity">bearing capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=bearing%20capacity%20factor%20N%CE%B3" title=" bearing capacity factor Nγ"> bearing capacity factor Nγ</a>, <a href="https://publications.waset.org/abstracts/search?q=irregular%20foundations" title=" irregular foundations"> irregular foundations</a>, <a href="https://publications.waset.org/abstracts/search?q=shape%20factor" title=" shape factor"> shape factor</a> </p> <a href="https://publications.waset.org/abstracts/134905/a-review-on-bearing-capacity-factor-ngh-of-foundations-with-different-shapes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134905.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">150</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">7184</span> Road Transition Design on Freeway Tunnel Entrance and Exit Based on Traffic Capacity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Han%20Bai">Han Bai</a>, <a href="https://publications.waset.org/abstracts/search?q=Tong%20Zhang"> Tong Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Lemei%20Yu"> Lemei Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Doudou%20Xie"> Doudou Xie</a>, <a href="https://publications.waset.org/abstracts/search?q=Liang%20Zhao"> Liang Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Road transition design on freeway tunnel entrance and exit is one vital factor in realizing smooth transition and improving traveling safety for vehicles. The goal of this research is to develop a horizontal road transition design tool that considers the transition technology of traffic capacity consistency to explore its accommodation mechanism. The influencing factors of capacity are synthesized and a modified capacity calculation model focusing on the influence of road width and lateral clearance is developed based on the VISSIM simulation to calculate the width of road transition sections. To keep the traffic capacity consistency, the right side of the transition section of the tunnel entrance and exit is divided into three parts: front arc, an intermediate transition section, and end arc; an optimization design on each transition part is conducted to improve the capacity stability and horizontal alignment transition. A case study on the Panlong Tunnel in Ji-Qing freeway illustrates the application of the tool. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20safety" title="traffic safety">traffic safety</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20transition" title=" road transition"> road transition</a>, <a href="https://publications.waset.org/abstracts/search?q=freeway%20tunnel" title=" freeway tunnel"> freeway tunnel</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20capacity" title=" traffic capacity"> traffic capacity</a> </p> <a href="https://publications.waset.org/abstracts/88916/road-transition-design-on-freeway-tunnel-entrance-and-exit-based-on-traffic-capacity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88916.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">326</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">7183</span> Preparation of Chemically Activated Carbon from Waste Tire Char for Lead Ions Adsorption and Optimization Using Response Surface Methodology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lucky%20Malise">Lucky Malise</a>, <a href="https://publications.waset.org/abstracts/search?q=Hilary%20Rutto"> Hilary Rutto</a>, <a href="https://publications.waset.org/abstracts/search?q=Tumisang%20Seodigeng"> Tumisang Seodigeng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of tires in automobiles is very important in the automobile industry. However, there is a serious environmental problem concerning the disposal of these rubber tires once they become worn out. The main aim of this study was to prepare activated carbon from waste tire pyrolysis char by impregnating KOH on pyrolytic char. Adsorption studies on lead onto chemically activated carbon was carried out using response surface methodology. The effect of process parameters such as temperature (°C), adsorbent dosage (g/1000ml), pH, contact time (minutes) and initial lead concentration (mg/l) on the adsorption capacity were investigated. It was found that the adsorption capacity increases with an increase in contact time, pH, temperature and decreases with an increase in lead concentration. Optimization of the process variables was done using a numerical optimization method. Fourier Transform Infrared Spectra (FTIR) analysis, XRay diffraction (XRD), Thermogravimetric analysis (TGA) and scanning electron microscope was used to characterize the pyrolytic carbon char before and after activation. The optimum points 1g/ 100 ml for adsorbent dosage, 7 for pH value of the solution, 115.2 min for contact time, 100 mg/l for initial metal concentration, and 25°C for temperature were obtained to achieve the highest adsorption capacity of 93.176 mg/g with a desirability of 0.994. Fourier Transform Infrared Spectra (FTIR) analysis and Thermogravimetric analysis (TGA) show the presence of oxygen-containing functional groups on the surface of the activated carbon produced and that the weight loss taking place during the activation step is small. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=waste%20tire%20pyrolysis%20char" title="waste tire pyrolysis char">waste tire pyrolysis char</a>, <a href="https://publications.waset.org/abstracts/search?q=chemical%20activation" title=" chemical activation"> chemical activation</a>, <a href="https://publications.waset.org/abstracts/search?q=central%20composite%20design%20%28CCD%29" title=" central composite design (CCD)"> central composite design (CCD)</a>, <a href="https://publications.waset.org/abstracts/search?q=adsorption%20capacity" title=" adsorption capacity"> adsorption capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20optimization" title=" numerical optimization"> numerical optimization</a> </p> <a href="https://publications.waset.org/abstracts/78877/preparation-of-chemically-activated-carbon-from-waste-tire-char-for-lead-ions-adsorption-and-optimization-using-response-surface-methodology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78877.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">226</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">7182</span> An Integrated Mathematical Approach to Measure the Capacity of MMTS </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayan%20Bevrani">Bayan Bevrani</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20L.%20Burdett"> Robert L. Burdett</a>, <a href="https://publications.waset.org/abstracts/search?q=Prasad%20K.%20D.%20V.%20Yarlagadda"> Prasad K. D. V. Yarlagadda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article focuses upon multi-modal transportation systems (MMTS) and the issues surrounding the determination of system capacity. For that purpose a multi-objective framework is advocated that integrates all the different modes and many different competing capacity objectives. This framework is analytical in nature and facilitates a variety of capacity querying and capacity expansion planning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytical%20model" title="analytical model">analytical model</a>, <a href="https://publications.waset.org/abstracts/search?q=capacity%20analysis" title=" capacity analysis"> capacity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=capacity%20query" title=" capacity query"> capacity query</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-modal%20transportation%20system%20%28MMTS%29" title=" multi-modal transportation system (MMTS)"> multi-modal transportation system (MMTS)</a> </p> <a href="https://publications.waset.org/abstracts/40444/an-integrated-mathematical-approach-to-measure-the-capacity-of-mmts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40444.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">359</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">7181</span> Curve Fitting by Cubic Bezier Curves Using Migrating Birds Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mitat%20Uysal">Mitat Uysal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new met heuristic optimization algorithm called as Migrating Birds Optimization is used for curve fitting by rational cubic Bezier Curves. This requires solving a complicated multivariate optimization problem. In this study, the solution of this optimization problem is achieved by Migrating Birds Optimization algorithm that is a powerful met heuristic nature-inspired algorithm well appropriate for optimization. The results of this study show that the proposed method performs very well and being able to fit the data points to cubic Bezier Curves with a high degree of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Bezier%20curves" title=" Bezier curves"> Bezier curves</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20optimization" title=" heuristic optimization"> heuristic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=migrating%20birds%20optimization" title=" migrating birds optimization"> migrating birds optimization</a> </p> <a href="https://publications.waset.org/abstracts/78026/curve-fitting-by-cubic-bezier-curves-using-migrating-birds-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78026.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">337</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">7180</span> Modeling Water Resources Carrying Capacity, Optimizing Water Treatment, Smart Water Management, and Conceptualizing a Watershed Management Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pius%20Babuna">Pius Babuna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sustainable water use is important for the existence of the human race. Water resources carrying capacity (WRCC) measures the sustainability of water use; however, the calculation and optimization of WRCC remain challenging. This study used a mathematical model (the Logistics Growth of Water Resources -LGWR) and a linear objective function to model water sustainability. We tested the validity of the models using data from Ghana. Total freshwater resources, water withdrawal, and population data were used in MATLAB. The results show that the WRCC remains sustainable until the year 2132 ±18, when half of the total annual water resources will be used. The optimized water treatment cost suggests that Ghana currently wastes GHȼ 1115.782± 50 cedis (~$182.21± 50) per water treatment plant per month or ~ 0.67 million gallons of water in an avoidable loss. Adopting an optimized water treatment scheme and a watershed management approach will help sustain the WRCC. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=water%20resources%20carrying%20capacity" title="water resources carrying capacity">water resources carrying capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20water%20management" title=" smart water management"> smart water management</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainable%20water%20use" title=" sustainable water use"> sustainable water use</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20withdrawal" title=" water withdrawal"> water withdrawal</a> </p> <a href="https://publications.waset.org/abstracts/159894/modeling-water-resources-carrying-capacity-optimizing-water-treatment-smart-water-management-and-conceptualizing-a-watershed-management-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159894.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">87</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">7179</span> Optimizing the Capacity of a Convolutional Neural Network for Image Segmentation and Pattern Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yalong%20Jiang">Yalong Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zheru%20Chi"> Zheru Chi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CNN" title="CNN">CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=capsule%20network" title=" capsule network"> capsule network</a>, <a href="https://publications.waset.org/abstracts/search?q=capacity%20optimization" title=" capacity optimization"> capacity optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title=" character recognition"> character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title=" data augmentation"> data augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20segmentation" title=" semantic segmentation"> semantic segmentation</a> </p> <a href="https://publications.waset.org/abstracts/95551/optimizing-the-capacity-of-a-convolutional-neural-network-for-image-segmentation-and-pattern-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95551.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">153</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">7178</span> Optimal Maintenance Clustering for Rail Track Components Subject to Possession Capacity Constraints</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cuong%20D.%20Dao">Cuong D. Dao</a>, <a href="https://publications.waset.org/abstracts/search?q=Rob%20J.I.%20Basten"> Rob J.I. Basten</a>, <a href="https://publications.waset.org/abstracts/search?q=Andreas%20Hartmann"> Andreas Hartmann</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper studies the optimal maintenance planning of preventive maintenance and renewal activities for components in a single railway track when the available time for maintenance is limited. The rail-track system consists of several types of components, such as rail, ballast, and switches with different preventive maintenance and renewal intervals. To perform maintenance or renewal on the track, a train free period for maintenance, called a possession, is required. Since a major possession directly affects the regular train schedule, maintenance and renewal activities are clustered as much as possible. In a highly dense and utilized railway network, the possession time on the track is critical since the demand for train operations is very high and a long possession has a severe impact on the regular train schedule. We present an optimization model and investigate the maintenance schedules with and without the possession capacity constraint. In addition, we also integrate the social-economic cost related to the effects of the maintenance time to the variable possession cost into the optimization model. A numerical example is provided to illustrate the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rail-track%20components" title="rail-track components">rail-track components</a>, <a href="https://publications.waset.org/abstracts/search?q=maintenance" title=" maintenance"> maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20clustering" title=" optimal clustering"> optimal clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=possession%20capacity" title=" possession capacity"> possession capacity</a> </p> <a href="https://publications.waset.org/abstracts/69824/optimal-maintenance-clustering-for-rail-track-components-subject-to-possession-capacity-constraints" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69824.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">262</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">7177</span> An Ant Colony Optimization Approach for the Pollution Routing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Parthiban">P. Parthiban</a>, <a href="https://publications.waset.org/abstracts/search?q=Sonu%20Rajak"> Sonu Rajak</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Kannan"> N. Kannan</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Dhanalakshmi"> R. Dhanalakshmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the Vehicle Routing Problem (VRP) with environmental considerations which is called Pollution Routing Problem (PRP). The objective is to minimize the operational and environmental costs. It consists of routing a number of vehicles to serve a set of customers, and determining fuel consumption, driver wages and their speed on each route segment, while respecting the capacity constraints and time windows. In this context, we presented an Ant Colony Optimization (ACO) approach, combined with a Speed Optimization Algorithm (SOA) to solve the PRP. The proposed solution method consists of two stages. Stage one is to solve a Vehicle Routing Problem with Time Window (VRPTW) using ACO and in the second stage a SOA is run on the resulting VRPTW solutions. Given a vehicle route, the SOA consists of finding the optimal speed on each arc of the route in order to minimize an objective function comprising fuel consumption costs and driver wages. The proposed algorithm tested on benchmark problem, the preliminary results show that the proposed algorithm is able to provide good solutions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title="ant colony optimization">ant colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=CO2%20emissions" title=" CO2 emissions"> CO2 emissions</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=speed%20optimization" title=" speed optimization"> speed optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20routing" title=" vehicle routing"> vehicle routing</a> </p> <a href="https://publications.waset.org/abstracts/52973/an-ant-colony-optimization-approach-for-the-pollution-routing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52973.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">322</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">7176</span> A Mean–Variance–Skewness Portfolio Optimization Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Metaxiotis">Kostas Metaxiotis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portfolio optimization is one of the most important topics in finance. This paper proposes a mean–variance–skewness (MVS) portfolio optimization model. Traditionally, the portfolio optimization problem is solved by using the mean–variance (MV) framework. In this study, we formulate the proposed model as a three-objective optimization problem, where the portfolio's expected return and skewness are maximized whereas the portfolio risk is minimized. For solving the proposed three-objective portfolio optimization model we apply an adapted version of the non-dominated sorting genetic algorithm (NSGAII). Finally, we use a real dataset from FTSE-100 for validating the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title="evolutionary algorithms">evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=skewness" title=" skewness"> skewness</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20selection" title=" stock selection"> stock selection</a> </p> <a href="https://publications.waset.org/abstracts/102472/a-mean-variance-skewness-portfolio-optimization-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102472.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">198</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">7175</span> Optimization and Operation of Charging and Discharging Stations for Hybrid Cars and their Effects on the Electricity Distribution Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Heydarimoghim">Ali Heydarimoghim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the optimal placement of charging and discharging stations is done to determine the location and capacity of the stations, reducing the cost of electric vehicle owners' losses, reducing the cost of distribution system losses, and reducing the costs associated with the stations. Also, observing the permissible limits of the bus voltage and the capacity of the stations and their distance are considered as constraints of the problem. Given the traffic situation in different areas of a city, we estimate the amount of energy required to charge and the amount of energy provided to discharge electric vehicles in each area. We then introduce the electricity distribution system of the city in question. Following are the scenarios for introducing the problem and introducing the objective and constraint functions. Finally, the simulation results for different scenarios are compared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=charging%20%26%20discharging%20stations" title="charging & discharging stations">charging & discharging stations</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20vehicles" title=" hybrid vehicles"> hybrid vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=replacement" title=" replacement"> replacement</a> </p> <a href="https://publications.waset.org/abstracts/133231/optimization-and-operation-of-charging-and-discharging-stations-for-hybrid-cars-and-their-effects-on-the-electricity-distribution-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133231.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">138</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">7174</span> Iterative Dynamic Programming for 4D Flight Trajectory Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kawser%20Ahmed">Kawser Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Bousson"> K. Bousson</a>, <a href="https://publications.waset.org/abstracts/search?q=Milca%20F.%20Coelho"> Milca F. Coelho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 4D flight trajectory optimization is one of the key ingredients to improve flight efficiency and to enhance the air traffic capacity in the current air traffic management (ATM). The present paper explores the iterative dynamic programming (IDP) as a potential numerical optimization method for 4D flight trajectory optimization. IDP is an iterative version of the Dynamic programming (DP) method. Due to the numerical framework, DP is very suitable to deal with nonlinear discrete dynamic systems. The 4D waypoint representation of the flight trajectory is similar to the discretization by a grid system; thus DP is a natural method to deal with the 4D flight trajectory optimization. However, the computational time and space complexity demanded by the DP is enormous due to the immense number of grid points required to find the optimum, which prevents the use of the DP in many practical high dimension problems. On the other hand, the IDP has shown potentials to deal successfully with high dimension optimal control problems even with a few numbers of grid points at each stage, which reduces the computational effort over the traditional DP approach. Although the IDP has been applied successfully in chemical engineering problems, IDP is yet to be validated in 4D flight trajectory optimization problems. In this paper, the IDP has been successfully used to generate minimum length 4D optimal trajectory avoiding any obstacle in its path, such as a no-fly zone or residential areas when flying in low altitude to reduce noise pollution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=4D%20waypoint%20navigation" title="4D waypoint navigation">4D waypoint navigation</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20dynamic%20programming" title=" iterative dynamic programming"> iterative dynamic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle%20avoidance" title=" obstacle avoidance"> obstacle avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=trajectory%20optimization" title=" trajectory optimization"> trajectory optimization</a> </p> <a href="https://publications.waset.org/abstracts/106496/iterative-dynamic-programming-for-4d-flight-trajectory-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106496.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">162</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">7173</span> Optimal Decisions for Personalized Products with Demand Information Updating and Limited Capacity </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Meimei%20Zheng">Meimei Zheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Product personalization could not only bring new profits to companies but also provide the direction of long-term development for companies. However, the characteristics of personalized product cause some new problems. This paper investigates how companies make decisions on the supply of personalized products when facing different customer attitudes to personalized product and service, constraints due to limited capacity and updates of personalized demand information. This study will provide optimal decisions for companies to develop personalized markets, resulting in promoting business transformation and improving business competitiveness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=demand%20forecast%20updating" title="demand forecast updating">demand forecast updating</a>, <a href="https://publications.waset.org/abstracts/search?q=limited%20capacity" title=" limited capacity"> limited capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=personalized%20products" title=" personalized products"> personalized products</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/66358/optimal-decisions-for-personalized-products-with-demand-information-updating-and-limited-capacity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66358.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">262</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">7172</span> Production and Distribution Network Planning Optimization: A Case Study of Large Cement Company</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lokendra%20Kumar%20Devangan">Lokendra Kumar Devangan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ajay%20Mishra"> Ajay Mishra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes the implementation of a large-scale SAS/OR model with significant pre-processing, scenario analysis, and post-processing work done using SAS. A large cement manufacturer with ten geographically distributed manufacturing plants for two variants of cement, around 400 warehouses serving as transshipment points, and several thousand distributor locations generating demand needed to optimize this multi-echelon, multi-modal transport supply chain separately for planning and allocation purposes. For monthly planning as well as daily allocation, the demand is deterministic. Rail and road networks connect any two points in this supply chain, creating tens of thousands of such connections. Constraints include the plant’s production capacity, transportation capacity, and rail wagon batch size constraints. Each demand point has a minimum and maximum for shipments received. Price varies at demand locations due to local factors. A large mixed integer programming model built using proc OPTMODEL decides production at plants, demand fulfilled at each location, and the shipment route to demand locations to maximize the profit contribution. Using base SAS, we did significant pre-processing of data and created inputs for the optimization. Using outputs generated by OPTMODEL and other processing completed using base SAS, we generated several reports that went into their enterprise system and created tables for easy consumption of the optimization results by operations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=production%20planning" title="production planning">production planning</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20integer%20optimization" title=" mixed integer optimization"> mixed integer optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20model" title=" network model"> network model</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20optimization" title=" network optimization"> network optimization</a> </p> <a href="https://publications.waset.org/abstracts/179220/production-and-distribution-network-planning-optimization-a-case-study-of-large-cement-company" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179220.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">66</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">7171</span> Improved Whale Algorithm Based on Information Entropy and Its Application in Truss Structure Optimization Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serges%20Mendomo%20%20Meye">Serges Mendomo Meye</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Guowei"> Li Guowei</a>, <a href="https://publications.waset.org/abstracts/search?q=Shen%20Zhenzhong"> Shen Zhenzhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Gan%20Lei"> Gan Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Xu%20Liqun"> Xu Liqun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given the limitations of the original whale optimization algorithm (WAO) in local optimum and low convergence accuracy in truss structure optimization problems, based on the fundamental whale algorithm, an improved whale optimization algorithm (SWAO) based on information entropy is proposed. The information entropy itself is an uncertain measure. It is used to control the range of whale searches in path selection. It can overcome the shortcomings of the basic whale optimization algorithm (WAO) and can improve the global convergence speed of the algorithm. Taking truss structure as the optimization research object, the mathematical model of truss structure optimization is established; the cross-sectional area of truss is taken as the design variable; the objective function is the weight of truss structure; and an improved whale optimization algorithm (SWAO) is used for optimization design, which provides a new idea and means for its application in large and complex engineering structure optimization design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20entropy" title="information entropy">information entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20optimization" title=" structural optimization"> structural optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=truss%20structure" title=" truss structure"> truss structure</a>, <a href="https://publications.waset.org/abstracts/search?q=whale%20algorithm" title=" whale algorithm"> whale algorithm</a> </p> <a href="https://publications.waset.org/abstracts/139986/improved-whale-algorithm-based-on-information-entropy-and-its-application-in-truss-structure-optimization-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139986.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">249</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">7170</span> Improved Particle Swarm Optimization with Cellular Automata and Fuzzy Cellular Automata</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Javadzadeh">Ramin Javadzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The particle swarm optimization are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Particle swarm optimization is introduced for the first time to overcome its problems. The fuzzy cellular automata is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the Particle swarm optimization algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cellular%20automata" title="cellular automata">cellular automata</a>, <a href="https://publications.waset.org/abstracts/search?q=cellular%20learning%20automata" title=" cellular learning automata"> cellular learning automata</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=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/24739/improved-particle-swarm-optimization-with-cellular-automata-and-fuzzy-cellular-automata" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24739.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">607</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">7169</span> Non-Stationary Stochastic Optimization of an Oscillating Water Column</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mar%C3%ADa%20L.%20Jal%C3%B3n">María L. Jalón</a>, <a href="https://publications.waset.org/abstracts/search?q=Feargal%20Brennan"> Feargal Brennan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A non-stationary stochastic optimization methodology is applied to an OWC (oscillating water column) to find the design that maximizes the wave energy extraction. Different temporal cycles are considered to represent the long-term variability of the wave climate at the site in the optimization problem. The results of the non-stationary stochastic optimization problem are compared against those obtained by a stationary stochastic optimization problem. The comparative analysis reveals that the proposed non-stationary optimization provides designs with a better fit to reality. However, the stationarity assumption can be adequate when looking at averaged system response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-stationary%20stochastic%20optimization" title="non-stationary stochastic optimization">non-stationary stochastic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=oscillating%20water" title=" oscillating water"> oscillating water</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20variability" title=" temporal variability"> temporal variability</a>, <a href="https://publications.waset.org/abstracts/search?q=wave%20energy" title=" wave energy"> wave energy</a> </p> <a href="https://publications.waset.org/abstracts/75300/non-stationary-stochastic-optimization-of-an-oscillating-water-column" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75300.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">373</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">7168</span> Two-Stage Approach for Solving the Multi-Objective Optimization Problem on Combinatorial Configurations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liudmyla%20Koliechkina">Liudmyla Koliechkina</a>, <a href="https://publications.waset.org/abstracts/search?q=Olena%20Dvirna"> Olena Dvirna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The statement of the multi-objective optimization problem on combinatorial configurations is formulated, and the approach to its solution is proposed. The problem is of interest as a combinatorial optimization one with many criteria, which is a model of many applied tasks. The approach to solving the multi-objective optimization problem on combinatorial configurations consists of two stages; the first is the reduction of the multi-objective problem to the single criterion based on existing multi-objective optimization methods, the second stage solves the directly replaced single criterion combinatorial optimization problem by the horizontal combinatorial method. This approach provides the optimal solution to the multi-objective optimization problem on combinatorial configurations, taking into account additional restrictions for a finite number of steps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20set" title="discrete set">discrete set</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20combinatorial%20optimization" title=" linear combinatorial optimization"> linear combinatorial optimization</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%20solutions" title=" Pareto solutions"> Pareto solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20permutation%20set" title=" partial permutation set"> partial permutation set</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20graph" title=" structural graph"> structural graph</a> </p> <a href="https://publications.waset.org/abstracts/133824/two-stage-approach-for-solving-the-multi-objective-optimization-problem-on-combinatorial-configurations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133824.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">167</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">7167</span> Co-Evolutionary Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Unconstrained Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20M.%20Rizk-Allah">R. M. Rizk-Allah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents co-evolutionary fruit fly optimization algorithm based on firefly algorithm (CFOA-FA) for solving unconstrained optimization problems. The proposed algorithm integrates the merits of fruit fly optimization algorithm (FOA), firefly algorithm (FA) and elite strategy to refine the performance of classical FOA. Moreover, co-evolutionary mechanism is performed by applying FA procedures to ensure the diversity of the swarm. Finally, the proposed algorithm CFOA- FA is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm for finding the global optimal solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=firefly%20algorithm" title="firefly algorithm">firefly algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fruit%20fly%20optimization%20algorithm" title=" fruit fly optimization algorithm"> fruit fly optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization%20problems" title=" unconstrained optimization problems"> unconstrained optimization problems</a> </p> <a href="https://publications.waset.org/abstracts/15923/co-evolutionary-fruit-fly-optimization-algorithm-and-firefly-algorithm-for-solving-unconstrained-optimization-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15923.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">536</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">7166</span> Model of Optimal Centroids Approach for Multivariate Data Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pham%20Van%20Nha">Pham Van Nha</a>, <a href="https://publications.waset.org/abstracts/search?q=Le%20Cam%20Binh"> Le Cam Binh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analysis%20of%20optimization" title="analysis of optimization">analysis of optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence%20based%20optimization" title=" artificial intelligence based optimization"> artificial intelligence based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20for%20learning%20and%20data%20analysis" title=" optimization for learning and data analysis"> optimization for learning and data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20optimization" title=" global optimization"> global optimization</a> </p> <a href="https://publications.waset.org/abstracts/126058/model-of-optimal-centroids-approach-for-multivariate-data-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126058.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">208</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">7165</span> Cuckoo Search (CS) Optimization Algorithm for Solving Constrained Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sait%20Ali%20Uymaz">Sait Ali Uymaz</a>, <a href="https://publications.waset.org/abstracts/search?q=G%C3%BClay%20Tezel"> Gülay Tezel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the comparison results on the performance of the Cuckoo Search (CS) algorithm for constrained optimization problems. For constraint handling, CS algorithm uses penalty method. CS algorithm is tested on thirteen well-known test problems and the results obtained are compared to Particle Swarm Optimization (PSO) algorithm. Mean, best, median and worst values were employed for the analyses of performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cuckoo%20search" title="cuckoo search">cuckoo search</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=constrained%20optimization%20problems" title=" constrained optimization problems"> constrained optimization problems</a>, <a href="https://publications.waset.org/abstracts/search?q=penalty%20method" title=" penalty method"> penalty method</a> </p> <a href="https://publications.waset.org/abstracts/13991/cuckoo-search-cs-optimization-algorithm-for-solving-constrained-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13991.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">557</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">7164</span> Parametric Estimation of U-Turn Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yonas%20Masresha%20Aymeku">Yonas Masresha Aymeku</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of capacity modelling at U-turns is to develop a relationship between capacity and its geometric characteristics. In fact, the few models available for the estimation of capacity at different transportation facilities do not provide specific guidelines for median openings. For this reason, an effort is made to estimate the capacity by collecting the data sets from median openings at different lane roads in Hyderabad City, India. Wide difference (43% -59%) among the capacity values estimated by the existing models shows the limitation to consider for mixed traffic situations. Thus, a distinct model is proposed for the estimation of the capacity of U-turn vehicles at median openings considering mixed traffic conditions, which would further prompt to investigate the effect of different factors that might affect the capacity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geometric" title="geometric">geometric</a>, <a href="https://publications.waset.org/abstracts/search?q=guiddelines" title=" guiddelines"> guiddelines</a>, <a href="https://publications.waset.org/abstracts/search?q=median" title=" median"> median</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicles" title=" vehicles"> vehicles</a> </p> <a href="https://publications.waset.org/abstracts/184454/parametric-estimation-of-u-turn-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184454.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">67</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">7163</span> An Algorithm of Set-Based Particle Swarm Optimization with Status Memory for Traveling Salesman Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takahiro%20Hino">Takahiro Hino</a>, <a href="https://publications.waset.org/abstracts/search?q=Michiharu%20Maeda"> Michiharu Maeda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is an optimization approach that achieves the social model of bird flocking and fish schooling. PSO works in continuous space and can solve continuous optimization problem with high quality. Set-based particle swarm optimization (SPSO) functions in discrete space by using a set. SPSO can solve combinatorial optimization problem with high quality and is successful to apply to the large-scale problem. In this paper, we present an algorithm of SPSO with status memory to decide the position based on the previous position for solving traveling salesman problem (TSP). In order to show the effectiveness of our approach. We examine SPSOSM for TSP compared to the existing algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combinatorial%20optimization%20problems" title="combinatorial optimization problems">combinatorial optimization problems</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=set-based%20particle%20swarm%20optimization" title=" set-based particle swarm optimization"> set-based particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a> </p> <a href="https://publications.waset.org/abstracts/47282/an-algorithm-of-set-based-particle-swarm-optimization-with-status-memory-for-traveling-salesman-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47282.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">552</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">7162</span> Capacity Loss of Urban Arterial Roads under the Influence of Bus Stop</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sai%20Chand">Sai Chand</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashish%20Dhamaniya"> Ashish Dhamaniya</a>, <a href="https://publications.waset.org/abstracts/search?q=Satish%20Chandra"> Satish Chandra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Curbside bus stops are provided on urban roads when sufficient land is not available to construct bus bays. The present study demonstrates the effect of curbside bus stops on midblock capacity of an urban arterial road. Data were collected on seven sections of 6-lane urban arterial roads in New Delhi. Three sections were selected without any side friction to estimate the base value of capacity. Remaining four sections were with curbside bus stop. Speed and volume data were collected in field and these data were used to estimate the capacity of a section. The average base midblock capacity of a 6–lane divided urban road was found to be 6314 PCU/hr which was further referred as base capacity. Effect of curbside bus stop on midblock capacity of urban road was evaluated by comparing the capacity of a section with curbside bus stop with that of the base capacity. Finally, a mathematical relation has been developed between bus frequency and capacity loss. Also a relation has been suggested between dwell time and capacity loss. The developed relations would be very useful for practising engineers to estimate capacity loss due to bus stop. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bus%20frequency" title="bus frequency">bus frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=bus%20stops" title=" bus stops"> bus stops</a>, <a href="https://publications.waset.org/abstracts/search?q=capacity%20loss" title=" capacity loss"> capacity loss</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20arterial" title=" urban arterial"> urban arterial</a> </p> <a href="https://publications.waset.org/abstracts/8595/capacity-loss-of-urban-arterial-roads-under-the-influence-of-bus-stop" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8595.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 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