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optimization</title> <meta name="description" content="Search results for: discrete optimization"> <meta name="keywords" content="discrete 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="discrete 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> 3852</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: discrete optimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3852</span> Discretization of Cuckoo Optimization Algorithm for Solving Quadratic Assignment Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elham%20Kazemi">Elham Kazemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quadratic Assignment Problem (QAP) is one the combinatorial optimization problems about which research has been done in many companies for allocating some facilities to some locations. The issue of particular importance in this process is the costs of this allocation and the attempt in this problem is to minimize this group of costs. Since the QAP’s are from NP-hard problem, they cannot be solved by exact solution methods. Cuckoo Optimization Algorithm is a Meta-heuristicmethod which has higher capability to find the global optimal points. It is an algorithm which is basically raised to search a continuous space. The Quadratic Assignment Problem is the issue which can be solved in the discrete space, thus the standard arithmetic operators of Cuckoo Optimization Algorithm need to be redefined on the discrete space in order to apply the Cuckoo Optimization Algorithm on the discrete searching space. This paper represents the way of discretizing the Cuckoo optimization algorithm for solving the quadratic assignment problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Quadratic%20Assignment%20Problem%20%28QAP%29" title="Quadratic Assignment Problem (QAP)">Quadratic Assignment Problem (QAP)</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Cuckoo%20Optimization%20Algorithm%20%28DCOA%29" title=" Discrete Cuckoo Optimization Algorithm (DCOA)"> Discrete Cuckoo Optimization Algorithm (DCOA)</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-heuristic%20algorithms" title=" meta-heuristic algorithms"> meta-heuristic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20algorithms" title=" optimization algorithms"> optimization algorithms</a> </p> <a href="https://publications.waset.org/abstracts/25249/discretization-of-cuckoo-optimization-algorithm-for-solving-quadratic-assignment-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25249.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">517</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">3851</span> Optimization of Fourth Order Discrete-Approximation Inclusions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elimhan%20N.%20Mahmudov">Elimhan N. Mahmudov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper concerns the necessary and sufficient conditions of optimality for Cauchy problem of fourth order discrete (PD) and discrete-approximate (PDA) inclusions. The main problem is formulation of the fourth order adjoint discrete and discrete-approximate inclusions and transversality conditions, which are peculiar to problems including fourth order derivatives and approximate derivatives. Thus the necessary and sufficient conditions of optimality are obtained incorporating the Euler-Lagrange and Hamiltonian forms of inclusions. Derivation of optimality conditions are based on the apparatus of locally adjoint mapping (LAM). Moreover in the application of these results we consider the fourth order linear discrete and discrete-approximate inclusions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=difference" title="difference">difference</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=fourth" title=" fourth"> fourth</a>, <a href="https://publications.waset.org/abstracts/search?q=approximation" title=" approximation"> approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=transversality" title=" transversality"> transversality</a> </p> <a href="https://publications.waset.org/abstracts/25199/optimization-of-fourth-order-discrete-approximation-inclusions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25199.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">374</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">3850</span> A Ground Structure Method to Minimize the Total Installed Cost of Steel Frame Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Filippo%20Ranalli">Filippo Ranalli</a>, <a href="https://publications.waset.org/abstracts/search?q=Forest%20Flager"> Forest Flager</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Fischer"> Martin Fischer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a ground structure method to optimize the topology and discrete member sizing of steel frame structures in order to minimize total installed cost, including material, fabrication and erection components. The proposed method improves upon existing cost-based ground structure methods by incorporating constructability considerations well as satisfying both strength and serviceability constraints. The architecture for the method is a bi-level Multidisciplinary Feasible (MDF) architecture in which the discrete member sizing optimization is nested within the topology optimization process. For each structural topology generated, the sizing optimization process seek to find a set of discrete member sizes that result in the lowest total installed cost while satisfying strength (member utilization) and serviceability (node deflection and story drift) criteria. To accurately assess cost, the connection details for the structure are generated automatically using accurate site-specific cost information obtained directly from fabricators and erectors. Member continuity rules are also applied to each node in the structure to improve constructability. The proposed optimization method is benchmarked against conventional weight-based ground structure optimization methods resulting in an average cost savings of up to 30% with comparable computational efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cost-based%20structural%20optimization" title="cost-based structural optimization">cost-based structural optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=cost-based%20topology%20and%20sizing" title=" cost-based topology and sizing"> cost-based topology and sizing</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20frame%20ground%20structure%20optimization" title=" steel frame ground structure optimization"> steel frame ground structure optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multidisciplinary%20optimization%20of%20steel%20structures" title=" multidisciplinary optimization of steel structures"> multidisciplinary optimization of steel structures</a> </p> <a href="https://publications.waset.org/abstracts/73293/a-ground-structure-method-to-minimize-the-total-installed-cost-of-steel-frame-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73293.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">341</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">3849</span> An Improved Discrete Version of Teaching–Learning-Based ‎Optimization for Supply Chain Network Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Yadegari">Ehsan Yadegari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> While there are several metaheuristics and exact approaches to solving the Supply Chain Network Design (SCND) problem, there still remains an unfilled gap in using the Teaching-Learning-Based Optimization (TLBO) algorithm. The algorithm has demonstrated desirable results with problems with complicated combinational optimization. The present study introduces a Discrete Self-Study TLBO (DSS-TLBO) with priority-based solution representation that can solve a supply chain network configuration model to lower the total expenses of establishing facilities and the flow of materials. The network features four layers, namely suppliers, plants, distribution centers (DCs), and customer zones. It is designed to meet the customer’s demand through transporting the material between layers of network and providing facilities in the best economic Potential locations. To have a higher quality of the solution and increase the speed of TLBO, a distinct operator was introduced that ensures self-adaptation (self-study) in the algorithm based on the four types of local search. In addition, while TLBO is used in continuous solution representation and priority-based solution representation is discrete, a few modifications were added to the algorithm to remove the solutions that are infeasible. As shown by the results of experiments, the superiority of DSS-TLBO compared to pure TLBO, genetic algorithm (GA) and firefly Algorithm (FA) was established. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20network%20design" title="supply chain network design">supply chain network design</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching%E2%80%93learning-based%20optimization" title=" teaching–learning-based optimization"> teaching–learning-based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=improved%20metaheuristics" title=" improved metaheuristics"> improved metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20solution%20representation" title=" discrete solution representation"> discrete solution representation</a> </p> <a href="https://publications.waset.org/abstracts/184885/an-improved-discrete-version-of-teaching-learning-based-optimization-for-supply-chain-network-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184885.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">52</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">3848</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">3847</span> A Simulation Modeling Approach for Optimization of Storage Space Allocation in Container Terminal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gamal%20Abd%20El-Nasser%20A.%20Said">Gamal Abd El-Nasser A. Said</a>, <a href="https://publications.waset.org/abstracts/search?q=El-Sayed%20M.%20El-Horbaty"> El-Sayed M. El-Horbaty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Container handling problems at container terminals are NP-hard problems. This paper presents an approach using discrete-event simulation modeling to optimize solution for storage space allocation problem, taking into account all various interrelated container terminal handling activities. The proposed approach is applied on a real case study data of container terminal at Alexandria port. The computational results show the effectiveness of the proposed model for optimization of storage space allocation in container terminal where 54% reduction in containers handling time in port is achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=container%20terminal" title="container terminal">container terminal</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete-event%20simulation" title=" discrete-event simulation"> discrete-event simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=storage%20space%20allocation" title=" storage space allocation "> storage space allocation </a> </p> <a href="https://publications.waset.org/abstracts/19699/a-simulation-modeling-approach-for-optimization-of-storage-space-allocation-in-container-terminal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19699.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">3846</span> Discrete Group Search Optimizer for the Travelling Salesman Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raed%20Alnajjar">Raed Alnajjar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Zakree"> Mohd Zakree</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Nazri"> Ahmad Nazri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we apply Discrete Group Search Optimizer (DGSO) for solving Traveling Salesman Problem (TSP). The DGSO is a nature inspired optimization algorithm that imitates the animal behavior, especially animal searching behavior. The proposed DGSO uses a vector representation and some discrete operators, such as destruction, construction, differential evolution, swap and insert. The TSP is a well-known hard combinatorial optimization problem, which seeks to find the shortest path among numbers of cities. The performance of the proposed DGSO is evaluated and tested on benchmark instances which listed in LIBTSP dataset. The experimental results show that the performance of the proposed DGSO is comparable with the other methods in the state of the art for some instances. The results show that DGSO outperform Ant Colony System (ACS) in some instances whilst outperform other metaheuristic in most instances. In addition to that, the new results obtained a number of optimal solutions and some best known results. DGSO was able to obtain feasible and good quality solution across all dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20group%20search%20optimizer%20%28DGSO%29%3B%20Travelling%20salesman%20problem%20%28TSP%29%3B%20Variable%20neighborhood%20search%28VNS%29" title="discrete group search optimizer (DGSO); Travelling salesman problem (TSP); Variable neighborhood search(VNS)">discrete group search optimizer (DGSO); Travelling salesman problem (TSP); Variable neighborhood search(VNS)</a> </p> <a href="https://publications.waset.org/abstracts/36200/discrete-group-search-optimizer-for-the-travelling-salesman-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36200.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">324</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">3845</span> Discrete Swarm with Passive Congregation for Cost Minimization of the Multiple Vehicle Routing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Aboueldahab">Tarek Aboueldahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20Farag"> Hanan Farag</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cost minimization of Multiple Vehicle Routing Problem becomes a critical issue in the field of transportation because it is NP-hard optimization problem and the search space is complex. Many researches use the hybridization of artificial intelligence (AI) models to solve this problem; however, it can not guarantee to reach the best solution due to the difficulty of searching the whole search space. To overcome this problem, we introduce the hybrid model of Discrete Particle Swarm Optimization (DPSO) with a passive congregation which enable searching the whole search space to compromise between both local and global search. The practical experiment shows that our model obviously outperforms other hybrid models in cost minimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cost%20minimization" title="cost minimization">cost minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-vehicle%20routing%20problem" title=" multi-vehicle routing problem"> multi-vehicle routing problem</a>, <a href="https://publications.waset.org/abstracts/search?q=passive%20congregation" title=" passive congregation"> passive congregation</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20swarm" title=" discrete swarm"> discrete swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=passive%20congregation" title=" passive congregation"> passive congregation</a> </p> <a href="https://publications.waset.org/abstracts/157025/discrete-swarm-with-passive-congregation-for-cost-minimization-of-the-multiple-vehicle-routing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157025.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">98</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">3844</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">553</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">3843</span> Multi-Objective Discrete Optimization of External Thermal Insulation Composite Systems in Terms of Thermal and Embodied Energy Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Berfin%20Yildiz">Berfin Yildiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> These days, increasing global warming effects, limited amount of energy resources, etc., necessitates the awareness that must be present in every profession group. The architecture and construction sectors are responsible for both the embodied and operational energy of the materials. This responsibility has led designers to seek alternative solutions for energy-efficient material selection. The choice of energy-efficient material requires consideration of the entire life cycle, including the building's production, use, and disposal energy. The aim of this study is to investigate the method of material selection of external thermal insulation composite systems (ETICS). Embodied and in-use energy values of material alternatives were used for the evaluation in this study. The operational energy is calculated according to the u-value calculation method defined in the TS 825 (Thermal Insulation Requirements) standard for Turkey, and the embodied energy is calculated based on the manufacturer's Energy Performance Declaration (EPD). ETICS consists of a wall, adhesive, insulation, lining, mechanical, mesh, and exterior finishing materials. In this study, lining, mechanical, and mesh materials were ignored because EPD documents could not be obtained. The material selection problem is designed as a hypothetical volume area (5x5x3m) and defined as a multi-objective discrete optimization problem for external thermal insulation composite systems. Defining the problem as a discrete optimization problem is important in order to choose between materials of various thicknesses and sizes. Since production and use energy values, which are determined as optimization objectives in the study, are often conflicting values, material selection is defined as a multi-objective optimization problem, and it is aimed to obtain many solution alternatives by using Hypervolume (HypE) algorithm. The enrollment process started with 100 individuals and continued for 50 generations. According to the obtained results, it was observed that autoclaved aerated concrete and Ponce block as wall material, glass wool, as insulation material gave better results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=embodied%20energy" title="embodied energy">embodied energy</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20discrete%20optimization" title=" multi-objective discrete optimization"> multi-objective discrete optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=performative%20design" title=" performative design"> performative design</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20insulation" title=" thermal insulation"> thermal insulation</a> </p> <a href="https://publications.waset.org/abstracts/122118/multi-objective-discrete-optimization-of-external-thermal-insulation-composite-systems-in-terms-of-thermal-and-embodied-energy-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122118.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">3842</span> Optimization of Structures with Mixed Integer Non-linear Programming (MINLP)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stojan%20Kravanja">Stojan Kravanja</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrej%20Ivani%C4%8D"> Andrej Ivanič</a>, <a href="https://publications.waset.org/abstracts/search?q=Toma%C5%BE%20%C5%BDula"> Tomaž Žula</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This contribution focuses on structural optimization in civil engineering using mixed integer non-linear programming (MINLP). MINLP is characterized as a versatile method that can handle both continuous and discrete optimization variables simultaneously. Continuous variables are used to optimize parameters such as dimensions, stresses, masses, or costs, while discrete variables represent binary decisions to determine the presence or absence of structural elements within a structure while also calculating discrete materials and standard sections. The optimization process is divided into three main steps. First, a mechanical superstructure with a variety of different topology-, material- and dimensional alternatives. Next, a MINLP model is formulated to encapsulate the optimization problem. Finally, an optimal solution is searched in the direction of the defined objective function while respecting the structural constraints. The economic or mass objective function of the material and labor costs of a structure is subjected to the constraints known from structural analysis. These constraints include equations for the calculation of internal forces and deflections, as well as equations for the dimensioning of structural components (in accordance with the Eurocode standards). Given the complex, non-convex and highly non-linear nature of optimization problems in civil engineering, the Modified Outer-Approximation/Equality-Relaxation (OA/ER) algorithm is applied. This algorithm alternately solves subproblems of non-linear programming (NLP) and main problems of mixed-integer linear programming (MILP), in this way gradually refines the solution space up to the optimal solution. The NLP corresponds to the continuous optimization of parameters (with fixed topology, discrete materials and standard dimensions, all determined in the previous MILP), while the MILP involves a global approximation to the superstructure of alternatives, where a new topology, materials, standard dimensions are determined. The optimization of a convex problem is stopped when the MILP solution becomes better than the best NLP solution. Otherwise, it is terminated when the NLP solution can no longer be improved. While the OA/ER algorithm, like all other algorithms, does not guarantee global optimality due to the presence of non-convex functions, various modifications, including convexity tests, are implemented in OA/ER to mitigate these difficulties. The effectiveness of the proposed MINLP approach is demonstrated by its application to various structural optimization tasks, such as mass optimization of steel buildings, cost optimization of timber halls, composite floor systems, etc. Special optimization models have been developed for the optimization of these structures. The MINLP optimizations, facilitated by the user-friendly software package MIPSYN, provide insights into a mass or cost-optimal solutions, optimal structural topologies, optimal material and standard cross-section choices, confirming MINLP as a valuable method for the optimization of structures in civil engineering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MINLP" title="MINLP">MINLP</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed-integer%20non-linear%20programming" title=" mixed-integer non-linear programming"> mixed-integer non-linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=structures" title=" structures"> structures</a> </p> <a href="https://publications.waset.org/abstracts/185274/optimization-of-structures-with-mixed-integer-non-linear-programming-minlp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185274.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">46</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">3841</span> Role of Discrete Event Simulation in the Assessment and Selection of the Potential Reconfigurable Manufacturing Solutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohsin%20Raza">Mohsin Raza</a>, <a href="https://publications.waset.org/abstracts/search?q=Arne%20Bilberg"> Arne Bilberg</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Ditlev%20Brun%C3%B8"> Thomas Ditlev Brunø</a>, <a href="https://publications.waset.org/abstracts/search?q=Ann-Louise%20Andersen"> Ann-Louise Andersen</a>, <a href="https://publications.waset.org/abstracts/search?q=Filip%20SK%C3%A4rin"> Filip SKärin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Shifting from a dedicated or flexible manufacturing system to a reconfigurable manufacturing system (RMS) requires a significant amount of time, money, and effort. Therefore, it is vital to verify beforehand that the potential reconfigurable solution will be able to achieve the organizational objectives. Discrete event simulation offers the opportunity of assessing several reconfigurable alternatives against the set objectives. This study signifies the importance of using discrete-event simulation as a tool to verify several reconfiguration options. Two different industrial cases have been presented in the study to elaborate on the role of discrete event simulation in the implementation methodology of RMSs. The study concluded that discrete event simulation is one of the important tools to consider in the RMS implementation methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reconfigurable%20manufacturing%20system" title="reconfigurable manufacturing system">reconfigurable manufacturing system</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20event%20simulation" title=" discrete event simulation"> discrete event simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=Tecnomatix%20plant%20simulation" title=" Tecnomatix plant simulation"> Tecnomatix plant simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=RMS" title=" RMS"> RMS</a> </p> <a href="https://publications.waset.org/abstracts/150254/role-of-discrete-event-simulation-in-the-assessment-and-selection-of-the-potential-reconfigurable-manufacturing-solutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150254.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">124</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">3840</span> A New Approach of Preprocessing with SVM Optimization Based on PSO for Bearing Fault Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tawfik%20Thelaidjia">Tawfik Thelaidjia</a>, <a href="https://publications.waset.org/abstracts/search?q=Salah%20Chenikher"> Salah Chenikher </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=condition%20monitoring" title="condition monitoring">condition monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=kurtosis" title=" kurtosis"> kurtosis</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</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=roller%20bearing" title=" roller bearing"> roller bearing</a>, <a href="https://publications.waset.org/abstracts/search?q=rotating%20machines" title=" rotating machines"> rotating machines</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20measurement" title=" vibration measurement "> vibration measurement </a> </p> <a href="https://publications.waset.org/abstracts/2554/a-new-approach-of-preprocessing-with-svm-optimization-based-on-pso-for-bearing-fault-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2554.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">3839</span> Particle Swarm Optimization Algorithm vs. Genetic Algorithm for Image Watermarking Based Discrete Wavelet Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omaima%20N.%20Ahmad%20AL-Allaf">Omaima N. Ahmad AL-Allaf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over communication networks, images can be easily copied and distributed in an illegal way. The copyright protection for authors and owners is necessary. Therefore, the digital watermarking techniques play an important role as a valid solution for authority problems. Digital image watermarking techniques are used to hide watermarks into images to achieve copyright protection and prevent its illegal copy. Watermarks need to be robust to attacks and maintain data quality. Therefore, we discussed in this paper two approaches for image watermarking, first is based on Particle Swarm Optimization (PSO) and the second approach is based on Genetic Algorithm (GA). Discrete wavelet transformation (DWT) is used with the two approaches separately for embedding process to cover image transformation. Each of PSO and GA is based on co-relation coefficient to detect the high energy coefficient watermark bit in the original image and then hide the watermark in original image. Many experiments were conducted for the two approaches with different values of PSO and GA parameters. From experiments, PSO approach got better results with PSNR equal 53, MSE equal 0.0039. Whereas GA approach got PSNR equal 50.5 and MSE equal 0.0048 when using population size equal to 100, number of iterations equal to 150 and 3×3 block. According to the results, we can note that small block size can affect the quality of image watermarking based PSO/GA because small block size can increase the search area of the watermarking image. Better PSO results were obtained when using swarm size equal to 100. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20watermarking" title="image watermarking">image watermarking</a>, <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=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/76473/particle-swarm-optimization-algorithm-vs-genetic-algorithm-for-image-watermarking-based-discrete-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76473.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">3838</span> Discrete Breeding Swarm for Cost Minimization of Parallel Job Shop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Aboueldahab">Tarek Aboueldahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20Farag"> Hanan Farag</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Parallel Job Shop Scheduling Problem (JSP) is a multi-objective and multi constrains NP- optimization problem. Traditional Artificial Intelligence techniques have been widely used; however, they could be trapped into the local minimum without reaching the optimum solution, so we propose a hybrid Artificial Intelligence model (AI) with Discrete Breeding Swarm (DBS) added to traditional Artificial Intelligence to avoid this trapping. This model is applied in the cost minimization of the Car Sequencing and Operator Allocation (CSOA) problem. The practical experiment shows that our model outperforms other techniques in cost minimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parallel%20job%20shop%20scheduling%20problem" title="parallel job shop scheduling problem">parallel job shop scheduling problem</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20breeding%20swarm" title=" discrete breeding swarm"> discrete breeding swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=car%20sequencing%20and%20operator%20allocation" title=" car sequencing and operator allocation"> car sequencing and operator allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20minimization" title=" cost minimization"> cost minimization</a> </p> <a href="https://publications.waset.org/abstracts/132701/discrete-breeding-swarm-for-cost-minimization-of-parallel-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132701.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">188</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">3837</span> Multidimensional Integral and Discrete Opial–Type Inequalities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maja%20Andri%C4%87">Maja Andrić</a>, <a href="https://publications.waset.org/abstracts/search?q=Josip%20Pe%C4%8Dari%C4%87"> Josip Pečarić</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the last five decades, an enormous amount of work has been done on Opial’s integral inequality, dealing with new proofs, various generalizations, extensions and discrete analogs. The Opial inequality is recognized as a fundamental result in the analysis of qualitative properties of solution of differential equations. We use submultiplicative convex functions, appropriate representations of functions and inequalities involving means to obtain generalizations and extensions of certain known multidimensional integral and discrete Opial-type inequalities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Opial%27s%20inequality" title="Opial's inequality">Opial's inequality</a>, <a href="https://publications.waset.org/abstracts/search?q=Jensen%27s%20inequality" title=" Jensen's inequality"> Jensen's inequality</a>, <a href="https://publications.waset.org/abstracts/search?q=integral%20inequality" title=" integral inequality"> integral inequality</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20inequality" title=" discrete inequality"> discrete inequality</a> </p> <a href="https://publications.waset.org/abstracts/41583/multidimensional-integral-and-discrete-opial-type-inequalities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41583.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">439</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">3836</span> Finite Element and Split Bregman Methods for Solving a Family of Optimal Control Problem with Partial Differential Equation Constraint</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Lot">Mahmoud Lot</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we will discuss the solution of elliptic optimal control problem. First, by using the nite element method, we obtain the discrete form of the problem. The obtained discrete problem is actually a large scale constrained optimization problem. Solving this optimization problem with traditional methods is diﬃcult and requires a lot of CPU time and memory. But split Bergman method converts the constrained problem to an unconstrained, and hence it saves time and memory requirement. Then we use the split Bregman method for solving this problem, and examples show the speed and accuracy of split Bregman methods for solving these types of problems. We also use the SQP method for solving the examples and compare with the split Bregman method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Split%20Bregman%20Method" title="Split Bregman Method">Split Bregman Method</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control%20with%20elliptic%20partial%20differential%20equation%20constraint" title=" optimal control with elliptic partial differential equation constraint"> optimal control with elliptic partial differential equation constraint</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</a> </p> <a href="https://publications.waset.org/abstracts/123437/finite-element-and-split-bregman-methods-for-solving-a-family-of-optimal-control-problem-with-partial-differential-equation-constraint" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123437.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">152</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">3835</span> Numerical Modelling of Dry Stone Masonry Structures Based on Finite-Discrete Element Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=%C5%BD.%20Nikoli%C4%87">Ž. Nikolić</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Smoljanovi%C4%87"> H. Smoljanović</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20%C5%BDivalji%C4%87"> N. Živaljić</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents numerical model based on finite-discrete element method for analysis of the structural response of dry stone masonry structures under static and dynamic loads. More precisely, each discrete stone block is discretized by finite elements. Material non-linearity including fracture and fragmentation of discrete elements as well as cyclic behavior during dynamic load are considered through contact elements which are implemented within a finite element mesh. The application of the model was conducted on several examples of these structures. The performed analysis shows high accuracy of the numerical results in comparison with the experimental ones and demonstrates the potential of the finite-discrete element method for modelling of the response of dry stone masonry structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dry%20stone%20masonry%20structures" title="dry stone masonry structures">dry stone masonry structures</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20load" title=" dynamic load"> dynamic load</a>, <a href="https://publications.waset.org/abstracts/search?q=finite-discrete%20element%20method" title=" finite-discrete element method"> finite-discrete element method</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20load" title=" static load"> static load</a> </p> <a href="https://publications.waset.org/abstracts/47740/numerical-modelling-of-dry-stone-masonry-structures-based-on-finite-discrete-element-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47740.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">414</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">3834</span> Fundamental Solutions for Discrete Dynamical Systems Involving the Fractional Laplacian</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jorge%20Gonzalez%20Camus">Jorge Gonzalez Camus</a>, <a href="https://publications.waset.org/abstracts/search?q=Valentin%20Keyantuo"> Valentin Keyantuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahamadi%20Warma"> Mahamadi Warma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we obtain representation results for solutions of a time-fractional differential equation involving the discrete fractional Laplace operator in terms of generalized Wright functions. Such equations arise in the modeling of many physical systems, for example, chain processes in chemistry and radioactivity. The focus is on the linear problem of the simplified Moore - Gibson - Thompson equation, where the discrete fractional Laplacian and the Caputo fractional derivate of order on (0,2] are involved. As a particular case, we obtain the explicit solution for the discrete heat equation and discrete wave equation. Furthermore, we show the explicit solution for the equation involving the perturbed Laplacian by the identity operator. The main tool for obtaining the explicit solution are the Laplace and discrete Fourier transforms, and Stirling's formula. The methodology mainly is to apply both transforms in the equation, to find the inverse of each transform, and to prove that this solution is well defined, using Stirling´s formula. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20fractional%20Laplacian" title="discrete fractional Laplacian">discrete fractional Laplacian</a>, <a href="https://publications.waset.org/abstracts/search?q=explicit%20representation%20of%20solutions" title=" explicit representation of solutions"> explicit representation of solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20heat%20and%20wave%20equations" title=" fractional heat and wave equations"> fractional heat and wave equations</a>, <a href="https://publications.waset.org/abstracts/search?q=fundamental" title=" fundamental"> fundamental</a> </p> <a href="https://publications.waset.org/abstracts/99922/fundamental-solutions-for-discrete-dynamical-systems-involving-the-fractional-laplacian" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99922.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">209</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">3833</span> Grid Computing for Multi-Objective Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aouaouche%20Elmaouhab">Aouaouche Elmaouhab</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassina%20Beggar"> Hassina Beggar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Solving multi-objective discrete optimization applications has always been limited by the resources of one machine: By computing power or by memory, most often both. To speed up the calculations, the grid computing represents a primary solution for the treatment of these applications through the parallelization of these resolution methods. In this work, we are interested in the study of some methods for solving multiple objective integer linear programming problem based on Branch-and-Bound and the study of grid computing technology. This study allowed us to propose an implementation of the method of Abbas and Al on the grid by reducing the execution time. To enhance our contribution, the main results are presented. <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=integer%20linear%20programming" title=" integer linear programming"> integer linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=grid%20computing" title=" grid computing"> grid computing</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20computing" title=" parallel computing"> parallel computing</a> </p> <a href="https://publications.waset.org/abstracts/31941/grid-computing-for-multi-objective-optimization-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31941.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">486</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">3832</span> Conservativeness of Probabilistic Constrained Optimal Control Method for Unknown Probability Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent decades, probabilistic constrained optimal control problems have attracted much attention in many research field. Although probabilistic constraints are generally intractable in an optimization problem, several tractable methods haven been proposed to handle probabilistic constraints. In most methods, probabilistic constraints are reduced to deterministic constraints that are tractable in an optimization problem. However, there is a gap between the transformed deterministic constraints in case of known and unknown probability distribution. This paper examines the conservativeness of probabilistic constrained optimization method with the unknown probability distribution. The objective of this paper is to provide a quantitative assessment of the conservatism for tractable constraints in probabilistic constrained optimization with the unknown probability distribution. <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=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20time%20systems" title=" discrete time systems"> discrete time systems</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20constraints" title=" probabilistic constraints"> probabilistic constraints</a> </p> <a href="https://publications.waset.org/abstracts/35585/conservativeness-of-probabilistic-constrained-optimal-control-method-for-unknown-probability-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35585.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">581</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">3831</span> A Hybrid Watermarking Scheme Using Discrete and Discrete Stationary Wavelet Transformation For Color Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B%C3%BClent%20Kantar">Bülent Kantar</a>, <a href="https://publications.waset.org/abstracts/search?q=Numan%20%C3%9Cnald%C4%B1"> Numan Ünaldı</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new method which includes robust and invisible digital watermarking on images that is colored. Colored images are used as watermark. Frequency region is used for digital watermarking. Discrete wavelet transform and discrete stationary wavelet transform are used for frequency region transformation. Low, medium and high frequency coefficients are obtained by applying the two-level discrete wavelet transform to the original image. Low frequency coefficients are obtained by applying one level discrete stationary wavelet transform separately to all frequency coefficient of the two-level discrete wavelet transformation of the original image. For every low frequency coefficient obtained from one level discrete stationary wavelet transformation, watermarks are added. Watermarks are added to all frequency coefficients of two-level discrete wavelet transform. Totally, four watermarks are added to original image. In order to get back the watermark, the original and watermarked images are applied with two-level discrete wavelet transform and one level discrete stationary wavelet transform. The watermark is obtained from difference of the discrete stationary wavelet transform of the low frequency coefficients. A total of four watermarks are obtained from all frequency of two-level discrete wavelet transform. Obtained watermark results are compared with real watermark results, and a similarity result is obtained. A watermark is obtained from the highest similarity values. Proposed methods of watermarking are tested against attacks of the geometric and image processing. The results show that proposed watermarking method is robust and invisible. All features of frequencies of two level discrete wavelet transform watermarking are combined to get back the watermark from the watermarked image. Watermarks have been added to the image by converting the binary image. These operations provide us with better results in getting back the watermark from watermarked image by attacking of the geometric and image processing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=watermarking" title="watermarking">watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=DSWT" title=" DSWT"> DSWT</a>, <a href="https://publications.waset.org/abstracts/search?q=copy%20right%20protection" title=" copy right protection"> copy right protection</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB" title=" RGB "> RGB </a> </p> <a href="https://publications.waset.org/abstracts/16927/a-hybrid-watermarking-scheme-using-discrete-and-discrete-stationary-wavelet-transformation-for-color-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16927.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">3830</span> Superconvergence of the Iterated Discrete Legendre Galerkin Method for Fredholm-Hammerstein Equations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Payel%20Das">Payel Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Gnaneshwar%20Nelakanti"> Gnaneshwar Nelakanti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we analyse the iterated discrete Legendre Galerkin method for Fredholm-Hammerstein integral equations with smooth kernel. Using sufficiently accurate numerical quadrature rule, we obtain superconvergence rates for the iterated discrete Legendre Galerkin solutions in both infinity and $L^2$-norm. Numerical examples are given to illustrate the theoretical results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hammerstein%20integral%20equations" title="hammerstein integral equations">hammerstein integral equations</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20method" title=" spectral method"> spectral method</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20galerkin" title=" discrete galerkin"> discrete galerkin</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%0D%0Aquadrature" title=" numerical quadrature"> numerical quadrature</a>, <a href="https://publications.waset.org/abstracts/search?q=superconvergence" title=" superconvergence"> superconvergence</a> </p> <a href="https://publications.waset.org/abstracts/22260/superconvergence-of-the-iterated-discrete-legendre-galerkin-method-for-fredholm-hammerstein-equations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22260.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">471</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">3829</span> Solving the Set Covering Problem Using the Binary Cat Swarm Optimization Metaheuristic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Broderick%20Crawford">Broderick Crawford</a>, <a href="https://publications.waset.org/abstracts/search?q=Ricardo%20Soto"> Ricardo Soto</a>, <a href="https://publications.waset.org/abstracts/search?q=Natalia%20Berrios"> Natalia Berrios</a>, <a href="https://publications.waset.org/abstracts/search?q=Eduardo%20Olguin"> Eduardo Olguin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a binary cat swarm optimization for solving the Set covering problem. The set covering problem is a well-known NP-hard problem with many practical applications, including those involving scheduling, production planning and location problems. Binary cat swarm optimization is a recent swarm metaheuristic technique based on the behavior of discrete cats. Domestic cats show the ability to hunt and are curious about moving objects. The cats have two modes of behavior: seeking mode and tracing mode. We illustrate this approach with 65 instances of the problem from the OR-Library. Moreover, we solve this problem with 40 new binarization techniques and we select the technical with the best results obtained. Finally, we make a comparison between results obtained in previous studies and the new binarization technique, that is, with roulette wheel as transfer function and V3 as discretization technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20cat%20swarm%20optimization" title="binary cat swarm optimization">binary cat swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=binarization%20methods" title=" binarization methods"> binarization methods</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic" title=" metaheuristic"> metaheuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=set%20covering%20problem" title=" set covering problem"> set covering problem</a> </p> <a href="https://publications.waset.org/abstracts/47183/solving-the-set-covering-problem-using-the-binary-cat-swarm-optimization-metaheuristic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47183.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">396</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">3828</span> A Look at the Quantum Theory of Atoms in Molecules from the Discrete Morse Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dairo%20Jose%20Hernandez%20Paez">Dairo Jose Hernandez Paez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The quantum theory of atoms in molecules (QTAIM) allows us to obtain topological information on electronic density in quantum mechanical systems. The QTAIM starts by considering the electron density as a continuous mathematical object. On the other hand, the discretization of electron density is also a mathematical object, which, from discrete mathematics, would allow a new approach to its topological study. From this point of view, it is necessary to develop a series of steps that provide the theoretical support that guarantees its application. Some of the steps that we consider most important are mentioned below: (1) obtain good representations of the electron density through computational calculations, (2) design a methodology for the discretization of electron density, and construct the simplicial complex. (3) Make an analysis of the discrete vector field associating the simplicial complex. (4) Finally, in this research, we propose to use the discrete Morse theory as a mathematical tool to carry out studies of electron density topology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20mathematics" title="discrete mathematics">discrete mathematics</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Morse%20theory" title=" Discrete Morse theory"> Discrete Morse theory</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20density" title=" electronic density"> electronic density</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20calculations" title=" computational calculations"> computational calculations</a> </p> <a href="https://publications.waset.org/abstracts/163354/a-look-at-the-quantum-theory-of-atoms-in-molecules-from-the-discrete-morse-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163354.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">104</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">3827</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">3826</span> 3-D Visualization and Optimization for SISO Linear Systems Using Parametrization of Two-Stage Compensator Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kazuyoshi%20Mori">Kazuyoshi Mori</a>, <a href="https://publications.waset.org/abstracts/search?q=Keisuke%20Hashimoto"> Keisuke Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we consider the two-stage compensator designs of SISO plants. As an investigation of the characteristics of the two-stage compensator designs, which is not well investigated yet, of SISO plants, we implement three dimensional visualization systems of output signals and optimization system for SISO plants by the parametrization of stabilizing controllers based on the two-stage compensator design. The system runs on Mathematica by using “Three Dimensional Surface Plots,” so that the visualization can be interactively manipulated by users. In this paper, we use the discrete-time LTI system model. Even so, our approach is the factorization approach, so that the result can be applied to many linear models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=linear%20systems" title="linear systems">linear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=visualization" title=" visualization"> visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Mathematica" title=" Mathematica"> Mathematica</a> </p> <a href="https://publications.waset.org/abstracts/52497/3-d-visualization-and-optimization-for-siso-linear-systems-using-parametrization-of-two-stage-compensator-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52497.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">298</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">3825</span> Discrete Tracking Control of Nonholonomic Mobile Robots: Backstepping Design Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20S.%20Andreev">Alexander S. Andreev</a>, <a href="https://publications.waset.org/abstracts/search?q=Olga%20A.%20Peregudova"> Olga A. Peregudova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a discrete tracking control of nonholonomic mobile robots with two degrees of freedom. The electro-mechanical model of a mobile robot moving on a horizontal surface without slipping, with two rear wheels controlled by two independent DC electric, and one front roal wheel is considered. We present back-stepping design based on the Euler approximate discrete-time model of a continuous-time plant. Theoretical considerations are verified by numerical simulation. The work was supported by RFFI (15-01-08482). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=actuator%20dynamics" title="actuator dynamics">actuator dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20stepping" title=" back stepping"> back stepping</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete-time%20controller" title=" discrete-time controller"> discrete-time controller</a>, <a href="https://publications.waset.org/abstracts/search?q=Lyapunov%20function" title=" Lyapunov function"> Lyapunov function</a>, <a href="https://publications.waset.org/abstracts/search?q=wheeled%20mobile%20robot" title=" wheeled mobile robot "> wheeled mobile robot </a> </p> <a href="https://publications.waset.org/abstracts/15632/discrete-tracking-control-of-nonholonomic-mobile-robots-backstepping-design-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15632.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">415</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">3824</span> Analytical Solution of Non–Autonomous Discrete Non-Linear Schrodinger Equation With Saturable Non-Linearity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mishu%20Gupta">Mishu Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Rama%20Gupta"> Rama Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It has been elucidated here that non- autonomous discrete non-linear Schrödinger equation is associated with saturable non-linearity through photo-refractive media. We have investigated the localized solution of non-autonomous saturable discrete non-linear Schrödinger equations. The similarity transformation has been involved in converting non-autonomous saturable discrete non-linear Schrödinger equation to constant-coefficient saturable discrete non-linear Schrödinger equation (SDNLSE), whose exact solution is already known. By back substitution, the solution of the non-autonomous version has been obtained. We have analysed our solution for the hyperbolic and periodic form of gain/loss term, and interesting results have been obtained. The most important characteristic role is that it helps us to analyse the propagation of electromagnetic waves in glass fibres and other optical wave mediums. Also, the usage of SDNLSE has been seen in tight binding for Bose-Einstein condensates in optical mediums. Even the solutions are interrelated, and its properties are prominently used in various physical aspects like optical waveguides, Bose-Einstein (B-E) condensates in optical mediums, Non-linear optics in photonic crystals, and non-linear kerr–type non-linearity effect and photo refracting medium. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=B-E-Bose-Einstein" title="B-E-Bose-Einstein">B-E-Bose-Einstein</a>, <a href="https://publications.waset.org/abstracts/search?q=DNLSE-Discrete%20non%20linear%20schrodinger%20equation" title=" DNLSE-Discrete non linear schrodinger equation"> DNLSE-Discrete non linear schrodinger equation</a>, <a href="https://publications.waset.org/abstracts/search?q=NLSE-non%20linear%20schrodinger%20equation" title=" NLSE-non linear schrodinger equation"> NLSE-non linear schrodinger equation</a>, <a href="https://publications.waset.org/abstracts/search?q=SDNLSE%20-%20saturable%20discrete%20non%20linear%20Schrodinger%20equation" title=" SDNLSE - saturable discrete non linear Schrodinger equation"> SDNLSE - saturable discrete non linear Schrodinger equation</a> </p> <a href="https://publications.waset.org/abstracts/121074/analytical-solution-of-non-autonomous-discrete-non-linear-schrodinger-equation-with-saturable-non-linearity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121074.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">155</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">3823</span> Application of the Discrete-Event Simulation When Optimizing of Business Processes in Trading Companies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maxat%20Bokambayev">Maxat Bokambayev</a>, <a href="https://publications.waset.org/abstracts/search?q=Bella%20Tussupova"> Bella Tussupova</a>, <a href="https://publications.waset.org/abstracts/search?q=Aisha%20Mamyrova"> Aisha Mamyrova</a>, <a href="https://publications.waset.org/abstracts/search?q=Erlan%20Izbasarov"> Erlan Izbasarov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optimization of business processes in trading companies is reviewed in the report. There is the presentation of the “Wholesale Customer Order Handling Process” business process model applicable for small and medium businesses. It is proposed to apply the algorithm for automation of the customer order processing which will significantly reduce labor costs and time expenditures and increase the profitability of companies. An optimized business process is an element of the information system of accounting of spare parts trading network activity. The considered algorithm may find application in the trading industry as well. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20processes" title="business processes">business processes</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete-event%20simulation" title=" discrete-event simulation"> discrete-event simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=management" title=" management"> management</a>, <a href="https://publications.waset.org/abstracts/search?q=trading%20industry" title=" trading industry"> trading industry</a> </p> <a href="https://publications.waset.org/abstracts/8577/application-of-the-discrete-event-simulation-when-optimizing-of-business-processes-in-trading-companies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8577.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info 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