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linear programming</title> <meta name="description" content="Search results for: sequential linear programming"> <meta name="keywords" content="sequential linear programming"> <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 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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="sequential linear programming"> <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> 4490</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: sequential linear programming</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4490</span> Engineering Optimization of Flexible Energy Absorbers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Hedayati">Reza Hedayati</a>, <a href="https://publications.waset.org/abstracts/search?q=Meysam%20Jahanbakhshi"> Meysam Jahanbakhshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Elastic energy absorbers which consist of a ring-liked plate and springs can be a good choice for increasing the impact duration during an accident. In the current project, an energy absorber system is optimized using four optimizing methods Kuhn-Tucker, Sequential Linear Programming (SLP), Concurrent Subspace Design (CSD), and Pshenichny-Lim-Belegundu-Arora (PLBA). Time solution, convergence, Programming Length and accuracy of the results were considered to find the best solution algorithm. Results showed the superiority of PLBA over the other algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Concurrent%20Subspace%20Design%20%28CSD%29" title="Concurrent Subspace Design (CSD)">Concurrent Subspace Design (CSD)</a>, <a href="https://publications.waset.org/abstracts/search?q=Kuhn-Tucker" title=" Kuhn-Tucker"> Kuhn-Tucker</a>, <a href="https://publications.waset.org/abstracts/search?q=Pshenichny-Lim-Belegundu-Arora%20%28PLBA%29" title=" Pshenichny-Lim-Belegundu-Arora (PLBA)"> Pshenichny-Lim-Belegundu-Arora (PLBA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Sequential%20Linear%20Programming%20%28SLP%29" title=" Sequential Linear Programming (SLP)"> Sequential Linear Programming (SLP)</a> </p> <a href="https://publications.waset.org/abstracts/23842/engineering-optimization-of-flexible-energy-absorbers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23842.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">399</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">4489</span> Sensitivity Analysis in Fuzzy Linear Programming Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Nasseri">S. H. Nasseri</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ebrahimnejad"> A. Ebrahimnejad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fuzzy set theory has been applied to many fields, such as operations research, control theory, and management sciences. In this paper, we consider two classes of fuzzy linear programming (FLP) problems: Fuzzy number linear programming and linear programming with trapezoidal fuzzy variables problems. We state our recently established results and develop fuzzy primal simplex algorithms for solving these problems. Finally, we give illustrative examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20linear%20programming" title="fuzzy linear programming">fuzzy linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20numbers" title=" fuzzy numbers"> fuzzy numbers</a>, <a href="https://publications.waset.org/abstracts/search?q=duality" title=" duality"> duality</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a> </p> <a href="https://publications.waset.org/abstracts/16916/sensitivity-analysis-in-fuzzy-linear-programming-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16916.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">565</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">4488</span> A Fuzzy Programming Approach for Solving Intuitionistic Fuzzy Linear Fractional Programming Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sujeet%20Kumar%20Singh">Sujeet Kumar Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Shiv%20Prasad%20Yadav"> Shiv Prasad Yadav</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper develops an approach for solving intuitionistic fuzzy linear fractional programming (IFLFP) problem where the cost of the objective function, the resources, and the technological coefficients are triangular intuitionistic fuzzy numbers. Here, the IFLFP problem is transformed into an equivalent crisp multi-objective linear fractional programming (MOLFP) problem. By using fuzzy mathematical programming approach the transformed MOLFP problem is reduced into a single objective linear programming (LP) problem. The proposed procedure is illustrated through a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=triangular%20intuitionistic%20fuzzy%20number" title="triangular intuitionistic fuzzy number">triangular intuitionistic fuzzy number</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20programming%20problem" title=" linear programming problem"> linear programming problem</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20objective%20linear%20programming%20problem" title=" multi objective linear programming problem"> multi objective linear programming problem</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20mathematical%20programming" title=" fuzzy mathematical programming"> fuzzy mathematical programming</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20function" title=" membership function"> membership function</a> </p> <a href="https://publications.waset.org/abstracts/16411/a-fuzzy-programming-approach-for-solving-intuitionistic-fuzzy-linear-fractional-programming-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16411.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">566</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">4487</span> Fuzzy Linear Programming Approach for Determining the Production Amounts in Food Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20G%C3%BCney">B. Güney</a>, <a href="https://publications.waset.org/abstracts/search?q=%C3%87.%20Teke"> Ç. Teke</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, rapid and correct decision making is crucial for both people and enterprises. However, uncertainty makes decision-making difficult. Fuzzy logic is used for coping with this situation. Thus, fuzzy linear programming models are developed in order to handle uncertainty in objective function and the constraints. In this study, a problem of a factory in food industry is investigated, required data is obtained and the problem is figured out as a fuzzy linear programming model. The model is solved using Zimmerman approach which is one of the approaches for fuzzy linear programming. As a result, the solution gives the amount of production for each product type in order to gain maximum profit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=food%20industry" title="food industry">food industry</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20linear%20programming" title=" fuzzy linear programming"> fuzzy linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20programming" title=" linear programming "> linear programming </a> </p> <a href="https://publications.waset.org/abstracts/27880/fuzzy-linear-programming-approach-for-determining-the-production-amounts-in-food-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27880.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">650</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">4486</span> Use of Linear Programming for Optimal Production in a Production Line in Saudi Food Co.</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qasim%20M.%20Kriri">Qasim M. Kriri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Few Saudi Arabia production companies face financial profit issues until this moment. This work presents a linear integer programming model that solves a production problem of a Saudi Food Company in Saudi Arabia. An optimal solution to the above-mentioned problem is a Linear Programming solution. In this regard, the main purpose of this project is to maximize profit. Linear Programming Technique has been used to derive the maximum profit from production of natural juice at Saudi Food Co. The operations of production of the company were formulated and optimal results are found out by using Lindo Software that employed Sensitivity Analysis and Parametric linear programming in order develop Linear Programming. In addition, the parameter values are increased, then the values of the objective function will be increased. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parameter%20linear%20programming" title="parameter linear programming">parameter linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=objective%20function" title=" objective function"> objective function</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=optimize%20profit" title=" optimize profit"> optimize profit</a> </p> <a href="https://publications.waset.org/abstracts/86104/use-of-linear-programming-for-optimal-production-in-a-production-line-in-saudi-food-co" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86104.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">205</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">4485</span> Application of De Novo Programming Approach for Optimizing the Business Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Babic">Z. Babic</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Veza"> I. Veza</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Balic"> A. Balic</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Crnjac"> M. Crnjac</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The linear programming model is sometimes difficult to apply in real business situations due to its assumption of proportionality. This paper shows an example of how to use De Novo programming approach instead of linear programming. In the De Novo programming, resources are not fixed like in linear programming but resource quantities depend only on available budget. Budget is a new, important element of the De Novo approach. Two different production situations are presented: increasing costs and quantity discounts of raw materials. The focus of this paper is on advantages of the De Novo approach in the optimization of production plan for production company which produces souvenirs made from famous stone from the island of Brac, one of the greatest islands from Croatia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20process" title="business process">business process</a>, <a href="https://publications.waset.org/abstracts/search?q=De%20Novo%20programming" title=" De Novo programming"> De Novo programming</a>, <a href="https://publications.waset.org/abstracts/search?q=optimizing" title=" optimizing"> optimizing</a>, <a href="https://publications.waset.org/abstracts/search?q=production" title=" production"> production</a> </p> <a href="https://publications.waset.org/abstracts/80556/application-of-de-novo-programming-approach-for-optimizing-the-business-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80556.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">222</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">4484</span> Globally Convergent Sequential Linear Programming for Multi-Material Topology Optimization Using Ordered Solid Isotropic Material with Penalization Interpolation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Darwin%20Castillo%20Huaman%C3%AD">Darwin Castillo Huamaní</a>, <a href="https://publications.waset.org/abstracts/search?q=Francisco%20A.%20M.%20Gomes"> Francisco A. M. Gomes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of the multi-material topology optimization (MTO) is to obtain the optimal topology of structures composed by many materials, according to a given set of constraints and cost criteria. In this work, we seek the optimal distribution of materials in a domain, such that the flexibility of the structure is minimized, under certain boundary conditions and the intervention of external forces. In the case we have only one material, each point of the discretized domain is represented by two values from a function, where the value of the function is 1 if the element belongs to the structure or 0 if the element is empty. A common way to avoid the high computational cost of solving integer variable optimization problems is to adopt the Solid Isotropic Material with Penalization (SIMP) method. This method relies on the continuous interpolation function, power function, where the base variable represents a pseudo density at each point of domain. For proper exponent values, the SIMP method reduces intermediate densities, since values other than 0 or 1 usually does not have a physical meaning for the problem. Several extension of the SIMP method were proposed for the multi-material case. The one that we explore here is the ordered SIMP method, that has the advantage of not being based on the addition of variables to represent material selection, so the computational cost is independent of the number of materials considered. Although the number of variables is not increased by this algorithm, the optimization subproblems that are generated at each iteration cannot be solved by methods that rely on second derivatives, due to the cost of calculating the second derivatives. To overcome this, we apply a globally convergent version of the sequential linear programming method, which solves a linear approximation sequence of optimization problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=globally%20convergence" title="globally convergence">globally convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-material%20design%20ordered%20simp" title=" multi-material design ordered simp"> multi-material design ordered simp</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20linear%20programming" title=" sequential linear programming"> sequential linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=topology%20optimization" title=" topology optimization"> topology optimization</a> </p> <a href="https://publications.waset.org/abstracts/67066/globally-convergent-sequential-linear-programming-for-multi-material-topology-optimization-using-ordered-solid-isotropic-material-with-penalization-interpolation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67066.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">315</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">4483</span> Interval Bilevel Linear Fractional Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Hamidi">F. Hamidi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Amiri"> N. Amiri</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Mishmast%20Nehi"> H. Mishmast Nehi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Bilevel Programming (BP) model has been presented for a decision making process that consists of two decision makers in a hierarchical structure. In fact, BP is a model for a static two person game (the leader player in the upper level and the follower player in the lower level) wherein each player tries to optimize his/her personal objective function under dependent constraints; this game is sequential and non-cooperative. The decision making variables are divided between the two players and one’s choice affects the other’s beneﬁt and choices. In other words, BP consists of two nested optimization problems with two objective functions (upper and lower) where the constraint region of the upper level problem is implicitly determined by the lower level problem. In real cases, the coeﬃcients of an optimization problem may not be precise, i.e. they may be interval. In this paper we develop an algorithm for solving interval bilevel linear fractional programming problems. That is to say, bilevel problems in which both objective functions are linear fractional, the coefficients are interval and the common constraint region is a polyhedron. From the original problem, the best and the worst bilevel linear fractional problems have been derived and then, using the extended Charnes and Cooper transformation, each fractional problem can be reduced to a linear problem. Then we can ﬁnd the best and the worst optimal values of the leader objective function by two algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=best%20and%20worst%20optimal%20solutions" title="best and worst optimal solutions">best and worst optimal solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=bilevel%20programming" title=" bilevel programming"> bilevel programming</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional" title=" fractional"> fractional</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20coefficients" title=" interval coefficients"> interval coefficients</a> </p> <a href="https://publications.waset.org/abstracts/34778/interval-bilevel-linear-fractional-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34778.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">446</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">4482</span> A Comparison of Sequential Quadratic Programming, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization for the Design and Optimization of a Beam Column</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nima%20Khosravi">Nima Khosravi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes an integrated optimization technique with concurrent use of sequential quadratic programming, genetic algorithm, and simulated annealing particle swarm optimization for the design and optimization of a beam column. In this research, the comparison between 4 different types of optimization methods. The comparison is done and it is found out that all the methods meet the required constraints and the lowest value of the objective function is achieved by SQP, which was also the fastest optimizer to produce the results. SQP is a gradient based optimizer hence its results are usually the same after every run. The only thing which affects the results is the initial conditions given. The initial conditions given in the various test run were very large as compared. Hence, the value converged at a different point. Rest of the methods is a heuristic method which provides different values for different runs even if every parameter is kept constant. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=beam%20column" title="beam column">beam column</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=sequential%20quadratic%20programming" title=" sequential quadratic programming"> sequential quadratic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a> </p> <a href="https://publications.waset.org/abstracts/58973/a-comparison-of-sequential-quadratic-programming-genetic-algorithm-simulated-annealing-particle-swarm-optimization-for-the-design-and-optimization-of-a-beam-column" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58973.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">386</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">4481</span> Optimizing Human Diet Problem Using Linear Programming Approach: A Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Priyanka">P. Priyanka</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Shruthi"> S. Shruthi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Guruprasad"> N. Guruprasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Health is a common theme in most cultures. In fact all communities have their concepts of health, as part of their culture. Health continues to be a neglected entity. Planning of Human diet should be done very careful by selecting the food items or groups of food items also the composition involved. Low price and good taste of foods are regarded as two major factors for optimal human nutrition. Linear programming techniques have been extensively used for human diet formulation for quiet good number of years. Through the process, we mainly apply “The Simplex Method” which is a very useful statistical tool based on the theorem of Elementary Row Operation from Linear Algebra and also incorporate some other necessary rules set by the Simplex Method to help solve the problem. The study done by us is an attempt to develop a programming model for optimal planning and best use of nutrient ingredients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diet%20formulation" title="diet formulation">diet formulation</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20programming" title=" linear programming"> linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=nutrient%20ingredients" title=" nutrient ingredients"> nutrient ingredients</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=simplex%20method" title=" simplex method"> simplex method</a> </p> <a href="https://publications.waset.org/abstracts/19161/optimizing-human-diet-problem-using-linear-programming-approach-a-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19161.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">558</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">4480</span> Solving Fuzzy Multi-Objective Linear Programming Problems with Fuzzy Decision Variables</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahnaz%20Hosseinzadeh">Mahnaz Hosseinzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Aliyeh%20Kazemi"> Aliyeh Kazemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a method is proposed for solving Fuzzy Multi-Objective Linear Programming problems (FMOLPP) with fuzzy right hand side and fuzzy decision variables. To illustrate the proposed method, it is applied to the problem of selecting suppliers for an automotive parts producer company in Iran in order to find the number of optimal orders allocated to each supplier considering the conflicting objectives. Finally, the obtained results are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20multi-objective%20linear%20programming%20problems" title="fuzzy multi-objective linear programming problems">fuzzy multi-objective linear programming problems</a>, <a href="https://publications.waset.org/abstracts/search?q=triangular%20fuzzy%20numbers" title=" triangular fuzzy numbers"> triangular fuzzy numbers</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20ranking" title=" fuzzy ranking"> fuzzy ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=supplier%20selection%20problem" title=" supplier selection problem"> supplier selection problem</a> </p> <a href="https://publications.waset.org/abstracts/54020/solving-fuzzy-multi-objective-linear-programming-problems-with-fuzzy-decision-variables" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54020.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">383</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">4479</span> Solving Linear Systems Involved in Convex Programming Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yixun%20Shi">Yixun Shi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many interior point methods for convex programming solve an (n+m)x(n+m)linear system in each iteration. Many implementations solve this system in each iteration by considering an equivalent mXm system (4) as listed in the paper, and thus the job is reduced into solving the system (4). However, the system(4) has to be solved exactly since otherwise the error would be entirely passed onto the last m equations of the original system. Often the Cholesky factorization is computed to obtain the exact solution of (4). One Cholesky factorization is to be done in every iteration, resulting in higher computational costs. In this paper, two iterative methods for solving linear systems using vector division are combined together and embedded into interior point methods. Instead of computing one Cholesky factorization in each iteration, it requires only one Cholesky factorization in the entire procedure, thus significantly reduces the amount of computation needed for solving the problem. Based on that, a hybrid algorithm for solving convex programming problems is proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convex%20programming" title="convex programming">convex programming</a>, <a href="https://publications.waset.org/abstracts/search?q=interior%20point%20method" title=" interior point method"> interior point method</a>, <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=vector%20division" title=" vector division"> vector division</a> </p> <a href="https://publications.waset.org/abstracts/39573/solving-linear-systems-involved-in-convex-programming-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39573.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">402</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4478</span> Choice of Sleeper and Rail Fastening Using Linear Programming Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luciano%20Oliveira">Luciano Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Elsa%20V%C3%A1squez-Alvarez"> Elsa Vásquez-Alvarez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increase in rail freight transport in Brazil in recent years requires new railway lines and the maintenance of existing ones, which generates high costs for concessionaires. It is in this context that this work is inserted, whose objective is to propose a method that uses Binary Linear Programming for the choice of sleeper and rail fastening, from various options, including the way to apply these materials, with focus to minimize costs. Unit value information, the life cycle each of material type, and service expenses are considered. The model was implemented in commercial software using real data for its validation. The formulated model can be replicated to support decision-making for other railway projects in the choice of sleepers and rail fastening with lowest cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=linear%20programming" title="linear programming">linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=rail%20fastening" title=" rail fastening"> rail fastening</a>, <a href="https://publications.waset.org/abstracts/search?q=rail%20sleeper" title=" rail sleeper"> rail sleeper</a>, <a href="https://publications.waset.org/abstracts/search?q=railway" title=" railway"> railway</a> </p> <a href="https://publications.waset.org/abstracts/144639/choice-of-sleeper-and-rail-fastening-using-linear-programming-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144639.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">199</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">4477</span> A New Approach for Generalized First Derivative of Nonsmooth Functions Using Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Mehdi%20Mazarei">Mohammad Mehdi Mazarei</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Asghar%20Behroozpoor"> Ali Asghar Behroozpoor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we define an optimization problem corresponding to smooth and nonsmooth functions which its optimal solution is the first derivative of these functions in a domain. For this purpose, a linear programming problem corresponding to optimization problem is obtained. The optimal solution of this linear programming problem is the approximate generalized first derivative. In fact, we approximate generalized first derivative of nonsmooth functions as tailor series. We show the efficiency of our approach by some smooth and nonsmooth functions in some examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=general%20derivative" title="general derivative">general derivative</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20programming" title=" linear programming"> linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20problem" title=" optimization problem"> optimization problem</a>, <a href="https://publications.waset.org/abstracts/search?q=smooth%20and%20nonsmooth%20functions" title=" smooth and nonsmooth functions"> smooth and nonsmooth functions</a> </p> <a href="https://publications.waset.org/abstracts/19425/a-new-approach-for-generalized-first-derivative-of-nonsmooth-functions-using-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19425.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">4476</span> Generalized Central Paths for Convex Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li-Zhi%20Liao">Li-Zhi Liao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The central path has played the key role in the interior point method. However, the convergence of the central path may not be true even in some convex programming problems with linear constraints. In this paper, the generalized central paths are introduced for convex programming. One advantage of the generalized central paths is that the paths will always converge to some optimal solutions of the convex programming problem for any initial interior point. Some additional theoretical properties for the generalized central paths will be also reported. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=central%20path" title="central path">central path</a>, <a href="https://publications.waset.org/abstracts/search?q=convex%20programming" title=" convex programming"> convex programming</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20central%20path" title=" generalized central path"> generalized central path</a>, <a href="https://publications.waset.org/abstracts/search?q=interior%20point%20method" title=" interior point method"> interior point method</a> </p> <a href="https://publications.waset.org/abstracts/58039/generalized-central-paths-for-convex-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58039.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">327</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">4475</span> Integrated Approach of Quality Function Deployment, Sensitivity Analysis and Multi-Objective Linear Programming for Business and Supply Chain Programs Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20T.%20Tham">T. T. Tham</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study is to propose an integrated approach to determine the most suitable programs, based on Quality Function Deployment (QFD), Sensitivity Analysis (SA) and Multi-Objective Linear Programming model (MOLP). Firstly, QFD is used to determine business requirements and transform them into business and supply chain programs. From the QFD, technical scores of all programs are obtained. All programs are then evaluated through five criteria (productivity, quality, cost, technical score, and feasibility). Sets of weight of these criteria are built using Sensitivity Analysis. Multi-Objective Linear Programming model is applied to select suitable programs according to multiple conflicting objectives under a budget constraint. A case study from the Sai Gon-Mien Tay Beer Company is given to illustrate the proposed methodology. The outcome of the study provides a comprehensive picture for companies to select suitable programs to obtain the optimal solution according to their preference. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20program" title="business program">business program</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20linear%20programming%20model" title=" multi-objective linear programming model"> multi-objective linear programming model</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20function%20deployment" title=" quality function deployment"> quality function deployment</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20management" title=" supply chain management"> supply chain management</a> </p> <a href="https://publications.waset.org/abstracts/121149/integrated-approach-of-quality-function-deployment-sensitivity-analysis-and-multi-objective-linear-programming-for-business-and-supply-chain-programs-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121149.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">123</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">4474</span> Three-Stage Multivariate Stratified Sample Surveys with Probabilistic Cost Constraint and Random Variance </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanam%20Haseen">Sanam Haseen</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Bari"> Abdul Bari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a three stage multivariate programming problem with random survey cost and variances as random variables has been formulated as a non-linear stochastic programming problem. The problem has been converted into an equivalent deterministic form using chance constraint programming and modified E-modeling. An empirical study of the problem has been done at the end of the paper using R-simulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chance%20constraint%20programming" title="chance constraint programming">chance constraint programming</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20E-model" title=" modified E-model"> modified E-model</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20programming" title=" stochastic programming"> stochastic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=stratified%20sample%20surveys" title=" stratified sample surveys"> stratified sample surveys</a>, <a href="https://publications.waset.org/abstracts/search?q=three%20stage%20sample%20surveys" title=" three stage sample surveys"> three stage sample surveys</a> </p> <a href="https://publications.waset.org/abstracts/14515/three-stage-multivariate-stratified-sample-surveys-with-probabilistic-cost-constraint-and-random-variance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14515.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">458</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">4473</span> Using Historical Data for Stock Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Stoica">Sofia Stoica</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finance" title="finance">finance</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=opening%20price" title=" opening price"> opening price</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market" title=" stock market"> stock market</a> </p> <a href="https://publications.waset.org/abstracts/159522/using-historical-data-for-stock-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159522.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">4472</span> A Mixed Integer Linear Programming Model for Flexible 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=Mohsen%20Ziaee">Mohsen Ziaee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a mixed integer linear programming (MILP) model is presented to solve the flexible job shop scheduling problem (FJSP). This problem is one of the hardest combinatorial problems. The objective considered is the minimization of the makespan. The computational results of the proposed MILP model were compared with those of the best known mathematical model in the literature in terms of the computational time. The results show that our model has better performance with respect to all the considered performance measures including relative percentage deviation (RPD) value, number of constraints, and total number of variables. By this improved mathematical model, larger FJS problems can be optimally solved in reasonable time, and therefore, the model would be a better tool for the performance evaluation of the approximation algorithms developed for the problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=scheduling" title="scheduling">scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop" title=" flexible job shop"> flexible job shop</a>, <a href="https://publications.waset.org/abstracts/search?q=makespan" title=" makespan"> makespan</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20integer%20linear%20programming" title=" mixed integer linear programming"> mixed integer linear programming</a> </p> <a href="https://publications.waset.org/abstracts/92281/a-mixed-integer-linear-programming-model-for-flexible-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92281.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">186</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">4471</span> Timetabling Communities’ Demands for an Effective Examination Timetabling Using Integer Linear Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20F.%20Jamaluddin">N. F. Jamaluddin</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20A.%20H.%20Aizam"> N. A. H. Aizam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explains the educational timetabling problem, a type of scheduling problem that is considered as one of the most challenging problem in optimization and operational research. The university examination timetabling problem (UETP), which involves assigning a set number of exams into a set number of timeslots whilst fulfilling all required conditions, has been widely investigated. The limitation of available timeslots and resources with the increasing number of examinations are the main reasons in the difficulty of solving this problem. Dynamical change in the examination scheduling system adds up the complication particularly in coping up with the demand and new requirements by the communities. Our objective is to investigate these demands and requirements with subjects taken from Universiti Malaysia Terengganu (UMT), through questionnaires. Integer linear programming model which reflects the preferences obtained to produce an effective examination timetabling was formed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=demands" title="demands">demands</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20timetabling" title=" educational timetabling"> educational timetabling</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=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=university%20examination%20timetabling%20problem%20%28UETP%29" title=" university examination timetabling problem (UETP)"> university examination timetabling problem (UETP)</a> </p> <a href="https://publications.waset.org/abstracts/49545/timetabling-communities-demands-for-an-effective-examination-timetabling-using-integer-linear-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49545.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">4470</span> Production Plan and Technological Variants Optimization by Goal Programming Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tunjo%20Peri%C4%87">Tunjo Perić</a>, <a href="https://publications.waset.org/abstracts/search?q=Franjo%20Brati%C4%87"> Franjo Bratić</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper the goal programming methodology for solving multiple objective problem of the technological variants and production plan optimization has been applied. The optimization criteria are determined and the multiple objective linear programming model for solving a problem of the technological variants and production plan optimization is formed and solved. Then the obtained results are analysed. The obtained results point out to the possibility of efficient application of the goal programming methodology in solving the problem of the technological variants and production plan optimization. The paper points out on the advantages of the application of the goal programming methodolohy compare to the Surrogat Worth Trade-off method in solving this problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=goal%20programming" title="goal programming">goal programming</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20objective%20programming" title=" multi objective programming"> multi objective programming</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20plan" title=" production plan"> production plan</a>, <a href="https://publications.waset.org/abstracts/search?q=SWT%20method" title=" SWT method"> SWT method</a>, <a href="https://publications.waset.org/abstracts/search?q=technological%20variants" title=" technological variants"> technological variants</a> </p> <a href="https://publications.waset.org/abstracts/31123/production-plan-and-technological-variants-optimization-by-goal-programming-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31123.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">379</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">4469</span> Optimal Production Planning in Aromatic Coconuts Supply Chain Based on Mixed-Integer Linear Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chaimongkol%20Limpianchob">Chaimongkol Limpianchob</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work addresses the problem of production planning that arises in the production of aromatic coconuts from Samudsakhorn province in Thailand. The planning involves the forwarding of aromatic coconuts from the harvest areas to the factory, which is classified into two groups; self-owned areas and contracted areas, the decisions of aromatic coconuts flow in the plant, and addressing a question of which warehouse will be in use. The problem is formulated as a mixed-integer linear programming model within supply chain management framework. The objective function seeks to minimize the total cost including the harvesting, labor and inventory costs. Constraints on the system include the production activities in the company and demand requirements. Numerical results are presented to demonstrate the feasibility of coconuts supply chain model compared with base case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aromatic%20coconut" title="aromatic coconut">aromatic coconut</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20management" title=" supply chain management"> supply chain management</a>, <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-integer%20linear%20programming" title=" mixed-integer linear programming"> mixed-integer linear programming</a> </p> <a href="https://publications.waset.org/abstracts/6619/optimal-production-planning-in-aromatic-coconuts-supply-chain-based-on-mixed-integer-linear-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6619.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">460</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">4468</span> Energy Management System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Periyadharshini">S. Periyadharshini</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Ramkumar"> K. Ramkumar</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Jayalalitha"> S. Jayalalitha</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20GuruPrasath"> M. GuruPrasath</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Manikandan"> R. Manikandan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a formulation and solution for industrial load management and product grade problem. The formulation is created using linear programming technique thereby optimizing the electricity cost by scheduling the loads satisfying the process, storage, time zone and production constraints which will create an impact of reducing maximum demand and thereby reducing the electricity cost. Product grade problem is formulated using integer linear programming technique of optimization using lingo software and the results show that overall increase in profit margin. In this paper, time of use tariff is utilized and this technique will provide significant reductions in peak electricity consumption. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cement%20industries" title="cement industries">cement industries</a>, <a href="https://publications.waset.org/abstracts/search?q=integer%20programming" title=" integer programming"> integer programming</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20formulation" title=" optimal formulation"> optimal formulation</a>, <a href="https://publications.waset.org/abstracts/search?q=objective%20function" title=" objective function"> objective function</a>, <a href="https://publications.waset.org/abstracts/search?q=constraints" title=" constraints"> constraints</a> </p> <a href="https://publications.waset.org/abstracts/29389/energy-management-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29389.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">593</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">4467</span> Software Transactional Memory in a Dynamic Programming Language at Virtual Machine Level</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Szu-Kai%20Hsu">Szu-Kai Hsu</a>, <a href="https://publications.waset.org/abstracts/search?q=Po-Ching%20Lin"> Po-Ching Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As more and more multi-core processors emerge, traditional sequential programming paradigm no longer suffice. Yet only few modern dynamic programming languages can leverage such advantage. Ruby, for example, despite its wide adoption, only includes threads as a simple parallel primitive. The global virtual machine lock of official Ruby runtime makes it impossible to exploit full parallelism. Though various alternative Ruby implementations do eliminate the global virtual machine lock, they only provide developers dated locking mechanism for data synchronization. However, traditional locking mechanism error-prone by nature. Software Transactional Memory is one of the promising alternatives among others. This paper introduces a new virtual machine: GobiesVM to provide a native software transactional memory based solution for dynamic programming languages to exploit parallelism. We also proposed a simplified variation of Transactional Locking II algorithm. The empirical results of our experiments show that support of STM at virtual machine level enables developers to write straightforward code without compromising parallelism or sacrificing thread safety. Existing source code only requires minimal or even none modi cation, which allows developers to easily switch their legacy codebase to a parallel environment. The performance evaluations of GobiesVM also indicate the difference between sequential and parallel execution is significant. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=global%20interpreter%20lock" title="global interpreter lock">global interpreter lock</a>, <a href="https://publications.waset.org/abstracts/search?q=ruby" title=" ruby"> ruby</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20transactional%20memory" title=" software transactional memory"> software transactional memory</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20machine" title=" virtual machine"> virtual machine</a> </p> <a href="https://publications.waset.org/abstracts/17152/software-transactional-memory-in-a-dynamic-programming-language-at-virtual-machine-level" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17152.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">285</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">4466</span> Approximation of Convex Set by Compactly Semidefinite Representable Set</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anusuya%20Ghosh">Anusuya Ghosh</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishnu%20Narayanan"> Vishnu Narayanan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The approximation of convex set by semidefinite representable set plays an important role in semidefinite programming, especially in modern convex optimization. To optimize a linear function over a convex set is a hard problem. But optimizing the linear function over the semidefinite representable set which approximates the convex set is easy to solve as there exists numerous efficient algorithms to solve semidefinite programming problems. So, our approximation technique is significant in optimization. We develop a technique to approximate any closed convex set, say K by compactly semidefinite representable set. Further we prove that there exists a sequence of compactly semidefinite representable sets which give tighter approximation of the closed convex set, K gradually. We discuss about the convergence of the sequence of compactly semidefinite representable sets to closed convex set K. The recession cone of K and the recession cone of the compactly semidefinite representable set are equal. So, we say that the sequence of compactly semidefinite representable sets converge strongly to the closed convex set. Thus, this approximation technique is very useful development in semidefinite programming. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semidefinite%20programming" title="semidefinite programming">semidefinite programming</a>, <a href="https://publications.waset.org/abstracts/search?q=semidefinite%20representable%20set" title=" semidefinite representable set"> semidefinite representable set</a>, <a href="https://publications.waset.org/abstracts/search?q=compactly%20semidefinite%20representable%20set" title=" compactly semidefinite representable set"> compactly semidefinite representable set</a>, <a href="https://publications.waset.org/abstracts/search?q=approximation" title=" approximation"> approximation</a> </p> <a href="https://publications.waset.org/abstracts/36914/approximation-of-convex-set-by-compactly-semidefinite-representable-set" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36914.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">386</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">4465</span> Optimization of Tundish Geometry for Minimizing Dead Volume Using OpenFOAM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prateek%20Singh">Prateek Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Dilshad%20Ahmad"> Dilshad Ahmad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Growing demand for high-quality steel products has inspired researchers to investigate the unit operations involved in the manufacturing of these products (slabs, rods, sheets, etc.). One such operation is tundish operation, in which a vessel (tundish) acts as a buffer of molten steel for the solidification operation in mold. It is observed that tundish also plays a crucial role in the quality and cleanliness of the steel produced, besides merely acting as a reservoir for the mold. It facilitates removal of dissolved oxygen (inclusions) from the molten steel thus improving its cleanliness. Inclusion removal can be enhanced by increasing the residence time of molten steel in the tundish by incorporation of flow modifiers like dams, weirs, turbo-pad, etc. These flow modifiers also help in reducing the dead or short circuit zones within the tundish which is significant for maintaining thermal and chemical homogeneity of molten steel. Thus, it becomes important to analyze the flow of molten steel in the tundish for different configuration of flow modifiers. In the present work, effect of varying positions and heights/depths of dam and weir on the dead volume in tundish is studied. Steady state thermal and flow profiles of molten steel within the tundish are obtained using OpenFOAM. Subsequently, Residence Time Distribution analysis is performed to obtain the percentage of dead volume in the tundish. Design of Experiment method is then used to configure different tundish geometries for varying positions and heights/depths of dam and weir, and dead volume for each tundish design is obtained. A second-degree polynomial with two-term interactions of independent variables to predict the dead volume in the tundish with positions and heights/depths of dam and weir as variables are computed using Multiple Linear Regression model. This polynomial is then used in an optimization framework to obtain the optimal tundish geometry for minimizing dead volume using Sequential Quadratic Programming optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=design%20of%20experiments" title="design of experiments">design of experiments</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20linear%20regression" title=" multiple linear regression"> multiple linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=OpenFOAM" title=" OpenFOAM"> OpenFOAM</a>, <a href="https://publications.waset.org/abstracts/search?q=residence%20time%20distribution" title=" residence time distribution"> residence time distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20quadratic%20programming%20optimization" title=" sequential quadratic programming optimization"> sequential quadratic programming optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=steel" title=" steel"> steel</a>, <a href="https://publications.waset.org/abstracts/search?q=tundish" title=" tundish"> tundish</a> </p> <a href="https://publications.waset.org/abstracts/80652/optimization-of-tundish-geometry-for-minimizing-dead-volume-using-openfoam" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80652.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">4464</span> Interactive Solutions for the Multi-Objective Capacitated Transportation Problem with Mixed Constraints under Fuzziness</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aquil%20Ahmed">Aquil Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Srikant%20Gupta"> Srikant Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Irfan%20Ali"> Irfan Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we study a multi-objective capacitated transportation problem (MOCTP) with mixed constraints. This paper is comprised of the modelling and optimisation of an MOCTP in a fuzzy environment in which some goals are fractional and some are linear. In real life application of the fuzzy goal programming (FGP) problem with multiple objectives, it is difficult for the decision maker(s) to determine the goal value of each objective precisely as the goal values are imprecise or uncertain. Also, we developed the concept of linearization of fractional goal for solving the MOCTP. In this paper, imprecision of the parameter is handled by the concept of fuzzy set theory by considering these parameters as a trapezoidal fuzzy number. α-cut approach is used to get the crisp value of the parameters. Numerical examples are used to illustrate the method for solving MOCTP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=capacitated%20transportation%20problem" title="capacitated transportation problem">capacitated transportation problem</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20objective%20linear%20programming" title=" multi objective linear programming"> multi objective linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20fractional%20programming" title=" multi-objective fractional programming"> multi-objective fractional programming</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20goal%20programming" title=" fuzzy goal programming"> fuzzy goal programming</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20sets" title=" fuzzy sets"> fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=trapezoidal%20fuzzy%20number" title=" trapezoidal fuzzy number"> trapezoidal fuzzy number</a> </p> <a href="https://publications.waset.org/abstracts/77380/interactive-solutions-for-the-multi-objective-capacitated-transportation-problem-with-mixed-constraints-under-fuzziness" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77380.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">434</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">4463</span> Method of Parameter Calibration for Error Term in Stochastic User Equilibrium Traffic Assignment Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiang%20Zhang">Xiang Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Rey"> David Rey</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Travis%20Waller"> S. Travis Waller</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stochastic User Equilibrium (SUE) model is a widely used traffic assignment model in transportation planning, which is regarded more advanced than Deterministic User Equilibrium (DUE) model. However, a problem exists that the performance of the SUE model depends on its error term parameter. The objective of this paper is to propose a systematic method of determining the appropriate error term parameter value for the SUE model. First, the significance of the parameter is explored through a numerical example. Second, the parameter calibration method is developed based on the Logit-based route choice model. The calibration process is realized through multiple nonlinear regression, using sequential quadratic programming combined with least square method. Finally, case analysis is conducted to demonstrate the application of the calibration process and validate the better performance of the SUE model calibrated by the proposed method compared to the SUE models under other parameter values and the DUE model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parameter%20calibration" title="parameter calibration">parameter calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20quadratic%20programming" title=" sequential quadratic programming"> sequential quadratic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20user%20equilibrium" title=" stochastic user equilibrium"> stochastic user equilibrium</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20assignment" title=" traffic assignment"> traffic assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=transportation%20planning" title=" transportation planning"> transportation planning</a> </p> <a href="https://publications.waset.org/abstracts/17091/method-of-parameter-calibration-for-error-term-in-stochastic-user-equilibrium-traffic-assignment-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17091.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">299</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">4462</span> An Analysis of Sequential Pattern Mining on Databases Using Approximate Sequential Patterns</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20Suneetha">J. Suneetha</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijayalaxmi"> Vijayalaxmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sequential Pattern Mining involves applying data mining methods to large data repositories to extract usage patterns. Sequential pattern mining methodologies used to analyze the data and identify patterns. The patterns have been used to implement efficient systems can recommend on previously observed patterns, in making predictions, improve usability of systems, detecting events, and in general help in making strategic product decisions. In this paper, identified performance of approximate sequential pattern mining defines as identifying patterns approximately shared with many sequences. Approximate sequential patterns can effectively summarize and represent the databases by identifying the underlying trends in the data. Conducting an extensive and systematic performance over synthetic and real data. The results demonstrate that ApproxMAP effective and scalable in mining large sequences databases with long patterns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multiple%20data" title="multiple data">multiple data</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20analysis" title=" performance analysis"> performance analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20pattern" title=" sequential pattern"> sequential pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=sequence%20database%20scalability" title=" sequence database scalability"> sequence database scalability</a> </p> <a href="https://publications.waset.org/abstracts/46782/an-analysis-of-sequential-pattern-mining-on-databases-using-approximate-sequential-patterns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46782.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">342</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">4461</span> Vendor Selection and Supply Quotas Determination by Using Revised Weighting Method and Multi-Objective Programming Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tunjo%20Peri%C4%8D">Tunjo Perič</a>, <a href="https://publications.waset.org/abstracts/search?q=Marin%20Fatovi%C4%87"> Marin Fatović</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a new methodology for vendor selection and supply quotas determination (VSSQD) is proposed. The problem of VSSQD is solved by the model that combines revised weighting method for determining the objective function coefficients, and a multiple objective linear programming (MOLP) method based on the cooperative game theory for VSSQD. The criteria used for VSSQD are: (1) purchase costs and (2) product quality supplied by individual vendors. The proposed methodology is tested on the example of flour purchase for a bakery with two decision makers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cooperative%20game%20theory" title="cooperative game theory">cooperative game theory</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20objective%20linear%20programming" title=" multiple objective linear programming"> multiple objective linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=revised%20weighting%20method" title=" revised weighting method"> revised weighting method</a>, <a href="https://publications.waset.org/abstracts/search?q=vendor%20selection" title=" vendor selection"> vendor selection</a> </p> <a href="https://publications.waset.org/abstracts/31124/vendor-selection-and-supply-quotas-determination-by-using-revised-weighting-method-and-multi-objective-programming-methods" class="btn btn-primary 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