Multi-objective models for lot-sizing with supplier selection

In this paper, two multi-objective mixed integer non-linear models are developed for multi-period lot-sizing problems involving multiple products and multiple suppliers. Each model is constructed on the basis of three objective functions (cost, quality and service level) and a set of constraints. Th...

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Vydané v:	International journal of production economics Ročník 130; číslo 1; s. 77 - 86
Hlavní autori:	Rezaei, Jafar, Davoodi, Mansoor
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 01.03.2011 Elsevier Elsevier Sequoia S.A
Edícia:	International Journal of Production Economics
Predmet:	Comparative analysis Cost engineering Costs Evolutionärer Algorithmus Ganzzahlige Optimierung Genetic algorithm Genetic algorithms Integer programming Inventory Inventory management Lieferantenbewertung Losgröße Lot-sizing Lot-sizing Supplier selection Inventory Multi-objective mixed integer non-linear programming Genetic algorithm Mathematical models Mehrproduktfertigung Mixed integer Multi-objective mixed integer non-linear programming Nichtlineare Optimierung Order disorder Pareto optimum Shortages Studies Supplier selection Suppliers Transportation Multi-objective mixed integer non-linear programming Supplier selection Lot-sizing Genetic algorithm Inventory
ISSN:	0925-5273, 1873-7579
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Abstract	In this paper, two multi-objective mixed integer non-linear models are developed for multi-period lot-sizing problems involving multiple products and multiple suppliers. Each model is constructed on the basis of three objective functions (cost, quality and service level) and a set of constraints. The total costs consist of purchasing, ordering, holding (and backordering) and transportation costs. Ordering cost is seen as an ‘ordering frequency’-dependent function, whereas total quality and service level are seen as time-dependent functions. The first model represents this problem in situations where shortage is not allowed while in the second model, all the demand during the stock-out period is backordered. Considering the complexity of these models on the one hand, and the ability of genetic algorithms to obtain a set of Pareto-optimal solutions, we apply a genetic algorithm in an innovative approach to solve the models. Comparison results indicate that, in a backordering situation, buyers are better able to optimize their objectives compared to situations where there is no shortage. If we take ordering frequency into account, the total costs are reduced significantly.
AbstractList	In this paper, two multi-objective mixed integer non-linear models are developed for multi-period lot-sizing problems involving multiple products and multiple suppliers. Each model is constructed on the basis of three objective functions (cost, quality and service level) and a set of constraints. The total costs consist of purchasing, ordering, holding (and backordering) and transportation costs. Ordering cost is seen as an 'ordering frequency'-dependent function, whereas total quality and service level are seen as time-dependent functions. The first model represents this problem in situations where shortage is not allowed while in the second model, all the demand during the stock-out period is backordered. Considering the complexity of these models on the one hand, and the ability of genetic algorithms to obtain a set of Pareto-optimal solutions, we apply a genetic algorithm in an innovative approach to solve the models. Comparison results indicate that, in a backordering situation, buyers are better able to optimize their objectives compared to situations where there is no shortage. If we take ordering frequency into account, the total costs are reduced significantly. In this paper, two multi-objective mixed integer non-linear models are developed for multi-period lot-sizing problems involving multiple products and multiple suppliers. Each model is constructed on the basis of three objective functions (cost, quality and service level) and a set of constraints. The total costs consist of purchasing, ordering, holding (and backordering) and transportation costs. Ordering cost is seen as an 'ordering frequency'-dependent function, whereas total quality and service level are seen as time-dependent functions. The first model represents this problem in situations where shortage is not allowed while in the second model, all the demand during the stock-out period is backordered. Considering the complexity of these models on the one hand, and the ability of genetic algorithms to obtain a set of Pareto-optimal solutions, we apply a genetic algorithm in an innovative approach to solve the models. Comparison results indicate that, in a backordering situation, buyers are better able to optimize their objectives compared to situations where there is no shortage. If we take ordering frequency into account, the total costs are reduced significantly. [PUBLICATION ABSTRACT]
Author	Davoodi, Mansoor Rezaei, Jafar
Author_xml	– sequence: 1 givenname: Jafar surname: Rezaei fullname: Rezaei, Jafar email: j.rezaei@tudelft.nl organization: Department of Technology, Strategy and Entrepreneurship, Faculty of Technology, Policy and Management, Delft University of Technology, P.O. Box 5015, 2600 GA Delft, The Netherlands – sequence: 2 givenname: Mansoor surname: Davoodi fullname: Davoodi, Mansoor email: mdmonfared@aut.ac.ir organization: Laboratory of Algorithms and Computational Geometry, Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
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Snippet	In this paper, two multi-objective mixed integer non-linear models are developed for multi-period lot-sizing problems involving multiple products and multiple...
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SubjectTerms	Comparative analysis Cost engineering Costs Evolutionärer Algorithmus Ganzzahlige Optimierung Genetic algorithm Genetic algorithms Integer programming Inventory Inventory management Lieferantenbewertung Losgröße Lot-sizing Lot-sizing Supplier selection Inventory Multi-objective mixed integer non-linear programming Genetic algorithm Mathematical models Mehrproduktfertigung Mixed integer Multi-objective mixed integer non-linear programming Nichtlineare Optimierung Order disorder Pareto optimum Shortages Studies Supplier selection Suppliers Transportation
Title	Multi-objective models for lot-sizing with supplier selection
URI	https://dx.doi.org/10.1016/j.ijpe.2010.11.017 http://www.econis.eu/PPNSET?PPN=647220970 http://econpapers.repec.org/article/eeeproeco/v_3a130_3ay_3a2011_3ai_3a1_3ap_3a77-86.htm https://www.proquest.com/docview/847535695 https://www.proquest.com/docview/907949687
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