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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Popis
Shrnutí: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.
Bibliografia:SourceType-Scholarly Journals-1
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ISSN:0925-5273
1873-7579
DOI:10.1016/j.ijpe.2010.11.017