Dynamic Lot Size Models for Multi-Stage Assembly Systems

Crowston, Wallace B. S.; Wagner, Michael

doi:10.1287/mnsc.20.1.14

Cited by 124 publications

(30 citation statements)

References 6 publications

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“…Zangwill's models are nevertheless based on the assumption that any component has at most one predecessor and one successor. This assumption is partially relaxed by Crowston and Wagner [4] who developed a model providing the optimal solution to the multi-stage lot-sizing problem where each item may have any number of predecessors, but at most one successor. Afentakis et al [1] proposed a formulation of the problem in terms of echelon stock which is also based on the no component commonality assumption.…”

Section: Overview Of Related Literaturementioning

confidence: 99%

Evolutionary algorithms for solving unconstrained multilevel lot-sizing problem with series structure

Han

Cai

Kaku

et al. 2012

J. Shanghai Jiaotong Univ. (Sci.)

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This paper presents a comparative study of evolutionary algorithms which are considered to be effective in solving the multilevel lot-sizing problem in material requirement planning (MRP) systems. Three evolutionary algorithms (simulated annealing (SA), particle swarm optimization (PSO) and genetic algorithm (GA)) are provided. For evaluating the performances of algorithms, the distribution of total cost (objective function) and the average computational time are compared. As a result, both GA and PSO have better cost performances with lower average total costs and smaller standard deviations. When the scale of the multilevel lot-sizing problem becomes larger, PSO is of a shorter computational time.In this paper, we compare several evolutionary algorithms for solving the multilevel lot-sizing (MLLS) problem in material requirement planning (MRP) systems. MLLS problem with a time-invariant cost structure is an important decision making process in the manufacturing production systems. For solving such problem there are many researches involving both optimal and heuristic approaches . We provide an overview about it later. The major drawback of the existing approaches is undoubtedly their inability to provide cost-efficient solutions in a reasonable computation time for the realistic size problems. Recently, some researchers [7,27] used evolutionary algorithms to develop a cost efficient solution method that required a low computational effort for solving the MLLS problem. They reported that their algorithms provided high cost effectiveness solutions in a moderate execution time. Then the evolutionary algorithms seem to become useful tools for solving the MLLS problem. On the other hand, because each evolutionary method has its own suitable application scope and constraint condition, it is necessary to evaluate performance for those algorithms in solving the MLLS problem. It is the objective of this paper.The evolutionary algorithms (or memetic algorithms) are a large and diverse class of intelligent algorithms inspired by models of the natural evolution of biological species. The typical examples of the evolutionary algorithms are the simulated annealing (SA) [13] , genetic algorithm (GA) [12] , analog neural networks [23] , ant colony algorithm [6] , particle swarm optimization (PSO) [15] and various hybrid methods [11,18] . In this paper three evolutionary algorithms, SA, PSO and GA, are provided for the performance evaluation. We provide a brief summary of the three evolutionary algorithms in the next section. To assess the output of the algorithms, we compare the algorithms with several lotsizing models, which have different numbers of items

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Section: Overview Of Related Literaturementioning

confidence: 99%

Evolutionary algorithms for solving unconstrained multilevel lot-sizing problem with series structure

Han

Cai

Kaku

et al. 2012

J. Shanghai Jiaotong Univ. (Sci.)

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show abstract

“…Starting with Wagner and Whitin (1958), Crowston and Wagner (1973), etc., the solution approaches for such problems have involved either dynamic programming approaches, specialized algorithms, or integer programming formulations and solutions (Belvaux & Wolsey, 2000;Wolsey, 2002). As noted in Wolsey (2002), many real-world lot sizing problems can now be adequately solved using commercial-off-the-shelf mathematical programming software.…”

Section: Modeling Inventory Effectivenessmentioning

confidence: 99%

Maximizing Effectiveness Using a Flexible Inventory

Sodhi¹,

Ferguson²,

Bussiere³

et al. 2011

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“…Several optimization formulations and algorithms have been developed to solve variant MLLS problems. Early dynamic programming formulations used a network representation of the problem with a series structure (Zhangwill, 1968(Zhangwill, , 1969 or an assembly structure (Crowston & Wagner, 1973). Other approaches involve the branch and bound algorithms (Afentakis, Gavish, & Kamarkar, 1984, 1986) that used a converting approach to change the classical formulation of the general structure into a simple but expanded assembly structure.…”

Section: Introductionmentioning

confidence: 99%