Abstract
ABSTRACTThis paper presents a novel linear mathematical model for integrated cell formation and task scheduling in the cellular manufacturing system (CMS). It is suitable for the dual-resource constrained setting, such as garment process, component assembly, and electronics manufacturing. The model can handle the manufacturing project composing of some tasks with precedence constraints. It provides a method to assign the multi-skilled workers to appropriate machines. The workers are allowed to move among the machines such that the processing time of tasks might be reduced. A hybrid simulated annealing (HSA) is proposed to minimize the makespan of manufacturing project in the CMS. The approach combines the priority rule based heuristic algorithm (PRBHA) and revised forward recursion algorithm (RFRA) with conventional simulated annealing (SA). The result of extensive numerical experiments shows that the proposed HSA outperforms the conventional SA accurately and efficiently.
Highlights
With rapidly changing customer expectation and global competition, cellular manufacturing system (CMS) has been an important way of producing goods in the last several decades
We develop a priority rule based heuristic algorithm (PRBHA) that is embedded in simulated annealing (SA) for determining an initial feasible schedule
We propose a hybrid simulated annealing (HSA) which combines the PRBHA approach with conventional SA algorithm
Summary
With rapidly changing customer expectation and global competition, cellular manufacturing system (CMS) has been an important way of producing goods in the last several decades. For the cell formation problem with worker and machine assignment, Mahdavi et al [11] presented a fuzzy goal programming-based approach for solving a multi-objective mathematical model of cell formation problem and production planning in a dynamic virtual cellular manufacturing system. Arkat et al [20] presented a mathematical model to concurrently identify the formation of cells, cellular layout and the operations sequence with the objective of minimizing total transportation cost of parts as well as minimizing makespan. Liu et al [21] developed a discrete bacteria foraging algorithm to solve the model of CMS with the objective of minimizing the material handling costs as well as the fixed and operating costs of machines and workers.
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