Abstract
Machines are a key element in the production system and their failure causes irreparable effects in terms of cost and time. In this paper, a new multi-objective mathematical model for dynamic cellular manufacturing system (DCMS) is provided with consideration of machine reliability and alternative process routes. In this dynamic model, we attempt to resolve the problem of integrated family (part/machine cell) formation as well as the operators’ assignment to the cells. The first objective minimizes the costs associated with the DCMS. The second objective optimizes the labor utilization and, finally, a minimum value of the variance of workload between different cells is obtained by the third objective function. Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in small-sized problems, and then the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems. Finally, the results of the two algorithms are compared with respect to five different comparison metrics.
Highlights
Since 1970, increase in competitiveness among American industries have begun and adoption of new ideas such as just-in-time (JIT) and group technology (GT) have intensified
Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in smallsized problems, and the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems
The first objective of the proposed model is to minimize the miscellaneous expenses of the dynamic cellular manufacturing system such as fixed or variable costs, inter-/intracellular part movement costs, and machine reconfiguration costs, labor transfer between cells, delay in the date of parts delivery, and failure of the machines
Summary
Since 1970, increase in competitiveness among American industries have begun and adoption of new ideas such as just-in-time (JIT) and group technology (GT) have intensified. Since CM is an NP-hard problem, a GA is used to tackle the model in large-sized problems From another point of view, according to the technological advancements and introduction of multi-function machines, few parts may have only one production route (Karim and Biswas 2015). Zhao and Wu (2000) presented a multi-objective CFP model by taking into account different routes for the production of parts. They aimed to optimize three conflicting objective functions simultaneously. Mehdizadeh and Rahimi (2016) proposed an integrated mathematical model to tackle the dynamic cell formation problem by simultaneous consideration of inter-/intra-cell layout problems with machine duplication and operator assignment.
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