Abstract
The efficiency and sustainability of a cellular manufacturing system (CMS) in batch type manufacturing is highly valued. This is done using a systematic method of equipment into machine cells, and components into part families, based on the suitable similar criteria. The present work discusses the cell formation problem, with the objective of minimizing the cumulative cell load variation and cumulative intercellular moves. The quantity of parts, operation sequences, processing time, capacity of machines, and workload of machineries were considered as parameters. For the grouping of equipment, the modified artificial bee colony (MABC) algorithm is considered. The computational procedure of this approach is explained by using up to 40 machines and 100 part types. The result obtained from MABC is compared with the findings acquired from the genetic algorithm (GA) and ant colony system (ACS) in the literature.
Highlights
Group technology (GT) is a production strategy where the similar components are picked out and grouped together to gain the benefits of their similarities in design and/or production character [1]
The modified artificial bee colony (MABC) algorithm, a meta heuristic population based algorithms is used in cell formation problem with the aim of reducing cumulative cell load variation and cumulative intercellular moves
The cell load variation is calculated by the difference between the equipment workload and the machine cell average load
Summary
Group technology (GT) is a production strategy where the similar components are picked out and grouped together to gain the benefits of their similarities in design and/or production character [1]. Ghezavati and Saidi-Mehrabad [27] proposed an efficient hybrid self-learning method for stochastic cellular manufacturing problems It addresses a new version of stochastic mixed-integer model to design cellular manufacturing systems under random parameters, described by continuous distributions. Sakhaii et al [34] developed a robust optimization approach for a new integrated mixed-integer linear programming model to solve a dynamic cellular manufacturing system with unreliable machines and a production planning problem simultaneously. The MABC algorithm, a meta heuristic population based algorithms is used in cell formation problem with the aim of reducing cumulative cell load variation and cumulative intercellular moves. The outcome of this approach is compared with that of both GA and ACO
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