Solving cell formation and task scheduling in cellular manufacturing system by discrete bacteria foraging algorithm

Abstract

We consider a joint decision model of cell formation and task scheduling in cellular manufacturing system under dual-resource constrained (DRC) setting. On one hand, machines and workers are multi-functional and/or multi-skilled, and they are grouped into workstations and cells. On the other hand, there is a processing sequence among operations of the parts which needs to be dispatched to the desirable workstations for processing. Inter-cell movements of parts can reduce the processing times and the makespan but will increase the inter-cell material handling costs. The objective of the problem is to minimise the material handling costs as well as the fixed and operating costs of machines and workers. Due to the NP-hardness of the problem, we propose an efficient discrete bacteria foraging algorithm (DBFA) with elaborately designed solution representation and bacteria evolution operators to solve the proposed problem. We tested our algorithm using randomly generated instances with different sizes and settings by comparing with the original bacteria foraging algorithm and a genetic algorithm. Our results show that the proposed DBFA has better performance than the two compared algorithms with the same running time.

Keywords:

• cellular manufacturing system
• cell formation
• group scheduling
• bacteria foraging algorithm
• operation sequence

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the Humanities and Social Sciences Youth Foundation of the Ministry of Education [grant number 14YJC630089]; Zhejiang Provincial Natural Science Foundation of China [grant number LY14G020014]; China Scholarship Council, Zhejiang Provincial Key Research Base of Humanities and Social Sciences in Hangzhou Dianzi University [grant number ZD03-201501], the Research Center of Information Technology & Economic and Social Development, and the National Natural Science Foundation of China [grant number 71471052]. The authors are grateful for the financial supports.