Distributed assembly permutation flow shop problem; Single seekers society algorithm


Hamzadayı A., Arvas M. A. , Elmi A.

JOURNAL OF MANUFACTURING SYSTEMS, vol.61, pp.613-631, 2021 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 61
  • Publication Date: 2021
  • Doi Number: 10.1016/j.jmsy.2021.10.012
  • Journal Name: JOURNAL OF MANUFACTURING SYSTEMS
  • Journal Indexes: Science Citation Index Expanded, Scopus, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Computer & Applied Sciences, INSPEC
  • Page Numbers: pp.613-631
  • Keywords: Distributed manufacturing, Assembly systems, Permutation flow shop, Mixed integer programming, Single seekers society algorithm, TABU SEARCH ALGORITHM, GENETIC ALGORITHM, SCHEDULING PROBLEM, OPTIMIZATION ALGORITHMS, MAKESPAN, HEURISTICS, FORMULATIONS, SYSTEM

Abstract

The distributed manufacturing and assembly systems have an important role at the point of overcoming the difficulties faced by today's mass-production industry. By using both of these systems together in the same production system, the advantages of this integration can make industries more flexible and stronger. Besides, optimizing these systems is more complicated since the multiple production systems can undoubtfully affect the production system's performance. In this paper, two new mixed-integer linear programming (MILP) models are proposed for the distributed assembly permutation flow shop problem (DAPFSP), inspiring by the multipletravelling salesman structure. Moreover, a single seekers society (SSS) algorithm is proposed for solving the DAPFSP to minimize the maximum completion time of all products. The performance of the proposed MILP models is evaluated using 900 small-sized benchmark instances. The proposed MILP models were effective and were able to find more optimal solutions or improve the best-found solutions for the small-sized DAPFSP benchmark instances. Similarly, the SSS algorithm is statistically compared with the best-known algorithms developed for solving the DAPFSP on 900 small and 810 large-sized benchmark instances. The proposed SSS algorithm shows superior performance compared to other algorithms in solving the small-sized DAPFSP instances in terms of finding better solutions. In addition, it is as effective as the best performing algorithms developed to solve the large-sized DAPFSP instances. Furthermore, the best-found solutions for 40 numbers of test problems reported to be improved in this paper.