Energy-efficient distributed permutation flow shop scheduling problem using a multi-objective whale swarm algorithm
Yükleniyor...
Dosyalar
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier B.V.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Production scheduling is of great significance in improving production effectiveness while the energy-efficient problem is one of most concerned problems for researchers and manufacturers. Thus, this study investigates the energy-efficient distributed permutation flow shop scheduling problem (DPFSP) with the objectives of makespan and energy consumption. The DPFSP is an extension of permutation flow shop problem (PFSP) considering a set of identical factories. This paper presents a multi-objective mixed integer programming model based on the three sub-problems: allocating jobs among factories, scheduling the jobs in each factory and determining speed upon each job. A multi-objective whale swarm algorithm (MOWSA) is proposed to solve this energy-efficient DPFSP. A new problem-dependent local search is developed to improve the exploitation capability of MOWSA. Moreover, the updating exploitation mechanism is presented to enhance energy efficiency without affecting production efficiency. Finally, the extensive comparison experiments are designed to demonstrate the effectiveness of proposed MOWSA, problem-dependent local search and updating exploitation mechanism. The results indicate the effectiveness of MOWSA and the superior performance over NSGA-II, SPEA2, PAES and MDEA, and also demonstrate that the proposed algorithm can significantly reduce the energy consumption compared with other algorithms. © 2020 Elsevier B.V.
Açıklama
Taşgetiren, Mehmet Fatih (isu author)
Anahtar Kelimeler
Distributed Permutation Flow Shop, Energy-Efficient Scheduling, Setup Times, Whale Swarm Algorithm
Kaynak
Swarm and Evolutionary Computation
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
57
Sayı
Künye
Wang, G., Gao, L., Li, X., Li, P., & Tasgetiren, M. F. (2020). Energy-efficient distributed permutation flow shop scheduling problem using a multi-objective whale swarm algorithm. Swarm and Evolutionary Computation, 100716.
