Hybrid enhanced discrete fruit fly optimization algorithm for scheduling blocking flow-shop in distributed environment

•The distributed blocking flow-shop scheduling problem is investigated.•A new efficient heuristic is proposed.•A discrete fruit fly optimization algorithm is presented.•State-of-the-art results are obtained by the proposed methods. Scheduling in distributed production environments is becoming widesp...

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Vydané v:Expert systems with applications Ročník 145; s. 113147
Hlavní autori: Shao, Zhongshi, Pi, Dechang, Shao, Weishi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.05.2020
Predmet:
Blocking flow-shop scheduling
Constructive heuristic
Distributed production environment
Makespan
Metaheuristic
Metaheuristic
Makespan
Blocking flow-shop scheduling
Distributed production environment
Constructive heuristic
ISSN:0957-4174, 1873-6793
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Popis
Shrnutí:•The distributed blocking flow-shop scheduling problem is investigated.•A new efficient heuristic is proposed.•A discrete fruit fly optimization algorithm is presented.•State-of-the-art results are obtained by the proposed methods. Scheduling in distributed production environments is becoming widespread in recent years due to the increasing advantages of multi-factory manufacture. This paper investigates the distributed blocking flow-shop scheduling problem (DBFSP) with the objective of minimizing the makespan. To solve this problem, a hybrid enhanced discrete fruit fly optimization algorithm (HEDFOA) is proposed. In the proposed algorithm, an effective constructive heuristic is developed based on a new assignment rule of jobs and an insertion-based improvement procedure to initialize the common central location of all fruit fly swarms. In the smell-based foraging, an effective insertion-based neighborhood operator is designed for exploration in global scope. In the vision-based foraging, a local search is embedded to intensify the exploitation ability of algorithm in local region. Meanwhile, a simulated annealing-like acceptance criterion is employed to help algorithm escape from the local optimum. Finally, an extensive computational experiment is conducted. Experimental results show that the proposed HEDFOA is more effective than the existing state-of-the-art methods. Furthermore, 516 best known solutions out of 720 benchmark instances are also updated.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2019.113147