A green scheduling algorithm for the distributed flowshop problem

In recent years, sustainable development and green manufacturing have attracted widespread attention to environmental problems becoming increasingly serious. Meanwhile, affected by the intensification of market competition and economic globalization, distributed manufacturing systems have become inc...

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Veröffentlicht in:Applied soft computing Jg. 109; S. 107526
Hauptverfasser: Li, Yuan-Zhen, Pan, Quan-Ke, Gao, Kai-Zhou, Tasgetiren, M. Fatih, Zhang, Biao, Li, Jun-Qing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.09.2021
Schlagworte:
Distributed permutation flowshop scheduling
Energy efficient
Multi-objective optimization
NSGA-II
Total energy consumption
Total flowtime
NSGA-II
Multi-objective optimization
Total flowtime
Total energy consumption
Distributed permutation flowshop scheduling
Energy efficient
ISSN:1568-4946, 1872-9681
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Zusammenfassung:In recent years, sustainable development and green manufacturing have attracted widespread attention to environmental problems becoming increasingly serious. Meanwhile, affected by the intensification of market competition and economic globalization, distributed manufacturing systems have become increasingly common. This paper addresses the energy-efficient scheduling of the distributed permutation flowshop (EEDPFSP) with the criteria of minimizing both total flow time and total energy consumption. Considering the distributed and multi-objective optimization complexity, an improved NSGAII algorithm (INSGAII) is proposed. First, we analyze the problem-specific characteristics and designed new operators based on the knowledge of the problem. Second, four constructive heuristic algorithms are proposed to produce high-quality initial solutions. Third, inspired by the artificial bee colony algorithm, we propose a new colony generation method using the operators designed. Fourth, a local intensification is designed for exploiting better non-dominated solutions. The influence of parameter settings is investigated by experiments to determine the optimal parameter configuration of the INSGAII. Finally, a large number of computational tests and comparisons have been carried out to verify the effectiveness of the proposed INSGAII in solving EEDPFSP.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2021.107526