Abstract
Distributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention; however, DHFSP with uncertainty and energy-related element is seldom studied. In this paper, distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with fuzzy processing time is considered and a cooperated shuffled frog-leaping algorithm (CSFLA) is presented to optimize fuzzy makespan, total agreement index and fuzzy total energy consumption simultaneously. Iterated greedy, variable neighborhood search and global search are designed using problem-related features; memeplex evaluation based on three quality indices is presented, an effective cooperation process between the best memeplex and the worst memeplex is developed according to evaluation results and performed by exchanging search times and search ability, and an adaptive population shuffling is adopted to improve search efficiency. Extensive experiments are conducted and the computational results validate that CSFLA has promising advantages on solving the considered DEHFSP.
Highlights
Production scheduling is a decision-making process that plays an important role in manufacturing and production systems
In cooperated shuffled frog-leaping algorithm (CSFLA), IG, VNS and global search (GS) are designed based on features of the problem and memeplex evaluation is done in terms of three quality indices: solution quality, evolution quality and contribution degree of archive; to improve search efficiency, an effective cooperation between the best memeplex and the worst memeplex is performed in search process of memeplexes and an adaptive population shuffling is presented
We found that CSFLA, its two variants and four comparative algorithms can converge well when the above time reaches, so we first choose 0.1×n×m CPU time as stopping condition
Summary
Production scheduling is a decision-making process that plays an important role in manufacturing and production systems. In CSFLA, IG, VNS and global search (GS) are designed based on features of the problem and memeplex evaluation is done in terms of three quality indices: solution quality, evolution quality and contribution degree of archive; to improve search efficiency, an effective cooperation between the best memeplex and the worst memeplex is performed in search process of memeplexes and an adaptive population shuffling is presented.
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