A Pareto-based discrete particle swarm optimization for parallel casting workshop scheduling problem with fuzzy processing time

The parallel casting workshop scheduling problem is essentially a type of unrelated parallel machine scheduling (UPMS) problem with fuzzy order processing time. In this paper, a tri-objective parallel casting workshop optimization model is investigated to minimize the order advance/delay penalty, ma...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 256; p. 109872
Main Authors: Zhou, Wenhao, Chen, Fayuan, Ji, Xiaoyuan, Li, Hailong, Zhou, Jianxin
Format: Journal Article
Language:English
Published: Elsevier B.V 28.11.2022
Subjects:
Discrete particle swarm optimization
Fuzzy processing time
Multi-objective algorithm
Parallel machine scheduling
Simulated annealing
Discrete particle swarm optimization
Parallel machine scheduling
Multi-objective algorithm
Fuzzy processing time
Simulated annealing
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:The parallel casting workshop scheduling problem is essentially a type of unrelated parallel machine scheduling (UPMS) problem with fuzzy order processing time. In this paper, a tri-objective parallel casting workshop optimization model is investigated to minimize the order advance/delay penalty, makespan, and workload imbalance, and a formula of the solution scale with the number of orders and workshops is derived. In line with the multi-objective integer programming model, a new Pareto-based discrete particle swarm optimization (PDPSO) is proposed. In this algorithm, the particles are encoded with integers. A novel two-phase heuristic is developed for integer-encoded particle updates. The first phase determines the order assignment through a weighting vote, and the second phase sorts the orders according to their dominance in each workshop. Every particle’s update is simultaneously guided by the best compromise solution selected from the personal and global Pareto solution. Meanwhile, the Pareto simulated annealing strategy is introduced in PDPSO to enhance its exploration ability. Multiple simulations under different scales are tested to validate the effectiveness of our proposed model and algorithm. The results show that the proposed PDPSO outperforms the other two multi-objective algorithms and has large advantages in finding the Pareto frontier (PF) with an acceptable solution number and solving speed, especially for small and medium-sized instances.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2022.109872