A Self-adaptive two stage iterative greedy algorithm based job scales for energy-efficient distributed permutation flowshop scheduling problem

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2024-11-30 DOI:10.1016/j.swevo.2024.101777

Yang Yu , Quan Zhong , Liangliang Sun , Yuyan Han , Qichun Zhang , Xuelei Jing , Zhujun Wang

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引用次数: 0

Abstract

The production form of distributed manufacturing combined with energy-efficient scheduling has attracted great attention. In this paper, the energy-efficient distributed permutation flowshop scheduling problem with sequence-dependent setup times (EEDPFSP/SDST) is studied with the criterion of minimizing the total flowtime (TF) and total energy consumption (TEC). Firstly, by changing the initial ordering rule according to the total flowtime, an improved multi-objective NEH heuristic is presented to generate better initial individuals. Secondly, by analyzing the feature of EEDPFSP/SDST, a self-adaptive two stage iterative greedy algorithm based on the job scales (SAIG_bjs) is proposed, which includes a self-adaptive local search according to the job scale, and the energy-saving strategy with the sequence-dependent setup times characteristics based on the time margin between adjacent operations on the same job. Finally, the extensive experiments are adopted to test the performance of the proposed algorithm, and the experimental results demonstrate that the proposed SAIG_bjs algorithm is superior to the other five well-known algorithms.

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.