Energy-efficient scheduling for flexible job shop under multi-resource constraints using non-dominated sorting teaching-learning-based optimization algorithm

• Published in: Journal of intelligent & fuzzy systems Vol. 43; no. 1; pp. 409 - 423
• Main Authors: Wang, Jianhua, Zhu, Kai, Peng, Yongtao, Zhu, Kang
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.06.2022; Sage Publications Ltd
• Subjects: Algorithms; Completion time; Constraints; Energy consumption; Job shop scheduling; Job shops; Machine learning; Optimization; Resource scheduling; Sorting algorithms; Teaching; NSTLBO; energy-efficient; Scheduling; multi-resource constraint; flexible job shop
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-212258

Due to the fact that the real manufacturing processes are often constrained by many kinds of resources and the trend that the energy consumption of factories is regulated more and more strictly, this paper studies the energy-efficient multi-resource flexible job shop scheduling problem (EE-MRFJSP). The goal is to minimize the energy consumption and completion time for all of the jobs’ production. Firstly, a general mathematic model for EE-MRFJSP is set up, in which the unit energy consumptions of the main resource’s different states are varied, and a constraint formula to ensure no crossover working periods for any resource is included. Then, a non-dominated sorting teaching-learning-based optimization(NSTLBO) algorithm is proposed to solving the problem, the details of NSTLBO include the real encoding method, Giffler Thompson rule for decoding, non-dominated sorting rule to rank the pareto sets and crowding distance of solution for maintaining the population’s diversity, and the traditional two evolving stages: teacher education and student mutual study. Finally, comparative experiments are made based on some new designed instances, and the results verify our proposed NSTLBO algorithm can effectively solve the EE-MMFJSP, and has obvious advantages by comparing with NSGA-II, NRGA, and MOPSO.