A bi-objective evolutionary algorithm for minimizing maximum lateness and total pollution cost on non-identical parallel batch processing machines. (October 2022)

• Record Type: Journal Article
• Authors: Li, Kai; Zhang, Han; Chu, Chengbin; Jia, Zhao-hong; Wang, Yan
• Is Part Of: Computers & industrial engineering. Volume 172:Part A(2022)
• Publication Date: 2022-10
• Subjects: Scheduling -- Batch processing machine -- Lateness -- Total pollution cost -- Evolutionary algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108608
• Languages: English
• ISSNs: 0360-8352

Abstract

Abstract: The concept of sustainable development, which strives to protect the environment while developing economic and industrial sectors, has received widespread attention. This paper takes the fabric dyeing process in the textile industry as an example to study the problem of minimizing the total pollution cost and the maximum lateness on non-identical parallel batch processing machines with different unit pollution costs. This paper analyzes the characteristics of the batch scheduling problem and proposes a method of constructively generating the initial solutions based on these characteristics. Meanwhile, An environment selection based on hierarchical clustering is proposed. This paper develops an NSGA-CC algorithm combining the above two methods in the NSGA-II framework to solve this bi-objective problem. Through extensive simulation experiments, NSGA-CC is compared with several latest algorithms, and then the experimental results show that the proposed algorithm outperforms compared algorithms. Highlights: A bi-objective problem on non-identical parallel BPMs with costs is presented. An NSGA-CC algorithm is developed to solve the presented problem. A constructively generating solutions strategy is proposed to improve convergence. A cluster-based environmental selection strategy is designed to improve diversity.