Effective Improved NSGA-II Algorithm for Multi-Objective Integrated Process Planning and Scheduling

Xiaoyu Wen, Qingbo Song, Yunjie Qian, Dongping Qiao, Haoqi Wang (), Yuyan Zhang and Hao Li
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Xiaoyu Wen: Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Qingbo Song: Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Yunjie Qian: Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Dongping Qiao: Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Haoqi Wang: Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Yuyan Zhang: Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Hao Li: Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China

Mathematics, 2023, vol. 11, issue 16, 1-17

Abstract: Integrated process planning and scheduling (IPPS) is important for modern manufacturing companies to achieve manufacturing efficiency and improve resource utilization. Meanwhile, multiple objectives need to be considered in the realistic decision-making process for manufacturing systems. Based on the above realistic manufacturing system requirements, it becomes increasingly important to develop effective methods to deal with multi-objective IPPS problems. Therefore, an improved NSGA-II (INSGA-II) algorithm is proposed in this research, which uses the fast non-dominated ranking method for multiple optimization objectives as an assignment scheme for fitness. A multi-layer integrated coding method is adopted to address the characteristics of the integrated optimization model, which involves many optimization parameters and interactions. Elite and mutation strategies are employed during the evolutionary process to enhance population diversity and the quality of solutions. An external archive is also used to store and update the Pareto solution. The experimental results on the Kim test set demonstrate the effectiveness of the proposed INSGA-II algorithm.

Keywords: integrated process planning and scheduling; multi-objective optimization; mutation strategy; elite strategy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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