Dual-Resource Scheduling with Improved Forensic-Based Investigation Algorithm in Smart Manufacturing

Yuhang Zeng, Ping Lou, Jianmin Hu (), Chuannian Fan, Quan Liu and Jiwei Hu
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Yuhang Zeng: School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Ping Lou: School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Jianmin Hu: School of Information Engineering, Hubei University of Economics, Wuhan 430205, China
Chuannian Fan: School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Quan Liu: School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Jiwei Hu: School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China

Mathematics, 2025, vol. 13, issue 9, 1-30

Abstract: With increasing labor costs and rapidly dynamic changes in the market demand, as well as realizing the refined management of production, more and more attention is being given to considering workers, not just machines, in the process of flexible job shop scheduling. Hence, a new dual-resource flexible job shop scheduling problem (DRFJSP) is put forward in this paper, considering workers with flexible working time arrangements and machines with versatile functions in scheduling production, as well as a multi-objective mathematical model for formalizing the DRFJSP and tackling the complexity of scheduling in human-centric manufacturing environments. In addition, a two-stage approach based on a forensic-based investigation (TSFBI) is proposed to solve the problem. In the first stage, an improved multi-objective FBI algorithm is used to obtain the Pareto front solutions of this model, in which a hybrid real and integer encoding–decoding method is used for exploring the solution space and a fast non-dominated sorting method for improving efficiency. In the second stage, a multi-criteria decision analysis method based on an analytic hierarchy process (AHP) is used to select the optimal solution from the Pareto front solutions. Finally, experiments validated the TSFBI algorithm, showing its potential for smart manufacturing.

Keywords: smart manufacturing; flexible job shop scheduling; workforce costs; forensic-based investigation; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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