A hybrid multi-objective approach for real-time flexible production scheduling and rescheduling under dynamic environment in Industry 4.0 context. (August 2021)

• Record Type: Journal Article
• Authors: Zhang, Sicheng; Tang, Fangcheng; Li, Xiang; Liu, Jiaming; Zhang, Bowen
• Is Part Of: Computers & operations research. Volume 132(2021)
• Publication Date: 2021-08
• Subjects: Integrated processing planning and assembly scheduling -- Dynamic production scheduling -- Multi-objective optimisation -- Genetic algorithm -- Constraint programming; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2021.105267
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: Incorporation of CP into genetic algorithm. Consideration of rescheduling upon disruptions. Detailed discussion on parameter adaption. Abstract: With the advent of industry-4.0 era, industrial production are evolving towards high flexibility, diversity, customisation, and dynamism. We address a realistic scenario of a smart manufacturing system, which concerns the production scheduling of complex multi-level products under a dynamic flexible job shop environment with shop floor disruptions incorporated. The products are assembled from multiple basic parts, whose fabrication processes are highly flexible, involving alternative process plans, alternative machines and alternative processing sequences of operations. We aim at providing Pareto solutions, with consideration of three typical optimisation objectives, including makespan, maximum machine workload, and total tardiness. A hybrid MPGA-CP approach is designed for the problem. To the best our knowledge, this is the first attempt to embed an exact optimisation technique into a meta-heuristic algorithm in the domain of production scheduling. Compared with other alternative approaches, its efficiency and performance are proven to be outstanding in solving medium-to-large scale problems, covering the largest proportion of Pareto solutions among all tested approaches. Furthermore, we constructed a simulation model of a real-time production scheduling control system, in which our approach is embedded as the kernel algorithm, to study the impacts of some uncertainties that are concerned in practice. Based on the results of simulation experiments and sensitivity analysis, meaningful managerial insights have been provided.