Unrelated Parallel Machine Scheduling with Job Splitting, Setup Time, Learning Effect, Processing Cost and Machine Eligibility

Feifeng Zheng (), Kaiyuan Jin, Yinfeng Xu () and Ming Liu ()
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Feifeng Zheng: Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, P. R. China
Kaiyuan Jin: Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, P. R. China
Yinfeng Xu: School of Management, Xi’an Jiaotong University, Xi’an 710049, P. R. China
Ming Liu: School of Economics and Management, Tongji University, Shanghai 200092, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2023, vol. 40, issue 03, 1-30

Abstract: This work investigates an unrelated parallel machine scheduling problem in the shared manufacturing environment. Based on practical production complexity, five job and machine-related factors, including job splitting, setup time, learning effect, processing cost and machine eligibility constraint, are integrated into the considered problem. Parallel machines with uniform speed but non-identical processing capabilities are shared on a sharing service platform, and jobs with different types can only be processed by the machines with matching eligibilities. The platform pays an amount of processing cost for using any machine to process the jobs. To balance the processing cost paid and the satisfaction of customers, we aim to minimize the weighted sum of total processing cost and total completion time of jobs in the considered problem. We establish a mixed integer linear programming model, and provide a lower bound by relaxing the machine eligibility constraint. The CPLEX solver is employed to generate optimal solutions for small-scale instances. For large-scale instances, we propose an efficient heuristic algorithm. Experimental results demonstrate that for various instance settings, the proposed algorithm can always produce near optimal solutions. We further present several managerial insights for the shared manufacturing platform.

Keywords: Scheduling; shared manufacturing; unrelated parallel machines; job splitting; heuristic algorithm (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0217595922500233

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