Simulation Optimization on Complex Job Shop Scheduling with Non-Identical Job Sizes

Lingxuan Liu and Leyuan Shi ()
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Lingxuan Liu: Department of Industrial Engineering & Management, Peking University, BJ 100871, P. R. China
Leyuan Shi: Department of Industrial & Systems Engineering, University of Wisconsin-Madison, WI 53706, USA

Asia-Pacific Journal of Operational Research (APJOR), 2019, vol. 36, issue 05, 1-26

Abstract: This paper addresses the complex job shop scheduling problem with the consideration of non-identical job sizes. By simultaneously considering practical constraints of sequence dependent setup times, incompatible job families and job dependent batch processing time, we formulate this problem into a simulation optimization problem based on the disjunctive graph representation. In order to find scheduling policies that minimise the expectation of mean weighted tardiness, we propose a genetic programming based hyper heuristic to generate efficient dispatching rules. And then, based on the nested partition framework together with the optimal computing budget allocation technique, a hybrid rule selection algorithm is proposed for searching machine group specified rule combinations. Numerical results show that the proposed algorithms outperform benchmark algorithms in both solution quality and robustness.

Keywords: Complex job shop scheduling; non-identical job sizes; stochastic simulation; nested partition; genetic programming (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S021759591950026X

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