A Genetic Hyper-Heuristic for an Order Scheduling Problem with Two Scenario-Dependent Parameters in a Parallel-Machine Environment

Lung-Yu Li, Jian-You Xu, Shuenn-Ren Cheng, Xingong Zhang, Win-Chin Lin (), Jia-Cheng Lin, Zong-Lin Wu and Chin-Chia Wu
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Lung-Yu Li: Department of Computer Science and Information Engineering, Cheng Shiu University, Kaohsiung City 83347, Taiwan
Jian-You Xu: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Shuenn-Ren Cheng: Department of E-Sport Technology Management, Cheng Shiu University, Kaohsiung City 83347, Taiwan
Xingong Zhang: Key Lab for OCME, School of Mathematical Science, Chongqing Normal University, Chongqing 401331, China
Win-Chin Lin: Department of Statistics, Feng Chia University, Taichung 40724, Taiwan
Jia-Cheng Lin: Department of Statistics, Feng Chia University, Taichung 40724, Taiwan
Zong-Lin Wu: Department of Statistics, Feng Chia University, Taichung 40724, Taiwan
Chin-Chia Wu: Department of Statistics, Feng Chia University, Taichung 40724, Taiwan

Mathematics, 2022, vol. 10, issue 21, 1-22

Abstract: Studies on the customer order scheduling problem have been attracting increasing attention. Most current approaches consider that either component processing times for customer orders on each machine are constant or all customer orders are available at the outset of production planning. However, these assumptions do not hold in real-world applications. Uncertainty may be caused by multiple issues including a machine breakdown, the working environment changing, and workers’ instability. On the basis of these factors, we introduced a parallel-machine customer order scheduling problem with two scenario-dependent component processing times, due dates, and ready times. The objective was to identify an appropriate and robust schedule for minimizing the maximum of the sum of weighted numbers of tardy orders among the considered scenarios. To solve this difficult problem, we derived a few dominant properties and a lower bound for determining an optimal solution. Subsequently, we considered three variants of Moore’s algorithm, a genetic algorithm, and a genetic-algorithm-based hyper-heuristic that incorporated the proposed seven low-level heuristics to solve this problem. Finally, the performances of all proposed algorithms were evaluated.

Keywords: order scheduling; scenario-dependent; genetic algorithm; genetic hyper-heuristic; low-level heuristics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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