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Open Access

A Penalty Groups-Assisted Iterated Greedy Integrating Idle Time Insertion: Solving the Hybrid Flow Shop Group Scheduling with Delivery Time Windows

School of Computer Science, Liaocheng University, Liaocheng 252059, China
Macau Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China
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Abstract

The hybrid flow shop group scheduling problem (HFGSP) with the delivery time windows has been widely studied owing to its better flexibility and suitability for the current just-in-time production mode. However, there are several unresolved challenges in problem modeling and algorithmic design tailored for HFGSP. In our study, we place emphasis on the constraint of timeliness. Therefore, this paper first constructs a mixed integer linear programming model of HFGSP with sequence-dependent setup time and delivery time windows to minimize the total weighted earliness and tardiness (TWET). Then a penalty groups-assisted iterated greedy integrating idle time insertion ( PG_IG_ITI) is proposed to solve the above problem. In the PG_IG_ITI, a double decoding strategy is proposed based on the earliest available machine rule and the idle time insertion rule to calculate the TWET value. Subsequently, to reduce the amount of computation, a skip-based destruction and reconstruction strategy is designed, and a penalty groups-assisted local search is proposed to further improve the quality of the solution by disturbing the penalized groups, i.e., early and tardy groups. Finally, through comprehensive statistical experiments on 270 test instances, the results prove that the proposed algorithm is effective compared to four state-of-the-art algorithms.

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Complex System Modeling and Simulation
Pages 137-165
Cite this article:
Ji Q, Han Y, Wang Y, et al. A Penalty Groups-Assisted Iterated Greedy Integrating Idle Time Insertion: Solving the Hybrid Flow Shop Group Scheduling with Delivery Time Windows. Complex System Modeling and Simulation, 2024, 4(2): 137-165. https://doi.org/10.23919/CSMS.2024.0005

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Received: 31 January 2024
Revised: 02 April 2024
Accepted: 21 April 2024
Published: 30 June 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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