Open Access

ARTICLE

An Iterated Greedy Algorithm with Memory and Learning Mechanisms for the Distributed Permutation Flow Shop Scheduling Problem

by Binhui Wang, Hongfeng Wang^*

College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

* Corresponding Author: Hongfeng Wang. Email:

(This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)

Computers, Materials & Continua 2025, 82(1), 371-388. https://doi.org/10.32604/cmc.2024.058885

Received 23 September 2024; Accepted 25 November 2024; Issue published 03 January 2025

Abstract

The distributed permutation flow shop scheduling problem (DPFSP) has received increasing attention in recent years. The iterated greedy algorithm (IGA) serves as a powerful optimizer for addressing such a problem because of its straightforward, single-solution evolution framework. However, a potential draw-back of IGA is the lack of utilization of historical information, which could lead to an imbalance between exploration and exploitation, especially in large-scale DPFSPs. As a consequence, this paper develops an IGA with memory and learning mechanisms (MLIGA) to efficiently solve the DPFSP targeted at the mini-mal makespan. In MLIGA, we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search, by extending, reconstructing, and reinforcing the information from previous solutions. In addition, we design a two-layer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism. Meanwhile, to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA, a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules. At last, a discrete adaptive learning rate is employed to enhance the stability of the memory and learning mechanisms. Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism, and the results show that this mechanism is capable of improving the performance of IGA to a large extent. Furthermore, through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks, we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs. This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.

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Keywords

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Supplementary Material

Supplementary Material File

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