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Affinity guided learning Iterated Greedy for permutation flow shop scheduling: A sugar manufacturing application
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A. Olalekan Olasupo   , F. Damilare Ibikunle , A. Jeremiah Amogu |
| Department of Industrial and Production Engineering, University of Ibadan, Nigeria |
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Abstract: (4 Views) |
This study proposes a mutual-love learning iterated greedy (IGML) algorithm for the permutation flow shop scheduling problem (PFSP) with the objective of minimizing makespan. The proposed method integrates a mutual-love learning matrix and affinity-guided destruction and reconstruction to improve the search for promising job sequences. The performance of IGML was evaluated against classical iterated greedy (IG) and the Nawaz–Enscore–Ham (NEH) heuristic using 120 Taillard benchmark instances. IGML achieved the lowest average makespan of 6871.717, compared with 6877.792 for IG and 6899.942 for NEH. Similarly, IGML obtained the lowest average RPD of 3.066%, compared with 3.211% for IG and 3.864% for NEH. The Wilcoxon signed-rank test at the 5% significance level confirmed statistically significant differences between IGML and NEH (p < 0.001) and between IGML and IG (p = 0.0027). A practical case study involving eight replacement components in a sugar manufacturing workshop was further used to demonstrate the applicability of the proposed approach. The results indicate that IGML provides a statistically significant improvement in PFSP makespan performance over the conventional methods considered.
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| Keywords: Permutation Flow Shop Scheduling, Iterated Greedy, Mutual-Love Learning, Affinity, Makespan, Taillard Benchmark. |
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Full-Text [PDF 729 kb]
(6 Downloads)
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Type of Study: Research |
Subject:
General
Received: 2026/05/13 | Accepted: 2026/06/20 | Published: 2026/07/10
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