Computational experiments showed that MSALS is either superior to current heuristics for Pareto sets or is incomparable due to other performance indicators of multiobjective problems.
📌 3 Key Takeaways
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Objective & Hypothesis:Interest in multiobjective permutation flow shop scheduling (PFSS) has increased in the last decade to ensure effective resource utilization.
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Key Findings & Data:This study presents a modified self-adaptive local search (MSALS) algorithm for the biobjective permutation flow shop scheduling problem where both makespan and total flow time objectives are minimized.
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Conclusion & Impact:Computational experiments showed that MSALS is either superior to current heuristics for Pareto sets or is incomparable due to other performance indicators of multiobjective problems.
Interest in multiobjective permutation flow shop scheduling (PFSS) has increased in the last decade to ensure effective resource utilization. This study presents a modified self-adaptive local search (MSALS) algorithm for the biobjective permutation flow shop scheduling problem where both makespan and total flow time objectives are minimized. Compared to existing sophisticated heuristic algorithms, MSALS is quite simple to apply to different biobjective PFSS instances without requiring effort or time for parameter tuning. Computational experiments showed that MSALS is either superior to current heuristics for Pareto sets or is incomparable due to other performance indicators of multiobjective problems.
USLU, Ç. A., DENGİZ, B., AĞLAN, C., SABUNCUOĞLU, İ. (2019). Modified self-adaptive local search algorithm for a biobjective permutation flow shop scheduling problem. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 27 - Sayı: 4, 2730–2745. https://ajindex.com/articles/186933