A Novel Pareto-Optimal Algorithm for Flow Shop Scheduling Problem

Nasser Shahsavari-Pour, Azim Heydari (), Afef Fekih and Hamed Asadi
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Nasser Shahsavari-Pour: Department of Industrial Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan 7718897111, Iran
Azim Heydari: Department of Astronautics, Electrical and Energetic Engineering (DIAEE) Sapienza University, 00184 Rome, Italy
Afef Fekih: Department of Electrical and Computer Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Hamed Asadi: Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran

Mathematics, 2024, vol. 12, issue 18, 1-15

Abstract: Minimizing job waiting time for completing related operations is a critical objective in industries such as chemical and food production, where efficient planning and production scheduling are paramount. Addressing the complex nature of flow shop scheduling problems, which pose significant challenges in the manufacturing process due to the vast solution space, this research employs a novel multiobjective genetic algorithm called distance from ideal point in genetic algorithm (DIPGA) to identify Pareto-optimal solutions. The effectiveness of the proposed algorithm is benchmarked against other powerful methods, namely, NSGA, MOGA, NSGA-II, WBGA, PAES, GWO, PSO, and ACO, using analysis of variance (ANOVA). The results demonstrate that the new approach significantly improves decision-making by evaluating a broader range of solutions, offering faster convergence and higher efficiency for large-scale scheduling problems with numerous jobs. This innovative method provides a comprehensive listing of Pareto-optimal solutions for minimizing makespan and total waiting time, showcasing its superiority in addressing highly complex problems.

Keywords: flow shop scheduling; multiobjective optimization; Pareto-optimal solutions; genetic algorithm; ANOVA (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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