Year: 2024
Type: article
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.
Source: Mathematics
DOI: 10.3390/math12182951
Authors: Nasser Shahsavari-Pour, Azim Heydari, Afef Fekih, Hamed Asadi
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4402747159