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DISCRETE SINE OPTIMIZATION ALGORITHM FOR MULTI-OBJECTIVE DISTRIBUTED HETEROGENEOUS NO-IDLE FLOWSHOP SCHEDULING PROBLEM
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DOI：10.3934/jimo.2025164.png)
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Abstract
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In this paper, a Multi-objective Distributed Heterogeneous No-Idle Flowshop Scheduling Problem with Sequence-Dependent Setup Time (MDHNIFSP-SDST) is studied. Firstly, a multi-objective optimization model was established with the objective of minimizing makespan (completion time) and Total Tardiness (TT). According to the characteristics of the problem, a Discrete Sine Optimization Algorithm (DSOA) is proposed. This algorithm mainly includes four core stages: In the multi-neighborhood search stage, a search strategy based on key factories was proposed, and the Q-learning algorithm was used to enable individuals to select appropriate operators during evolution; In the stage of destruction and reconstruction, an iterative search strategy was designed to guide the evolutionary direction of individuals, while the sinusoidal optimization algorithm was used to balance global development and local search. In the selection stage, the method of non-dominated sorting and reference points is used to screen for high-quality solutions, and the external file set is used to store all non-dominated solutions. In the collaborative stage, the sharing and competition mechanisms among populations are designed to balance the optimization of the two objectives. Finally, 270 instances were solved by DSOA algorithm and advanced algorithms at the same time and measured by multi-objective indicators. The results show that DSOA algorithm ranks first in all test instances compared with other algorithms, which verifies the effectiveness of DSOA algorithm.
Keywords:
Distributed manufacturing
heterogeneity
discrete sine optimization algorithm
distributed no-idle flowshop scheduling
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