Adaptive crayfish optimization algorithm for multi-objective scheduling optimization in distributed production workshops

The increasing demand for wind turbines and cost pressures in the wind energy industry have made the Wind Turbine Pultruded Panels Production Scheduling Problem (WTPP-PSP) a critical challenge. To address the production scheduling requirements of WTPP-PSP, an intelligent platform is proposed for win...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 19680 - 23
Hauptverfasser: Yang, Xin, Yang, Xiaoying, Du, Jinhao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Nature Publishing Group 04.06.2025
Nature Publishing Group UK
Nature Portfolio
Schlagworte:
Algorithms
Convergence
Crayfish optimization algorithm
Decision making
Distributed production workshops
Immunoglobulin D
Linear programming
Multi-objective optimization
Optimization algorithms
Production scheduling
Reinforcement learning
Turbines
Wind power
Multi-objective optimization
Production scheduling
Crayfish optimization algorithm
Distributed production workshops
Reinforcement learning
ISSN:2045-2322, 2045-2322
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Zusammenfassung:The increasing demand for wind turbines and cost pressures in the wind energy industry have made the Wind Turbine Pultruded Panels Production Scheduling Problem (WTPP-PSP) a critical challenge. To address the production scheduling requirements of WTPP-PSP, an intelligent platform is proposed for wind turbine pultruded panel production systems, leveraging intelligent decision-making to tackle the problem. A multi-objective model based on mixed-integer linear programming is developed, considering sequence-dependent completion and setup time constraints. The model aims to maximize customer satisfaction, minimize total setup time, and reduce deviations in workshop machine loads. To solve this problem, an Adaptive Crayfish Optimization Algorithm (ACOA) is introduced. This algorithm incorporates crossover and mutation operators, making it effective for discrete optimization problems. Furthermore, an improved crowding distance calculation enhances the algorithm's performance in multi-objective optimization by improving solution distribution. Reinforcement learning is employed to dynamically adjust temperature parameters, improving both exploration and exploitation capabilities and thus enhancing the convergence of the algorithm. The performance comparison using multi-objective metrics such as HV, IGD, GD, and NR demonstrates that ACOA significantly outperforms COA, WOA, and NSGA-II, with average improvements of 76%, 80%, 28%, and 220%, respectively. These results highlight ACOA's consistent advantages in coverage, convergence, and solution diversity. In the application to WTPP-PSP, the proposed algorithm outperforms COA by approximately 13%, 10%, and 8% in the three objectives.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-02218-1