An Improved Differential Evolution Algorithm for Solving a Distributed Flexible Job Shop Scheduling Problem

Wu, Xiuli; Xiajing, Liu

doi:10.1109/coase.2018.8560396

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…The processing time depends on the performance of the selected equipment and the complexity of the process. 7 Multi-objective flexible job-shop scheduling involves mainly parallel processing and multi-function machines. 8 The machine can process multiple processes without mutual interference.…”

Section: Description and Model Construction Of Multi-objective Flexibmentioning

confidence: 99%

NSGA-II algorithm and application for multi-objective flexible workshop scheduling

Wang

Shi

Cai

et al. 2020

Journal of Algorithms & Computational Technology

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Based on the study of multi-objective flexible workshop scheduling problem and the learning of traditional genetic algorithm, a non-dominated sorting genetic algorithm is proposed to solve and optimize the scheduling model with the objective functions of processing cycle, advance/delay penalty and processing cost. In the process of optimization, non-dominated fast ranking operator and competition operator are used to select the descendant operator, which improves the computational efficiency and optimization ability of the algorithm. Non-repetitive non-dominant solutions and frontier sets are found in the iteration operation to retain the optimal results. Finally, taking a manufacturing workshop as an example, the practicability of the proposed algorithm is verified by the simulation operation of the workshop scheduling information and the comparison with other algorithms. The results show that the algorithm can obtain the optimal solution more quickly than the unimproved algorithm. The improved algorithm is faster and more effective in searching, and has certain feasibility in solving the job shop scheduling problem, which is more suitable for industrial processing and production.

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Section: Description and Model Construction Of Multi-objective Flexibmentioning

confidence: 99%

NSGA-II algorithm and application for multi-objective flexible workshop scheduling

Wang

Shi

Cai

et al. 2020

Journal of Algorithms & Computational Technology

View full text Add to dashboard Cite

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An effective reformative memetic algorithm for distributed flexible job-shop scheduling problem with order cancellation

Zhu,

Gong,

et al. 2024

Expert Systems with Applications

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Applying Hybrid Genetic Simplified Swarm Optimization for Distributed Flexible Job-Shop Scheduling Problem

Perng¹,

Yeh²,

et al. 2022

SSRN Journal

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An Improved Differential Evolution Algorithm for Solving a Distributed Flexible Job Shop Scheduling Problem

Cited by 3 publications

References 12 publications

NSGA-II algorithm and application for multi-objective flexible workshop scheduling

NSGA-II algorithm and application for multi-objective flexible workshop scheduling

An effective reformative memetic algorithm for distributed flexible job-shop scheduling problem with order cancellation

Applying Hybrid Genetic Simplified Swarm Optimization for Distributed Flexible Job-Shop Scheduling Problem

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