An Adaptive Multi-Objective Differential Evolution Algorithm for Batch Scheduling Problem in Shared Manufacturing

In shared manufacturing, the flexible collaboration between manufacturing units presents significant challenges for production scheduling. Furthermore, batch manufacturing is often employed in practical production, which makes solving the scheduling problem even more difficult, yet there is limited research on this topic. Therefore, this paper investigates the batch scheduling problem in shared manufacturing. A mathematical model for this problem is developed with the objective of minimizing the makespan and total cost. To solve this problem, an adaptive multi-objective differential evolution algorithm is proposed. The algorithm incorporates an adaptive batch decomposition method for efficient batch result optimization and a discrete differential evolution strategy for process scheduling optimization. Finally, experimental results are provided to validate the effectiveness and superiority of the proposed method.

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Publication Details

Published
2025-02-28
DOI
https://doi.org/10.1109/isbdas64762.2025.11117146
Primary Topic
Scheduling and Optimization Algorithms
Type
article
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article

An Adaptive Multi-Objective Differential Evolution Algorithm for Batch Scheduling Problem in Shared Manufacturing

Jun Guo, Shunsheng Guo, Yanhui Huang
Scheduling and Optimization Algorithms
article

An Adaptive Multi-Objective Differential Evolution Algorithm for Batch Scheduling Problem in Shared Manufacturing

Jun Guo, Shunsheng Guo, Yanhui Huang
article en

Abstract

In shared manufacturing, the flexible collaboration between manufacturing units presents significant challenges for production scheduling. Furthermore, batch manufacturing is often employed in practical production, which makes solving the scheduling problem even more difficult, yet there is limited research on this topic. Therefore, this paper investigates the batch scheduling problem in shared manufacturing. A mathematical model for this problem is developed with the objective of minimizing the makespan and total cost. To solve this problem, an adaptive multi-objective differential evolution algorithm is proposed. The algorithm incorporates an adaptive batch decomposition method for efficient batch result optimization and a discrete differential evolution strategy for process scheduling optimization. Finally, experimental results are provided to validate the effectiveness and superiority of the proposed method.

Wuhan University of Technology (CN)
Openalex Percentile: Top 70%
Scheduling and Optimization Algorithms
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An Adaptive Multi-Objective Differential Evolution Algorithm for Batch Scheduling Problem in Shared Manufacturing — Jun Guo, Shunsheng Guo, et al. · (2025) | TGRS Research Map | TGRS