Open Access

ARTICLE

An SAC-AMBER Algorithm for Flexible Job Shop Scheduling with Material Kit

Bo Li, Xiaoying Yang^*, Zhijie Pei, Xin Yang, Yaqi Wu

School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China

* Corresponding Author: Xiaoying Yang. Email:

(This article belongs to the Special Issue: Applications of Artificial Intelligence in Smart Manufacturing)

Computers, Materials & Continua 2025, 84(2), 3649-3672. https://doi.org/10.32604/cmc.2025.066267

Received 03 April 2025; Accepted 20 May 2025; Issue published 03 July 2025

Abstract

It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage. This paper studies the flexible job shop scheduling problem (FJSP) with the objective of material kitting, where a material kit is a collection of components that ensures that a batch of components can be ready at the same time during the product assembly process. In this study, we consider completion time variance and maximum completion time as scheduling objectives, continue the weighted summation process for multiple objectives, and design adaptive weighted summation parameters to optimize productivity and reduce the difference in completion time between components in the same kit. The Soft Actor Critic (SAC) algorithm is designed to be combined with the Adaptive Multi-Buffer Experience Replay (AMBER) mechanism to propose the SAC-AMBER algorithm. The AMBER mechanism optimizes the experience sampling and policy updating process and enhances learning efficiency by categorically storing the experience into the standard buffer, the high equipment utilization buffer, and the high productivity buffer. Experimental results show that the SAC-AMBER algorithm can effectively reduce the maximum completion time on multiple datasets, reduce the difference in component completion time in the same kit, and thus optimize the readiness of the part kits, demonstrating relatively good stability and convergence. Compared with traditional heuristics, meta-heuristics, and other deep reinforcement learning methods, the SAC-AMBER algorithm performs better in terms of solution quality and computational efficiency, and through extensive testing on multiple datasets, the algorithm has been confirmed to have good generalization ability, providing an effective solution to the FJSP problem.

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Keywords

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