Title: Deep reinforcement learning for dynamic cellular manufacturing systems with deterioration effect

Authors: Mostafa Jafari; Amir Hossein Akbari

Addresses: Department of Industrial Engineering, Iran University of Science and Technology, Tehran, 1684933511, Iran ' Department of Industrial Engineering, Iran University of Science and Technology, Tehran, 1684933511, Iran

Abstract: This study presents an integrated framework for optimising machine layout and production planning in dynamic cellular manufacturing systems under uncertainty. The framework addresses key challenges including machine deterioration and breakdowns, order rejection, and tardiness costs, which are often treated separately in traditional approaches. A multi-objective mathematical model is developed to maximise profit, increase the number of accepted orders, and balance machine workloads to reduce failures. The solution employs a three-step hierarchical approach: heuristic machine-to-cell assignment, deep reinforcement learning for real-time order acceptance and scheduling while considering machine deterioration, and heuristic layout refinement. Computational results show that the proposed method accepts 2.63% more orders with a 5.7% profit reduction, enhancing customer attraction and competitiveness. Workload balancing decreases machine repairs by 11.5%, improving system stability and reducing maintenance costs. Despite an average profit loss of 9.77% due to machine deterioration, the framework significantly improves efficiency and operational resilience in dynamic manufacturing environments.

Keywords: CMS; cellular manufacturing system; OAS; order acceptance and scheduling; deterioration effect; DRL; deep reinforcement learning.

DOI: 10.1504/IJCSM.2026.151974

International Journal of Computing Science and Mathematics, 2026 Vol.23 No.1, pp.39 - 80

Received: 05 Jan 2025
Accepted: 05 Nov 2025
Published online: 28 Feb 2026 *

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