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All Journal International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Naufan Raharya
Universitas Indonesia
Computer Science & IT Electrical & Electronics Engineering Materials Science & Nanotechnology Medicine & Pharmacology

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Energy Consumption Optimization for Flexible Job-Shop Scheduling in Manufacturing Industry Using Multi-Agent Reinforcement Learning Satwika Bintang Bahana; Naufan Raharya
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 4 No. 1 (2026)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v4i1.227

Abstract

Modern manufacturing industries are increasingly demanding production systems that are both flexible and efficient in handling dynamic operational conditions. Traditional scheduling approaches, such as the job-shop scheduling Problem (JSSP), are limited by the constraint that each operation must be executed on a predefined machine. To address this limitation, the flexible job-shop scheduling Problem (FJSSP) was introduced, allowing alternative machine options for each operation and thereby enhancing system flexibility. This study proposes a scheduling optimization approach based on Multi-Agent Reinforcement Learning (MARL), to support decision-making in complex production environments. Experimental results demonstrate that the proposed method reduces energy consumption by up to 40.62% compared to the First Come First Serve (FCFS) method and by 35.23% compared to the Fastest Available Agent (FAA) method. Moreover, the model shows superior performance in controlling theworst-case makespan and achieves significantly higher success rates in satisfying various production constraints compared to all tested rule-based methods.
Co-Authors Satwika Bintang Bahana