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Jérôme, Jesca Fell
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science

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Journal : journal of computer science and research

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Hybrid Grid Partition and Rought Set Methods for Generating Fuzzy Rules in Supply Chain Marsoit, Patrisius Michaud Felix; Pernadate, Park Vrançoisee; Jérôme, Jesca Fell
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 3 (2023): July: Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v1i3.20

Abstract

Supply chain management in today's dynamic and complex business environment demands innovative approaches to decision support. This research introduces a novel hybrid framework that combines grid partition, rough set methods, and fuzzy logic to generate adaptive fuzzy rules tailored to supply chain data. By integrating these techniques, the study provides a comprehensive decision support system capable of addressing the intricacies and uncertainties prevalent in supply chain operations. A numerical example illustrates the practical application of this framework in optimizing inventory management within an e-commerce supply chain. The results showcase the effectiveness of the adaptive fuzzy rules in minimizing stockouts, reducing excess inventory, and optimizing inventory costs. Additionally, the study emphasizes the importance of balancing rule quality and complexity using a tunable parameter, offering flexibility for rule customization. The interpretability of the generated fuzzy rules further enhances their practical utility, enabling domain experts to comprehend and adjust decision criteria. This research not only contributes to advancing decision support systems in supply chain management but also lays the groundwork for future exploration of real-world data integration, adaptability to dynamic environments, and scalability challenges, thus promising significant enhancements in supply chain performance and resilience.
Optimizing Multi-Objective Flexible Job-Shop Scheduling Using Hybrid Bat Algorithm and Simulated Annealing Lee, See Cheng; Lee, Jian-Cheng; Jérôme, Jesca Fell
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 3 (2023): July: Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v1i3.22

Abstract

This research investigates the application of a Hybrid Bat Algorithm (BA) and Simulated Annealing (SA) approach to solve the Multi-Objective Flexible Job-Shop Scheduling Problem (MOFJSSP) within contemporary manufacturing settings. MOFJSSP embodies the complexities of scheduling in modern industries, encompassing multiple conflicting objectives such as minimizing makespan, reducing idle time, optimizing machine utilization, and minimizing production costs. Traditional approaches often struggle to address these complexities adequately. To confront these challenges, a hybrid algorithm integrating BA and SA is proposed, leveraging their respective strengths in exploration and exploitation of solution spaces. The methodology involves problem formulation, solution representation, parameter settings, initialization strategies, iterative evolution mechanisms, and comprehensive evaluation. Experimental results showcase the hybrid approach's superior convergence rates, solution quality, and robustness in comparison to individual algorithms and state-of-the-art methods. The implications suggest potential applications in optimizing manufacturing scheduling, logistics, and diverse industries. Moreover, the research paves the way for future exploration into hybridization with emerging techniques, integration with Industry 4.0 technologies, and adaptation to dynamic manufacturing environments. Embracing these findings promises enhanced operational efficiency, informed decision-making, and continuous innovation in manufacturing scheduling practices.
Co-Authors Lee, Jian-Cheng Lee, See Cheng Marsoit, Patrisius Michaud Felix Pernadate, Park Vrançoisee