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All Journal Jurnal Teknik Informatika C.I.T. Medicom
Sihotang , Jonhariono
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Computer Science & IT

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Graph-based Exploration for Mining and Optimization of Yields (GEMOY Method) Sihotang, Hengki Tamando; Riandari, Fristi; Sihotang , Jonhariono
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 2 (2024): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.777.pp70-81

Abstract

This research explores the application of graph-based optimization techniques to enhance yield management and minimize transportation costs in industrial operations, particularly focusing on mining. By representing mining sites and processing plants as nodes and transportation routes as edges in a graph, we formulated an optimization problem aimed at maximizing yields while minimizing associated costs. Utilizing linear programming, we demonstrated significant cost savings, reducing transportation costs from 2100 units to 1700 units through optimized flow distribution. The study integrates elements of graph theory, optimization algorithms, and machine learning, providing a robust framework for efficient resource allocation and operational planning. The numerical example underscores the practical applicability of these techniques, paving the way for further research and refinement to accommodate additional constraints and dynamic changes in resource availability. This research highlights the potential of graph-based methods to achieve substantial economic and operational improvements across various industrial contexts.
Optimizing supply chain efficiency: Advanced decision support systems for enhanced performance Judijanto, Loso; Lemos, Sgarbossa Carlo; Sihotang , Jonhariono; Sihotang , Hengki Tamando
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 3 (2024): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.857.pp185-198

Abstract

This research investigates the optimization of supply chain efficiency through the application of advanced Decision Support Systems (DSS), focusing on minimizing operational costs while maintaining high service levels. The main objective is to explore how DSS, integrated with real-time data, artificial intelligence (AI), and machine learning (ML), can enhance decision-making processes across production, inventory management, and transportation. The research employs a multi-objective optimization model, developed to minimize production, inventory, transportation, and shortage costs, while dynamically adjusting decisions based on real-time demand and supply data. A numerical example is used to test the model’s effectiveness, revealing significant cost reductions in production and transportation but highlighting challenges in maintaining consistent service levels. The results indicate that DSS can substantially improve supply chain efficiency by enabling data-driven decisions in real time, though its adoption remains limited by technical and scalability challenges, particularly for small-to-medium enterprises (SMEs). This study contributes to the growing body of knowledge on supply chain optimization, offering practical insights into DSS implementation and its potential impact on operational performance. The conclusions suggest that future research should focus on developing more sophisticated DSS models capable of handling uncertainty, sustainability, and resilience, as well as enhancing scalability to make DSS more accessible to a broader range of businesses.
Co-Authors Fristi Riandari Hengki Tamando Sihotang Judijanto, Loso Lemos, Sgarbossa Carlo