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Quantum computing approach in uncertain data optimization problem for vehicle routing problem Patrisia Teresa Marsoit; Liu Wang Zhang; Deodoro Lakonde; Firta Sari Panjaitan
International Journal of Enterprise Modelling Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (519.285 KB) | DOI: 10.35335/emod.v15i3.52

Abstract

This research addresses the Vehicle Routing Problem (VRP) with uncertain data and proposes a novel approach using quantum computing techniques. The problem involves optimizing vehicle routes considering uncertain customer demands, time windows, and vehicle capacities. We formulate the problem mathematically and develop an algorithmic framework to tackle it. The approach incorporates multiple scenarios based on the uncertainty distribution and selects the one with the minimum cost to optimize the vehicle routes. Through a numerical example, we demonstrate the effectiveness of the proposed approach in generating optimal routes that minimize the total distance traveled by the vehicles. The results highlight the solution quality, adaptability to uncertainty, and potential benefits in terms of cost reduction and resource utilization. While the computational efficiency of quantum computing approaches is a consideration, this research provides a promising direction for addressing uncertain optimization problems in logistics and transportation. Future research should focus on scalability and refinement of the algorithm to further enhance its applicability in real-world scenarios.
Web-Based on-line learning (e-learning) decision support system Firta Sari Panjaitan; Sonya Enjelina Gorat
Vertex Vol. 11 No. 1 (2021): December: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/kpwbk147

Abstract

The rapid advancement of technology has revolutionized education, paving the way for innovative learning methods such as E-Learning. However, optimizing the effectiveness of online education poses challenges in data management and decision-making processes. This research investigates the integration of Web-Based Decision Support Systems (DSS) in E-Learning to enhance learning outcomes. The study develops a mathematical formulation that quantifies the impact of DSS by considering student engagement, knowledge retention, and academic achievement. A numerical example is presented to demonstrate the application of the formulation, showcasing the positive influence of the DSS on individual students and the overall cohort. The results emphasize the potential benefits of personalized learning experiences, data-driven insights, and informed decision-making facilitated by the DSS. Nonetheless, the limitations of the study are acknowledged, warranting further research with larger and more diverse samples. Overall, this research contributes to the discourse surrounding the role of Web-Based DSS in shaping the future of online education, empowering educators and learners to unlock the full potential of E-Learning in the digital age