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All Journal International Journal of Enterprise Modelling
Patrisia Teresa Marsoit
International Enterprise Integration Association, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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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.
Integrating machine learning and real-time optimization for heterogeneous instant delivery orders scheduling and routing Patrisia Teresa Marsoit; Patrisius Michaud Felix Marsoit; Ralp Varene
International Journal of Enterprise Modelling Vol. 16 No. 1 (2022): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.784 KB) | DOI: 10.35335/emod.v16i1.56

Abstract

This research aims to integrate machine learning and real-time optimization for heterogeneous instant delivery order scheduling and routing. The objective is to minimize the total delivery time while considering factors such as demand, time windows, predicted demand, and vehicle capacity constraints. By leveraging machine learning algorithms and real-time data, the proposed approach provides adaptive decision-making capabilities, allowing for dynamic adjustments in response to changing conditions. A mathematical formulation is developed to model the problem, and an algorithm is proposed to solve it. A numerical example is presented to demonstrate the effectiveness of the approach. The results highlight the optimal assignment of orders to vehicles at different time periods, leading to efficient delivery routes and minimized delivery time. The integration of machine learning and real-time optimization offers promising opportunities for enhancing the efficiency and responsiveness of delivery operations. This research contributes to advancing the field of instant delivery order scheduling and routing and paves the way for further developments in real-time logistics optimization
Co-Authors Deodoro Lakonde Firta Sari Panjaitan Liu Wang Zhang Patrisius Michaud Felix Marsoit Ralp Varene