Embedded Vehicle Routing Problem: A modelling and optimization framework for real-world multi-purpose truck network
3
Issued Date
2024-01-01
Resource Type
eISSN
21693536
Scopus ID
2-s2.0-85214101125
Journal Title
IEEE Access
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Access (2024)
Suggested Citation
Krityakierne T., Boonkangwan C., Phunnasorn C., Laesanklang W. Embedded Vehicle Routing Problem: A modelling and optimization framework for real-world multi-purpose truck network. IEEE Access (2024). doi:10.1109/ACCESS.2024.3524395 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/102668
Title
Embedded Vehicle Routing Problem: A modelling and optimization framework for real-world multi-purpose truck network
Corresponding Author(s)
Other Contributor(s)
Abstract
This paper introduces original contributions to the field of vehicle routing problems, specifically addressing the optimization of multi-purpose truck routing within the agricultural third-party logistics domain. The study presents a novel approach aimed at optimizing the routing of a 3PL provider's truck fleet, considering efficient commodity delivery and hygienic practices for transporting agricultural products. To tackle the complexities associated with multi-purpose truck routing, we propose the concept of the Embedded Vehicle Routing Problem (EMBDD-VRP). The EMBDD-VRP adopts a two-phase framework, embedding multiple local Vehicle Routing Problems (VRPs) into a global VRP structure. By leveraging existing VRP solutions in each phase, the EMBDD-VRP retains the desirable characteristics of classical VRP models while effectively addressing the unique challenges faced by 3PL providers. Case studies and practical implementations conducted within the Thai agriculture sector demonstrate the applicability and viability of the proposed methodology. Compared to the exact VRP method, our analysis shows that the proposed EMBDD-VRP reduces the search space size by approximately 100-fold for medium-sized problems. The computational results indicate that the solution gap between EMBDD-VRP and the exact VRP method (using Gurobi) ranges from 0% to 6.2% in the simplified problem, where the exact VRP is applicable. While the exact method becomes infeasible for more complex problems, EMBDD-VRP can efficiently solve much larger instances, including real-world cases. This research not only fills gaps in the literature but also offers a promising avenue for optimizing multi-purpose truck routing within agricultural third-party logistics operations.