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Online Optimization of Pickup and Delivery Problem Considering Feasibility

Author

Listed:
  • Ryo Matsuoka

    (Graduate School of Information Science and Technoloty, Hokkaido University, Sapporo 060-0814, Japan)

  • Koichi Kobayashi

    (Graduate School of Information Science and Technoloty, Hokkaido University, Sapporo 060-0814, Japan)

  • Yuh Yamashita

    (Graduate School of Information Science and Technoloty, Hokkaido University, Sapporo 060-0814, Japan)

Abstract

A pickup and delivery problem by multiple agents has many applications, such as food delivery service and disaster rescue. In this problem, there are cases where fuels must be considered (e.g., the case of using drones as agents). In addition, there are cases where demand forecasting should be considered (e.g., the case where a large number of orders are carried by a small number of agents). In this paper, we consider an online pickup and delivery problem considering fuel and demand forecasting. First, the pickup and delivery problem with fuel constraints is formulated. The information on demand forecasting is included in the cost function. Based on the orders, the agents’ paths (e.g., the paths from stores to customers) are calculated. We suppose that the target area is given by an undirected graph. Using a given graph, several constraints such as the moves and fuels of the agents are introduced. This problem is reduced to a mixed integer linear programming (MILP) problem. Next, in online optimization, the MILP problem is solved depending on the acceptance of orders. Owing to new orders, the calculated future paths may be changed. Finally, by using a numerical example, we present the effectiveness of the proposed method.

Suggested Citation

  • Ryo Matsuoka & Koichi Kobayashi & Yuh Yamashita, 2024. "Online Optimization of Pickup and Delivery Problem Considering Feasibility," Future Internet, MDPI, vol. 16(2), pages 1-15, February.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:2:p:64-:d:1340448
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    References listed on IDEAS

    as
    1. Dumas, Yvan & Desrosiers, Jacques & Soumis, Francois, 1991. "The pickup and delivery problem with time windows," European Journal of Operational Research, Elsevier, vol. 54(1), pages 7-22, September.
    2. Hess, Alexander & Spinler, Stefan & Winkenbach, Matthias, 2021. "Real-time demand forecasting for an urban delivery platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    3. Regue, Robert & Recker, Will, 2014. "Proactive vehicle routing with inferred demand to solve the bikesharing rebalancing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 72(C), pages 192-209.
    4. Jun, Sungbum & Lee, Seokcheon & Yih, Yuehwern, 2021. "Pickup and delivery problem with recharging for material handling systems utilising autonomous mobile robots," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1153-1168.
    5. Denissa Sari Darmawi Purba & Eleftheria Kontou & Chrysafis Vogiatzis, 2021. "Evacuation Route Planning for Alternative Fuel Vehicles," Papers 2109.01578, arXiv.org, revised May 2022.
    Full references (including those not matched with items on IDEAS)

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