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Optimal Delivery Area Assignment for the Capital Vehicle Routing Problem Based on a Maximum Likelihood Approach

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Optimization and Learning (OLA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1684))

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Abstract

In this study, we constructed an optimization model for the maximum likelihood estimation of delivery areas from a capacitated vehicle routing problem. The aim is to develop a method that combines the advantages of two methods of delivery planning: the efficiency of the routing software-based method and the flexibility of the area-in-charge method. We first conduct computer experiments to derive the optimal cycling plan for each stochastic demand pattern. We then solve the optimal delivery area assignment that is globally consistent with the data from these experiments. We focused on whether the optimal route for each demand pattern was contained in the same area and found the assigning area that maximized the probability. This model is designed for daily use because it is an easy-to-interpret area map, while the optimization of the circulation problem is solved using computers in advance. In experiments using the data, we confirmed that the model can provide correct area creation.

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References

  1. Newell, F., Gordon, D., Carlos, F.: Design of multiple vehicle delivery tours-II other metrics. Transp. Res B-Meth 20(5), 365–376 (1986). https://doi.org/10.1016/0191-2615(86)90009-3

    Article  MATH  Google Scholar 

  2. Ouyang, Y.: Design of vehicle routing zones for large-scale distribution systems. Transp. Res B-Meth: Methodol. 41(10), 1079–1093 (2007). https://doi.org/10.1016/j.trb.2007.04.010

    Article  MATH  Google Scholar 

  3. Galvão, L.C., Novaes, A.G.N., Souza de Cursi, J.E., Souza, J.C.: A multiplicatively-weighted Voronoi diagram approach to logistics districting. Comput. Oper. Res. 33, 93–114 (2006). https://doi.org/10.1016/j.cor.2004.07.001

  4. García-Ayala, G., González-Velarde, J.L., Ríos-Mercado, R.Z., Fernández, E.: A novel model for arc territory design: promoting Eulerian districts. Int. Trans. Oper. Res. 23, 433–458 (2016). https://doi.org/10.1111/itor.12219

    Article  MathSciNet  MATH  Google Scholar 

  5. Zhong, H., Hall, R.W., Dessouky, M.: Territory planning and vehicle dispatching with driver learning. Transp. Sci. 4(1), 74–89 (2007). https://doi.org/10.1287/trsc.1060.0167

    Article  Google Scholar 

  6. Sung, I., Nielsen, P.: Zoning a service area of unmanned aerial vehicles for package delivery services. J. Intell. Rob. Syst. 97(3–4), 719–731 (2019). https://doi.org/10.1007/s10846-019-01045-7

    Article  MATH  Google Scholar 

  7. Yudai, H., Atsushi, S.: New clustering method to estimate overlapped exchange area from regional flow matrix –characteristics of population migrations and freight flow. J. City Plan. Inst. Japan 55(3), 475–481 (2020). https://doi.org/10.11361/journalcpij.55.475

    Article  MATH  Google Scholar 

  8. Mikio, K., Pedroso, J.P., Masakazu, M., Rais, A.: Mathematical Optimization: Solving Problems Using Gurobi and Python, 2nd edn. Kindai Kagaku sha Co., Ltd., Japan (2013)

    MATH  Google Scholar 

  9. Densham, P.J., Rushton, G.: A more efficient heuristic for solving large p-median problems. J. RSAI 71(3), 307–329 (1992). https://doi.org/10.1007/BF01434270

    Article  MATH  Google Scholar 

  10. Capacitated Vehicle Routing (CVRP) package, LocalSolver. https://www.localsolver.com/docs/last/exampletour/vrp.html. Accessed 20 Jan 2022

  11. LocalSolver. https://www.localsolver.com/. Accessed 20 Jan 2022

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Author information

Authors and Affiliations

  1. The University of Tokyo, Bunkyo, Tokyo, Japan

    Junya Maruyama, Yudai Honma & Daisuke Hasegawa

  2. Tokyo Gas Co., Ltd., Minato, Tokyo, Japan

    Soma Toki

  3. Kanagawa Institute of Technology, Atsugi, Kanagawa, Japan

    Naoshi Shiono

Authors
  1. Junya Maruyama
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  2. Yudai Honma
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  3. Daisuke Hasegawa
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  4. Soma Toki
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  5. Naoshi Shiono
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Corresponding author

Correspondence to Yudai Honma .

Editor information

Editors and Affiliations

  1. Universidad de Cádiz, Cadiz, Spain

    Bernabé Dorronsoro

  2. University of Catania, Catania, Italy

    Mario Pavone

  3. University of Paris-Est, Paris, France

    Amir Nakib

  4. Université de Lille, Lille, France

    El-Ghazali Talbi

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Maruyama, J., Honma, Y., Hasegawa, D., Toki, S., Shiono, N. (2022). Optimal Delivery Area Assignment for the Capital Vehicle Routing Problem Based on a Maximum Likelihood Approach. In: Dorronsoro, B., Pavone, M., Nakib, A., Talbi, EG. (eds) Optimization and Learning. OLA 2022. Communications in Computer and Information Science, vol 1684. Springer, Cham. https://doi.org/10.1007/978-3-031-22039-5_13

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  • DOI: https://doi.org/10.1007/978-3-031-22039-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22038-8

  • Online ISBN: 978-3-031-22039-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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