Optimizing Distribution Route of Convenience Vegetable Stores Considering Transit Nodes

Expand
  • School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China

Received date: 2019-01-09

  Revised date: 2020-05-28

  Online published: 2020-09-10

Supported by

This research was supported by the National Natural Science Foundation of China (No.G71390334), Beijing Social Science Fund (No. 16JDGLB012) and Beijing Logistics Informatics Research Base.

Abstract

Convenience vegetable stores are distributed in various districts of the city, which is difficult to distribute, mainly reflected in the high cost of distribution and transportation. In order to solve this problem, combining with the characteristics of convenience vegetable stores and urban transportation, an optimization model of distribution route considering transit nodes is established. The model takes into account both soft time window and hard time window. Soft time window is used to calculate the cost increase caused by an urban traffic jam. Hard time window is the unified service time of convenience vegetable stores, and the cost of transit damage is considered to make the model more realistic. The genetic algorithm is used to solve the model with a onestage solution method. The effectiveness and feasibility of the model and algorithm are verified by an example. The effectiveness of the proposed algorithm is verified by comparing the optimal solutions in the case of setting up transit and not setting up transit. The result shows that when the number of convenience vegetable stores is large, the establishment of transit can reduce the overall transportation cost. Quantitative analysis shows that with the further increase in convenience vegetable stores in cities, the distribution of convenience vegetable stores can be improved by planning scientific path and by increasing transit nodes.

Cite this article

Pu Li, Hong-Jie Lan, You-Hua Chen . Optimizing Distribution Route of Convenience Vegetable Stores Considering Transit Nodes[J]. Journal of the Operations Research Society of China, 2020 , 8(3) : 515 -531 . DOI: 10.1007/s40305-020-00319-4

References

[1] Duan, Z.Y., Lei, Z.X., Sun, S., et al.:Multi-objective robust optimization model and algorithm for stochastic time-dependent vehicle routing problem. J. Southwest Jiaotong Univ. 2018, 1-9(2018)
[2] Hu, C., Lu, J., Liu, X., et al.:Robust vehicle routing problem with hard time windows under demand and travel time uncertainty. Comput. Oper. Res. 94, 139-153(2018)
[3] Errico, F., Desaulniers, G., Gendreau, M., et al.:A priori optimization with recourse for the vehicle routing problem with hard time windows and stochastic service times. Eur. J. Oper. Res. 259(1), 55-66(2016)
[4] Miranda, D.M., Branke, J., Conceição, S.V.:Algorithms for the multi-objective vehicle routing problem with hard time windows and stochastic travel time and service time. Appl. Soft Comput. 70, 66-79(2018)
[5] Fu, Z., Liu, W., Qiu, M.:A tabu search algorithm for the vehicle routing problem with soft time windows and split deliveries by order. Chin. J. Manag. Sci. 25(05), 78-86(2017)
[6] Barkaoui, M.:A co-evolutionary approach using information about future requests for dynamic vehicle routing problem with soft time windows. Memet. Comput. 10(7), 1-13(2017)
[7] Paolucci, M., Anghinolfi, D., Tonelli, F.:Field services design and management of natural gas distribution networks:a class of vehicle routing problem with time windows approach. Int. J. Prod. Res. 56(1), 1-17(2018)
[8] Osta, J.P.V., Veen, B.V., Krevelen, R.V., et al.:Dynamic vehicle routing with time windows in theory and practice. Nat. Comput. 16(1), 119-134(2017)
[9] Han, S., Zhao, L., Chen, K., et al.:Appointment scheduling and routing optimization of attended home delivery system with random customer behavior. Eur. J. Oper. Res. 262(3), 966-980(2017)
[10] Xia, Y., Zhuo, F.:Improved tabu search algorithm for the open vehicle routing problem with soft time windows and satisfaction rate. Clust. Comput. 1(2), 1-9(2018)
[11] Wang, W.Y., Li, S.B., Peng, Y., et al.:Optimization of refrigerated container shipping network considering transportation time constraint. J. Dalian Univ. Technol. 58(03), 254-260(2018)
[12] Ruan, J.H., Wang, X.P.:Disruption management of emergency medical supplies intermodal transportation with updated transit centers. Oper. Res. Manag. Sci. 25(04), 114-124(2016)
[13] Ge, X.L., Zou, D.H.:Vehicle routing problem of multi-distribution centers with cross-docking in the supply chain. Control Decis. 33(12), 2169-2176(2018)
[14] Alshujeary, M.:Solving vehicle routing problem by using improved genetic algorithm for optimal solution. J. Comput. Sci. 21, 255-262(2017)
[15] Zhang, W.F., Liang, K.H.:Optimization of cold-chain network nodes and delivery for fresh agricultural products. Syst. Eng. 35(01), 119-123(2017)
[16] Ren, L., Fang, Q., Shao, Y., et al.:Fourth party logistics routing problem with time window in uncertain environments. Inf. Control 47(05), 583-588(2018)
[17] Villanueva, J.F., Sanchez, A.I., Carlos, S., et al.:Genetic algorithm-based optimization of testing and maintenance under uncertain unavailability and cost estimation:a survey of strategies for harmonizing evolution and accuracy. Reliab. Eng. Syst. Saf. 93(12), 1830-1841(2017)
[18] Mohammed, M.A., Gani, M.K.A., Hamed, R.I., et al.:Solving vehicle routing problem by using improved genetic algorithm for optimal solution. J. Comput. Sci. 21, 255-262(2017)
Options
Outlines

/