Two-Echelon Location-Routing Problem with Fuzzy Demand of Rural E-Commerce Logistics

Xiaojuan LU; Jianjun WANG; Shuai WU; Shiyu ZHENG; Qian LIU

doi:10.7307/ptt.v37i1.578

Authors

Xiaojuan LU Chang’an University, School of Transportation Engineering
Jianjun WANG Chang’an University, School of Transportation Engineering
Shuai WU Chang’an University, School of Transportation Engineering
Shiyu ZHENG Chang’an University, School of Transportation Engineering
Qian LIU Chang’an University, School of Transportation Engineering

DOI:

https://doi.org/10.7307/ptt.v37i1.578

Keywords:

two-echelon location-routing problem, fuzzy demand, k-means clustering, improved NSGA-II, rural e-commerce logistics

Abstract

To promote the green and high-quality development of rural e-commerce logistics, we propose the Two-Echelon Location-Routing Problem with Fuzzy Demand (2E-LRP-FD) of the rural e-commerce logistics network. Considering fuzzy demand, government subsidies and simultaneous delivery, the objective function aims to maximise the profit of enterprises considering government subsidies. The fuzzy chance-constrained programming method is used to deal with the triangular fuzzy variables of pickup demands. Additionally, we present a two-stage Improved Non-Dominated Sorting Genetic Algorithm II (INSGA-II) that integrates stochastic simulation and a K-means clustering algorithm to effectively solve the problem. In the end, the numerical experiments of algorithm and model design are verified. The experimental results demonstrate that the proposed INSGA-II is significantly efficient and effective. Furthermore, we discuss the relationship between subsidy strategies and logistics enterprise profits. This research contributes valuable insights for the establishment of rural e-commerce logistics systems.

References

Ministry of Commerce of the People's Republic of China, China E-commerce Report 2021. Beijing: 2022.

Novotná M, Gottwald D. Current challenges for the postal services sector in the context of E-commerce – Predictive analysis of V4 States. Promet - Traffic and Transportation. 2021;33(6):919-930. DOI: 10.7307/ptt.v33i6.3927.

Kshetri N. Rural e-commerce in developing countries. IT Professional. 2018;20(2):91-95. DOI: 10.1109/MITP.2018.021921657.

Liu Z, et al. A measurement model and empirical analysis of the coordinated development of rural e-commerce logistics and aagricultural modernization. Sustainability. 2022;14(21): 13758. DOI: 10.3390/su142113758.

Markowska M, Marcinkowski J. Rural e-customers' preferences for last mile delivery: Evidence from Poland. Energies. 2022;15(22): 8351. DOI: 10.3390/en15228351.

Seghezzi A, Siragusa C, Mangiaracina R. Parcel lockers vs. home delivery: a model to compare last-mile delivery cost in urban and rural areas. International Journal of Physical Distribution & Logistics Management. 2022;52(3):213-237. DOI: 10.1108/IJPDLM-03-2020-0072.

Liu W. Route optimization for last-mile distribution of rural e-commerce logistics based on ant colony optimization. IEEE Access. 2020;8:12179-12187. DOI: 10.1109/ACCESS.2020.2964328.

Yang F, Dai Y, Ma ZJ. A cooperative rich vehicle routing problem in the last-mile logistics industry in rural areas. Transportation Research Part E: Logistics and Transportation Review. 2020;141: 102024. DOI: 10.1016/j.tre.2020.102024.

Watson-Gandy C, Dohrn P. Depot location with van salesmen - A practical approach. Omega. 1973;1(3):321-329. DOI: 10.1016/0305-0483(73)90108-4.

Salhi S, Rand GK. The effect of ignoring routes when locating depots. European Journal of Operational Research. 1989;39(2):150-156. DOI: 10.1016/0377-2217(89)90188-4.

Prodhon C, Prins C. A survey of recent research on location-routing problems. European Journal of Operational Research. 2014;238(1):1-17. DOI: 10.1016/j.ejor.2014.01.005.

Jacobsen SK, Madsen OBG. A comparative study of heuristics for a two-level routing-location problem. European Journal of Operational Research. 1980;5(6):378-387. DOI:10.1016/0377-2217(80)90124-1.

Wang Y, et al. Two-echelon multi-depot multi-period location-routing problem with pickup and delivery. Computers & Industrial Engineering. 2023;182:109385. DOI: 10.1016/j.cie.2023.109385.

Tian XD, Hu ZH. A branch-and-price method for a two-echelon location routing problem with recommended satellites. Computers & Industrial Engineering. 2023;184:109593. DOI: 10.1016/j.cie.2023.109593.

Yıldız EA, Altiparmak F, Karaoğlan I. An exact algorithm for two-echelon location-routing problem with simultaneous pickup and delivery. Expert Systems with Applications. 2023;231:120598. DOI: 10.1016/j.eswa.2023.120598.

Zadeh LA. Fuzzy sets. Information and Control, 1965;8(3):338-353. DOI: 10.1016/S0019-9958(65)90241-X.

Kaufmann A . Introduction to the theory of fuzzy subsets. Academic Press; 1975. Accessed December 18, 2023. https://research.ebsco.com/linkprocessor/plink?id=6bda5edb-19b8-3bd4-a6a9-1d96e96098a7.

Liu L, Gao X. Fuzzy weighted equilibrium multi-job assignment problem and genetic algorithm. Applied Mathematical Modelling. 2009;33(10):3926-3935. DOI: 10.1016/j.apm.2009.01.014.

Zarandi MHF, Hemmati A, Davari S. The multi-depot capacitated location-routing problem with fuzzy travel times. Expert Systems with Applications. 2011; 38(8):10075-10084. DOI: 10.1016/j.eswa.2011.02.006.

Wei M, Yu L, Li X. Credibilistic location-routing model for hazardous materials transportation. International Journal of Intelligent Systems. 2015;30(1):23-39. DOI: 10.1002/int.21679.

Ghezavati V, Morakabatchian S. Application of a fuzzy service level constraint for solving a multi-objective location-routing problem for the industrial hazardous wastes. Journal of Intelligent and Fuzzy Systems. 2015;28(5):2003-2013. DOI: 10.3233/IFS-141341.

Nadizadeh A, Kafash B. Fuzzy capacitated location-routing problem with simultaneous pickup and delivery demands. Transportation Letters. 2019;11(1):1-19. DOI: 10.1080/19427867.2016.1270798.

Zhang H, et al. A hybrid heuristic based on a particle swarm algorithm to solve the capacitated location-routing problem with fuzzy demands. IEEE Access. 2020; 8:153671-153691. DOI:10.1109/ACCESS.2020.3018490.

Tirkolaee EB, Abbasian P, Weber GW. Sustainable fuzzy multi-trip location-routing problem for medical waste management during the COVID-19 outbreak. Science of the Total Environment. 2021;756:143607. DOI: 10.1016/j.scitotenv.2020.143607.

Li N, Fan Z. A hybrid algorithm for location-routing sustainable optimization under fuzzy demand. IEEE Access. 2022;10:12191-12200. DOI: 10.1109/ACCESS.2022.3145157.

Fazayeli S, Eydi A, Kamalabadi IN. Location-routing problem in multimodal transportation network with time windows and fuzzy demands: Presenting a two-part genetic algorithm. Computers & Industrial Engineering. 2018;119:233-246. DOI: 10.1016/j.cie.2018.03.041.

Tonči C, Juraj F. Using congestion zones for solving the time dependent vehicle routing problem. Promet - Traffic and Transportation. 2020;32(1):25-38. DOI: 10.7307/ptt.v32i1.3296.

Wu Y, Qureshi AG, Yamada T. Adaptive large neighborhood decomposition search algorithm for multi-allocation hub location routing problem. European Journal of Operational Research. 2022;302(3):1113-1127. DOI: 10.1016/j.ejor.2022.02.002.

Lv C, et al. A fuzzy correlation based heuristic for dual-mode integrated location routing problem. Computers & Operations Research. 2022;146:105923. DOI: 10.1016/j.cor.2022.105923.

Yang S, et al. Integrated electric logistics vehicle recharging station location–routing problem with mixed backhauls and recharging strategies. Transportation Research Part C: Emerging Technologies. 2022;140:103695. DOI: 10.1016/j.trc.2022.103695.

Herrera-Cobo JS, Escobar JW, Álvarez-Martínez D. Metaheuristic algorithm for the location, routing and packing problem in the collection of recyclable waste. International Journal of Industrial Engineering Computations. 2023;14(1):157-172. DOI: 10.5267/j.ijiec.2022.8.004.

Cordeau JF, Laporte G, Mercier A. A unified tabu search heuristic for vehicle routing problems with time windows. Journal of the Operational Research Society. 2001;52(8):928-936. DOI: 10.1057/palgrave.jors.2601163.

Nguyen VP, Prins C, Prodhon C. A multi-start evolutionary local search for the two-echelon location routing Problem. Lecture Notes in Computer Science. 2010;(6373):88-102. DOI: 10.1007/978-3-642-16054-7_7.

Nguyen VP, Prins C, Prodhon C. GRASP with learning process for a two-echelon location-routing problem. Proceedings of the 8th Conference on Modeling and Simulation. 2010.

Nguyen VP, Prins C, Prodhon C. A multi-start iterated local search with tabu list and path relinking for the two-echelon location-routing problem. Engineering Applications of Artificial Intelligence. 2012; 25(1):56-71.DOI: 10.1016/j.engappai.2011.09.012.