Designing a Mathematical Model for Two-Echelon Allocation-Routing Problem by Applying the Route and Transportation Fleet Conditions

Document Type : CFP- Metaheuristic Algorithms and Applications to Production and Service Systems

Authors

1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University,Tehran, Iran

3 Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

10.22070/jqepo.2024.18421.1272

Abstract

A novel mixed integer non-linear mathematical model is presented in this paper for the two-echelon allocation-routing problem by applying the conditions of the route and transportation fleet under uncertainty. The cost of allocating drivers to non-homogeneous vehicles is calculated in this model based on the type of the vehicle, the lifecycle of the car, the experience of the driver, and different degrees of hardness that are defined for various routes. The cost of passing the route is defined based on an initial fixed cost and the degree of hardness of the route. Also, the reliability of the routes in each section is defined as an objective in the second echelon of the model aimed at enhancing the reliability rate. Two metaheuristic algorithms, NSGAII and MOPSO, are utilized to solve the model. Then, their performance rates in problems with different sizes are statistically evaluated and compared by different indices, following the adjustment of their parameters by Taguchi's method, through which results indicated the high efficiency of the model. A sensitivity analysis is ultimately performed on the results obtained from the solution, and some suggestions are made for the development of the model.

Keywords


 
Anderluh, A., Larsen, R., Hemmelmayr, V. C., & Nolz, P. C. (2020). Impact of travel time uncertainties on the solution cost of a two-echelon vehicle routing problem with synchronization. Flexible Services and Manufacturing Journal, 32, 806-828.
Asefi, A. H., Bozorgi-Amiri, A., & ghezavati, V. (2020). Location-Routing Problem in Humanitarian Relief Chain Considering the Reliability of Road Network. Emergency Management, 9(1), 29-41.
Cheng, C., Zhu, R., Costa, A. M., Thompson, R. G., & Huang, X. (2022). Multi-period two-echelon location routing problem for disaster waste clean-up. Transportmetrica A: Transport Science, 18(3), 1053-1083.
Coello, C. C., & Lechuga, M. S. (2002, May). MOPSO: A proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600), (2), 1051-1056. IEEE.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2000). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Parallel Problem Solving from Nature PPSN VI: 6th International Conference Paris, France, September 18–20, 2000 Proceedings 6, 849-858. Springer Berlin Heidelberg.
do C. Martins, L., Hirsch, P., & Juan, A. A. (2021). Agile optimization of a two‐echelon vehicle routing problem with pickup and delivery. International Transactions in Operational Research, 28(1), 201-221.
Du, J., Wang, X., Wu, X., Zhou, F., & Zhou, L. (2023). Multi-objective optimization for two-echelon joint delivery location routing problem considering carbon emission under online shopping. Transportation Letters, 15(8), 907-925.
Dumez, D., Tilk, C., Irnich, S., Lehuédé, F., Olkis, K., & Péton, O. (2023). A matheuristic for a 2-echelon vehicle routing problem with capacitated satellites and reverse flows. European Journal of Operational Research, 305(1), 64-84.
Escobar-Vargas, D., & Crainic, T. G. (2024). Multi-attribute two-echelon location routing: Formulation and dynamic discretization discovery approach. European Journal of Operational Research, 314(1), 66-78.
Esmaeili, M., & Sahraeian, R. (2019). Comparing two-echelon and single-echelon multi-objective capacitated vehicle routing problems. Journal of Quality Engineering and Production Optimization, 4(1), 1-16.
Fakhrzad, M., Sadri Esfahani, A. (2014). Modeling the time windows vehicle routing problem in cross-docking strategy using two meta-heuristic algorithms. International Journal of Engineering-Transactions ABasics 27(7): 1113-1126.
Fallahtafti, A., Ardjmand, E., Young Ii, W. A., & Weckman, G. R. (2021). A multi-objective two-echelon location-routing problem for cash logistics: A metaheuristic approach. Applied Soft Computing, 111, 107685.
Fernando, W. M., Thibbotuwawa, A., Perera, H. N., Nielsen, P., & Kilic, D. K. (2024). An integrated vehicle routing model to optimize agricultural products distribution in retail chains. Cleaner Logistics and Supply Chain, 100137.
Gandra, V. M. S., Çalık, H., Wauters, T., Toffolo, T. A., Carvalho, M. A. M., & Berghe, G. V. (2021). The impact of loading restrictions on the two-echelon location routing problem. Computers & Industrial Engineering, 160, 107609.
Hajghani, M., Forghani, M. A., Heidari, A., Khalilzadeh, M., & Kebriyaii, O. (2023). A two-echelon location routing problem considering sustainability and hybrid open and closed routes under uncertainty. Heliyon, 9(3).
Hasanpour Jesri, Z. S., Eshghi, K., Rafiee, M., & Van Woensel, T. (2022). The Multi-Depot Traveling Purchaser Problem with Shared Resources. Sustainability, 14(16), 10190.
Hosseini, M.H., Hassani, A.A. (2017). Modeling and solving the problem of vehicle routing in the distribution sector of the supply chain by considering traffic restrictions. Sharif Industrial Engineering and Management, 1(1), 147-155.
Hosseini-Motlagh, S. M., Samani, M. R. G., & Saadi, F. A. (2020). A novel hybrid approach for synchronized development of sustainability and resiliency in the wheat network. Computers and electronics in agriculture, 168, 105095.
Huang, N., Li, J., Zhu, W., & Qin, H. (2021). The multi-trip vehicle routing problem with time windows and unloading queue at depot. Transportation Research Part E: Logistics and Transportation Review, 152, 102370.
Jafarzadeh, J., Khalili, H. A., & Shoja, N. (2022). A multiobjective optimization model for a dynamic and sustainable cellular manufacturing system under uncertainty. Computational Intelligence and Neuroscience, 2022(1), 1334081.
Jiao, L., Peng, Z., Xi, L., Guo, M., Ding, S., & Wei, Y. (2023). A multi-stage heuristic algorithm based on task grouping for vehicle routing problem with energy constraint in disasters. Expert Systems with Applications, 212, 118740.
Kahfi, A., & Tavakkoli-Moghaddam, R. (2014). Solving a multi-depot vehicle routing problem based on reduction risk by a multi-objective bat algorithm. Quarterly Journal of Transportation Engineering, 6(2), 507-522.
Lenstra, J. K., & Kan, A. R. (1981). Complexity of vehicle routing and scheduling problems. Networks, 11(2), 221-227.
Neira, D. A., Aguayo, M. M., De la Fuente, R., & Klapp, M. A. (2020). New compact integer programming formulations for the multi-trip vehicle routing problem with time windows. Computers & Industrial Engineering, 144, 106399.
Nozari, H., Tavakkoli-Moghaddam, R., & Gharemani-Nahr, J. (2022). A neutrosophic fuzzy programming method to solve a multi-depot vehicle routing model under uncertainty during the covid-19 pandemic. International Journal of Engineering, 35(2), 360-371.
Rabbani, M., Akbarian-Saravi, N., Ansari, M., & Musavi, M. (2020). A bi-objective vehicle-routing problem for optimization of a bioenergy supply chain by using NSGA-II algorithm. Journal of Quality Engineering and Production Optimization, 5(1), 87-102.
Rabbani, M., & Farrokhi-Asl, H. (2019). Using Metaheuristic Algorithms Combined with Clustering Approach to Solve a Sustainable Waste Collection Problem. Journal of Quality Engineering and Production Optimization, 4(1), 153-174.
Rahmanifar, G., Mohammadi, M., Hajiaghaei-Keshteli, M., Fusco, G., & Colombaroni, C. (2024). An integrated temporal and spatial synchronization for two-echelon vehicle routing problem in waste collection system. Journal of Industrial Information Integration, 40, 100611.
Rezaeipanah, A., Ahmadi, G., Hajiani, M., & Darzi, M. R. (2019). An Improved Hybrid Cuckoo Search Algorithm for Vehicle Routing Problem with Time Windows. Journal of Quality Engineering and Production Optimization, 4(2), 189-208.
Rezaei Kallaj, M., Abolghasemian, M., Moradi Pirbalouti, S., Sabk Ara, M., & Pourghader Chobar, A. (2021). Vehicle routing problem in relief supply under a crisis condition considering blood types. Mathematical Problems in Engineering, 2021, 1-10.
Pirabán-Ramírez, A., Guerrero-Rueda, W. J., & Labadie, N. (2022). The multi-trip vehicle routing problem with increasing profits for the blood transportation: An iterated local search metaheuristic. Computers & Industrial Engineering, 170, 108294.
Sabbagh, M.S., Alinaghian, M., & Zamanloo, K. (2014). Two-dimensional loading time-dependent vehicle routing problem: mathematical modeling and solving approaches, Journal of Industrial Engineering Research in Production Systems, third year, fifth issue, spring and summer 2014, 43-59.
Salavati-Khoshghalb, M., Gendreau, M., Jabali, O., & Rei, W. (2019). An exact algorithm to solve the vehicle routing problem with stochastic demands under an optimal restocking policy. European Journal of Operational Research, 273(1), 175-189.
Wang, Y., Sun, Y., Guan, X., Fan, J., Xu, M., & Wang, H. (2021). Two-echelon multi-period location routing problem with shared transportation resource. Knowledge-Based Systems, 226, 107168.
Wang, Y., Zhe, J., Wang, X., Sun, Y., & Wang, H. (2022). Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows. Sustainability, 14(11), 6709.
Xue, G., Wang, Y., Guan, X., & Wang, Z. (2022). A combined GA-TS algorithm for two-echelon dynamic vehicle routing with proactive satellite stations. Computers & Industrial Engineering, 164, 107899.
Yu, X., Zhou, Y., & Liu, X. F. (2020). The two-echelon multi-objective location routing problem inspired by realistic waste collection applications: The composable model and a metaheuristic algorithm. Applied Soft Computing, 94, 106477.