Abstract
During epidemics, the transportation of medical resources from storage places to communities plays a crucial role, which can be regarded as a vehicle routing problem (VRP). However, the traditional VRP primarily focuses on the transportation cost of vehicles and typically revolves around a single depot, limiting its applicability. Therefore, this paper extends the VRP model and introduces a multi-objective and multi-depot aspect of VRP for epidemic (MOMDVRP4E) model, which can effectively address the multi-depot scenarios and additional costs stemming from the preventive policies in high-risk regions. For this new model, existing multi-objective optimization algorithms still encounter the issues with low-quality initial solutions and incomplete searches. To address these challenges, this paper proposes a Multi-objective Variable Tabu Neighborhood Search algorithm named MOVTNS. Initially, the MOVTNS utilizes the fuzzy clustering to generate high-quality initial solutions. Subsequently, a new two-stage three-population algorithm framework is proposed to enhance the search coverage. In the first stage, two populations are deployed to seek optimal solutions for two objectives parallelly, which effectively explores the edge-part solutions. In the second stage, a new population is employed to pursue the cooperative objective based on two independent populations, which explores the central part of the Pareto front from the edge part solutions. Extensive experiments on benchmarks validate the effectiveness of MOVTNS, showcasing its superior performance over various state-of-the-art algorithms.
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References
Gibbs, H., et al.: Changing travel patterns in china during the early stages of the covid-19 pandemic. Nat. Commun.Commun. 11(1), 5012 (2020)
Li, J.Y., et al.: A multi-population multiobjective ant colony system considering travel and prevention costs for vehicle routing in COVID-19-like epidemics. IEEE Trans. Intell. Transport. Syst. 23(12), 25062–25076 (Dec 2022)
Sadati, M.E.H., Catay, B., Aksen, D.: An efficient variable neighborhood search with tabu shaking for a class of multi-depot vehicle routing problems. Comput. Oper. Res.. Oper. Res. 133, 105269 (2021)
Kubil, V.N., Mokhov, V.A., Grinchenkov, D.V.: Multi-objective ant colony optimization for multi-depot heterogenous vehicle routing problem. In: 2018 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), pp. 1–6. IEEE (2018)
Zhan, Z.H., Li, J., Cao, J., Zhang, J., Chung, H.S.H., Shi, Y.H.: Multiple populations for multiple objectives: a coevolutionary technique for solving multiobjective optimization problems. IEEE Trans. Cybern. 43(2), 445–463 (2013)
Tsai, Y.L., Rastogi, C., Kitanidis, P.K., Field, C.B.: Routing algorithms as tools for integrating social distancing with emergency evacuation. Sci. Rep. 11(1), 19623 (2021)
Montoya-Torres, J.R., Lopez Franco, J., Nieto Isaza, S., Felizzola Jimenez, H., HerazoPadilla, N.: A literature review on the vehicle routing problem with multiple depots. Comput. Ind. Eng.. Ind. Eng. 79, 115–129 (2015)
Online: Centers for disease control and prevention (cdc) (Feb 2021). https://www.cdc.gov/
Lv, C., Zhang, C., Lian, K., Ren, Y., Meng, L.: A two-echelon fuzzy clustering based heuristic for large-scale bike sharing repositioning problem. Transp. Res. Pt. B-Methodol. 160, 54–75 (2022)
Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Comput. Oper. Res.. Oper. Res. 34(8), 2403–2435 (2007)
Erzin, A.I., Mladenovic, N., Plotnikov, R.V.: Variable neighborhood search variants for Min-power symmetric connectivity problem. Comput. Oper. Res. 78, 557–563 (2017)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Computat. 3(4), 257–271 (1999)
Zhang, Z., Qin, H., Li, Y.: Multi-objective optimization for the vehicle routing problem with outsourcing and profit balancing. IEEE Trans. Intell. Transport. Syst. 21(5), 1987–2001 (2020)
Men, J., et al.: Robust multi-objective vehicle routing problem with time windows for hazardous materials transportation. IEEE Trans. Intell. Transp. Syst.Intell. Transp. Syst. 14(3), 154–163 (2020)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput.Evol. Comput. 6(2), 182–197 (2002)
Acknowledgments
This work was supported by the National Key R&D Program of China (No. 2022YFE0112300), the National Natural Science Foundation of China (No. U22A2098), Fok Ying-Tong Education Foundation, China (No. 171105), and the Tencent Foundation and XPLORER PRIZE.
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Luo, M., Teng, M., Gao, C., Li, X., Wang, Z. (2024). A Multi-objective Variable Tabu Neighborhood Search Algorithm for Multiple Depot Vehicle Routing Problem in Epidemics. In: Huang, DS., Zhang, X., Chen, W. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14862. Springer, Singapore. https://doi.org/10.1007/978-981-97-5578-3_42
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