Research on path optimization for multimodal transportation of hazardous materials under uncertain demand

Wei Han; Huo Chai; Jianpeng Zhang; Yuanping Li

doi:10.5604/01.3001.0053.7259

Authors

Wei Han School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou Author https://orcid.org/0009-0009-8489-5414
Huo Chai School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou Author https://orcid.org/0000-0001-6036-8390
Jianpeng Zhang School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou Author https://orcid.org/0009-0005-3069-5493
Yuanping Li School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou Author https://orcid.org/0000-0003-0275-2946

DOI:

https://doi.org/10.5604/01.3001.0053.7259

Keywords:

hazardous materials, multimodal transport, routing optimization, fuzzy random numbers, NSGA-II

Abstract

In the process of long-distance and large-volume transportation of hazardous materials (HAZMAT), multimodal transportation plays a crucial role with its unique advantages. In order to effectively reduce the transportation risk and improve the reliability of transportation, it is particularly important to choose a suitable transportation plan for multimodal transport of HAZMAT. In this paper, we study the transportation of HAZMAT in multimodal transport networks. Considering the fluctuation in demand for HAZMAT during the actual transportation process, it is difficult for decision makers to obtain the accurate demand for HAZMAT orders in advance, leading to uncertainty in the final transportation plan. Therefore, in this paper, the uncertain demand of HAZMAT is set as a triangular fuzzy random number, and a multi-objective mixed integer linear programming model is established with the objective of minimizing the total risk exposure population and the total cost in the transportation process of HAZMAT. In order to facilitate the solution of the model, we combined the fuzzy random expected value method with the fuzzy random chance constraint method based on credibility measures to reconstruct the uncertain model clearly and equivalently, and designed a non-dominated sorting genetic algorithm (NSGA-Ⅱ) to obtain the Pareto boundary of the multi-objective optimization problem. Finally, we conducted a numerical example experiment to verify the rationality of the model proposed in this paper. The experimental results indicate that uncertain demand can affect the path decision-making of multimodal transportation of HAZMAT. In addition, the confidence level of fuzzy random opportunity constraints will have an impact on the risk and economic objectives of optimizing the multimodal transportation path of HAZMAT. When the confidence level is higher than 0.7, it will lead to a significant increase in transportation risks and costs. Through sensitivity analysis, it can provide useful decision-making references for relevant departments to formulate HAZMAT transportation plans.

References

Assadipour, G., Ke, G. Y., & Verma, M. (2015). Planning and managing intermodal transportation of hazardous materials with capacity selection and congestion. Transportation research. Part E, Logistics and transportation review, 76(apr.), 45-57.

Assadipour, G., Ke, G. Y., & Verma, M. (2016). A toll-based bi-level programming approach to managing hazardous materials shipments over an intermodal transportation net-work. Transportation research. Part D, Transport and environment, 47D(Aug.), 208-221.

Bubbico, R., Maschio, G., Mazzarotta, B., Milazzo, M. F., & Parisi, E. (2006). Risk management of road and rail transport of hazardous materials in Sicily. Journal of loss prevention in the process industries, 19(1), 32-38.

Chai, H., He, R., Jia, X., Ma, C., & Dai, C. (2018). Generalized route planning approach for hazardous materials transportation with equity consideration. Archives of Transport, 46(2), 33-46.

Chen, X., Zuo, T., Lang, M., Li, S., & Li, S. (2022). Integrated optimization of transfer station selection and train timetables for road-rail intermodal transport network. Computers & industrial engineering, 165, 107929.

Chiou, S. (2017). A risk-averse signal setting policy for regulating hazardous material trans-portation under uncertain travel demand. Transportation Research Part D: Transport and Environment, 50, 446-472.

Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000, 2000-1-1). A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II, Berlin, Heidelberg.

Du, M., Zhou, J., Chen, A., & Tan, H. (2022). Modeling the capacity of multimodal and intermodal urban transportation networks that incorporate emerging travel modes. Transportation research. Part E, Logistics and transportation review, 168, 102937.

Esfandeh, T., Batta, R., & Kwon, C. (2018). Time-Dependent Hazardous-Materials Network Design Problem. Transportation science, 52(2), 454-473.

Ghaderi, A., & Burdett, R. L. (2019). An integrated location and routing approach for transporting hazardous materials in a bimodal transportation network. Transportation Research Part E: Logistics and Transportation Review, 127(JUL.), 49-65.

Hu, Q., Gu, W., & Wang, S. (2022). Optimal subsidy scheme design for promoting intermodal freight transport. Transportation Re-search Part E: Logistics and Transportation Review, 157, 102561.

Huang, L., & Shuai, B. (2014). Multi-objective optimized algorithm for routing optimization problem in intermodal transportation of hazardous materials. J. Saf. Sci. Technol, 10(9), 10-16.

Jabbarzadeh, A., Azad, N., & Verma, M. (2020). An optimization approach to planning rail hazmat shipments in the presence of random disruptions. Omega (Oxford), 96, 102078.

Ke, G. Y., Zhang, H., & Bookbinder, J. H. (2020). A dual toll policy for maintaining risk equity in hazardous materials transportation with fuzzy incident rate. International Journal of Production Economics, 227, 107650.

Leleń, P., & Wasiak, M. (2019). The model of selecting multimodal technologies for the transport of perishable products. Archives of Transport, 50(2), 17-33.

Liu, B., & Liu, Y. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE transactions on fuzzy systems, 10(4), 445-450.

Liu, Y. K., & Gao, J. (2008). The independence of fuzzy variables with applications to fuzzy random optimization. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(supp02), 1-20.

Mohammadi, M., Jula, P., & Tavakkoli-Moghaddam, R. (2017). Design of a reliable multi-modal multi-commodity model for hazardous materials transportation under uncertainty. European Journal of Operational Research, 257(3), 792-809.

Mohri, S. S., Mohammadi, M., Gendreau, M., Pirayesh, A., Ghasemaghaei, A., & Salehi, V. (2022). Hazardous material transportation problems: A comprehensive overview of models and solution approaches. European journal of operational research, 302(1), 1-38.

Sun, Y. (2020). A Fuzzy Multi-Objective Routing Model for Managing Hazardous Materials Door-to-Door Transportation in the Road-Rail Multimodal Network With Uncertain Demand and Improved Service Level. IEEE Access, 8, 172808-172828.

Sun, Y., Li, X., Liang, X., & Zhang, C. (2019). A Bi-Objective Fuzzy Credibilistic Chance-Constrained Programming Approach for the Hazardous Materials Road-Rail Multimodal Routing Problem under Uncertainty and Sustainability. Sustainability, 11(9), 2577.

Toumazis, I., & Kwon, C. (2013). Routing hazardous materials on time-dependent networks using conditional value-at-risk. Transportation Research Part C: Emerging Technologies, 37, 73-92.

Verma, M., & Verter, V. (2010). A lead-time based approach for planning rail-truck inter-modal transportation of dangerous goods. European journal of operational research, 202(3), 696-706.

Verma, S., Pant, M., & Snasel, V. (2021). A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems. IEEE access, 9, 57757-57791.

Xie, Y., Lu, W., Wang, W., & Quadrifoglio, L. (2012). A multimodal location and routing model for hazardous materials transportation. J Hazard Mater, 227-228, 135-141.

Xin, C., Feng, J., & Zhang, J. (2016a). Problem of distribution center location-routing optimization for multi-modal hazardous materials transportation. China Saf. Sci. J, 26(9), 73-78.

Xin, C., Feng, J., & Zhang, J. (2016b). Routing optimization for multi-modal hazardous materials transportation under a time-varying condition. China Saf. Sci. J, 26(6), 104-110.

Yang, K., Yang, L., & Gao, Z. (2016). Planning and optimization of intermodal hub-and-spoke network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 95, 248-266.

Zhu, S., Zhang, S., Lang, H., Jiang, C., & Xing, Y. (2022). The Situation of Hazardous Materials Accidents during Road Transportation in China from 2013 to 2019. International Journal of Environmental Research and Public Health, 19(15), 9632.