In this paper, an urban hazmat transportation problem considering multiple factors that tangle with real-world applications (i.e., weather conditions, traffic conditions, population density, time window, link closure and half link closure) is investigated. Based on multiple depot capacitated vehicle routing problem, we provide a multi-level programming formulation for urban hazmat transportation. To obtain the Pareto optimal solution, an improved biogeography-based optimization (improved BBO) algorithm is designed, comparing with the original BBO and genetic algorithm, with both simulated numerical examples and a real-world case study, demonstrating the effectiveness of the proposed approach.
This is a preview of subscription content, log in to check access.
This study was supported by grants from National Natural Science Foundation of China of No. 71722007, and the Fundamental Research Funds for the Central Universities (No. XK1802-5).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human or animal participants performed by the author.
Abkowitz M, Cheng PDM (1988) Developing a risk/cost framework for routing truck movements of hazardous materials. Accid Anal Prev 20(1):39–51CrossRefGoogle Scholar
Akgün V, Parekh A, Batta R, Rump CM (2007) Routing of a hazmat truck in the presence of weather systems. Comput Oper Res 34(5):1351–1373CrossRefzbMATHGoogle Scholar
Androutsopoulos KN, Zografos KG (2012) A bi-objective time-dependent vehicle routing and scheduling problem for hazardous materials distribution. EURO J Transp Logist 1(1–2):157–183CrossRefGoogle Scholar
Assadipour G, Ke GY, Verma M (2015) Planning and managing intermodal transportation of hazardous materials with capacity selection and congestion. Transp Res Part E Logist Transp Rev 76:45–57CrossRefGoogle Scholar
Batta R, Chiu SS (1988) Optimal obnoxious paths on a network: transportation of hazardous materials. Oper Res 36(1):84–92CrossRefGoogle Scholar
Bronfman A, Marianov V, Paredes-Belmar G, Lüer-Villagra A (2016) The maxisum and maximin-maxisum HAZMAT routing problems. Transp Res Part E Logist Transp Rev 93:316–333CrossRefzbMATHGoogle Scholar
Bula GA, Prodhon C, Gonzalez FA, Afsar HM, Velasco N (2017) Variable neighborhood search to solve the vehicle routing problem for hazardous materials transportation. J Hazard Mater 324:472–480CrossRefGoogle Scholar
Carotenuto P, Giordani S, Ricciardelli S (2007) Finding minimum and equitable risk routes for hazmat shipments. Comput Oper Res 34(5):1304–1327CrossRefzbMATHGoogle Scholar
Clarke G, Wright JW (1964) Scheduling of vehicles from a central depot to a number of delivery points. Oper Res 12(4):568–581CrossRefGoogle Scholar
De Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9(1):67–103CrossRefGoogle Scholar
Erkut E, Tjandra SA, Verter V (2007) Hazardous materials transportation. Handb Oper Res Manag Sci 14:539–621Google Scholar
Esfandeh T, Kwon C, Batta R (2016) Regulating hazardous materials transportation by dual toll pricing. Transp Res Part B Methodol 83:20–35CrossRefGoogle Scholar
Fan T, Chiang WC, Russell R (2015) Modeling urban hazmat transportation with road closure consideration. Transp Res Part D Transp Environ 35:104–115CrossRefGoogle Scholar
Filipec M, Skrlec D, Krajcar S (1997) Darwin meets computers: new approach to multiple depot capacitated vehicle routing problem. In: IEEE International conference on systems, man, and cybernetics, pp 421–426Google Scholar
Filipec M, Skrlec D, Krajcar S (2000) Genetic algorithm approach for multiple depot capacitated vehicle routing problem solving with heuristic improvements. Int J Model Simul 20(4):320–328CrossRefGoogle Scholar
Hassan R, Cohanim B, De Weck O, Venter G (2005) A comparison of particle swarm optimization and the genetic algorithm. In: 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conferenceGoogle Scholar
Karkazis J, Boffey TB (1995) Optimal location of routes for vehicles transporting hazardous materials. Eur J Oper Res 86(2):201–215CrossRefzbMATHGoogle Scholar
List G, Mirchandani P (1991) An integrated network/planar multiobjective model for routing and siting for hazardous materials and wastes. Transp Sci 25(2):146–156CrossRefGoogle Scholar
Lozano A, Munoz A, Antun JP, Granados F, Guarneros L (2010) Analysis of hazmat transportation accidents in congested urban areas, based on actual accidents in Mexico. Procedia Soc Behav Sci 2(3):6053–6064CrossRefGoogle Scholar
Ma H, Simon D, Fei M, Xie Z (2013) Variations of biogeography-based optimization and Markov analysis. Inf Sci 220:492–506CrossRefGoogle Scholar
Meng Q, Lee DH, Cheu RL (2005) Multiobjective vehicle routing and scheduling problem with time window constraints in hazardous material transportation. J Transp Eng 131(9):699–707CrossRefGoogle Scholar
Mirjalili S, Mirjalili SM, Lewis A (2014) Let a biogeography-based optimizer train your multi-layer perceptron. Inf Sci 269:188–209MathSciNetCrossRefGoogle Scholar
Patel MH, Horowitz AJ (1994) Optimal routing of hazardous materials considering risk of spill. Transp Res Part A Policy Pract 28(2):119–132CrossRefGoogle Scholar
Pradhananga R, Taniguchi E, Yamada T, Qureshi AG (2014) Bi-objective decision support system for routing and scheduling of hazardous materials. Socio Econ Plan Sci 48(2):135–148CrossRefGoogle Scholar
Satterthwaite SP (1976) An assessment of seasonal and weather effects on the frequency of road accidents in California. Accid Anal Prev 8(2):87–96CrossRefGoogle Scholar
Toumazis I, Kwon C (2015) Worst-case conditional value-at-risk minimization for hazardous materials transportation. Transp Sci 50(4):1174–1187CrossRefGoogle Scholar
Toumazis I, Kwon C, Batta R (eds) (2013) Value-at-risk and conditional value-at-risk minimization for hazardous materials routing. In: Handbook of OR/MS models in hazardous materials transportation. Springer, pp 127–154Google Scholar
Wang X, Zhu J, Ma F, Li C, Cai Y, Yang Z (2016) Bayesian network-based risk assessment for hazmat transportation on the Middle Route of the South-to-North Water Transfer Project in China. Stoch Environ Res Risk Assess 30(3):841–857CrossRefGoogle Scholar