Urban hazmat transportation with multi-factor

  • Jiaoman Du
  • Xiang LiEmail author
  • Lei Li
  • Changjing Shang


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.


Urban hazmat transportation Multiple factors Multi-level programming Biogeography-based optimization Pareto optimization 



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.


  1. 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
  2. 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
  3. 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
  4. 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
  5. Batta R, Chiu SS (1988) Optimal obnoxious paths on a network: transportation of hazardous materials. Oper Res 36(1):84–92CrossRefGoogle Scholar
  6. Branston D (1976) Link capacity functions: a review. Transp Res 10(4):223–236CrossRefGoogle Scholar
  7. Bronfman A, Marianov V, Paredes-Belmar G, Lüer-Villagra A (2015) The maximin HAZMAT routing problem. Eur J Oper Res 241(1):15–27MathSciNetCrossRefzbMATHGoogle Scholar
  8. 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
  9. 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
  10. Carotenuto P, Giordani S, Ricciardelli S (2007) Finding minimum and equitable risk routes for hazmat shipments. Comput Oper Res 34(5):1304–1327CrossRefzbMATHGoogle Scholar
  11. 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
  12. De Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9(1):67–103CrossRefGoogle Scholar
  13. Du J, Yu L, Li X (2016) Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation. Int J Gen Syst 45(3):286–310MathSciNetCrossRefzbMATHGoogle Scholar
  14. Du J, Li X, Yu L, Dan R, Zhou J (2017) Multi-depot vehicle routing problem for hazardous materials transportation: a fuzzy bilevel programming. Inf Sci 399:201–218CrossRefGoogle Scholar
  15. Elbeltagi E, Hegazy T, Grierson D (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Inform 19(1):43–53CrossRefGoogle Scholar
  16. Erkut E, Verter V (1998) Modeling of transport risk for hazardous materials. Oper Res 46(5):625–642CrossRefzbMATHGoogle Scholar
  17. Erkut E, Tjandra SA, Verter V (2007) Hazardous materials transportation. Handb Oper Res Manag Sci 14:539–621Google Scholar
  18. Esfandeh T, Kwon C, Batta R (2016) Regulating hazardous materials transportation by dual toll pricing. Transp Res Part B Methodol 83:20–35CrossRefGoogle Scholar
  19. 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
  20. 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
  21. 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
  22. 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
  23. Kang Y, Batta R, Kwon C (2014) Value-at-risk model for hazardous material transportation. Ann Oper Res 222(1):361–387MathSciNetCrossRefzbMATHGoogle Scholar
  24. Karkazis J, Boffey TB (1995) Optimal location of routes for vehicles transporting hazardous materials. Eur J Oper Res 86(2):201–215CrossRefzbMATHGoogle Scholar
  25. 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
  26. 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
  27. Ma H, Simon D, Fei M, Xie Z (2013) Variations of biogeography-based optimization and Markov analysis. Inf Sci 220:492–506CrossRefGoogle Scholar
  28. 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
  29. Mirjalili S, Mirjalili SM, Lewis A (2014) Let a biogeography-based optimizer train your multi-layer perceptron. Inf Sci 269:188–209MathSciNetCrossRefGoogle Scholar
  30. 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
  31. 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
  32. 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
  33. Simon D (2008a) Biogeography-based optimization. IEEE Trans Evolut Comput 12(6):702–713CrossRefGoogle Scholar
  34. Simon D (2008b) The Matlab code of biogeography-based optimization. Accessed 8 May 2009
  35. Toumazis I, Kwon C (2015) Worst-case conditional value-at-risk minimization for hazardous materials transportation. Transp Sci 50(4):1174–1187CrossRefGoogle Scholar
  36. 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
  37. Wang J, Kang Y, Kwon C, Batta R (2012) Dual toll pricing for hazardous materials transport with linear delay. Netw Spat Econ 12(1):147–165MathSciNetCrossRefzbMATHGoogle Scholar
  38. 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

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Economics and ManagementBeijing University of Chemical TechnologyBeijingChina
  2. 2.Faculty of Science and EngineeringHosei UniversityTokyoJapan
  3. 3.Department of Computer ScienceAberystwyth UniversityAberystwythUK
  4. 4.Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijingChina

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