4.7 Article

Link-based multi-class hazmat routing-scheduling problem: A multiple demon approach

期刊

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 261, 期 1, 页码 337-354

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.ejor.2017.01.048

关键词

Transportation; Vehicle routing; Non-cooperative game; Hazardous materials; Scheduling

资金

  1. University Research Committee, University of Hong Kong [201311159123]
  2. National Natural Science Foundation of China, China [71271183]

向作者/读者索取更多资源

This paper addresses a hazmat routing and scheduling problem for a general transportation network with multiple hazmat classes when incident probabilities are unknown or inaccurate. A multi-demon formulation is proposed for this purpose. This formulation is link-based (i.e., the decision variables are link flows) and can be transformed into other forms so that a wide range of solution methods can be used to obtain solutions. This paper also proposes a solution strategy to obtain route flow solutions without relying on exhaustive route enumeration and route generation heuristics. Examples are set up to illustrate the problem properties, the method of obtaining route flows from link flows, and the computational efficiency of the solution strategy. Moreover, a case study is used to illustrate our methodology for real-life hazmat shipment problems. From this case study, we obtain four key insights. First, to have the safest shipment of one type of hazmat, different trucks carrying the same type of hazmat need to take different routes and links. Second, in case of multiple-hazmat transportation, it is recommended to use different routes and links for the shipment of different hazmat types. This may increase travel time but can result in safer shipment. Third, if the degree of connectivity in a transportation network is high, the shipment company may have multiple solutions. Fourth, the hazmat flows on critical links (whose removal would make the network disconnected) must be distributed or scheduled over different periods to have safer shipment. (C) 2017 Elsevier B.V. All rights reserved.

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