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dc.contributor.authorErkut, E.en_US
dc.contributor.authorAlp, O.en_US
dc.date.accessioned2016-02-08T10:15:23Z
dc.date.available2016-02-08T10:15:23Z
dc.date.issued2007en_US
dc.identifier.issn0041-1655
dc.identifier.urihttp://hdl.handle.net/11693/23540
dc.description.abstractWe consider an integrated routing and scheduling problem in hazardous materials transportation where accident rates, population exposure, and link durations on the network vary with time of day. We minimize risk (accident probability multiplied by exposure) subject to a constraint on the total duration of the trip. We allow for stopping at the nodes of the network. We consider four versions of this problem with increasingly more realistic constraints on driving and waiting periods, and propose pseudopolynomial dynamic programming algorithms for each version. We use a realistic example network to experiment with our algorithms and provide examples of the solutions they generate. The computational effort required for the algorithms is reasonable, making them good candidates for implementation in a decision-support system. Many of the routes generated by our models do not exhibit the circuitous behavior common in risk-minimizing routes. The en route stops allow us to take full advantage of the time-varying nature of accident probabilities and exposure and result in the generation of routes that are associated with much lower levels of risk than those where no waiting is allowed.en_US
dc.language.isoEnglishen_US
dc.source.titleTransportation Scienceen_US
dc.relation.isversionofhttps://doi.org/10.1287/trsc.1060.0176en_US
dc.subjectDynamic programmingen_US
dc.subjectHazardous materialsen_US
dc.subjectRouting and schedulingen_US
dc.titleIntegrated routing and scheduling of hazmat trucks with stops en routeen_US
dc.typeArticleen_US
dc.departmentDepartment of Managementen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.citation.spage107en_US
dc.citation.epage122en_US
dc.citation.volumeNumber41en_US
dc.citation.issueNumber1en_US
dc.identifier.doi10.1287/trsc.1060.0176en_US
dc.publisherInstitute for Operations Research and the Management Sciences (INFORMS)en_US
dc.identifier.eissn1526-5447


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