Heuristic Multiobjective Search for Hazmat Transportation Problems

Enrique Machuca, Lawrence Mandow, J.L. Pérez de la Cruz, and Antonio Iovanella.
Heuristic multiobjective search for hazmat transportation problems.
In Jose Lozano, Jose Gómez, and Jose Moreno, editors, Advances in Artificial Intelligence, volume 7023 of Lecture Notes in Computer Science, pages 243-252. Springer Berlin / Heidelberg, 2011. 10.1007/978-3-642-25274-7_25.


This paper describes the application of multiobjective heuristic search algorithms to the problem of hazardous material (hazmat) transportation. The selection of optimal routes inherently involves the consideration of multiple conflicting objectives. These include the minimization of risk (e.g. the exposure of the population to hazardous substances in case of accident ), transportation cost, time, or distance. Multiobjective analysis is an important tool in hazmat transportation decision making. This paper evaluates the application of multiobjective heuristic search techniques to hazmat route planning. The efficiency of existing algorithms is known to depend on factors like the number of objectives and their correlations. The use of an informed multiobjective heuristic function is shown to significantly improve efficiency in problems with two and three objectives. Test problems are defined over random graphs and over a real road map.


  affiliation={Universidad de M{\'a}laga Dpto. Lenguajes y Ciencias de la Computaci{\'o}n 29071 M{\'a}laga Spain},
  keyword={Computer Science},
  url={http: //},
  author={Machuca, Enrique and Mandow, Lawrence and P{\'e}rez de la Cruz, J.L. and Iovanella, Antonio},
  title={Heuristic Multiobjective Search for Hazmat Transportation Problems},
  editor={Lozano, Jose and G{\'o}mez, Jose and Moreno, Jose},
  booktitle={Advances in Artificial Intelligence},
  series={Lecture Notes in Computer Science},
  publisher={Springer Berlin / Heidelberg},
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