A genetic algorithm for multiobjective dangerous goods route planning

Transportation of dangerous goods (DGs) can significantly affect human life and the environment if accidents occur during the transportation process. Therefore, safe DG transportation is of vital importance, especially in high-density living environments. Effective routing of DG shipments is thus essential to the lowering of risk associated with DG transportation. DG routing is inherently a multicriteria, multiobjective problem in which various factors, such as cost, safety, public and environmental exposure, need to be simultaneously considered. We develop in this paper a multiobjective genetic algorithm (MOGA) for the determination of optimal routes for DG transportation under conflicting objectives. Implemented within the geographical information system environment, the MOGA approach is applied to the transportation of liquefied petroleum gas in the road network of Hong Kong. Experimental results in this case study substantiate the conceptual arguments and demonstrate the good performance of the proposed approach.