Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Journal of Intelligent Manufacturing
Using graphical information systems to improve vehicle routing problem instances
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Traditionally, the uncapacitated facility location problem (UFLP) is solved as a single-objective optimization exercise, and focusses on minimizing the cost of operating a distribution network. This paper presents an exploratory study in which the environmental impact is modelled as a separate objective to the economic cost. We assume that the environmental cost of transport is large in comparison to the impact involved in operating distribution centres or warehouses (in terms of CO2 emissions, for example). We further conjecture that the whole impact on the environment is not fully reflected in the costs incurred by logistics operators. Based on these ideas, we investigate a number of "what if ?" scenarios, using a Fast Non-Dominated Sorting Genetic Algorithm (NSGA-II), to provide sets of non-dominated solutions to some test instances. The analysis is conducted on both two-objective (economic cost versus environmental impact) and three objective (economic cost, environmental impact and uncovered demand) models. Initial results are promising, indicating that this approach could indeed be used to provide informed choices to a human decision maker.