Multi-Objective Optimization for Site Location Problems through Hybrid Genetic Algorithm with Neural Networks.

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  • Multi-ObiGctive Optimization for Site Location Problems through Hybrid Genetic Algorithm with Neural Networks

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Abstract

With the aim of developing a flexible optimization method for managing conflict resolution, this paper concerns itself with site location problems under multi-objectives. As known from the term NIMBY (Not In My Back Yard), disposal site location problems of hazardous waste is an eligible case study associated with environmental and economic concerns. After describing the problem generally as a multi-objective mixed-integer program, we have proposed an intelligence supported approach that extends the hybrid genetic algorithm developed by the author to derive the best-compromise solution. For this purpose, we have developed a novel modeling method of value function using neural networks, and incorporated it into the approach. As a result, we can provide a practical and effective method in which the hybrid strategy maintains its advantages of relying on good matches between the solution methods and the problem properties such as a genetic algorithm for unconstrained discrete optimization and a mathematical program for constrained continuous ones. Finally, by taking an example formulated as a multi-objective mixed-integer linear program, we have examined the effectiveness of the proposed approach numerically.

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