With the aim of developing a flexible optimization method for managing conflict resolution in a variety of real world problems, this paper concerns itself with multi-objective mixed-integer programs. Here we have proposed an intelligence supported approach that combines the genetic algorithm with the mathematical programs in a hierarchical structure.
To implement the procedure, we have also 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 a site location problem of hazardous waste disposal as an eligible case study associated with the conflict resolution, we have applied the method to examine its effectiveness through numerical experiments. After describing the problem generally as a two-objective mixed-integer linear program, we have confirmed that the proposed method can derive the best-compromise solution very effectively and reliably while the conventional methods of multi-objective genetic algorithm are limited to derive only the Pareto optimal solution set.
System Create Lab. - Research Areas