An effective genetic algorithm approach to multiobjective routing problems

Dynamic programming (DP) is a mathematical procedure designed primarily to improve the computational efficiency of solving select mathematical programming problems by decomposing them into smaller, and hence computationally simpler, subproblems. In solving multiple objectives dynamic programming problem (MODP), classical approaches reduce the multiple objectives into a single objective of minimizing a weighted sum of objectives. The determination of these weights indicates the relative importance of the various objectives. Also, if the problem scale increases, it becomes difficult to be dealt with even in the case of single objective because of the rapid expansion of the number of states to be considered. In this paper, we investigated the possibility of using genetic algorithms (GAs) to solve multiobjective routing problems (MORPs). This procedure eliminates the need for any user defined weight factor for each objective. Also, the proposed approach is developed to deal with the problems with both single and multiple objectives. The simulation results for MORPs show that genetic algorithms (GAs) may hopefully be a new approach for such kinds of difficult-to-solve problems.