An optimized genetic routing approach for constrained shortest path selections

This paper presented a new methodology to determine the population, a set of feasible paths, chromosomes of genetic algorithms (GA) given multi-constraints, i.e., distance, deadline, and budget, in a shortest path and modified vehicle routing problem. Several aspects of GA have been explored and optimized including population generation, crossover, mutation, ranking, and path selection criteria. Our GA optimization proposal was evaluated with benchmark instances and compared with other heuristics in the literature resulting in the outstanding performance in terms of quality and computational time complexity given a heterogeneous of network sizes.