Abstract
Vehicle routing, being a major concern of any industry with transportation requirements (manufacturing, supply-chain, travel and tourism, etc.), offers immense scope for research. Due consideration of this fact has motivated the development of the approach illustrated hereafter. In this article, a vehicle routing problem (VRP) with generalized fuzzy travel times, multiple depots, split delivery (including inter depot split), and heterogenous, capacitated, alternative fuel driven vehicles, is studied. A hybrid genetic algorithm (GA) is designed to produce efficient solutions. Since traditional methods are incapable of computing the expected values of such fuzzy variables, the technique of fuzzy simulation is incorporated in the GA. Five alternative fuel vehicles-electric, hybrid, diesel, biodiesel, and CNG, are evaluated vis-a-vis multiple criteria using fuzzy hierarchical technique for order preference by similarity to ideal solution (TOPSIS) and their respective scores are input in the hybrid GA for sustainable, apt, and low-cost assignment of vehicles to routes. The algorithm is run for multiple combinations of crossover and mutation probabilities, vehicle capacities, location instances, and number of generations. The experimental results substantiate the robustness of the proposed approach and suffice to project the strength of the computationally challenging model.