Pham Minh Hoan

Optimizing transportation costs regarding many constraints on depots, customers, vehicles, and time windows is an urgent need of logistic businesses to get the highest profit and customer satisfaction. In this paper, we propose a genetic algorithm for optimizing the transportation cost in vehicle routing problem, comprising the penalty cost while constraints are violated. The considered constraints are: goods must be picked up from or delivered to different depots or customers at their requested time windows; vehicles are subject to a capacity limit, an operation time limit, a distance limit, and a type of goods limit. Three experiment scenarios with real-world locations on Google maps were conducted, showing significant results on transportation costs in terms of traveled distance and number of used vehicles. The experimental results, compared to other methods, show the effectiveness and practical applicability of the proposed algorithm.