Solving a Vehicle Routing Problem under Uncertainty by a Differential Evolution Algorithm
In the real world, because of decreasing the related cost, the vehicles should return to the depot after serving the last customer’s location. This paper investigates the problem of the increasing service time by using the stochastic time for each tour such that the total traveling time of the vehicles is limited to a specific limit based on a defined probability.
It is proven that classic models in vehicle routing problems (VRPs) belong to the class of NP-hard ones; thus, due to its complexity using exact methods in large-scale problems, a meta-heuristic based differential evolution (DE) algorithm.
The obtained results indicate the efficiency of the proposed DE algorithm.
Originality/Value:
The total travel time is limited to a definite probability percent, and also other constraints (e.g., capacity and time distribution restrictions) are considered while the total cost of the transportation is minimized.
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