A metaheuristic solution approach based on Variable Neighborhood Search (VNS) is proposed for solving a Dynamic Rich Vehicle Routing Problem with Time Windows (DRVRPTW). It combines a set of real-world constraints proposed by some companies in the Canary Islands, Spain. The designed algorithm manages two possibilities: rejecting the customers that cannot be feasibly inserted taking into account priorities, or permitting time windows infeasibilities in customers in order to provide a solution with all customers.
In order to assess the behavior of our approach, we have compared the obtained results with the best results in the literature using the standard test problem instances. In some cases, our results are not only competitive with the related literature, but also even better. Moreover, our insertion times are substantially lower than the best ones.
Taking into account that the method proposed in this work has been developed to solve a real problem with a real set of constraints, it is not supposed to be the most competitive with the standard Solomon instances, which have other features. However, in that case, we have obtained results very close to the best ones in the literature.
Additionally, we propose solutions with infeasibilities in order to include all customers in the final solutions. In this case, logically, the total distance increases, but our results are still very close to the best ones in the literature. It is important to note that we are considering the time needed to insert any new dynamic customer in the plan, which can influence in the final results.
Finally, we have also analyzed the effect of the different restrictions in the final solutions using instances based on the real ones provided by a company. In this case, the importance of customers priorities on the final plan has become clear.
A metaheuristic solution approach based on Variable Neighborhood Search (VNS) is proposed for solving a Dynamic Rich Vehicle Routing Problem with Time Windows (DRVRPTW). It combines a set of real-world constraints proposed by some companies in the Canary Islands, Spain. The designed algorithm manages two possibilities: rejecting the customers that cannot be feasibly inserted taking into account priorities, or permitting time windows infeasibilities in customers in order to provide a solution with all customers.In order to assess the behavior of our approach, we have compared the obtained results with the best results in the literature using the standard test problem instances. In some cases, our results are not only competitive with the related literature, but also even better. Moreover, our insertion times are substantially lower than the best ones.Taking into account that the method proposed in this work has been developed to solve a real problem with a real set of constraints, it is not supposed to be the most competitive with the standard Solomon instances, which have other features. However, in that case, we have obtained results very close to the best ones in the literature.Additionally, we propose solutions with infeasibilities in order to include all customers in the final solutions. In this case, logically, the total distance increases, but our results are still very close to the best ones in the literature. It is important to note that we are considering the time needed to insert any new dynamic customer in the plan, which can influence in the final results.Finally, we have also analyzed the effect of the different restrictions in the final solutions using instances based on the real ones provided by a company. In this case, the importance of customers priorities on the final plan has become clear.
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