A hybrid metaheuristic for solving asymmetric distance-constrained vehicle routing problem

The Asymmetric Distance-Constrained Vehicle Routing Problem (ADVRP) is NP-hard as it is a natural extension of the NP-hard Vehicle Routing Problem. In ADVRP problem, each customer is visited exactly once by a vehicle; every tour starts and ends at a depot; and the traveled distance by each vehicle is not allowed to exceed a predetermined limit. We propose a hybrid metaheuristic algorithm combining the Randomized Variable Neighborhood Search (RVNS) and the Tabu Search (TS) to solve the problem. The combination of multiple neighborhoods and tabu mechanism is used for their capacity to escape local optima while exploring the solution space. Furthermore, the intensification and diversification phases are also included to deliver optimized and diversified solutions. Extensive numerical experiments and comparisons with all the state-of-the-art algorithms show that the proposed method is highly competitive in terms of solution quality and computation time, providing new best solutions for a number of instances.

A hybrid metaheuristic for Solving Asymmetric Distance-Constrained Vehicle Routing Problem

The Asymmetric Distance-Constrained Vehicle Routing Problem (ADVRP) is an NP-hard problems. In ADVRP problem, each customer is visited once by one vehicle; every tour starts and ends at a depot; and the travelled distance by each vehicle is required to be less than or equal to the given maximum value. The problem is a natural extension of Vehicle Routing Problem case. In our work, we propose a hybrid metaheuristic algorithm combining the Randomized Variable Neighborhood Search (RVNS) and the Tabu Search (TS) to solve the problem. The combination of multiple neighborhoods and tabu mechanism is used for their capacity to escape local optima while exploring the solution space. Furthermore, the intensification and diversification phases are also included to deliver optimized and diversified solutions for the search. Extensive numerical experiments on benchmark instances show that our algorithm can be comparable with the state-of-the-art previous algorithms in terms of solution quality and computation time. In many cases our proposed method is able to improve the best-known solution available from the literature.

A Bucket Graph–Based Labeling Algorithm with Application to Vehicle Routing

We consider the shortest path problem with resource constraints arising as a subproblem in state-of-the-art branch-cut-and-price algorithms for vehicle routing problems. We propose a variant of the bidirectional label-correcting algorithm in which the labels are stored and extended according to the so-called bucket graph. This organization of labels helps to significantly decrease the number of dominance checks and the running time of the algorithm. We also show how the forward/backward route symmetry can be exploited and how to eliminate arcs from the bucket graph using reduced costs. The proposed algorithm can be especially beneficial for vehicle routing instances with large vehicle capacity and/or with time window constraints. Computational experiments were performed on instances from the distance-constrained vehicle routing problem, including multidepot and site-dependent variants, on the vehicle routing problem with time windows, and on the “nightmare” instances of the heterogeneous fleet vehicle routing problem. Significant improvements over the best algorithms in the literature were achieved, and many instances could be solved for the first time.

A Hybrid Metaheuristic For Solving Asymmetric Distance-Constrained Vehicle Routing Problem

The Asymmetric Distance-Constrained Vehicle Routing Problem (ADVRP) is an NP-hard problems. In ADVRP problem, each customer is visited once by one vehicle; every tour starts and ends at a depot; and the travelled distance by each vehicle is required to be less than or equal to the given maximum value. The problem is a natural extension of Vehicle Routing Problem case. In our work, we propose a hybrid metaheuristic algorithm combining the Randomized Variable Neighborhood Search (RVNS) and the Tabu Search (TS) to solve the problem. The combination of multiple neighborhoods and tabu mechanism is used for their capacity to escape local optima while exploring the solution space. Furthermore, the intensification and diversification phases are also included to deliver optimized and diversified solutions for the search. Extensive numerical experiments on benchmark instances show that our algorithm can be comparable with the state-of-the-art previous algorithms in terms of solution quality and computation time. In many cases our proposed method is able to improve the best-known solution available from the literature.

The Distributionally Robust Chance-Constrained Vehicle Routing Problem

Vehicle Routing under Uncertainty