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.

The information complexity of learning tasks, their structure and their distance

We introduce an asymmetric distance in the space of learning tasks and a framework to compute their complexity. These concepts are foundational for the practice of transfer learning, whereby a parametric model is pre-trained for a task, and then fine tuned for another. The framework we develop is non-asymptotic, captures the finite nature of the training dataset and allows distinguishing learning from memorization. It encompasses, as special cases, classical notions from Kolmogorov complexity and Shannon and Fisher information. However, unlike some of those frameworks, it can be applied to large-scale models and real-world datasets. Our framework is the first to measure complexity in a way that accounts for the effect of the optimization scheme, which is critical in deep learning.

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 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.