Exact and heuristic solution of the consistent vehicle-routing problem

Providing consistent service by satisfying customer demands with the same driver (driver consistency) at approximately the same time (arrival-time consistency) allows companies in last-mile distribution to stand out among competitors. The consistent vehicle-routing problem (ConVRP) is a multiday problem addressing such consistency requirements along with traditional constraints on vehicle capacity and route duration. The literature offers several heuristics but no exact method for this problem. The state-of-the-art exact technique to solve VRPs-column generation (CG) applied to route-based formulations in which columns are generated via dynamic programming-cannot be successfully extended to the ConVRP because the linear relaxation of route-based formulations is weak. We propose the first exact method for the ConVRP, which can solve medium-sized instances with five days and 30 customers. The method solves, via CG, a formulation in which each variable represents the set of routes assigned to a vehicle over the planning horizon. As an upper bounding procedure, we develop a large neighborhood search (LNS) featuring a repair procedure specifically designed to improve the arrival-time consistency of solutions. Used as stand-alone heuristic, the LNS is able to significantly improve the solution quality on benchmark instances from the literature compared with state-of-the-art heuristics

Emerging Trends in Logistics: New Models and Algorithms for Vehicle Routing

This thesis studies recent trends in logistics that are driven by technological advancements, organizational transformation, and changing customer behavior. Heuristic and Exact Algorithms are developed to solve central vehicle routing problems arising in this context

Emerging Trends in Logistics: New Models and Algorithms for Vehicle Routing

This thesis studies recent trends in logistics that are driven by technological advancements, organizational transformation, and changing customer behavior. Heuristic and Exact Algorithms are developed to solve central vehicle routing problems arising in this context

A Variable Neighborhood Search heuristic for the maximum ratio clique problem

International audienc

The prize-collecting vehicle routing problem with single and multiple depots and non-linear cost

In this paper, we propose a new routing problem to model a highly relevant planning task in small package shipping. We consider the Prize-Collecting Vehicle Routing Problem with Non-Linear cost in its single and multi-depot version, which integrates the option of outsourcing customers to subcontractors instead of serving them with the private fleet. Thereby, a lower bound on the total customer demand to be served by the private fleet guarantees a high utilization of the fleet capacity. To represent the practical situation, where a discount is given by a subcontractor if larger amounts of packages are outsourced, subcontracting costs follow a non-linear function. The considered problem is NP-hard and we propose an Adaptive Variable Neighborhood Search algorithm to solve instances of realistic size. We propose new benchmark sets for the single and the multi-depot problem, which are adapted from test instances of the capacitated VRP and the closely related Multi-Depot VRP with Private fleet and Common carrier. In numerical studies, we investigate the performance of our algorithm on the newly generated test instances and on standard benchmark problems of related problems. Moreover, we study the effect of different cost functions and different values of the minimal demand to be served by the private fleet on the routing solutions obtained