Preface: Special issue on the future of route optimization/vehicle routing

On June 12-13, 2017, the SGH Warsaw School of Economics and Loyola University Chicago co-hosted the Route Optimization/Vehicle Routing (ROVER) Workshop in Warsaw, Poland. In attendance were 17 leading scholars in the field of vehicle routing from throughout Europe and North America. Over the course of this workshop, numerous future directions for ROVER were discussed, identifying outstanding/important problems for further exploration in a variety of areas. Topics identified during the workshop included collaborative vehicle routing, autonomous vehicle fleet operations, dynamic vehicle routing, last mile delivery in an urban and/or omnichannel environment, real-time vehicle routing, data-rich vehicle routing, and service network design. While there has been considerable research on efficient algorithms that can be applied to these problems, rapidly changing business models in transportation and logistics, as well as advances in technology, create new challenges that are yet unsolved. It is increasingly important to be able to incorporate such aspects as: demand and supply uncertainty, real-time flow of information from customers and drivers, autonomous transportation modes, exponentially increasing sources of data, a sharing economy, and synchronized supply chain partners. This special issue focuses on several of these developing concerns. We received 18 manuscripts, 7 of which were selected for this special issue through a rigorous review process. The first four papers focus on the development of new methodologies for vehicle routing problems (VRP) and the latter three consider the application of vehicle routing concepts to areas of growing importance. With numerous stakeholders in today’s global supply chain, it becomes difficult to focus solely on an objective of cost minimization. When considering the VRP, other factors are gaining importance, such as service quality, workload balance, and environmental impact. With this in mind, Matl et al. [4] propose a generic framework for solving bi-objective problems using a single-objective heuristic. The authors adapt the Box Splitting methodology for heuristics, which is capable of rapidly generating representative sets of heuristic compromise solutions. They use the VRP with Route Balancing as a benchmark to show the added value of Heuristic Box Splitting and demonstrate that it significantly outperforms the current state-of-the-art multiobjective metaheuristics with respect to several quality metrics. Another development that has changed how we look at route optimization is the ever-increasing amount of data that is collected about our road networks. Utilizing this data with a traditional customer-based graph representation of the network, with nodes representing customers and arcs the best path between those nodes, omits some insight offered by the data and may degrade solution quality. Ben Ticha et al. [1] develop a branch-and-price scheme that relies on a graph that mimics the original road network, as well as a similar scheme that uses a multigraph, in which a customer-based graph is extended to include an arc for every efficient path between two nodes. Both schemes are applied to the VRP with Time Windows, showing that the efficiency of the two approaches depends on customer density in the road network, time and capacity constraints. The multigraph approach does have a general advantage, especially for the more realistic instances, but using the road network also has benefit. With another perspective on the proliferation of data related to the road network underlying route optimization problems, Merchan and Winkenbach [5] focus on the real-world validation of continuum approximation methods to estimate urban route distances in large-scale applications. In particular, they look at the use of contemporary geo-spatial and traffic-related datasets