This project considers the utilization of a fleet of vehicles during freight and service delivery operations, and aims to enhance the ability to optimize such processes in view of operational uncertainty, such as uncertainty related to the transportation network (e.g., traffic conditions, road closings or vehicle break-downs), or uncertainty related to the delivery targets themselves (e.g., customer demands, time availabilities, or geographic locations). From a practical perspective, failure to take into account uncertainty when making operational decisions in this context may have significant economic and reputational repercussions for operators, as slight changes in operational conditions may lead even a carefully planned and "optimized" operation to become highly suboptimal or outright infeasible. Furthermore, efficient optimization and risk-management frameworks for vehicle routing operations, such as the one developed in this project, can benefit the environment by reducing unwanted side-effects of delivery systems such as traffic congestion and pollution emissions. In addressing this important issue, this project applies and advances the theoretical field of Robust Optimization (RO), which has emerged in the scientific literature as a promising framework to optimize mathematical models subject to parameter uncertainty. The project seeks to systematize the application of RO in the context of mathematical models used for the optimization of vehicle routing operations and to streamline the adoption of its theoretical and methodological innovations by practitioners. The project further impacts Education by providing training to graduate and undergraduate students on issues of freight and service delivery operations, optimization methods and algorithms, uncertainty quantification and analysis, and scientific computation, as well as enabling STEM outreach to K-12 students.
This project aims to develop an optimization framework for the systematic treatment of uncertainty in rich Vehicle Routing Problems (VRPs). VRPs consider a fleet of vehicles and their optimal utilization in freight and service delivery operations. Rich VRP settings particularly account for complicated operational realities that are answered in practice. From a practical perspective, it is of interest to design freight and service delivery systems that take into account operational uncertainties, since failure to do so may lead to solutions that are infeasible or highly suboptimal. The project applies the Robust Optimization (RO) framework, which has not been considered much in the context of VRPs. The framework seeks to optimize the problem in view of a "worst-case" scenario, as dictated by an uncertainty set that is suitably selected to reflect the decision maker's tolerance for risk and ambiguity. An RO-based approach can be advantageous from a tractability viewpoint and does not require precise distributional knowledge. The expected methodological contributions include the development of (a) robust formulations, cutting planes, and efficient exact solution approaches, (b) robust feasibility checks and efficient metaheuristic solution approaches, and (c) associated uncertainty sets that are tractable, practically relevant, and easily tuned according to risk tolerance. Furthermore, we plan to compile comprehensive collections of new benchmark instances and computational performance profiles, which will help to stimulate research efforts in the area. Efficient rich-VRP optimization and risk-management frameworks, such as the one developed in this project, can have an important impact in the competitiveness, service quality and sustainability of companies that adopt them. Furthermore, they benefit the environment by reducing unwanted side-effects of delivery systems, such as traffic congestion and pollution emissions. The project will further impact Education via providing training to graduate and undergraduate students on issues of freight and service delivery operations, optimization methods and algorithms, uncertainty quantification and analysis, and scientific computation, as well as enabling STEM outreach to K-12 students.
