Solving the Distributionally Robust Split Delivery Vehicle Routing Problem via exact methods

This Mitacs Globalink project, part of the PhD thesis titled “Robust Optimization for Logistic Problems,” aims to develop new optimization techniques for solving the distributionally robust split delivery vehicle routing problem under uncertainty. In split delivery routing, customer demands can be served in multiple partial shipments rather than a single delivery, an approach that can reduce total delivery costs by up to 50%, though it significantly increases computational complexity to find the optimal solution. This project will study the problem through the distributionally robust optimization framework, which allows the incorporation of limited probabilistic information without requiring perfect knowledge of the underlying distribution of parameters, a common limitation in real-world applications. As this variant has not yet been explored in the literature, it holds strong potential for improving the efficiency and resilience of modern logistics and delivery systems. Building on the applicant’s previous successful implementations of branch-and-cut algorithms for deterministic and robust variants of the problem, this project seeks to develop an exact method for the new distributionally robust framework. The collaboration between experts from Université Laval and the University of Groningen combines expertise in transportation optimization and stochastic and distributionally robust programming, a promising outcomes that will advance both domains.

Faculty Supervisor:

Leandro Callegari Coelho

Student:

Partner:

University of Groningen

Discipline:

Engineering

Sector:

Education

University:

Université Laval

Program: