Robust Inventory Routing Problem Under Uncertainty

We dedicate to inventory routing problems with uncertain demand, arising from supply chain systems. We determine replenishment times, quantities, and vehicle routes to serve customers. To predict customer demand, we use machine learning techniques to extract information from historical data, which is normally available in big data era. We use distributionally robust optimization method to build mathematical models and develop software packages that can directly be used by suppliers to plan their supply chain activities. The outcomes of our project can help suppliers effectively control inventories and allocate transportation vehicles, thereby optimizing resource allocations, reducing operation costs, and better serving customers. Furthermore, optimizing vehicle schedules makes a good environmental sense, because transportation produces most of the CO2 in supply chain activities.

Faculty Supervisor:

Louis-Martin Rousseau

Student:

Partner:

Massachusetts Institute of Technology

Discipline:

Engineering

Sector:

Education

University:

Polytechnique Montréal

Program: