Developing a hybrid optimization model for matching and pricing strategies in an intelligent freight transportation platform under uncertainties

The freight industry market is complex and dynamic and has many challenges such as inefficient operations, coordination issues, lack of shipment visibility, market volatility, cargo thefts, road incidents, and quality assurance of sensitive products. Recently, the COVID-19 pandemic situation has brought new difficulties to the freight industry in Canada and worldwide. An intelligent platform for proactive operations with predictive intelligence capability is needed to manage the dynamics and complexity of freight transportation and provide mitigation strategies for the different risks and disruptions factors. In this research, a hybrid model will be developed to improve the delivery time and the quality of services in the logistics platform under uncertainty. This model integrates the real-time data to jointly optimize matching and pricing strategies considering the risks and uncertainties in the dynamic freight marketplace.

Faculty Supervisor:

Samira Keivanpour;Maha Ben Ali

Student:

Partner:

ShipHaul Logistics Inc.

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Transportation and warehousing

University:

Polytechnique Montréal

Program: