Many contexts involve managing a fleet of vehicles to serve customers. This notoriously hard problem is too often handled manually or with very sub-optimal splitting strategies. We manage to drastically cut planning times while maintaining the quality of service and reducing travel times, distances and CO2 emissions by more than 20%.
Shop-floor scheduling problems require to take into account delivery delays, machine availability, operator working hours and skills. The combinatorial nature of the problem makes it hard to tackle by hand, while computed solutions are cost-effective and ensure production constraint satisfaction.
Many other optimization problems arise in industrial contexts: assignment, cutting, loading... A widely-spread response is to use human expertise and "common sense" to get practical solutions. The resulting process is usually very time-consuming, offers no flexibility and yields largely sub-optimal solutions.
Julien holds a master's degree in both mathematics and computer science. He loves to tackle optimization challenges, mixing his taste for mathematical modeling and efficient implementations.