2019_INFORMS_Student_Competition
General Motors: Redefining Vehicle Delivery with Autonomous Cars
Current Logistics Network
Every day, General Motors ships out an average of 29,000 vehicles from approximately 70 assembly plants to approximately 20,000 dealers by truck, rail and ship. Geographical groups of dealers are assigned to vehicle distribution centers (VDC).
The traditional approach to designing the logistics network assumes constant manufacturing and demand volumes, and uses an optimization model to minimize total shipping and operating cost. The model determines the lowest cost path from each plant to each dealer, given their locations, existing VDC locations, forecasted demand by demand areas, availability, and the cost and duration of transportation between each node.
Vehicles must also be delivered to dealers in a timely manner. Target delivery times are established based on a dealer’s distance from the plant, and GM must pay a penalty to the dealer if vehicles arrive late.
Impact of the Autonomous Vehicle
Autonomous vehicles (AVs) may dramatically change GM’s finished vehicle delivery and operating processes. For example, an AV could drive itself within the plant yard and VDC, and load itself onto a trailer or rail car, which would significantly reduce handling time. If the plant yard or VDC runs out of space, an AV could temporarily park itself in a nearby parking space.
More importantly, an AV could drive itself to the dealer and reduce the last mile delivery costs. It could also drive itself to nearby hubs to consolidate trailer loads. In particular, an AV driving itself could be treated as a new transportation mode. This would enable more flexible logistics network operations and decrease order fulfillment time and cost.
Competition Problem
The 2019 Competition problem will ask student teams to look into the future, helping GM analyze how autonomous vehicles may change the finished vehicle and delivery operation process. The problem involves designing a vehicle delivery network, including the routing for each plant-dealer-vehicle combination. The objective is to minimize total costs while satisfying all constraints. Students will be provided with GM datasets, as well as a description of key assumptions, defined output formats for the report, clear evaluation criteria, and references.