2019 Problem

2019 O.R. & Analytics Student Team Competition

PRELIMINARY PROBLEM STATEMENT

Important Note: This is a preliminary statement of the 2019 problem. Updates and changes may be made, with the final problem statement published on October 12.  Be sure to check back on October 12 to see the final problem and to register your team’s intent to compete. Teams must register in order to have access to company data, entry instructions and templates, links to complimentary software, and other competition materials. After October 12, there may be additional updates to the problem statement; all registered teams will be notified of any updates. 

Full Preliminary Problem Statement here

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.

Full Preliminary Problem Statement here

Timeline

  • September 18, 2018: Preliminary problem statement published, without data or software.
  • October 12, 2018: Registration opens. Final problem statement, data and software available.  Teams must register to receive access to the final problem, data and software.  Registered teams will also be invited to attend webinars and other opportunities to ask questions and interact with General Motors staff.
  • December 3, 2018: Deadline to register
  • January 25, 2019: Entry deadline.

Registration Closed

The registration deadline has passed. However, teams interested in registering late should email orastc@mail.informs.org to request permission.  The deadline to submit an entry is January 25, 2019. Teams must register before submitting an entry.  While some opportunities to interact with General Motors staff and Committee experts have already taken place, late-registering teams will have access to recordings of webinars, as well as written questions submitted by teams with answers provided.