Enabling congestion avoidance and reduction in the Michigan-Ohio transportation network to improve supply chain efficiency : freight ATIS.

Details

• Creators:
  Chinnam, Ratna Babu ; Murat, Alper ; Ulferts, Gregory
• Corporate Creators:
  Michigan Ohio University Transportation Center
• Corporate Contributors:
  Wayne State University. Industrial & Manufacturing Engineering ; University of Detroit Mercy. School of Business Administration ; United States. Federal Highway Administration ; Michigan. Dept. of Transportation ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; Michigan State Transportation Commission
• Subject/TRT Terms:
  • Traffic flow
  • Supply chain management
  • Freight service
  • Trucking
  • Traffic flow
  • Supply chain management
  • Freight service
  • Trucking
• Publication/ Report Number:
  MIOH UTC SC2p5 2010-Final
• Resource Type:
  Tech Report
• Geographical Coverage:
  Michigan
• Edition:
  Final report.
• Corporate Publisher:
  Michigan Ohio University Transportation Center
• Abstract:
  We consider dynamic vehicle routing under milk-run tours with time windows in congested ; transportation networks for just-in-time (JIT) production. The arc travel times are considered ; stochastic and time-dependent. The problem integrates TSP with dynamic routing to find a static ; yet robust recurring tour of a given set of sites (i.e., DC and suppliers) while dynamically routing ; the vehicle between site visits. The static tour is motivated by the fact that tours cannot be ; changed on a regular basis (e.g., daily or even weekly) for milk-run pickup and delivery in ; routine JIT production. We allow network arcs to experience recurrent congestion, leading to ; stochastic and time-dependent travel times and requiring dynamic routing decisions. While the ; tour cannot be changed, we dynamically route the vehicle between pair of sites using real-time ; traffic information (e.g. speeds) from Intelligent Transportation System (ITS) sources to improve ; delivery performance. Traffic dynamics for individual arcs are modeled with congestion states ; and state transitions based on time-dependent Markov chains. Based on vehicle location, time of ; day, and current and projected network congestion states, we generate dynamic routing policies ; for every pair of sites using a stochastic dynamic programming formulation. The dynamic ; routing policies are then simulated to find travel time distributions for each pair of sites. These ; time-dependent stochastic travel time distributions are used to build the robust recurring tour ; using an efficient stochastic forward dynamic programming formulation. Results are very ; promising when the algorithms are tested in a simulated network of Southeast-Michigan ; freeways using historical traffic data from the Michigan ITS Center and Traffic.com.
  More +
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/24579/dot_24579_DS1.pdf
• File Type:
  [PDF - 3.51 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:bc6e36aef27e08a008218dc9c6ddc902ff500c60ed6a02cdf825d746b7f81372