United States Department of Transportation
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Enabling congestion avoidance and reduction in the Michigan-Ohio transportation network to improve supply chain efficiency : freight ATIS.
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2010-01-01
[PDF-3.51 MB]
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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 ; ... More +
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Edition:Final report.
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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.
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