An analysis of stochastic shortest path problems
Mathematics of Operations Research
Dynamic shortest paths in acyclic networks with Markovian arc costs
Operations Research
Least possible time paths in stochastic time-varying networks
Computers and Operations Research
Optimal paths in graphs with stochastic or multidimensional weights
Communications of the ACM
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Efficient greedy learning of Gaussian mixture models
Neural Computation
Least Expected Time Paths in Stochastic, Time-Varying Transportation Networks
Transportation Science
Optimal vehicle routing with real-time traffic information
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Expert Systems with Applications: An International Journal
Dynamic shortest path problems: Hybrid routing policies considering network disruptions
Computers and Operations Research
Hi-index | 0.01 |
In just-in-time (JIT) manufacturing environments, on-time delivery is a key performance measure for dispatching and routing of freight vehicles. Growing travel time delays and variability, attributable to increasing congestion in transportation networks, are greatly impacting the efficiency of JIT logistics operations. Recurrent and non-recurrent congestion are the two primary reasons for delivery delay and variability. Over 50% of all travel time delays are attributable to non-recurrent congestion sources such as incidents. Despite its importance, state-of-the-art dynamic routing algorithms assume away the effect of these incidents on travel time. In this study, we propose a stochastic dynamic programming formulation for dynamic routing of vehicles in non-stationary stochastic networks subject to both recurrent and non-recurrent congestion. We also propose alternative models to estimate incident induced delays that can be integrated with dynamic routing algorithms. Proposed dynamic routing models exploit real-time traffic information regarding speeds and incidents from Intelligent Transportation System (ITS) sources to improve delivery performance. Results are very promising when the algorithms are tested in a simulated network of South-East Michigan freeways using historical data from the MITS Center and Traffic.com.