Dynamic routing under recurrent and non-recurrent congestion using real-time ITS information
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 l...
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| Vydáno v: | Computers & operations research Ročník 39; číslo 2; s. 358 - 373 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Kidlington
Elsevier Ltd
01.02.2012
Elsevier Pergamon Press Inc |
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| ISSN: | 0305-0548, 1873-765X, 0305-0548 |
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| Abstract | 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. |
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| AbstractList | 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. 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. [PUBLICATION ABSTRACT] |
| Author | Güner, Ali R. Murat, Alper Chinnam, Ratna Babu |
| Author_xml | – sequence: 1 givenname: Ali R. surname: Güner fullname: Güner, Ali R. email: arguner@wayne.edu – sequence: 2 givenname: Alper surname: Murat fullname: Murat, Alper email: amurat@wayne.edu – sequence: 3 givenname: Ratna Babu surname: Chinnam fullname: Chinnam, Ratna Babu email: r_chinnam@wayne.edu |
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| Keywords | Dynamic routing ITS Incidents JIT logistics Congestion Transportation Performance evaluation Dispatching problem Recurrence Transportation system Transit time Vehicle routing problem Variability Non stationary process Modeling Routing algorithm Transportation network Freeway Delay time Dynamic programming Dynamic model Intelligent system Delayed time Travel time Non stationary condition Routing Real time Stochastic programming Production management Just in time Logistics |
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| SubjectTerms | Algorithms Applied sciences Computer networks Congestion Delay Dynamic programming Dynamic routing Dynamics Exact sciences and technology Ground, air and sea transportation, marine construction Incidents Inventory control, production control. Distribution ITS JIT logistics Just in time Logistics Mathematical models Mathematical programming Networks Operational research and scientific management Operational research. Management science Route optimization Routing (telecommunications) Studies Traffic congestion Transportation Transportation planning, management and economics |
| Title | Dynamic routing under recurrent and non-recurrent congestion using real-time ITS information |
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