Dynamic Truckload Truck Routing and Scheduling in Oversaturated Demand Situations
Abstract
The problem considered is dynamic online truckload routing and scheduling with time windows operating in oversaturated conditions, that is, when the demand for service exceeds the system’s average capacity. The relationship between computation time and the solution quality, measured by system efficiency and response time, is discussed. A dynamic operation process for implementing various algorithms and procedures is described, with the aim of fully utilizing computational resources without wasting available time between successive demand requests. In addition, particularly when demand arrival rates result in oversaturated system conditions, application of intelligent acceptance and filtering decision procedures that consider system status and demand characteristics can result in significant operational benefits. Simulation experiments conducted to evaluate the relative performance of the proposed approaches confirm the existence of significant improvement potential in terms of both system efficiency and response time over previous approaches.
Get full access to this article
View all access and purchase options for this article.
References
1. QUALCOMM Wireless Business Solutions Home Page. http://www.qualcomm.com/qwbs/products/omnitracs.html. Accessed July 29, 2001.
2. Powell W. B., Snow W., and Cheung R. K. Adaptive Labeling Algorithm for the Dynamic Assignment Problem. Transportation Science, Vol. 34, No. 1, 2000, pp. 50–66.
3. Regan A. C., Mahmassani H. S., and Jaillet P. Improving Efficiency of Commercial Vehicle Operations Using Real-Time Information: Potential Uses and Assignment Strategies. In Transportation Research Record 1493, TRB, National Research Council, Washington, D.C., 1995, pp. 188–198.
4. Regan A. C., Mahmassani H. S., and Jaillet P. Dynamic Decision Making for Commercial Fleet Operations Using Real-Time Information. In Transportation Research Record 1537, TRB, National Research Council, Washington, D.C., 1996, pp. 91–97.
5. Regan A. C., Mahmassani H. S., and Jaillet P. Dynamic Dispatching Strategies Under Real-Time Information for Carrier Fleet Management. In Transportation and Traffic Theory: Proceedings of the 13th International Symposium on Transportation and Traffic Theory, Lyon, France, July 24-26, 1996 (Lesort J. B., ed.), Elsevier Science Ltd., Oxford, United Kingdom, 1996, pp. 736–756.
6. Regan A. C., Mahmassani H. S., and Jaillet P. Evaluation of Dynamic Fleet Management Systems: Simulation Framework. In Transportation Research Record 1645, TRB, National Research Council, Washington, D.C., 1998, pp. 176–184.
7. Regan A. C. Real-Time Information for Improved Efficiency Commercial Vehicle Operations. Ph.D. dissertation. University of Texas, Austin, 1997.
8. Mahmassani H. S., Kim Y., and Jaillet P. Local Optimization Approaches to Solve Dynamic Commercial Fleet Management Problems. In Transportation Research Record: Journal of the Transportation Research Board, No. 1733, TRB, National Research Council, Washington, D.C., 2000, pp. 71–79.
9. Ichoua S., Gendreau M., and Potvin J-Y. Diversion Issues in Real-Time Vehicle Dispatching. Transportation Science, Vol. 34, No. 4, 2000, pp. 426–438.
10. Wang X., and Regan A. C. Assignment Models for Local Truckload Trucking Problems with Stochastic Service Times and Time Window Constraints. In Transportation Research Record: Journal of the Transportation Research Board, No. 1771, TRB, National Research Council, Washington, D.C., 2001, pp. 61–68.
11. Hu T.-Y. Evaluation Framework for Dynamic Vehicle Routing Strategies Under Real-Time Information. In Transportation Research Record: Journal of the Transportation Research Board, No. 1774, TRB, National Research Council, Washington, D.C., 2001, pp. 115–122.
12. Kim Y., Jaillet P., and Mahmassani H. S. Two Phase Optimization Approaches to Solve Dynamic Large Fleet Management Problem. Submitted to Transportation Science.
13. Yang J., Jaillet P., and Mahmassani H. S. On-Line Algorithms for Truck Fleet Assignment and Scheduling Under Real-Time Information. In Transportation Research Record: Journal of the Transportation Research Board, No. 1667, TRB, National Research Council, Washington, D.C., 1999, pp. 107–113.
Cite article
Cite article
Cite article
OR
Download to reference manager
If you have citation software installed, you can download article citation data to the citation manager of your choice
Information, rights and permissions
Information
Published In
Article first published: January 2002
Issue published: January 2002
Authors
Metrics and citations
Metrics
Journals metrics
This article was published in Transportation Research Record: Journal of the Transportation Research Board.
VIEW ALL JOURNAL METRICSArticle usage*
Total views and downloads: 14
*Article usage tracking started in December 2016
Altmetric
See the impact this article is making through the number of times it’s been read, and the Altmetric Score.
Learn more about the Altmetric Scores
Articles citing this one
Receive email alerts when this article is cited
Web of Science: 0
Crossref: 10
- Capacitated Location-Allocation-Routing Problem with Time Windows for ...
- Autonomous Mobility-on-Demand Real-Time Gaming Framework
- City Logistics...
- Repeated Auction Games and Learning Dynamics in Electronic Logistics M...
- Comparison of agent-based scheduling to look-ahead heuristics for real...
- Dynamic Decision Making for Less-Than-Truckload Trucking Operations
- Regional fleet design in truckload trucking
- Competitive Performance Assessment of Dynamic Vehicle Routing Technolo...
- Dynamic Truckload Routing, Scheduling, and Load Acceptance for Large F...
- Simulation Model for Real-Time Emergency Vehicle Dispatching and Routi...
Figures and tables
Figures & Media
Tables
View Options
Get access
Access options
If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:
loading institutional access options
Alternatively, view purchase options below:
Purchase 24 hour online access to view and download content.
Access journal content via a DeepDyve subscription or find out more about this option.
