VEHICLE ROUTING TO MINIMIZE MIXED-FLEET FUEL CONSUMPTION AND ENVIRONMENTAL IMPACT

O. Gusikhin, P. MacNeille, A. Cohn

2010

Abstract

Efficient vehicle routing is critical to the operational profitability and customer satisfaction of vehicle fleet-related businesses, especially in light of increasing, and highly volatile, fuel prices. Growing pressures to reduce negative environmental impacts have suggested that a second metric (vehicle emissions) should also be considered in vehicle routing. Currently, the majority of existing tools use distance as a surrogate for cost. When considering a mixed fleet of multiple vehicle types, with individual vehicles within a fleet type also varying by age and vehicle health, this surrogate becomes significantly less accurate. Furthermore, using distance as a surrogate fails to capture the variations between city and highway driving, which are particularly striking for hybrid vehicles. We thus propose a new approach to the vehicle routing problem, specifically targeting applications with mixed fleets including clean-vehicle technologies, in recognition of the limitations of the existing approaches.

References

  1. Chan, C. C. and Chau, K.T.; Modern Electric Vehicle Technology. Oxford University Press Nov. 15, 2001.
  2. Brundell-Freij, K. and Ericsson, E.; Influence of street characteristics, driver category and car performance on urban driving patterns, Transportation Research Part D 10, 2005, pp. 213-229.
  3. Tavares, G., Zsigraiova, Z., Semiao, V., Carvalho, M.G , Optimization of MSW collection routes for minimum fuel consumption using 3D GIS modeling, Waste Management 29, 2009, pp. 1176-1185.
  4. PSAT Overview, January 2008, http:// www.transportation.anl.gov/pdfs/HV/412.pdf
  5. Rose-Hulman Institute of Technology; Rose-Hulman Institute of Technology Students Design Hybrid Vehicle Powertrain with Simulink and SimDriveLine, 2005; http:// www.rose-hulman.edu/ challengex/ downloads/Rose-Hulman_user_story.pdf
  6. PTV AG, VISSIM - State-of-the-Art Multi-Modal Simulation; http:// www. ptvag. com/ fileadmin / files_ptvag.com/download/traffic/Broschures_Flyer/V ISSIM/VISSIM_Brochure_e_2009_HiRes.pdf, 2009.
  7. R. Wiedemann, U. Reiter, Wiedemann ModelMicroscopic Traffic Simulation The Simulation System Mission, 1991; http:// www.ptvamerica.com/fileadmin/files_ptvamerica.com /library/1970s%20Wiedemann%20VISSIM%20car%2 0following.pdf
  8. Roshan Busawon, M. David Checkel, CALMOB6: A Fuel Economy and Emissions Tool for Transportation Planners, presented to the Best Practices in Urban Transportation Planning session of the 2006 Annual Conference of the Transportation Association of Canada, Charlettetown, Price Edward Island, 2006.
  9. Robert B. Noland, Mohammed A. Quddus, Flow improvements and vehicle emissions: Effects of trip generation and emission control technology, Transportation Research Part D 11, 2006, pp. 1-14.
  10. Roberto Baldacci, Maria Battarra, and Daniele Vigo, Routing a Heterogeneous Fleet of Vehicles in B. Golden, et al. (Eds), The Vehicle Routing Problem: Latest Advances and New Challenges, Springer 2008, pp. 3-27.
  11. Cohn, A. “Composite Variable Modeling for Large-Scale Problems in Transportation and Logistics,” doctoral dissertation in Operations Research, Massachusetts Institute of Technology, advised by Dr. Cynthia Barnhart, April 2002.
  12. Barlatt A.,Cohn A.,FradkinY., Gusikhin O.; Morford C., Using composite variable modeling to achieve realism and tractability in production planning: An example from automotive stamping, IIE Transactions, Volume 41, Issue 5, 2009 , pp. 421 - 436.
  13. Desaulniers, G., J. Desrosiers, and M. Solomon, editors. “A Primer in Column Generation.” Springer US, 2005.
  14. Taillard E. D., A heuristic column generation method for the heterogeneous VRP, Operations Research -- Recherche opérationnelle 33 (1), 1999, pp. 1-14. (Publication CRT-96-03, Centre de recherche sur les transports, Université de Montréal, 1996. Revision 11.2005)
  15. Desrochers, M. and F. Soumis. A Generalization Generalized Permanent Labeling Algorithm for the Shortest Path Problem with Time Windows. INFOR 1988 26:191 - 212.
Download


Paper Citation


in Harvard Style

Gusikhin O., MacNeille P. and Cohn A. (2010). VEHICLE ROUTING TO MINIMIZE MIXED-FLEET FUEL CONSUMPTION AND ENVIRONMENTAL IMPACT . In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 1: IVC & ITS, (ICINCO 2010) ISBN 978-989-8425-00-3, pages 285-291. DOI: 10.5220/0003029002850291


in Bibtex Style

@conference{ivc & its10,
author={O. Gusikhin and P. MacNeille and A. Cohn},
title={VEHICLE ROUTING TO MINIMIZE MIXED-FLEET FUEL CONSUMPTION AND ENVIRONMENTAL IMPACT},
booktitle={Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 1: IVC & ITS, (ICINCO 2010)},
year={2010},
pages={285-291},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003029002850291},
isbn={978-989-8425-00-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 1: IVC & ITS, (ICINCO 2010)
TI - VEHICLE ROUTING TO MINIMIZE MIXED-FLEET FUEL CONSUMPTION AND ENVIRONMENTAL IMPACT
SN - 978-989-8425-00-3
AU - Gusikhin O.
AU - MacNeille P.
AU - Cohn A.
PY - 2010
SP - 285
EP - 291
DO - 10.5220/0003029002850291