Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
권호 표지
DOI QR코드

DOI QR Code

How Customer Adaptability Factors Affect Information Systems for Transportation: Vehicle Scheduling Models with Time Flexibility

  • Soonhui Lee (College of Business at Hankuk University of Foreign Studies)
  • Received : 2017.01.03
  • Accepted : 2017.02.27
  • Published : 2017.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

The need for effective information systems that can help in efficient transportation management has become essential. This study presents the potential benefits of developing a decision support system used by a trucking company for routing and scheduling. Our study investigates how customer exibility factors affect the utilization of transportation resources and establishes a vehicle scheduling model for better allocation of transportation resources with a time window. The results show vehicle savings from 25% up to 70% per day given different levels of exibility in delivery times. Increased capacity utilization can be achieved by considering only customer exibility in the model. Our study implies that incorporating customer exibility into the information system can help transportation organizations have the capability to gain control over management to cut costs and improve service.

Keywords

Acknowledgement

This work was supported by Hankuk University of Foreign Studies Research Fund of 2016.

References

  1. Agustina, D., Lee, C. and Piplani, R. (2014). Vehicle scheduling and routing at a cross docking center for food supply chains. International Journal of production economics, 152, 29-41.  https://doi.org/10.1016/j.ijpe.2014.01.002
  2. Arunapuram, S., Mathur, K. and Solow, D. (2003). Vehicle routing and schedueling with full truckloads. Transportation Science, 37(2), 170-182.  https://doi.org/10.1287/trsc.37.2.170.15248
  3. Biskup, D., Herrmann, J. and Gupta, J. N. (2008). Scheduling identical parallel machines to minimize total tardiness. International Journal of Production Economics, 115(1), 134-142.  https://doi.org/10.1016/j.ijpe.2008.04.011
  4. Cha, H. S. (2012). Factors influencing the adoption of location-based smartphone applications: An application of the privacy calculus model. Asia Pacific Journal of Information Systems, 22(4), 7-29. 
  5. Cohon, J. L. (1978). Multiobjective Programming and Planning. New York: Academic Press. 
  6. Cook, T. M. and Russell, R. A. (1978). Simulation and statistical analysis of stochastic vehicle routing with timing constraints. Decision Sciences, 9(4), 673-687.  https://doi.org/10.1111/j.1540-5915.1978.tb00753.x
  7. Gayialis, S. P. and Tatsiopoulos, I. P. (2004). Design of an it-driven decision support system for vehicle routing and scheduling. European Journal of Operational Research, 152(2), 382-398.  https://doi.org/10.1016/S0377-2217(03)00031-6
  8. Golumbic, M. C. (2004). Algorithmic graph theory and perfect graphs (Second ed.), Volume 57 of Annals of Discrete Mathematics. Amsterdam: Elsevier Science B.V. With a foreword by Claude Berge. 
  9. Lee, H. J. and Chang, Y. S. (2009). Intelligent optimal route planning based on context awareness. Asia Pacific Journal of Information Systems, 19(2), 117-137.
  10. Lee, S., Turner, J., Daskin, M. S., Homem-de Mello, T. and Smilowitz, K. (2012). Improving fleet utilization for carriers by interval scheduling. European Journal of Operational Research, 218(1), 261-269.  https://doi.org/10.1016/j.ejor.2011.10.019
  11. Lee, Y., Jeon, S. and Park, Y. (2016). Stochastic traffic congestion evaluation of korean highway traffic information system with structural changes. Asia Pacific Journal of Information Systems, 26(3), 427-448.  https://doi.org/10.14329/apjis.2016.26.3.427
  12. Mensendiek, A., Gupta, J. N. and Herrmann, J. (2015). Scheduling identical parallel machines with fixed delivery dates to minimize total tardiness. European Journal of Operational Research, 243(2), 514-522.  https://doi.org/10.1016/j.ejor.2014.12.002
  13. Mousavi, S. M., Vahdani, B., Tavakkoli-Moghaddam, R. and Hashemi, H. (2014). Location of cross-docking centers and vehicle routing scheduling under uncertainty: A fuzzy possibilistic -stochastic programming model. Applied Mathematical Modelling 38(7), 2249-2264.  https://doi.org/10.1016/j.apm.2013.10.029
  14. Rexing, B., Barnhart, C., Kniker, T., Jarrah, A. and Krishnamurthy N. (2000). Airline fleet assignment with time windows. Transportation Science, 34(1), 1-20.  https://doi.org/10.1287/trsc.34.1.1.12277
  15. Solomon, M. M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35(2), 254-265.  https://doi.org/10.1287/opre.35.2.254
  16. St. John, J., Visinescu, L. L., Guynes, C. S. and Prybutok, V. R. (2016). Information and communication technology offshoring logistics success: A social exchange perspective. Information Systems Management 33(3), 212-230.  https://doi.org/10.1080/10580530.2016.1188542
  17. Tilanus, B. (1997). Information systems in logistics and transportation. Pergamon London. 
  18. Turner, J. P., Lee, S., Daskin, M. S., Homem-de Mello, T. and Smilowitz K. (2012). Dynamic fleet scheduling with uncertain demand and customer flexibility. Computational Management Science, 9(4), 459-481.  https://doi.org/10.1007/s10287-012-0145-3
  19. Williamson, E. A., Harrison, D. K. and Jordan, M. (2004). Information systems development within supply chain management. International Journal of Information Management, 24(5), 375-385. https://doi.org/10.1016/j.ijinfomgt.2004.06.002