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Annals of Operations Research

, Volume 277, Issue 1, pp 95–118 | Cite as

Reliability of a stochastic intermodal logistics network under spoilage and time considerations

  • Yi-Kuei Lin
  • Cheng-Fu HuangEmail author
  • Yi-Chieh Liao
Reliability and Quality Management in Stochastic Systems
  • 132 Downloads

Abstract

In an intermodal logistics network, there is a carrier along each route whose capacity (number of available containers) is stochastic because the containers may be occupied by other customers. Hence this paper focuses on single commodity in a stochastic intermodal logistics network (SILN) with cargo terminals, transit stations, and routes. In particular, commodities may rot or be spoilt during delivery due to traffic accidents, collisions, natural disasters, weather, etc., and thus the intact commodities may not satisfy the market demand. The arrival time at the cargo terminal should be in the time window which is the interval between the earliest and latest acceptable arrival times. The delivery time depends on the number of containers and type of vehicle. So we propose an algorithm to evaluate the network reliability, the probability that the SILN can successfully deliver sufficient amount of commodity to meet market demand under the time and delivery spoilage constraints. Finally, a practical case of starting motor distribution between Taiwan and China is presented to demonstrate the effectiveness of the proposed algorithm.

Keywords

Delivery spoilage Stochastic intermodal logistics network (SILN) Time windows Network reliability Transit stations 

Notes

Acknowledgements

Funding was provided by Ministry of Science and Technology, Taiwan (Grant No. 104-2218-E-035-018-MY3).

References

  1. Arnold, P., Peeters, D., & Thomas, I. (2004). Modelling a rail/road intermodal transportation system. Transportation Research Part E: Logistics and Transportation Review, 40, 255–270.CrossRefGoogle Scholar
  2. Azad, N., Saharidis, G. K. D., Davoudpour, H., Malekly, H., & Alireza, S. (2013). Strategies for protecting supply chain networks against facility and transportation disruptions: An improved Benders decomposition approach. Annals of Operations Research, 210, 125–163.CrossRefGoogle Scholar
  3. Bai, G., Zuo, M. J., & Tian, Z. (2015). Ordering heuristics for reliability evaluation of multistate networks. IEEE Transactions on Reliability, 64, 1015–1123.CrossRefGoogle Scholar
  4. Bark, P., Bärthel, F., & Storhagen, N. G. (2009). Intermodal transports of non-durable consumer products. In 16th world congress on intelligent transport systems and services (pp. 1–11).Google Scholar
  5. Bijwaard, D. J. A., Van Kleunen, W. A. P., Havinga, P. J. M., Kleiboer, L., & Bijl, M. J. J. (2011). Industry: Using dynamic WSNs in smart logistics for fruits and pharmacy. In SenSys-proceedings on 9th ACM conference on embedded networked sensor systems (pp. 218–231).Google Scholar
  6. Bookbinder, J. H., & Fox, N. S. (1998). Intermodal routing of Canada–Mexico shipments under NAFTA. Transportation Research Part E: Logistics and Transportation Review, 34E, 289–303.CrossRefGoogle Scholar
  7. Chang, P. C., & Lin, Y. K. (2015). Fuzzy-based system reliability of a labor-intensive manufacturing network with repair. International Journal of Production Research, 53, 1980–1995.CrossRefGoogle Scholar
  8. Chang, T. S. (2008). Best routes selection in international intermodal networks. Computers & Operations Research, 35, 2877–2891.CrossRefGoogle Scholar
  9. Chen, S. G. (2012). Fuzzy-scorecard based logistics management in robust SCM. Computers and Industrial Engineering, 62, 740–745.CrossRefGoogle Scholar
  10. Chen, Y. L., & Yang, H. H. (2004). Finding the first k shortest paths in a time-window network. Computers & Operations Research, 31, 499–513.CrossRefGoogle Scholar
  11. Crainic, T. G., & Kim, K. H. (2007). Intermodal transportation (Chapter 8). In: Barnhart, C., & Laporte, G (Eds.), Handbooks in operations research and management science: 14 transportation (pp. 467–537). North-Holland, Amsterdam: Elsevier.Google Scholar
  12. Ghane-Ezabadi, M., & Vergara, H. A. (2016). Decomposition approach for integrated intermodal logistics network design. Transportation Research Part E, 89, 53–69.CrossRefGoogle Scholar
  13. Givoni, M., & Banister, D. (2006). Airline and railway integration. Transport Policy, 13, 386–397.CrossRefGoogle Scholar
  14. Grout, J. R. (1998). Influencing a supplier using delivery windows: Its effect on the variance of flow time and on-time delivery. Decision Sciences, 29, 747–762.CrossRefGoogle Scholar
  15. Hassan, M. R. (2012). Reliability evaluation of stochastic-flow network under quickest path and system capacity constraints. International Journal of Computer Networks, 4, 98–103.Google Scholar
  16. Hsieh, C. C., & Lin, M. H. (2006). Simple algorithms for updating multi-resource allocations in an unreliable flow network. Computers and Industrial Engineering, 50, 120–129.CrossRefGoogle Scholar
  17. Hsu, C. I., Hung, S. F., & Li, H. C. (2007). Vehicle routing problem with time-windows for perishable food delivery. Journal of Food Engineering, 80, 465–475.CrossRefGoogle Scholar
  18. Kolen, A. W. J., Rinnooy Kan, A. H. G., & Trienekens, H. W. J. M. (1987). Vehicle routing with time windows. Operations Research, 35, 266–273.CrossRefGoogle Scholar
  19. Limbourg, S., & Jourquin, B. (2009). Optimal rail-road container terminal locations on the European network. Transportation Research Part E: Logistics and Transportation Review, 45, 551–563.CrossRefGoogle Scholar
  20. Lin, Y. K. (2003). Extend the quickest path problem to the system reliability evaluation for a stochastic-flow network. Computers and Operations Research, 30, 567–575.CrossRefGoogle Scholar
  21. Lin, Y. K., & Huang, C. F. (2016). Reliability evaluation according to a routing scheme for multi-state computer networks under assured accuracy rate. Annals of Operations Research, 244, 221–240.CrossRefGoogle Scholar
  22. Lin, Y. K., & Yeh, C. T. (2010). Optimal carrier selection based on network reliability criterion for stochastic logistics networks. International Journal of Production Economics, 128, 510–517.CrossRefGoogle Scholar
  23. Lin, Y. K., Yeh, C. T., & Huang, C. F. (2016). A simple algorithm to evaluate supply-chain reliability for brittle commodity logistics under production and delivery constraints. Annals of Operations Research, 244, 67–83.CrossRefGoogle Scholar
  24. Low, C., Li, R. K., & Chang, C. M. (2012). Integrated scheduling of production and delivery with time windows. International Journal of Production Research, 51, 897–909.CrossRefGoogle Scholar
  25. Macharis, C., & Bontekoning, Y. M. (2004). Opportunities for OR in intermodal freight transport research: A review. European Journal of Operational Research, 153, 400–416.CrossRefGoogle Scholar
  26. Powell, W.B., & Topaloglu, H. (2003) . Stochastic programming in transportation and logistics. In: Handbooks in operations research and management science vol. 10.Google Scholar
  27. Ritzinger, U., Puchinger, J., & Hartl, R. F. (2016). A survey on dynamic and stochastic vehicle routing problems. International Journal of Production Research, 54, 215–231.CrossRefGoogle Scholar
  28. Rong, A., Akkerman, R., & Grunow, M. (2011). An optimization approach for managing fresh food quality throughout the supply chain. International Journal of Production Economics, 131, 421–429.CrossRefGoogle Scholar
  29. Ruan, J. H., Wang, X. P., Chan, F. T. S., & Shi, Y. (2016). Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering. International Journal of Production Research, 54, 4368–4386.CrossRefGoogle Scholar
  30. Solomon, M. M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35, 254–265.CrossRefGoogle Scholar
  31. Southworth, F., & Peterson, B. E. (2000). Intermodal and international freight network modeling. Transportation Research Part C, 8, 147–166.CrossRefGoogle Scholar
  32. Tsamboulas, D. (2008). Development strategies for intermodal transport in Europe. In: Konings, R., Priemus, H., & Nijkamp, P. (Eds.), The future of intermodal freight transport—operations, design and policy (pp. 271–301). Cheltenham, United Kingdom: Edward Elgar Publishing Ltd.Google Scholar
  33. Tu, J., Huang, M., & Zhao, S. J. (2015). Delivery time contract design under different task structures for outsourcing logistics. Control and Decision, 30, 1815–1819.Google Scholar
  34. Wang, J. J. (2010). The model of time-based logistics and its application. In: Proceedings of the 2nd IEEE international conference on information management and engineering (ICME) (pp. 517–521).Google Scholar
  35. Yarlagadda, R., & Hershey, J. (1991). Fast algorithm for computing the reliability of communication network. International Journal of Electronics, 70, 549–564.CrossRefGoogle Scholar
  36. Yu, C. S., & Li, H. L. (2005). A robust optimization model for stochastic logistic problems. International Journal of Production Economics, 98, 108–109.CrossRefGoogle Scholar
  37. Yu, M., & Nagurney, A. (2013). Competitive food supply chain networks with application to fresh produce. European Journal of Operational Research, 224, 273–282.CrossRefGoogle Scholar
  38. Zhang, J., Lam, H. K., & Chen, B. Y. (2016). On-time delivery probabilistic models for the vehicle routing problem with stochastic demands and time windows. European Journal of Operational Research, 249, 144–154.CrossRefGoogle Scholar
  39. Zuo, M. J., Tian, Z., & Huang, H. Z. (2007). An efficient method for reliability evaluation of multistate networks given all minimal path vectors. IIE Transactions, 39, 811–817.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Industrial ManagementNational Taiwan University of Science and TechnologyTaipeiTaiwan, ROC
  2. 2.Department of Business AdministrationFeng Chia UniversityTaichungTaiwan, ROC

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