JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Effect of Continuity Rate on Multistage Logistic Network Optimization under Disruption Risk
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Effect of Continuity Rate on Multistage Logistic Network Optimization under Disruption Risk
Rusman, Muhammad; Shimizu, Yoshiaki;
  PDF(new window)
 Abstract
Modern companies have been facing devastating impacts from unexpected events such as demand uncertainties, natural disasters, and terrorist attacks due to the increasing global supply chain complexity. This paper proposes a multi stage logistic network model under disruption risk. To formulate the problem practically, we consider the effect of continuity rate, which is defined as a percentage of ability of the facility to provide backup allocation to customers in the abnormal situation and affect the investments and operational costs. Then we vary the fixed charge for opening facilities and the operational cost according to the continuity rate. The operational level of the company decreases below the normal condition when disruption occurs. The backup source after the disrup-tion is recovered not only as soon as possible, but also as much as possible. This is a concept of the business continuity plan to reduce the recovery time objective such a continuity rate will affect the investments and op-erational costs. Through numerical experiments, we have shown the proposed idea is capable of designing a resilient logistic network available for business continuity management/plan.
 Keywords
Disruption Risk;Mixed-Integer Programming;Resilient Logistic Network;Continuity Rate;
 Language
English
 Cited by
 References
1.
Berman, O., Krass, D., and Menezes, M. B. C. (2007), Facility reliability issues in network p-median problems: strategic centralization and co-location effects, Operations Research, 55(2), 332-350. crossref(new window)

2.
Chopra, S. and Sodhi, M. S. (2004), Managing risk to avoid supply-chain breakdown, MIT Sloan Management Review, 46(1), 53-61.

3.
Cui, T., Ouyang, Y., and Shen, Z. J. M. (2010), Reliable facility location design under the risk of disruptions, Operations Research, 58(4-Part-1), 998-1011. crossref(new window)

4.
Kleindorfer, P. R. and Saad, G. H. (2005), Managing disruption risks in supply chain, Production and Operations Management, 14(1), 53-58.

5.
Klibi, W., Martel, A., and Guitouni, A. (2010), The design of robust value-creating supply chain net-works: a critical review, European Journal of Operational Research, 203(2), 283-293. crossref(new window)

6.
Lim, M., Daskin, M. S., Bassamboo, A., and Chopra, S. (2009), A facility reliability problem: formulation, properties, and algorithm, Naval Research Logistics, 57(1), 58-70.

7.
Melo, M. T., Nickel, S., and Saldanhada-Gama, F. (2009), Facility location and supply chain management: a review, European Journal of Operational Research, 196(2), 401-412. crossref(new window)

8.
Peng, P., Snyder, L. V., Lim, A., and Liu, Z. (2011), Reliable logistics networks design with facility disruptions, Transportation Research Part B, 45(8), 1190-1211. crossref(new window)

9.
Rusman, M. and Shimizu, Y. (2011), Comparison of multistage logistic network design as critical infrastructure under disruption risk, Proceedings of the 54th Japan Joint Automatic Control Conference, Aichi, Japan, 1563-1568.

10.
Rusman, M. and Shimizu, Y. (2012), Morphological analysis for multistage logistic network optimization under disruption risk, Journal of Japan Industrial Management Association, 63(4), 289-297.

11.
Schmitt, A. J. (2011), Strategies for customer service level protection under multi-echelon supply chain disruption risk, Transportation Research Part B, 45(8), 1266-1283. crossref(new window)

12.
Shimizu, Y., Yamazaki, Y., and Wada, T. (2006), A flexible design for logistic network under uncertain demands through hybrid meta-heuristic strategy, Transactions of the Institute of Systems, Control and Information Engineers, 19(9), 342-349. crossref(new window)

13.
Shimizu, Y., Yamazaki, Y., and Wada, T. (2008), Multimodal logistics network design over planning horizon through a hybrid meta-heuristic approach, Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2(5), 915-925. crossref(new window)

14.
Shimizu, Y., Fushimi, H., and Wada, T. (2011), Robust logistics network modeling and design against uncertainties, Journal of Advanced Mechanical Design, Systems, and Manufacturing, 5(2), 103-114. crossref(new window)

15.
Shimizu, Y. and Rusman, M. (2012), A hybrid approach for huge multi-stage logistic network optimization under disruption risk, Journal of Chemical Engineering of Japan, 45(8), 597-603. crossref(new window)

16.
Snyder, L.V. and Daskin, M. S. (2005), Reliability models for facility location: the expected failure cost case, Transportation Science, 39(3), 400-416. crossref(new window)

17.
Tang, C. S. (2006), Perspectives in supply chain risk management, International Journal of Production Economics, 103(2), 451-488. crossref(new window)

18.
Tang, O. and Nurmaya Musa, S. (2011), Identifying risk issues and research advancements in supply chain risk management, International Journal of Production Economics, 133(1), 25-34. crossref(new window)

19.
Tomlin, B. (2006), On the value of mitigation and contingency strategies for managing supply chain disruption risks, Management Science, 52(5), 639-657. crossref(new window)

20.
Yu, H., Zeng, A. Z., and Zao, L. (2009), Single or dual sourcing: decision-making in the presence of supply chain disruption risks, Omega, 37(4), 788-800. crossref(new window)