Advertisement

EOQ Models with Supply Disruptions

  • Zümbül AtanEmail author
  • Lawrence V. Snyder
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 197)

Abstract

Most of the early research in inventory theory concentrates purely on demand uncertainty. However, models which aim to capture the dynamics of real-world systems must also take uncertainties in the supply side into consideration. One type of supply uncertainty that has attracted considerable attention during the past decade is supply disruptions, such as those that arise as a result of customs delays, labor strikes, and natural disasters. Over the past several years, companies have developed many strategies to mitigate the effects of such disruptions. One strategy is to hold more inventory with the additional amount serving as a buffer against disruptions. Since it is among the most basic inventory models, the EOQ model features prominently in the earliest work on disruptions, as well as many subsequent models. This chapter summarizes the studies on EOQ models with supply disruptions.

Keywords

Supply Chain Order Quantity Safety Stock Economic Order Quantity Machine Breakdown 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Z. Atan and L. Snyder. Disruptions in one-warehouse multiple-retailer systems. Working Paper, P.C. Rossin College of Engineering and Applied Sciences, Lehigh University, Bethlehem, PA, 2012.Google Scholar
  2. S. K. Bar-Lev, M. Parlar and D. Perry. Impulse control of a Brownian inventory system with supplier uncertainty. Journal of Stochastic Analysis and Applications, 11:11–27, 1993.Google Scholar
  3. E. Berk and A. Arreola-Risa. Note on “Future supply uncertainty in EOQ models”. Naval Research Logistics, 41:129–132, 1994.Google Scholar
  4. M. S. Daskin, C. R. Coullard and Z. -J. M. Shen. An inventory-location model: Formulation, solution algorithm, and computational results. Annals of Operations Research, 110:83–106, 2002.Google Scholar
  5. G. A. De Croix. Inventory Management for an Assembly System Subject to Supply Disruptions. To appear in Management Science, 2013 Google Scholar
  6. H. Groenevelt, L. Pintelon and A. Seidmann. Production lot sizing with machine breakdowns. Management Science, 38(1):104–123, 1992a.Google Scholar
  7. H. Groenevelt, L. Pintelon and A. Seidmann. Production batching with machine breakdowns and safety stocks. Operations Research, 40(5):959–927, 1992b.Google Scholar
  8. K. B. Hendricks and V. R. Singhal. The effect of supply chain glitches on shareholder wealth. Journal of Operations Management, 21(5), 501–522, 2003.Google Scholar
  9. K. B. Hendricks and V. R. Singhal. Association between supply chain glitches and operating performance. Management Science, 51(5), 695–711, 2005a.Google Scholar
  10. K. B. Hendricks and V. R. Singhal. An empirical analysis of the effect of supply chain disruptions on long-run stock price performance and equity risk of the firm. Production and Operations Management, 14(1), 35–52, 2005b.Google Scholar
  11. M. Parlar and D. Berkin. Future supply uncertainty in EOQ models. Naval Research Logistics, 38:107–121, 1991.Google Scholar
  12. L. Qi, Z.-J. M. Shen and L. Snyder. A continuous review inventory model with disruptions at both supplier and retailer. Production and Operations Management, 18(5):516–532, 2009.Google Scholar
  13. L. Qi, Z.-J. M. Shen and L. Snyder. The effect of supply disruptions on supply chain network design. Transportation Science, 44(25):274–289, 2010.Google Scholar
  14. A. Ross, Y. Rong and L. Snyder. Supply disruptions with time-dependent parameters. Computers and Operations Research, 35(11):3504–3529, 2008.Google Scholar
  15. L. Snyder. A tight approximation for a continuous review inventory model with supplier disruptions. Working Paper, P.C. Rossin College of Engineering and Applied Sciences, Lehigh University, Bethlehem, PA, 2011.Google Scholar
  16. L. Snyder, Z. Atan, P. Peng, Y. Rong, A. J. Schmitt and B. Sinsoysal. OR/MS Models for Supply Chain Disruptions: A Review. Working Paper, P.C. Rossin College of Engineering and Applied Sciences, Lehigh University, Bethlehem, PA, 2012.Google Scholar
  17. H. Weiss and E. Rosenthal. Optimal ordering policies when anticipating a disruption in supply or demand. European Journal of Operational Research, 59(3):370–382, 1992.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Industrial EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Department of Industrial and Systems EngineeringLehigh UniversityBethlehemUSA

Personalised recommendations