Supply chain planning for hurricane response with wind speed information updates

https://doi.org/10.1016/j.cor.2007.09.003Get rights and content

Abstract

This paper introduces a stochastic inventory control problem that is relevant to proactive disaster recovery planning as it relates to preparing for potential hurricane activity. In particular, we consider a manufacturing or retail organization who experiences demand surge for items such as flashlights, batteries, and gas-powered generators, where the magnitude of demand surge is influenced by various characteristics of an ensuing storm. The planning horizon begins during the initial stages of storm development, when a particular tropical depression or disturbance is first observed, and ends when the storm dissipates. Since hurricane characteristics can be predicted with more accuracy during the later stages of the planning horizon relative to the earlier stages, the inventory control problem is formulated as an optimal stopping problem with Bayesian updates, where the updates are based on hurricane predictions. A dynamic programming algorithm is described to solve the problem, and several examples involving real hurricane wind speed data are presented to illustrate the methodology.

Section snippets

Problem description

Many government agencies, not-for-profit organizations, and private corporations assume leading roles in positioning supplies, equipment, and personnel both during and after a major hurricane. These organizations are faced with challenging supply chain and logistics decisions to ensure that supplies, equipment, and personnel are readily available at the right places, at the right times, and in the right quantities. In addition to the complexities associated with supply chain and logistics

Literature review

Disruptions in business continuity caused by natural and man-made disasters demonstrate the need for organizations to develop effective Disaster recovery planning (DRP). For example, immediately following the World Trade Center attacks of September 11, 2001, the United States government's protective measures inhibited the daily operations of many corporations. One such corporation was Ford Motor Company who eventually closed five US plants and reported a 13% decline in vehicle production [1].

Background and notations

The framework of sequential statistical decision problems is ideally suited to model the hurricane supply chain planning problem described in Section 1. Therefore, relevant concepts and terminology related to sequential decision problems based on [39], [40] are first introduced and a model related to hurricane planning is presented later as a special case.

Consider a decision problem in which a DM must specify a one-time decision δ that minimizes some loss function L. The loss function depends

Model formulation

In this section, the hurricane supply stocking problem described in Section 1 is formulated as an optimal stopping problem within the general framework presented in Section 3. We first present and elaborate upon several important assumptions used in developing an appropriate single period loss function, which is a variation of the single product newsboy problem. Then a risk function based on the loss function is derived and incorporated into an optimal stopping problem framework in the form of

Algorithm development

The hurricane supply stocking problem described in Section 1 and represented by Eq. (18) involves determining an order/production quantity Qt and single order/production period t* that minimizes expected costs due to ordering/producing, overstocking, and understocking. We first describe how Qt can be determined and then describe a procedure for obtaining t*.

Empirical study

We now demonstrate the solution methodology presented in Section 5 using real hurricane data from the HURDAT database. The objective is to use historical wind speed data to simulate the evolution of the wind speeds associated with an observed tropical depression, and then apply our solution approach to determine a one-time stocking decision, as well as which period this stocking decision should be given. A sample of N=143 hurricanes comprises our data set spanning the 10-year period 1995–2004.

Extension to ordering disruption

In this section, we describe how the base model presented in Section 5 can be extended such that damages from an observed storm could prevent an ordering/producing decision from being carried out. That is, if the solution to the base model suggests ordering/producing a quantity Qt in period t, then the extended model accounts for possible disruptions, such as damages to the transportation network or inaccessible overtime labor, that would prevent the decision from being implemented. To extend

Conclusion

In response to increased hurricane activity in the United States, particularly the devastating impact of Hurricane Katrina during the year 2005, this paper addresses a disaster recovery planning problem encountered by manufacturing and retail organizations who experience demand surge for various products if an observed storm evolves into a catastrophic hurricane. The proposed model and solution method are also applicable to a closely related disaster relief planning problem relevant to the

Acknowledgments

This research was financially supported by the Title VI program sponsored by the Auburn University Office of Diversity and Multi-cultural Affairs. The authors are also grateful for the consultations of Professor Mark Carpenter, Director of Statistics in the Auburn University, Department of Mathematics and Statistics.

References (41)

  • S. Chopra et al.

    Managing risk to avoid supply chain breakdown

    MIT Sloan Management Review

    (2004)
  • R.K. Iyer et al.

    Disaster recovery planning in an automated manufacturing environment

    IEEE Transactions on Engineering Management

    (1998)
  • P.R. Kleindorfer et al.

    Managing disruption risks in supply chains

    Production and Operations Management

    (2005)
  • Y. Sheffi

    Supply chain management under the threat of international terrorism

    The International Journal of Logistics Management

    (2001)
  • Lodree EJ, Taskin S. An insurance risk management framework for disaster relief and supply chain disruption inventory...
  • G. Yu et al.

    Disruption management: framework, models and applications

    (2004)
  • N. Atlay et al.

    OR/MS research in disaster operations management

    European Journal of Operational Research

    (2006)
  • K.-M.N. Bryson et al.

    Using formal MS/OR modeling to support disaster recovery planning

    Computers and Operations Research

    (2002)
  • N.D. Tura et al.

    Disaster recovery preparedness through continuous process optimization

    Bell Labs Technical Journal

    (2004)
  • Zou N, Yen S-T, Chang G-L, Marquess A, Zezeski M. Simulation-based emergency evacuation system for Ocean City Maryland...
  • Cited by (73)

    • Extending interdiction and median models to identify critical hurricane shelters

      2020, International Journal of Disaster Risk Reduction
      Citation Excerpt :

      There are several modeling approaches for different aspects of this growing branch of logistics planning. Some studies have focused on the inventory planning for disaster relief operations (e.g., Refs. [4–6]) whereas others focused on determining the optimal facility locations along with the quantity of relief items needed (e.g., Refs. [7,8]). There are also studies that focus on determining the optimum strategies to protect critical facilities in case of attacks [9,10].

    • An exact branch-and-price algorithm for scheduling rescue units during disaster response

      2019, European Journal of Operational Research
      Citation Excerpt :

      While mitigation and preparedness refer to the time before a disaster, response phase activities take place in the immediate aftermath of a disaster. The main objective here is the deployment of vital resources to affected people (Fiedrich, Gehbauer, & Rickers, 2000; Lodree & Taskin, 2009). Finally, the recovery stage includes tasks that restore the normal functioning of the community (Liberatore, Ortuño, Tirado, Vitoriano, & Scaparra, 2014; Sahebjamnia, Torabi, & Mansouri, 2015).

    View all citing articles on Scopus
    View full text