Supply chain planning for hurricane response with wind speed information updates
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 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 and single order/production period that minimizes expected costs due to ordering/producing, overstocking, and understocking. We first describe how can be determined and then describe a procedure for obtaining .
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 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 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.
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