Abstract
All organizations are susceptible to a non-zero risk of experiencing out-of-course events, whether natural or man-made, that can lead to internal "disasters" with respect to business operations. Different types of events (e.g. flood, earthquake, fire, theft, computer failure) have implications for the operations of modern organizations. Hence, there is a critical need for planning and recovery strategies for the effects of disasters. Disaster recovery plans (DRPs) aim at ensuring that organizations can function effectively during and following the occurrence of a disaster. As such, they possess cost, performance, reliability, and complexity characteristics that make their development and selection non-trivial. To date, there has been little modeling of disaster recovery issues in the MS/OR literature. We believe that many of the issues involved can benefit from the application of quantitative decision-making techniques. Consequently, in this paper our contribution is prescriptive rather than descriptive in nature and we propose the use of mathematical modeling as a decision support tool for successful development of a DRP. In arriving at a final DRP, decision-makers must consider a number of options or subplans and select a subset of these subplans for inclusion in the final plan. We present a mathematical programming model which helps the decision maker to select among competing subplans, a subset of subplans which maximizes the "value" of the recovery capability of a recovery strategy. We use hypothetical situations to illustrate how this technique can be used to support the planning process.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 679-688 |
| Number of pages | 10 |
| Journal | European Journal of Operational Research |
| Volume | 141 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 16 2002 |
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Keywords
- Disaster recovery planning
- Disaster recovery strategy
- Heuristics
- Subplan selection
ASJC Scopus subject areas
- Modeling and Simulation
- Management Science and Operations Research
- Information Systems and Management
Cite this
Using formal MS/OR modeling to support disaster recovery planning. / Bryson, Kweku Muata; Millar, Harvey; Joseph, Anito; Mobolurin, Ayodele.
In: European Journal of Operational Research, Vol. 141, No. 3, 16.09.2002, p. 679-688.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Using formal MS/OR modeling to support disaster recovery planning
AU - Bryson, Kweku Muata
AU - Millar, Harvey
AU - Joseph, Anito
AU - Mobolurin, Ayodele
PY - 2002/9/16
Y1 - 2002/9/16
N2 - All organizations are susceptible to a non-zero risk of experiencing out-of-course events, whether natural or man-made, that can lead to internal "disasters" with respect to business operations. Different types of events (e.g. flood, earthquake, fire, theft, computer failure) have implications for the operations of modern organizations. Hence, there is a critical need for planning and recovery strategies for the effects of disasters. Disaster recovery plans (DRPs) aim at ensuring that organizations can function effectively during and following the occurrence of a disaster. As such, they possess cost, performance, reliability, and complexity characteristics that make their development and selection non-trivial. To date, there has been little modeling of disaster recovery issues in the MS/OR literature. We believe that many of the issues involved can benefit from the application of quantitative decision-making techniques. Consequently, in this paper our contribution is prescriptive rather than descriptive in nature and we propose the use of mathematical modeling as a decision support tool for successful development of a DRP. In arriving at a final DRP, decision-makers must consider a number of options or subplans and select a subset of these subplans for inclusion in the final plan. We present a mathematical programming model which helps the decision maker to select among competing subplans, a subset of subplans which maximizes the "value" of the recovery capability of a recovery strategy. We use hypothetical situations to illustrate how this technique can be used to support the planning process.
AB - All organizations are susceptible to a non-zero risk of experiencing out-of-course events, whether natural or man-made, that can lead to internal "disasters" with respect to business operations. Different types of events (e.g. flood, earthquake, fire, theft, computer failure) have implications for the operations of modern organizations. Hence, there is a critical need for planning and recovery strategies for the effects of disasters. Disaster recovery plans (DRPs) aim at ensuring that organizations can function effectively during and following the occurrence of a disaster. As such, they possess cost, performance, reliability, and complexity characteristics that make their development and selection non-trivial. To date, there has been little modeling of disaster recovery issues in the MS/OR literature. We believe that many of the issues involved can benefit from the application of quantitative decision-making techniques. Consequently, in this paper our contribution is prescriptive rather than descriptive in nature and we propose the use of mathematical modeling as a decision support tool for successful development of a DRP. In arriving at a final DRP, decision-makers must consider a number of options or subplans and select a subset of these subplans for inclusion in the final plan. We present a mathematical programming model which helps the decision maker to select among competing subplans, a subset of subplans which maximizes the "value" of the recovery capability of a recovery strategy. We use hypothetical situations to illustrate how this technique can be used to support the planning process.
KW - Disaster recovery planning
KW - Disaster recovery strategy
KW - Heuristics
KW - Subplan selection
UR - http://www.scopus.com/inward/record.url?scp=0037120621&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0037120621&partnerID=8YFLogxK
U2 - 10.1016/S0377-2217(01)00275-2
DO - 10.1016/S0377-2217(01)00275-2
M3 - Article
AN - SCOPUS:0037120621
VL - 141
SP - 679
EP - 688
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 3
ER -