O.R. Applications
Using formal MS/OR modeling to support disaster recovery planning

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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.

Introduction

A disaster recovery plan (DRP) or disaster recovery strategy (DRS) is a system for internal control and security that focuses on quick restoration of service for critical organizational processes when there are operational failures due to natural or man-made disasters. A DRP aims to minimize potential loss by identifying, prioritizing and safeguarding those organizational assets that are most valuable and that need the most protection. In recent years various factors including government regulations in certain industries (e.g. banking, credit unions), the occurrence of natural disasters (e.g. hurricane Andrew of 1993), and the occurrence of social disasters (e.g. L.A. riots of 1992) have led to increasing interest in the development, testing and maintenance of DRPs. This has resulted in intense customer and vendor interest in the development of tools for disaster recovery (e.g. Sclafane, 1996; Violino, 1996; Tevis, 1996; Carson, 1997). For example in just the NT (operating system) recovery area, it is estimated that customers spent $ 102 million in 1996, with the projected amount being $ 324 million for the year 2000 (Torode, 1997).

Although the field of DRP has attracted the interests of information technology practitioners and vendors for a number of years, very little formal management science type research (e.g. Sarker et al., 1996) has been done in this area. Thus, while there is a large volume of publications on DRP in numerous and various practitioner-oriented journals (e.g. Banking Technology, Network World, Communications News), the subject has received scant attention in the management science (including information systems) research journals. It appears that while much attention has been paid by vendors and organization to the development of hardware and software tools for addressing specific aspects of a disaster (e.g. Dryden, 1997; Bucholz, 1997; Merrill, 1997), little attention has been paid to formal modeling of the DRP process. In the instances where the problem has appeared in the academic journals the focus has been on examining the general features of a disaster recovery plan rather than on decision support. But as noted by Jackson (1997), “Creating a good plan is no easy task”. For DRPs are inherently complex, given the various types of possible interactions that could occur between target resources, solution resources and the environment. Also as observed by Howard (1997), “the danger is not over once the fire has been put out”, so that survival is not just a matter of putting out the fire, and there must be a clear plan for recovery that requires various resources including personnel, hardware and infrastructure.

The dearth of models in the MS/OR literature shows quite clearly that the benefit of MS/OR methodology has not yet been brought to bear on this emerging discipline. Although recently Jenkins (2000) has explored the use of integer programming techniques for selecting disaster scenarios, it would appear that the largest body of relevant quantitative analysis exists primarily in the area of risk assessment (Levitt, 1997; Tamura et al., 2000), or has been reactive in focus, being restricted only to the operational post-disaster phase such as the deployment of crews (Pidd et al., 1996; Sarker et al., 1996). Our research, we believe, will be a welcomed addition to the contribution of MS/OR to disaster recovery research. Recognizing that little modeling of disaster recovery issues has taken place to date in the MS/OR literature, our model represents a forward step in providing what is a fast growing discipline with rigorous models for supporting decision making. Consequently, our contribution is prescriptive rather than descriptive in nature. We introduce the application of MS/OR to DRP at the pre-disaster planning/development phase. MS/OR has potential for contributing to the strategic levels of decision-making in DRP to ensure that plans operate as expected/desired when put into operation. There are opportunities for applying MS/OR at all levels of DRP planning, development, implementation and operation, however we focus on the development phase where MS/OR has been most noticeably absent and can have its greatest impact. In this paper we presents a mixed-integer mathematical decision model for selecting subplans for a DRP. We use randomly generated hypothetical problems to demonstrate the feasibility of MS/OR for decision support in DRP development.

The rest of the paper is organized as follows: Section 2 provides an overview of DRP concepts; Section 3 provides a sample of outstanding DRP research problems; Section 4 provides an OR-based solution approach for addressing one of the research problems described in Section 3; and finally in Section 5, the paper terminates with a brief discussion of the contribution of the paper and possible directions for future research.

Section snippets

Some general axiomatic statements about DRPs

We begin this subsection by listing some general axiomatic statements about DRPs:

  • Several possible disasters can occur.

  • Each disaster has a set of possible debilitating effects.

  • Each effect has the ability to affect a number of business functions.

  • To protect against each effect, a set of (solution) resources are necessary.

  • Two strategies are possible: prevention (risk mitigation) and recovery.

  • Some recovery resources may be substitutable.

  • Substitutable resources do not necessarily possess the same

A sample of outstanding DRP research problems

In this section we present an overview of three DRP problems that could be suitably addressed by mathematical modeling techniques. In the following section we present a mathematical model for addressing the subplan selection problem.

A mathematical model of the subplan selection problem

In this section, we present a mixed-integer model for the subplan selection problem (i.e. selecting a group of subplans from a number of alternatives in order to establish the disaster recovery capability for the organization).

    Notation

    K

    set of business functions

    R1

    set of solution resources that occur in real quantities (e.g., time)

    R2

    set of solution resources that occur in integer quantities (e.g., equipment)

    R

    set of solution resources: R=R1R2

    J

    set of disaster effects

    I

    set of disaster types

    S

    set of recovery

Conclusion

In this paper, we have shown how MS/OR modeling can be used to provide strategic decision support for DRP selection. We have presented a new model for selecting subplans in building a DRP/DRS that addresses the concerns of feasibility, consistency and completeness. The objective of the model is to maximize the total value of the coverage provided by the set of selected subplans subject to several constraints. The model, which contains, real, binary, and general integer variables can be solved

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