Optimally Stationing Army Forces. A Case Study

Optimum utilization of a nation’s armed forces is governed by many complex factors. This optimization includes force structuring, and force relocation in an efficient and cost-effective manner. During the Cold War, the US Army rapidly built up numerous bases which extended to over 4000 locations across the globe to counter the Soviet threat. With the fall of the Soviet Union, that level of threat no longer existed and the tremendous costs for maintaining such a large number of bases both across the globe and on the homeland were no longer valid. Thus there was a need to rationalize the stationing so that hard-earned taxpayers’ money was best utilized without sacrificing US national interests. Closing bases and cutting costs required an effective methodology that could address the myriad complexities associated with making decisions on the stationing of forces and base closures. Dell, Ewing and Tarantino (2008) through their paper titled ‘Optimally Stationing Army Forces’ argue that the US Army has been successful in ensuring this optimization by using an Operations Research process based on integer linear programming called the Optimal Stationing Army Forces (OSAF) program. The main thread of the paper is that the OSAF program was quite successful in predicting the costs savings that would accrue from the Base Realignment and Closure (BRAC) process. According to the authors, the BRAC process would cost more than $13 billion to implement and save $ 7.6 billion over a 20-year period. This essay highlights the main points of the paper as a case study.

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The objective of this paper is clearly encapsulated by the authors when they state that “This paper describes the OSAF with emphasis on its role in helping the Army make its 2005 BRAC recommendation” (Dell, Ewing, & Tarantino, 2008, p. 421). The paper very clearly brings out the practical field use of the OSAF, stating that the program has been effectively used by the US army to rationalize stationing of their forces worldwide.

The OSAF procedure is adequately described as the reader is clearly provided with the spatial, graphical, tabular, mathematical and quantitative data sets used in deriving the results of the OSAF program. The paper describes the locations of the bases, the number of people, the scales of installations and units. Then it goes on to quantify the usage of the installations and the cost per man and thereafter the overall costs. The paper then works out the costs of closures per installation, facilities, movement and then the variables and the actual linear programming methodology.

The relevance of the discussion in the paper is directly relative to the target audience which is supposed to read this paper. Considering the significant use of mathematical parameters and data provided and discussed, this paper is more relevant to OR specialists rather than to lay readers. For a layman to clearly understand the underlying theme, information could have been provided in a more simplified manner.

The underlying assumptions of the paper presume that firstly, the army’s stationing process is analogous to corporate location analysis. Secondly, the qualitative and quantitative metrics of the stationing plan given by the Army are accurate and thirdly, political dynamics are not included in the formulation of the OSAF linear programming. The authors have also assumed, though not overtly evident that the mathematical model does not factor the geostrategic calculus.

The authors clearly state their case for the factual relevance and utility of the OSAF program in optimizing stationing of army forces. The statements are buttressed with providing the actual linear programming methodology for those so inclined to test its mathematical validity.

There is a definite over-emphasis on the mathematical methodology of the OSAF programming and an under-emphasis on the actual gains made. The paper could have benefited had actual figures of savings accrued from the General Accounting Office (GAO) reports on the base closures carried out till date been quoted or comparisons made to buttress the claims of the ‘expected net savings’.

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The paper has completely missed making any comments on the geostrategic impact of the BRAC commission, which has been considerable especially with regard to the closures in the Asia-Pacific region. This portion of the analysis requires to be dovetailed. The environmental costs of the BRAC process for reclaiming and reutilizing the closed bases have come in for active debate within the DoD and the public. This could have formed part of the paper for expansion. The authors claim that “all assumptions and constraints for each scenario are documented and stated explicitly.( Dell, Ewing, & Tarantino, 2008, p.430)”. However, the same does not find sufficient mention in the paper. The section on these ‘assumptions and constraints’ could have been further explained in the paper.

The authors appear to be ‘selling’ the case for the continued use of the OSAF linear program for the stationing of US Army forces. The paper makes a case that since mathematically the programming allows net savings and has resulted in such, it must be continued to be used. The paper makes no reference to the weaknesses of the linear programming methodology or suggests that there could be other OR tools that could better serve the purpose.

There are many positive and negative lessons that can be learned from this paper. The positive aspects of the paper are that there are mathematical tools of proven validity that can be used for solving human dynamics and complex administrative problems. The paper gives a quantifiable estimate of how much savings will accrue when the entire range of BRAC activities has been completed. On the negative aspects, firstly, the authors must have a clear idea of whom are they addressing. This paper tries to reach out to the lay reader and at the same time address the specialists, in the end satisfying neither. Secondly, papers must be written in an impartial manner and while they may have a particular ‘slant’, attempting a ‘hard sell’ by not discussing the ‘cons’ of the proposal robs the credibility of the paper. The prime weakness of the OSAF methodology was proved by the United States GAO(2007) findings that “Compared to the BRAC Commission’s estimates, DOD’s cost estimates to implement BRAC recommendations increased from $21 billion to $31 billion (48 percent), and net annual recurring savings estimates decreased from $4.2 billion to $4 billion (5 percent)(p.1)”. Thus by not pointing out the discrepancies found by other organizations the objectivity of the paper is lost.

Works Cited

Dell, R. F., Ewing, L., & Tarantino, W. J. (2008). Optimally Stationing Army Forces. Interfaces, Vol.38, No.6, 421-435.

The United States General Accounting Office (2007). Military Base Realignments and Closures. 2009. Web.

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StudyCorgi. (2021) 'Optimally Stationing Army Forces. A Case Study'. 12 October.

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