Optimizing Military Capital Planning

Brown, Gerald G.; Dell, Robert F.; Newman, Alexandra M.

doi:10.1287/inte.1040.0107

Cited by 42 publications

(21 citation statements)

References 22 publications

(19 reference statements)

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“…is the ratio of 360K to 27798. The results, except for the smallest data set (6,10), show a clear superiority of our branch-and-price algorithm over Cplex.…”

Section: Comparing With Cplexmentioning

confidence: 85%

“…As indicated above, capital budgeting classically is formulated using variants of a multidimensional knapsack problem [e.g., 4,6,28,45]. Specifically, given a set of candidate projects, and given (point estimates of) the In this section, we first set notation and briefly describe a multidimensional knapsack formulation for the deterministic capital-budgeting problem, and then discuss the implications of instead having stochastic budget levels.…”

Section: Optimal Project Portfoliomentioning

confidence: 99%

“…The notation and formulation of the optimal prioritization model tion of projects may represent mutually exclusive alternatives; some projects are implemented in phases, with opportunities for acceleration or delay; colors, beyond yearly availability, can distinguish types of money; both fixed and variable costs play a role in asset replacement models; resources in addition to monetary budgets can limit project selection; and, demand constraints can drive project selection. See, for instance, Brown et al [6] and Hartman [15] for a discussion of these and related issues. surprising that when prioritizing a small set of candidate projects (in this case consisting of only 9 projects) using multiple heuristics (in this case 12 different heuristics; see Table 4.5) that some of those heuristics find the optimal solution.…”

Section: Optimal Project Prioritizationmentioning

confidence: 99%

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Prioritization via Stochastic Optimization

Koç

Morton

2015

Management Science

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We take a novel approach to decision problems involving binary activity-selection decisions competing for scarce resources. The literature approaches such problems by forming an optimal portfolio of activities. However, often practitioners instead form a rank-ordered list of activities and select those with the highest priority. We account for both viewpoints. We rank activities considering both the uncertainty in the problem parameters and the optimal portfolio that will be obtained once the uncertainty is revealed. We use stochastic integer programming as a modeling framework, and we apply our approach to a facility location problem and a multidimensional knapsack problem. We develop two sets of cutting planes to improve computation. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1865 . This paper was accepted by Dimitris Bertsimas, optimization.

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“…is the ratio of 360K to 27798. The results, except for the smallest data set (6,10), show a clear superiority of our branch-and-price algorithm over Cplex.…”

Section: Comparing With Cplexmentioning

confidence: 85%

Section: Optimal Project Portfoliomentioning

confidence: 99%

Section: Optimal Project Prioritizationmentioning

confidence: 99%

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Prioritization via Stochastic Optimization

Koç

Morton

2015

Management Science

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“…This is where the theory of a knapsack problem comes into play. The projects with the highest combined value within the limitations are chosen to produce the greatest value to the consumer (Brown, Dell, & Newman, 2004).…”

Section: A the Knapsack Problemmentioning

confidence: 99%

Optimizing Project Selection at the Department of Public Works, Presidio of Monterey

Whitfield¹

2012

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to ABSTRACT (maximum 200 words)Large organizations suffer from the problem of insufficient resources to complete all project requests submitted throughout the year. With the high volume of project requests received and the limited resources available, picking those projects to fund that return the highest value to the organization can be a daunting task. The purpose of this research is to help management make an optimal decision, and determine whether the introduction of an Excel-based optimization model would benefit an organization in its selection process. This research focuses on the project selection process for the Department of Public Works for the Presidio of Monterey Army instillations in the Monterey area. The results from the current fiscal year's selection process are compared with the results from the optimization model. This demonstrates how analytical tools, specifically an optimization model, can add value to an organization by increasing the number of projects selected. One of the conclusions of this thesis is that for the model to properly reflect the values of the organization, a different weighting system would be needed. Therefore, this research recommends that the optimization model be used, but only as a non-biased opinion on which projects should be selected. SUBJECT TERMS Knapsack, Excel, Optimization Model, DPW, POM, NUMBER OF PAGES 67 PRICE CODE SECURITY CLASSIFICATION OF REPORT Unclassified SECURITY CLASSIFICATION OF THIS PAGE Unclassified SECURITY CLASSIFICATION OF ABSTRACT Unclassified LIMITATION OF ABSTRACT UU

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“…There may be industry rules of thumb or policies on the admissible state of your enterprise (e.g., always have sufficient supply on hand to satisfy the next 90 days of demand). Lacking such guidance, we often plan further into the future than the planning horizon requires because we want to get some realistic representation of the actions up to exactly the end of the planning horizon (and discarding the further future results) (Brown et al 2004).…”

Section: Model Persistencementioning

confidence: 99%

Optimization Tradecraft: Hard-Won Insights from Real-World Decision Support

Brown

Rosenthal

2008

Interfaces

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This paper honors the memory of deceased coauthor Richard E. RosenthalPractitioners of optimization-based decision support advise commerce and government on how to coordinate the activities of millions of people who employ assets worth trillions of dollars. The contributions of these practitioners substantially improve planning methods that benefit our security and welfare. The success of realworld optimization applications depends on a few trade secrets that are essential, but that rarely, if at all, appear in textbooks. This paper summarizes a set of these secrets and uses examples to discuss each."Thou shalt never get such a secret from me but by a parable."Shakespeare, The Two Gentlemen of Verona

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Optimizing Military Capital Planning

Cited by 42 publications

References 22 publications

Prioritization via Stochastic Optimization

Prioritization via Stochastic Optimization

Optimizing Project Selection at the Department of Public Works, Presidio of Monterey

Optimization Tradecraft: Hard-Won Insights from Real-World Decision Support

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