Capital Rationing Under Risk: A Chance Constrained Approach Using Uniformly Distributed Cash Flows and Available Budgets

Sarper, Hüseyin

doi:10.1080/00137919308903112

Cited by 22 publications

(12 citation statements)

References 28 publications

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“…For illustrative purpose, projects values and resources consumption have been randomly generated from an uniform distribution (as in [35]) in the range [300, 600] and [10,1000], respectively. However, because of the generality of the proposed solution approach other distributions can be used as well.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…We also mention the more recent contribution [23] where a real option evaluation approach is used to determine the uncertain future project cash-flows included within a dynamic scenario-based model. The second approach is based on the use of chance constraints and it has in [10] and [35] some of the major exponents. Such an approach suggests a "proactive" strategy to select a portfolio of projects able to hedge against losses due to future adverse events with a given reliability value.…”

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confidence: 99%

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Capital rationing problems under uncertainty and risk

2011

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Capital rationing is a major problem in managerial decision making. The classical mathematical formulation of the problem relies on a multi-dimensional knapsack model with known input parameters. Since capital rationing is carried out in conditions where uncertainty is the rule rather than the exception, the hypothesis of deterministic data limits the applicability of deterministic formulations in real settings. This paper proposes a stochastic version of the capital rationing problem which explicitly accounts for uncertainty. In particular, a mathematical formulation is provided in the framework of stochastic programming with joint probabilistic constraints and a novel solution approach is proposed. The basic model is also extended to include specific risk measures. Preliminary computational results are presented and discussed.

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Section: Numerical Resultsmentioning

confidence: 99%

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confidence: 99%

Capital rationing problems under uncertainty and risk

2011

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“…The earliest work on the CCP use in capital rationing problems is due to Naslund (1966). An extensive review of stochastic investment planning is presented by Kelle (1987), Sarper (1993), and Gurgur and Luxhoj (2003).…”

Section: Related Literaturementioning

confidence: 99%

Optimal project portfolio selection with carryover constraint

Gurgur

2009

Journal of the Operational Research Society

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In a typical capital rationing problem, a project portfolio is selected to maximize expected return on investment while adhering to the capital budget constraint. Sometimes projects may be delayed and they have to be funded beyond their planned completion time. This type of 'unplanned carryovers' represents a financial obligation to the company. If future years' capital budgets cannot be expanded to cover such obligations, future projects may be cancelled or postponed to fund the unplanned carryover. In this paper, we develop a methodology based on multi-attribute utility theory and chance-constrained programming to optimize portfolio selection subject to the constraints that the selected portfolio does not exceed the available budget and that the carryover of the unspent funds to the next fiscal year does not exceed predetermined limits. We use this technique to select an optimal project portfolio for Lockheed Martin Space Systems' infrastructure investments.

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“…An extensive review of stochastic investment planning is presented by Kelle (1987) and Sarper (1993). When we apply CCP in our capital rationing problem the formulation in Eq.…”

Section: Sl P ( a X~b ) 2 Amentioning

confidence: 99%

“…Although CCP has been extensively used in the literature, most of its applications are based on normally distributed random variables. Recently, Sarper (1993) investigated the use of CCP under uniform distributed variables and it has been shown that when a uniform distribution is approximated into a normal distribution, CCP still produces reasonably good solutions. However, there has been no study to evaluate the performance of CCP ' under asymmetrically distributed random variables.…”

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confidence: 99%