Application of Chance-Constrained Programming to Capital Rationing Problems With Asymmetrically Distributed Cash Flows and Available Budget

Gurgur, Cigdem Z.; Luxhøj, James T.

doi:10.1080/00137910308965064

Cited by 23 publications

(11 citation statements)

References 11 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Related Literaturementioning

confidence: 99%

Optimal project portfolio selection with carryover constraint

Gurgur

2009

Journal of the Operational Research Society

View full text Add to dashboard Cite

show abstract

“…Naslund (1966) produced the earliest work on CCP use in capital-rationing problems. Kelle (1987), Sarper (1993), and Gurgur and Luxhoj (2003) provided an extensive review of stochastic investment planning.…”

Section: Related Literaturementioning

confidence: 99%

Lockheed Martin Space Systems Company Optimizes Infrastructure Project-Portfolio Selection

Gurgur

Morley

2008

Interfaces

Self Cite

View full text Add to dashboard Cite

Lockheed Martin Space Systems Company spends millions of dollars on the maintenance and modernization of its infrastructure each year. Projects often involve investments that cannot be justified purely in terms of net present value or other classical investment-evaluation methods. The options are also restricted because funds that are not spent within a given time frame must be relinquished. Furthermore, some projects may be delayed and the unplanned carryover of their costs moved into the next fiscal year; this causes the postponement or cancellation of other unrelated projects because of in-budget transfers. In this paper, we used multiattribute utility theory and chance-constrained programming to optimize the selection of infrastructure projects. Our solution ensured the selection of high-value projects to maximize the company's performance. These selections were subject to the constraints that a portfolio did not exceed the available budget and that the carryover of the unspent funds to the next fiscal year did not exceed predetermined limits. We used Microsoft Excel to ensure broad accessibility, transparency, user interaction, improved data collection and asset management, and ease-of-use by managers.

show abstract

“…Since the introduction by Charnes and Cooper in 1959 [2], chance constrained optimization has become a useful method to carry out this task. There have been various applications such as production planning [8], finance management [6], power supply operations [15] and reservoir management [10]. Recent applications have been in process systems engineering [13].…”

Section: Introductionmentioning

confidence: 99%

Evaluating integration approaches to robust process optimization and control using chance constrained programming

Klöppel

Geletu

Hoffmann

et al. 2010

2010 IEEE International Symposium on Computer-Aided Control System Design

View full text Add to dashboard Cite

This contribution concerns robust optimization and control of nonlinear steady-state and dynamic processes under uncertain disturbances or parameters. We use chance constrained programming to solve such stochastic optimization and control problems. While steady-state processes always possess time-independent uncertain parameters, in dynamic processes there may be both time-independent and time-dependent uncertain parameters. For problems with time-dependent uncertain parameters these parameters will be discretized in the time horizon and thus the total number of uncertain variables to be treated will be high, which leads to difficulties to compute probabilities and their gradients. A new approach to an efficient computation is developed by using the sparse grid technique with which CPU-time can be significantly reduced. Two application examples from process engineering are taken to demonstrate the effectiveness of our computational framework. The performance of sparse grid integration is verified through numerical experiments in comparison to Monte-Carlo and Quasi-Monte-Carlo techniques

show abstract

Application of Chance-Constrained Programming to Capital Rationing Problems With Asymmetrically Distributed Cash Flows and Available Budget

Cited by 23 publications

References 11 publications

Optimal project portfolio selection with carryover constraint

Optimal project portfolio selection with carryover constraint

Lockheed Martin Space Systems Company Optimizes Infrastructure Project-Portfolio Selection

Evaluating integration approaches to robust process optimization and control using chance constrained programming

Contact Info

Product

Resources

About