A robust goal programming model for the capital budgeting problem
Fatemeh Ghasemi Bojd and
Hamidreza Koosha
Journal of the Operational Research Society, 2018, vol. 69, issue 7, 1105-1113
Abstract:
Considering financial limitations, organizations should choose among various investment opportunities. Wrong decision making for selecting projects may lead to waste of resources as well as opportunity cost and negative long term consequences. Thus, capital budgeting problem can be solved to make proper decisions. Some of the main parameters of these problems, e.g., cash flows, are not deterministic. In addition, budget constraints of the capital budgeting problem are soft, i.e., they can be violated. In this paper, we propose a new model for the capital budgeting problem which can deal with uncertainty and benefits from soft constraints so that it can still provide a feasible solution. Goal programming is used to increase model flexibility and robust optimisation is applied to deal with uncertainty. The model is examined with different numerical illustrations. Finally, results are analysed and advantages of the new model are discussed. Results are promising and the approach is highly tractable and easy to implement.
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2017.1389673 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:69:y:2018:i:7:p:1105-1113
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20
DOI: 10.1080/01605682.2017.1389673
Access Statistics for this article
Journal of the Operational Research Society is currently edited by Tom Archibald
More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().