Dawkins, George S.2022-10-142022-10-1419762690945https://hdl.handle.net/10657/12287The use of mathematical programming(MP) in capital budgeting(CB) industry practice appears to be rare even though the concept has been in the literature for a number of years and seems well-suited to the problem. The hypothesis is proposed in this thesis that perhaps the inclusion of uncertainty in the MP formulation, via chance constrained programming (C2P), would enhance the industry utilization of MP in CB-since the uncertainty data would better fit the sophistication of a MP approach. A review of some previous CB theoretical works and industries' response to these is presented in order to address some of the practical difficulties in the adoption of more sophisticated CB techniques by industry. [...]application/pdfenThis item is protected by copyright but is made available here under a claim of fair use (17 U.S.C. Section 107) for non-profit research and educational purposes. Users of this work assume the responsibility for determining copyright status prior to reusing, publishing, or reproducing this item for purposes other than what is allowed by fair use or other copyright exemptions. Any reuse of this item in excess of fair use or other copyright exemptions requires express permission of the copyright holder.Thesisreformatted digital