Applied financial non-linear programming models for decision making

“

" There are many advantages of implementing mixed method integrative financial non-linear programming techniques which were improved further (for ease of use) by programming the models into standard spreadsheet software. Thus more examples should be completed beyond the energy creation utility industry so as to share the knowledge across the disciplines. While this study demonstrated complex linear and non-linear programming, there was very little application of statistical theories. Descriptive and inferential statistics can be used to corroborate and predict new estimates (Strang, 2009). For example, history data on coal costs, production volume, and electricity demand-pricing could have been used (only current year figures were provided by the case study company). Hypothesis testing using parametric and non-parametric techniques could be used to estimate the probability of success by comparing with competitors. Likewise there is an abundance of stock market data which could be leveraged to validate the probability of success with these investment portfolios." (p. 394)

”