The problem of scheduling resource‐constrained projects to maximize their Net Present Value (NPV) is studied in this paper. The performance of seventeen scheduling heuristics, ten of which are original to this work, is evaluated. Heuristic performance is rated separately on maximization of project NPV and minimization of project duration. Two of the new heuristics are significantly better than the standard heuristics at yielding higher project NPVs. These two heuristics also perform well at reducing project duration. Two heuristics, Minslack and Shortest Processing Time (SPT), that performed well in previous studies are shown to be poor performers at maximizing project NPV.
Business students taking business analytics courses that have significant predictive modeling components, such as marketing research, data mining, forecasting, and advanced financial modeling, are introduced to nonlinear regression using application software that is a "black box" to the students. Thus, although correct models are estimated, students often do not obtain a thorough understanding of the nonlinear estimation process. The exercise presented in this article was created to demonstrate to students the need for nonlinear regression estimation-rather than using linear transformations and Ordinary Least Squares (OLS) and subsequently demonstrate the nonlinear optimization process to estimate nonlinear regression models. Using the spreadsheet exercise, students can see effects on the fit of the model by changing the model parameters as they change the values of the decision variables. After applying the spreadsheet to further exercises, students have expressed a deep understanding of the linear regression software. This exercise is innovative because the active learning exercise requires the students to make the logical connections between the structure of the model, the model's parameters, and the objective function.
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