"PROJECT EVALUATION; A UNIFIED APPROACH FOR THE ANALYSIS OF CAPITAL INVESTMENTS by J. Morley English, Macmillan, New York, 1984, xiv + 401 pages. List $29.95.." THE ENGINEERING ECONOMIST, 31(1), pp. 54–55

Journal Article Considerations in Selecting a Mobile Master Medication Cart Get access Edward Superstine, Ph.D., Edward Superstine, Ph.D. Visiting Associate Professor Department of Pharmacy Practice, College of Pharmacy, The University of Utah, Salt Lake City. Search for other works by this author on: Oxford Academic Google Scholar Arthur G. Lipman, Pharm.D., Arthur G. Lipman, Pharm.D. Professor and Chairman Department of Pharmacy Practice, College of Pharmacy, The University of Utah, Salt Lake City. Search for other works by this author on: Oxford Academic Google Scholar Jan N. Bair, Ph.D., Jan N. Bair, Ph.D. Associate Professor Department of Pharmacy Practice, College of Pharmacy, The University of Utah, and Director of Pharmacy Services, University of Utah Hospital. Search for other works by this author on: Oxford Academic Google Scholar Sanford Baum, Ph.D. Sanford Baum, Ph.D. Professor Department of Mechanical and Industrial Engineering, and Director, Master of Engineering Administration Program, University of Utah. Search for other works by this author on: Oxford Academic Google Scholar American Journal of Hospital Pharmacy, Volume 40, Issue 2, 1 February 1983, Pages 293–297, https://doi.org/10.1093/ajhp/40.2.293 Published: 01 February 1983

"Review of: “PRINCIPLES OF ENGINEERING ECONOMICS” by E. L. Grant, W. G. Ireson and R. S. Leavenworth, New York: John Wiley & Sons, 7th Ed., 1982, x + 687 pp., list $24.95.." The Engineering Economist, 29(1), pp. 75–76

The use of the “better than most” (BTM) approach to solve mathematical programming problems was described in an earlier paper. The essence of the BTM approach is that it allows the analyst to formulate problems more realistically because he is not constrained by the necessity of formulating the problem in a way that will lead to an “optimal” solution. In the BTM approach a fixed number of solutions are generated in a random fashion, checked for feasibility, and ranked according to the decision maker’s criterion functions. The feasible random solution found to be most preferred in terms of these functions is “the” BTM solution. Although the earlier paper illustrated the BTM approach in terms of a MCDM decision problem with binary decision variables and linear criterion functions, it was argued that the BTM approach might be applicable to much more sophisticated formulations. The present paper explores such a formulation.

A programmatic approach to solving decision problems by the use of ‘better than most’ (BTM) solutions is introduced. The BTM approach is contrasted to approaches that seek a solution whose outcome would be the best of all outcomes for feasible solutions, again according to criteria taken to represent the decision maker's objectives. This BTM approach is illustrated in terms of a decision problem in binary decision variables. This is followed by a discussion of (1) some of the probabilistic aspects of BTM solutions, including calculational experience, (2) a central BTM problem, akin to the problem of finding optimal methods in the more traditional approaches, (3) some ad hoc methods for overcoming this and other problems associated with the BTM approach, and (4) similarities and differences between the BTM approach and other approaches.

Capital budgeting can be described as the problem of allocating scarce capital among a number of investment opportunities in such a manner that the outcome most preferred by a decision maker will result. When a single, mathematically explicit criterion is assumed, mathematical programming techniques can be applied. However, a single criterion, such as maximizing the return on investment or minimizing the risk of losing a sizable fraction of the original investment, is not appropriate for a significant number of real-world decision makers for whom two or more criteria, e.g., a judicious combination of return on investment and risk, are important. It has been argued [1] that it is usually not possible to obtain an explicit utility function for the decision maker and, consequently, that it is usually not possible to apply conventional (optimizing) mathematical programming techniques to find the most preferred outcome.

This paper describes research on the reformulation of the methods of engineering economics to include the effects of inflation. Starting with a rather common and rational approximation of inflation effects, the paper shows that there are actually two rates of return. The paper also shows that fundamental techniques of engineering economics can be readily adapted to this redefinition and that recognition of the two rates can lead to significantly improved sensitivity analyses.

A number of modern economists have accepted alternatives to the basic hypothesis of neo-classical economics that the overriding objective of any firm is to maximize the owner's (shareholder's) welfare. However, with one or two outstanding exceptions, these alternatives have been ignored by managerial scientists, whose economics related models continue to be formulated in terms of the neo-classical hypothesis. This paper treats the implications of those alternatives which deny that corporate management is trying to optimize any single goal—let alone shareholder welfare. More specifically, this paper discusses management science models involving the allocation of scarce resources to satisfy several goals. The resulting multi-goal “optimization” problem is identified as a vector “maximization” problem and is formulated in terms of the efficient point concept.