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The decision rules for the various measures of worth under constrained analysis are list below.
Constrained PW Decision Rule: Accept the decision alternative with the highest PW.
Constrained AW Decision Rule: Accept the decision alternative with the highest AW.
Constrained IRR Decision Rule: Accept the decision alternative with the highest IRR.
Constrained SIR Decision Rule: Accept the decision alternative with the highest SIR.
For the example problem, the highest present worth ($1203.21) is associated with accepting projects A and D (rejecting all others). This decision is guaranteed to be optimal (i.e., no feasible combination of projects has a higher PW, AW, IRR, or SIR).
4.8.4 Some Interesting Observations Regarding Constrained Analysis
Several interesting observations can be made regarding the approach, measures of worth, and decisions associated with constrained analysis. Detailed development of these observations is omitted here but may be found in many engineering economic analysis texts [White, et al., 1998].
• The present worth of a decision alternative is the sum of the present worths of the projects contained within the alternative. (From above PWa&d = PWa + PWd).
• The annual worth of a decision alternative is the sum of the annual worths of the projects contained within the alternative.
The internal rate of return of a decision alternative is NOT the sum of internal rates of returns of the projects contained within the alternative. The IRR for the decision alternative must be calculated by the trial and error process of finding the value of i that sets the PW of the decision alternative to zero.
The savings investment ratio of a decision alternative is NOT the sum of the savings investment ratios of the projects contained within the alternative. The SIR for the decision alternative must be calculated from the cash flows of the decision alternative.
A common, but flawed, procedure for selecting the projects to accept from the set of potential projects involves ranking the projects (not decision alternatives) in preferred order based on a measure of worth calculated for the project (e.g., decreasing project PW) and then accepting projects as far down the list as funds allow. While this procedure will select the optimal set under some conditions (e.g., it works well if the initial investments of all projects are small relative to the capital budget limit), it is not guaranteed to select the optimal set under all conditions. The procedure outlined above will select the optimal set under all conditions.
Table 4.10 illustrates that the number of decision alternatives in the collectively exhaustive, mutually exclusive set can grow prohibitively large as the number of potential projects increases. The mitigating factor in this combinatorial growth problem is that in most practical situations a high percentage of the possible decision alternatives are infeasible and do not require evaluation.
yr \ Alt 
A only 
C only 
D only 
A&C 
A&D 
C&D 
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