Traditionally, corporate decision making has revolved around one objective:
profit maximization (Diekmann, 1981). Indeed, the majority of current selection methods exhibit constraint and an overreliance on the principle of acceptance of the lowest bid (Holt et al., 1993). In contrast, comprehensive evaluation of structural frames should consider a frame's all-round performance potential as the conceptual framework (see Figure 4.2) has shown that there are multiple factors and indicators. However, such evaluation can be difficult to perform, as it is characterized by many decision parameters (economic matters, environmental issues and constructability objectives) and several outcome dimensions (decision alternatives). Fortunately, a technique exists in various forms for such analysis.
Termed Multiple Criteria Decision Making (MCDM), the method refers to the Levene‘s test for equality of variances
Sig. 0.05?
Equal variances not assumed Equal variances assumed
t-test (second row) t-test (first row)
T-test
Sig. (2-tailed) 0.05?
No Yes
Yes No
Difference is significant Difference is not significant
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making of decisions in the face of uncoupled, multiple decision criteria (Mohseli & Martinelli, 1990). It is an operational evaluation and decision support approach that is suitable for addressing complex problems featuring high uncertainty, conflicting objectives, different forms of data and information, multiple interests and perspectives, and the accounting for complex and evolving biophysical and socio-economic systems (Latham, 1993).
MCDM problems are commonly categorized as continuous or discrete, depending on the domain of alternatives. Hwang and Yoon (1981) classified them as: (i) Multiple Objective Decision Making (MODM), with decision variable values to be determined in a continuous or integer domain, of infinite or large number of choices, to best satisfy the decision making constraints, preferences or priorities; and (ii) Multiple Attribute Decision Making (MADM), with discrete, usually limited, number of pre-specified alternatives, requiring inter and intra-attribute comparisons, involving implicit or explicit tradeoffs.
MODM is a problem-solving technique in which the objectives (decision alternatives) are not predetermined, and it is therefore commonly used for design (that is, design the best option in respect of purchaser objectives). Such an approach is unrealistic for structural frame material selection because the cost of accruing 'perfect' (design/evaluation) data would be unacceptably high.
Evaluation should not make informational demands in excess of data commonly available (Diekmann, 1979). Furthermore, MODM is infeasible, as a 'perfect' solution would be practically impossible to find and would be likely to prove inordinately expensive. For these reasons MODM is not considered further in this study.
Conversely, MADM is capable of helping to select (identify) optimum choice in respect of the same objectives where the decision alternatives are predetermined (Holt et al., 1994). Hence it is suitable for the multi criteria nature of this selection problem.
Various MADM techniques have been developed. The frequently used
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techniques are: Analytic hierarchy process (AHP); Analytic network process (ANP); Fuzzy methodology; Ideal point method such as TOPSIS; and Multiple Attribute Value Technique (MAVT).
The AHP is a structured technique for organizing and analyzing complex decisions. Based on mathematics and psychology, it was developed by Saaty in the 1970s and has been extensively studied and refined since then (Saaty, 2008). The ANP is a more general form of the AHP used in multi- criteria decision analysis. AHP structures a decision problem into a hierarchy with a goal, decision criteria, and alternatives, while the ANP structures it as a network (Saaty and Vargas, 2006). Both then use a system of pairwise comparisons to measure the weights of the components of the structure, and finally to rank the alternatives in the decision (Saaty, 1996).
AHP and ANP are not suitable for this study because of too many criteria and attributes. Participants would be confused when they do the paired-wise comparison when there are too many criteria.
Fuzzy sets were introduced by Zadeh (1965) as an extension of the classical notion of sets. Kuzmin (1982) used fuzzy sets in decision-making area.
Thereafter, this idea is used now in many MCDM algorithms to model and solve fuzzy problems.
Fuzzy methodology is not suitable for this study because it causes rank disagreements and produces less consistent results (Buede and Maxwell, 1995) and does not have a unique method to derive importance weights.
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Hwang and Yoon (1981) with further developments by Yoon (1987), and Hwang et al. (1993). TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution and the longest geometric distance from the negative ideal solution. It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for
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each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.
TOPSIS relies on the assumption that the criteria are monotonically increasing or decreasing (Yoon, 1987). This method suffers from the low participation of decision makers because the information of decision maker‘s subjective preferences is not considered (Hwang, et al., 1993). TOPSIS is not suitable for this study because the criteria and indicators are not always monotonically increasing or decreasing, and the decision maker‘s preferences should be considered in the selection of structural material.
The MAVT method allows decisions with multiple attributes to be made by developing a scoring system. It involves three steps: ascertaining importance weights of each attribute; rating an option (RC frame or SS frame) against each attribute; and aggregating the weights with the rates.
MAVT is suitable for this study because it gives more consistent rankings (Ling, 1999) and the scores derived from the MAVT system enable structural frames to be ranked. This method is also adopted because it allows multiple attributes to be considered, and is a quantitative approach and hence rationalised decision-making to identify suitable structural frame can be carried out.