A Model for Systematic Analysis of
Multi-Criteria Decision Problems in Construction
C. KALAIARASAN
Dept. of Civil Engineering, Indian Institute of Technology Madras, Chennai, Tamilnadu, PIN- 600036, India
Abstract:
Selecting the right construction method/equipment is vital for a construction project’s success. Typically in Indian construction, during such selection process, experience and intuition of the decision makers are largely relied upon. In this paper development and testing of a decision analysis model that can aid the decision makers in construction projects in systematic evaluation of construction alternatives, involving multiple decision factors is discussed. The concept of non-structural fuzzy decision support system (NSFDSS) was utilized. Experienced construction professionals from different construction organizations were involved in testing the proposed model in terms of its evaluation methodology, usefulness of the concept utilized to the industry and the user interface provided. Feedback from the participants showed that, NSFDSS with its simplified fuzzy 3-point rating scale and automatic logical consistency check features, is a suitable modeling technique to use in the evaluation of construction alternatives that involves multiple numbers of decision factors.
Keywords: Construction projects; Decision analysis model; Non-structural fuzzy decision support system
1. Introduction
Selecting the right kind of method/equipment is important for the successful delivery of a construction project [10]. Typically in Indian construction, experience and the intuitive thinking of the decision makers are largely dependent upon in such selection process. However, the experiential knowledge is usually not codified properly and is poorly organized in the human memory to use systematically when needed [6]. Further, construction projects, nowadays, are increasingly complex and numerous types of equipment and technologies are available to facilitate the industry in executing various operations involved in a construction project in a speedy, quality and safe way. As a result, evaluating the different types of construction alternatives and selecting the right one for a construction project has become hard to the decision makers [5]. Thus, in order to assist the decision makers in handling the method/equipment selection problems in an efficient way and enhance the quality of decision-making in construction, using some kinds of structured decision analysis methods/tools seems important.
A study of literature pertaining to construction method/equipment selection models revealed that, most of the existing models, 1) limit themselves to use only the quantitative decision factors during the evaluation process (without considering the qualitative/soft factors), and/or 2) not allowing the users to exercise their experience and intuitive judgment during the evaluation of alternatives [10]. Subsequently, discussions were held with construction professionals from 24 different construction organizations. It was observed that, a model that allows the decision makers to utilize both quantitative and qualitative decision factors and that allows the users to use their intuitive knowledge in the decision analysis would be of assistance to the decision makers in Indian construction context.
2. Development of the Model
2.1. Technique
Based on an extensive literature study, two methods namely, analytic hierarchy process (AHP) [9] and the non-structural fuzzy decision support system (NSFDSS) [3] were observed suitable to handle the multi-criteria decision problems in construction in a simple and effective way. The basic working principles of both techniques were found to be similar involving, 1) decomposing the decision problem into different factors that constitutes the problem 2) performing pair-wise comparisons among the factors and 3) synthesizing their priorities. However, in the AHP method the phenomenon of rank reversal, imposed inconsistency due to using the 1-to-9 scoring scale and time consuming in correcting the matrix inputs in case of occurrence of inconsistent comparison matrices, were observed to be the limitations [1; 2;4; 7; 8; 10; 12]. With regards to the NSFDSS, the simplified fuzzy 3-point scoring scale and automatic logical consistency checking features were reported in the literature as advantages over the AHP method. Accordingly, NSFDSS was utilized in the proposed model.
2.2. Evaluation methodology
The evaluation methodology of the proposed decision analysis model (Figure 1)was charted out based on, that was proposed by Tam et al. [11] in analyzing the site layout planning problems. However, the methodology of the present model is more generalized compared to Tam et al.’s, intended to handle different types of decision-making problems in construction, involving multiple decision factors.
Revise Inputs
Information about the alternatives
Information about the decision criteria
Pairwise comparison of decision criteria
Fomulate a consistent output matrix
Priority ordering of decision criteria
Consistent or not?
NO
YES
Assign semantic scores to all criteria
Normalize the semantic scores and calculate the weight of each criteria
St ep 1 St ep 3 St ep 4 St ep 2
Pairwise comparison of alternatives
Consistent or not?
YES NO
Fomulate a consistent output matrix
Priority ordering of alternatives
Assign semantic scores to all alternatives
Calculate Euclidean distance, Hamming distance
Average of both distances to determine the priority vector of each alternative
Select the alternative with the highest priority vector
Revise Inputs
Figure 1. Evaluation Methodology of the Proposed Decision Analysis Model
In the model, the evaluation process is carried out in four different steps. In step-1, the user is required to provide brief details of all the alternatives that are to be evaluated.
In step-2, the user is required to give the list of decision factors (to a maximum of ten numbers) that are intended to be used in the evaluation of alternatives. Following that, pairwise comparison of the decision factors are performed by the user using the fuzzy 3-point rating scale (0 means one decision factor is less important
more important compared to the other). The system, checks the entries as and when entered by the user for any inconsistency and alerts the user to correct them in case of any inconsistencies. Then, after checking the consistency, the system forms a consistent output matrix and does the priority ordering of the decision factors to allow the user to assign the semantic scores. Once the semantic scores are assigned, they are normalized and the final weights of all decision factors are calculated by the system and displayed to the user as shown in Figure 2.
In step-3 (Figure 3), all the alternatives are evaluated with respect to each of the decision factors and the semantic scores (r) are assigned.
Figure 2 Screenshot of Step-2 in the Proposed Model
Figure 3 Screenshot of Step-3 in the Proposed Model
1
uj=
pm i p ij i m i p ij i r w r w / 2 1 1 ) ( ) 1 ( 1
(1)Where, wi=weights of decision factors (from step-2); rij = semantic score of alternatives (from step-3);
p=distance parameter; m = number of decision factors; uj = priority vector (calculated for p=1 and p=2).
2.3. User interface
The proposed decision analysis model was implemented in Microsoft excel (version 2003), since it was observed during discussion with the construction professionals that, they were more comfortable using the Microsoft excel-based system compared to the other IT tools.
3. Testing of the Model
A hypothetical test construction project was developed to sufficient details (basic drawings, bill of quantities and programme schedule) and a typical decision-making problem in construction problems involving multiple decision factors was assumed (selecting a suitable materials handling method/equipment). Ten construction professionals were identified from five different organizations to test the model with regards to its evaluation algorithm, concept utilized and the user interface. During the testing process, first, the participants were informed about the pre-assumed decision problem in the test construction project, handed over the available details of the test construction project and were asked about the various possible solutions to solve that particular problem. Then, out of the different solutions suggested by them, they were requested to select the best appropriate alternative in their perception (intuition). Explanations that justify the selection were recorded. Soon after, they were provided with the proposed decision analysis model and requested to try using it in solving the same decision problem. Feedbacks from the participants about using the model with respect to the evaluation sequence, usefulness of the concept to the industry and the user interface provided, were gathered. Important observations from the analysis of the feedbacks are discussed in the subsequent section.
4. Findings
4.1. Evaluation methodology
All the ten professionals participated in the testing process have commented that, the evaluation methodology of the model is simple to understand and logically sound.
4.2. Usefulness of the concept
With regards to the usefulness of the concept used in the model (NSFDSS) for the decision makers and the industry, it was appreciated by all the participants that, the model,
Allows providing the inputs easily and quickly while performing the pairwise comparison of decision factors (step-2) and alternatives (step-3) because of the simplified fuzzy 3-point scoring scale and the automatic logical consistency checking of the inputs.
Allows utilizing multiple decision factors (both quantitative and qualitative in nature) in the decision analysis and perform the evaluation using a single tool in a common platform.
Allows using the experience and the intuitive thinking of the decision makers during the process of evaluation, however, through a more systematic procedure.
4.3. User interface
All participants expressed their comfort in using the proposed model built in Microsoft excel (2003). However, there were suggestions to implement the model as a stand-alone tool using packages like Microsoft Visual Basic.
5. Limitations of the Model
Restricting the users to involve not more than four alternatives and ten decision factors in the model.
Lack of provision for group-decision making. Often in construction, decision-making appears to be a group process involving participants from different positions. Hence, a group decision-making feature allowing engaging more than one user at a time in the decision analysis and utilizing all their subjective inputs in the decision-making was suggested by the participants during the testing of the model.
With little modifications in the outline and evaluation methodology, however, these issues can be addressed to suit the users’ requirements.
6. Conclusions
Decision analysis tools that handles multiple decision factors, saves the inputs entry time of the users and allows the decision makers to exercise their past experience and intuition in the evaluation process, were found more attractive in the Indian construction context. Non-structural fuzzy decision support system, with its simplified fuzzy 3-point rating scale and automatic checking of consistency in the pairwise matrices was found to be a suitable modeling technique to use in the analysis of alternatives involving numerous decision factors. However, using the semantic operators in the technique during evaluating the decision factors and the alternatives, was found to cause some confusion to the users in their initial attempt.
References
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