Selecting Key Smart Building Technologies for UAE Prisons by Integrating Analytical Hierarchy Process (AHP) and Fuzzy-TOPSIS

Procedure of MCDM Process for the Selection of Smart Building Technologies
Step 1. Identification of Smart Building Technologies
Smart building technologies to be used in UAE prisons were identified by semi-structured interviews and questionnaire survey conducted in the prior stage of this study. The interview was accomplished by the participation of fourteen interviewees from the top management working in the UAE prison and construction sectors. Subsequently, 238 participants, including people with good knowledge and experience of prisons and construction, completed a questionnaire survey. As a result, nine smart building technologies were
selected for the pilot survey involving AHP and Fuzzy-TOPSIS. These smart technologies include the lighting system, fire protection system, safety and security system, HVAC, vertical transportation system, information and communication network system, electrical system, building automation system, and hydraulic and drainage system.
Step 2. Identification of Criteria and Sub-Criteria for Smart Building Technologies
The next step of the decision-making process is identifying the criteria and subcriteria for smart building technologies. This was accomplished based on the literature review, interviews, and survey. By collating evidence from these broad sources, a questionnaire was developed, comprising pairwise comparisons of main criteria, subcriteria and smart building technologies using Expert Choice online software and provided
to 14 experts from the top management level working in the construction and prison sector of the UAE. The demographic information of experts revealed that 11 out of 14 were male; half had master’s degrees, whereas five experts had bachelor’s degrees. The 11 experts had more than ten years of experience serving in higher positions in the government and private sectors. The experts were briefed about the study’s objectives and asked to compare the weights of each criterion and sub-criteria on a 9-point Saaty scale. 

Step 3. Deriving the Fuzzy Weights of Criteria Using the AHP
The AHP was employed to derive the weights of all pairwise comparisons from the questionnaire survey, resulting in local and global weights for each criterion and subcriteria. The pairwise comparison result of experts (k) on the relative weight of criteria i over sub-criteria j is given by ij(k).

Step 4. Aggregating the Fuzzy Weights from All Decision-Makers
The weights of different criteria obtained from the AHP process were aggregated to incorporate multiple decision-makers. This involved pooling the decision makers’ opinions on the relative weights of different criteria to get the aggregated fuzzy rating for each alternative.

Step 5. Assessing the suitability of smart technology applications in prisons using the Fuzzy TOPSIS

The fuzzy TOPSIS method was used to assess the suitability of different smart building technologies for applications in different prison scenarios. 

A closeness coefficient was used to rank the order of alternatives: