A Fuzzy Decision Support System for Sustainable Construction Project Selection: An Integrated FPP-Fis Model

Abstract

Sustainability has become a key concern for project selection in construction industries. Determining the best sustainable project based on various sustainability attributes is a very complicated decision. Accordingly, developing a suitable decision support framework can be very helpful for decision makers to attain planned business goals and complete projects at the right time with good quality. This research develops a decision support model which helps managers to understand the concept of sustainability in construction project selection and choose the best project using a new integrated Multi-Criteria Decision Making (MCDM) approach under uncertainty by integrating Fuzzy Preference Programming (FPP) as a modification of Fuzzy Analytical Hierarchy Process (FAHP), with Fuzzy Inference System (FIS) as a fuzzy rulebased expert system. In the first phase of the research, fifteen sustainability attributes were selected. In the second phase, the final weight of each attribute was computed by using FPP. In the last phase, the most appropriate project was selected by running the weighted FIS. The results showed that Project 3 (P3) is the best project. Finally, two different evaluative tests were also applied to verify the validity and robustness of the developed model.

Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches

“…After running the model, the results show that they are consistent with the findings of the studies of Fallahpour et al [36] and Hendiani et al [25]. In order to verify the results, after evaluating the consistency ratio for FBWM, the robustness and validity of the developed model was tested using several defuzzification techniques, i.e., Center of Area (COA), Bisector of Area (BOA), Mean of Maximum (MOM), Smallest of Maximum (SOM), and Largest of Maximum (LOM) for FIS [20].…”

“…Step 1: Measuring the performance of aspects and criteria In the second section and as the starting point of second layer, FIS technique is employed to finally rank the projects, imitating Fallahpour et al [36]. In this technique, fuzzy rules are defined through the experts' opinions, such that if "C" and "M" are the criteria and membership functions, respectively, the number of rules becomes M C .…”

“…In this technique, fuzzy rules are defined through the experts' opinions, such that if "C" and "M" are the criteria and membership functions, respectively, the number of rules becomes M C . In this regard, the experts develop the rules by two inputs (if C = 2 and M = 5, the number of rules is 25), in order to prevent a larger number of rules and attributes [36]. For this research, according to the opinions of the relevant experts, the fuzzy rule base is developed based on the following five membership functions: The fuzzy performance numbers can be defuzzified into precise values using Equation (5) (the Graded Mean Integration (GMI) representation method) [37]:…”

A New Decision-Making Approach Based on Fermatean Fuzzy Sets and WASPAS for Green Construction Supplier Evaluation

“…The proposed approach was used in a case study to rank and prioritize the factors related to development and renewal of cycling routes. In another study, Fallahpour et al [62] provided a decision support framework for selecting sustainable construction projects. In their approach, they used an integrated MCDM model to take into account uncertain conditions, and they used fuzzy preference programming (FPP) to modify the fuzzy analytical hierarchy process (FAHP).…”

Life Cycle Sustainability Assessment for Multi-Criteria Decision Making in Bridge Design: A Review

“…However, when it comes to complex decision making problems, such as those related with sustainability, this assumption has become object of strong criticism (Radwan et al, 2016). As a means to model and include the non-probabilistic uncertainties associated to subjective judgements, alternative mathematical logics have been used during the past years to handle linguistic variables and deal with the vagueness of human thinking (Fallahpour et al, 2020;del Caño et al, 2016). Figure 10 shows how different mathematical logics have been incorporated in the sustainability assessment of bridges and its distribution over time.…”