Fuzzy Multicriteria Decision‐Making Model for Time‐Cost‐Risk Trade‐Off Optimization in Construction Projects

Abstract

As is often the case in project scheduling, when the project duration is shortened to decrease total cost, the total float is lost resulting in added critical or nearly critical activities. This, in turn, results in decreasing the probability of completing the project on time and increases the risk of schedule delays. To solve this problem, this research developed a fuzzy multicriteria decision-making (FMCDM) model. The objective of this model is to help project managers improve their decisions regarding time-cost-risk trade-offs (TCRTO) in construction projects. In this model, an optimization algorithm based on fuzzy logic and analytic hierarchy process (AHP) has been used to analyze the time-cost-risk trade-off alternatives and select the best one based on selected criteria. The algorithm was implemented in the MATLAB software and applied to two case studies to verify and validate the presented model. The presented FMCDM model could help produce a more reliable schedule and mitigate the risk of projects running overbudget or behind schedule. Further, this model is a powerful decision-making instrument to help managers reduce uncertainties and improve the accuracy of time-cost-risk trade-offs. The presented FMCDM model employed fuzzy linguistic terms, which provide decision-makers with the opportunity to give their judgments as intervals comparing to fixed value judgments. In conclusion, the presented FMCDM model has high robustness, and it is an attractive alternative to the traditional methods to solve the time-cost-risk trade-off problem in construction.

A measurement model of project control systems success for engineering and construction projects case study: contractor companies in Saudi's petroleum and chemical industry

“…Typically, FAHP breaks a complex problem into three hierarchical structure levels: (1) main goal, (2) main decision elements and (3) subelements. Elements are compared at each level using fuzzy numbers in pairs to assess their relative importance, and mathematical calculations are then used to rank the results (Alzarrad et al , 2019; Leśniak et al , 2018; Nieto-Morote and Ruz-Vila, 2012). In this research, the (16) subelements to measure PCS were classified within (6) main decision elements: Project Planning, Tracking Project Progress, Analyzing Project Performance, Project Forecasting, Corrective Action and Corrective Action Verification.…”

“…Geometric mean used in FAHP for aggregating group decisions is shown in Eqn (3):where true(lijk, mijk, uijktrue) is the fuzzy evaluation of sample members k ( k = 1, 2, …, k ). The priorities of each element have been calculated by applying the geometric means method of Buckley as shown as in Eqns (4) and (5) (Alzarrad et al , 2019; Csutora and Buckley, 2001):where rˇi is the geometric mean of fuzzy comparison values of criterion i and wˇi is the fuzzy weights.Step 3: Calculating Final Weight…”

“…These decision elements were identified through the literature review and are clearly structured in these three levels of FAHP (see Figure 2). The analysis process of FAHP employed in this study is explained in the three following steps (Alzarrad et al , 2019; Leśniak et al , 2018; Plebankiewicz and Kubek, 2016; Taylan et al , 2014). Step 1: Pairwise Comparison…”

“…., k). The priorities of each element have been calculated by applying the geometric means method of Buckley as shown as in Eqns ( 4) and ( 5) (Alzarrad et al, 2019;Csutora and Buckley, 2001):…”

Fuzzy Monte Carlo Simulation to Optimize Resource Planning and Operations

“…However, this can lead to pointless and unreliable results since the results are obtained based on experts' subjective judgments and assumptions [4]. Fuzzy Logic has been used successfully for representing such uncertainties in experts' judgments [5].…”