A Framework to Evaluate Project Complexity Using the Fuzzy TOPSIS Method

Abstract

This work aims to help managers anticipate, detect, and keep under control complex situations before facing negative consequences. This article explores complexity modeling theory and develops a framework and associated score sheet to measure project complexity. A framework comprising ninety factors is presented and divided into seven categories: stakeholders, project team, project governance, product, project characteristics, resources, and environment. For the project complexity assessment grid, the project manager prioritizes and weighs its factors using linguistic variables. The score sheet is customizable in its handling of the factors and their weights. A critical state of the art on multi-criteria methodologies is presented, as well as reasons for using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. This method provides early-warning signs with the possibility of comparing multiple projects. It also enables one to measure and prioritize areas and domains where complexity may have the highest impact. Practical applications on three projects within an automotive manufacturer highlight the benefits of such an approach for managers. Project managers could use both a project complexity rating system and a measure of risk criticality to decide on the level of proactive actions needed. This research work differs from traditional approaches that have linked proactive actions to risk criticality but not project complexity.

Experiencing Stress among Different Professional Groups in the Context of Their Age

“…C.J. Park et al used the FUZZY TOPSIS method to examine the factors responsible for employee retention in the construction sector [ 48 ] (Rank Determinants of Employee Retention in Construction Companies), while H. Jaber et al investigated the A Framework to Evaluate Project Complexity [ 49 ]. Jellali et al [ 50 ] carried out a sustainable configuration of the Tunisian olive oil supply chain using a FUZZY TOPSIS-based approach (Sustainable Configuration of the Tunisian Olive Oil Supply Chain Using a FUZZY TOPSIS-Based Approach).…”

“…The inspiration for the choice of the FUZZY TOPSIS method was the repeated observations of researchers, which allowed them to notice that, in the surveys conducted, people participating in the study often have problems with giving clearly formulated, unambiguous answers. Introducing ambiguity in the response leads to a situation in which a choice must be made among at least two alternatives [ 49 ]. Therefore, fuzzy logic was introduced, combining numerical and symbolic modeling.…”

Green Fuzzy Tourist Trip Design Problem

“…Fuzzy models and solution techniques have been developed in the TTDP. e early work considers a fuzzy routing problem for sightseeing [38]. [9] developed a Fuzzy Greedy Randomized Adaptive Search Procedures (GRASP).…”

Multi-Objective Fuzzy Tourist Trip Design Problem with Heterogeneous Preferences and Sustainable Itineraries

“…On the contrary, if a distribution can be generated that represents the behaviour of the data, then the stochastic approach could be used [75]. Nevertheless, tourists in general almost always incompletely express the information necessary for planning with the available knowledge, and, as such, their preferences and their decision criteria are also expressed in an imprecise and vague way, instead using natural language which take on the role of linguistic variables [39]. Given these circumstances, it is necessary to address these optimisation problems with ambiguous and imprecise parameters and components expressed with linguistic variables, with fuzzy approaches, and generate solutions of the same nature without using exact calculations [76].…”

“…Nevertheless, information is not the only element which is vague and ambiguous; the criteria and restrictions defined by the decision-makers are also vague and ambiguous, and in many cases the natural way to express them is with the use of a linguistic term [24,37,38]. Linguistic variables describe imprecise and subjective information from human judgment such as preference ratings [39,40]. This type of component of planning problems can be approached as fuzzy components [41,42] because it does not allow the construction of distributions under stochastic approaches [43].…”