## 1 Introduction
Consequently, determining interrelationships among key CLP factors and strategies and determining the impact of each strategy on CLP is crucial for prioritizing appropriate CLP improvement strategies for a given project. In order to address the mentioned research gaps, the objective of this study is to identify the most effective CLP improvement strategies and quantify their impact on CLP through a decision support model that considers the causal relationships among CLP factors as well as the project characteristics and experts' perspective. The decision support model is developed by integrating two fuzzy multi-criteria decision making (fuzzy MCDM) methods, called fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy techniques for order of preference by similarity to idea solution (fuzzy TOPSIS), with fuzzy cognitive maps (FCM). Improvement strategy selection is a decision-making problem in which various criteria, such as impact on CLP and workers' adaptation, influence the effectiveness of selected strategies. MCDM techniques were specifically designed to mathematically solve this type of problem by providing formalized tools for dealing with multiple criteria [15]. However, the problem of selecting improvement strategies is too complex to be understood quantitatively with numerical values since the criteria of strategy selection (i.e., implementation feasibility) are qualitative. This type of problem can be addressed by using knowledge that is imprecise and qualitative [16]. Fuzzy MCDM incorporates MCDM methods with fuzzy set theory to resemble human reasoning by using approximate information and uncertainty to generate decisions. Hence, this study proposes the combination of two fuzzy MCDM methods--fuzzy AHP and fuzzy TOPSIS -to determine the top-ranked CLP improvement strategies. The combination of fuzzy AHP and fuzzy TOPSIS captures experts' opinions and project characteristics and represents uncertainty and vagueness in the decision-making process of CLP improvement strategies selection without involving cumbersome mathematics [16]. CLP is affected by both quantitative factors (e.g., temperature) and qualitative factors (e.g., team spirit of crew), and the combined effect these factors have on CLP is difficult to determine. Thus, developing a precise model for CLPmight not be feasible [17]. FCM as a knowledge representation and reasoning method originates from the combination of fuzzy logic, cognitive mapping, and neural networks to represent knowledge of systems such as a CLP environment, which are characterized by uncertainty, causality, and complex processes [18; 19; 20; 21]. FCM depicts a CLP system in a form that corresponds closely to the way humans perceive it, through approximate reasoning and taking into account the imprecision and uncertainty of CLP factors [22]. [PERSON] et al. [19] showed the effectiveness of FCM as tool for modeling CLP since FCM considers causal relationships among CLP factors as well as the imprecision and uncertainty of CLP factors. Therefore, the proposed decision support model in this study develops an FCM model for capturing the causal relationships among CLP factors and quantifying the impact of improvement strategies. A main contribution of this study is providing a novel decision support model that integrates the combination of fuzzy AHP and fuzzy TOPSIS, as two fuzzy MCDM methods, with FCM to prioritize CLP improvement strategies based on their impact on CLP, causal relationships among CLP factors, and project characteristics. In addition to representing uncertainty and vagueness, the combination of fuzzy AHP and fuzzy TOPSIS supports FCM modeling by reducing the number of improvement strategies required for FCM modeling with respect to project characteristics and experts' opinions. Thereafter, FCM, by taking into account the imprecision and uncertainty of CLP factors, models the causal relationships among CLP factors in order to quantify the impact of the strategies on CLP improvement.