# Systematic Review on the Use of CCPM in Project Management: Empirical Applications and Trends
## 3 Results
The results show that the approach can generate high-quality solutions efficiently and competitively when compared to reference algorithms for small- and medium-sized instances. Another study [44] uses modeling and simulation with support vector machines and empirical data to propose a dynamic buffer monitoring method that combines monitoring and forecasting. The results show a comparison between the costs and duration of projects using the traditional and proposed methods, with the latter reducing costs and total project time. In a case study [43], a construction company conducted an experimental study to assess the practical impact of CCPM on shortening the schedule of a construction project. The results indicated a significant saving of 36 days (approximately 20%) in the project's completion time, with the project completed in 151 days using CCPM compared to the 187 days planned using CPM. In a paper published in 2024 [5], using the Design Science Research method, a framework for implementing blockchain-based CCPM buffer monitoring was presented. The findings highlight blockchain's potential for delivering real-time updates, early risk detection, and enhanced collaboration, ultimately benefiting project stakeholders. The research [46] aimed to propose a model with modeling and simulation support to define the buffer size and fulfill 90% of the project stages in the appropriate time. The results indicate that the model tested with empirical data achieved satisfactory results. Finally, in the last text evaluated [47], the authors integrate the Early Late Start (ELS) technique with the Critical Chain approach to manage resource allocation in construction projects, supported by modeling and simulation. The model shows a reduction in project time from 154 to 120 days and buffers from 47 to 40 days, generating relevant results and validating the model. The next paper proposes a buffer sizing method based on comprehensive resource tightness, combining physical and informational resource constraints. The technique improves buffer accuracy using a design structure matrix (DSM) and rework logic, overcoming the shortcomings of traditional methods (such as C&PM and RSEM). Simulated design tests demonstrated a 9.9% reduction in project duration and an 8.56% reduction in project cost compared to RSEM, with a lower probability of delay (2.7%) [48]. The following study proposes an integrated schedule monitoring model that combines Buffer Management (BM) with Schedule Risk Analysis (SRA), introducing the Activity Criticality Index (CRI) as a trigger for control actions. Simulations demonstrate that the CRI-BMA model is more efficient than traditional methods (such as RBMA), generating fewer interventions and crash costs, with similar time performance and a higher probability of on-time completion. The model also evaluates dynamic sensitivity strategies, showing that adaptive limits increase tracking efficiency [49]. The following research proposes a multi-objective optimization model for multi-project scheduling based on the Critical Chain (CCPM), considering time, cost, quality, and robustness. The model uses weighted utility functions and a cloud-based genetic algorithm to generate optimized solutions. Experiments have demonstrated that the model enhances overall multi-project scheduling performance, exhibiting high robustness and utility (value of 0.893). The main limitation reported is the subjectivity of defining variable weights [50]. The following article proposes a bi-objective model for optimizingtime and cost in resource-constrained multi-project environments, utilizing the Critical Chain method and the NSGA-II genetic algorithm. Several scenarios were simulated with different daily resource limits and release configurations. The model presented Pareto optimal results for time and cost, demonstrating that the release of resources K2 and K3 significantly reduces time and total cost. The analysis revealed that having more available resources leads to shorter lead times, with a corresponding reduction in total costs [51].