Given the complex and dispersed nature of construction projects, which demand collaboration among varied experts, it is crucial to reevaluate project management and execution approaches to enhance their performance. Agile principles offer a path towards heightened responsiveness within tasks and processes, addressing the construction industry’s fast-paced nature and growing client demands for timely project completion. However, adopting new management methodologies, such as Agile, needs a significant cultural shift, enabling the cultivation of multi-skilled and self-managing teams. Projectification, a growing trend in contemporary society where more activities are managed as projects, has gained significant relevance in recent years. This study examines the application of Agile project management (specifically Agile Scrum methodology) during the pre-construction phase of a construction project through the lens of Projectification. As evidenced by the research results, applying the Agile methodology in the pre-construction phase may help enhance flexibility and collaboration, enabling teams to adapt quickly to evolving project requirements and unforeseen challenges. The findings demonstrate that Agile methodologies offer numerous benefits, including enhanced adaptability, flexibility, accountability, transparency, and teamwork. Additionally, there were significant reductions in time and costs, emphasizing the value of Agile practices in construction project management.

The objective of this article is to present the results ob- tained after using a simulation optimization methodology applied to a production line from a secondary manufactur- ing wood processing plant of a well known Chilean mill. For this reason a simulation model constructed in ARENA, was integrated to a genetic algorithms heuristic. The results obtained show that using a different configuration of the plant resources, it is possible to reduce the total average cycle time in 18%. The resource configuration needed to reach this result was obtained evaluating just 1.6% of the total number of possible combinations.

This research deals with the high project completion variability by presenting a new method to decrease such variability in repetitive construction projects. To achieve this, a Fixed Start Method (FSM) —where the starts of each activity with a high level of probabilistic confidence for the planned project duration are fixed— was applied, where the high level of probabilistic confidence obtained was optimized with the use of the metaheuristic algorithm called Simulated Annealing (SA). This procedure evaluated the project completion in a case study based on the coefficient of variance (COV) of the resulting standard deviation, mean, and temperature selected for the SA. Thus, the combined FSM and SA (FSSA) is a new method that decreases the variability of scheduling duration while achieving project completion times. Finally, the proposed FSSA method for construction scheduling was validated in a case study using Discrete Event Simulation.

This research focuses on applying Game Theory to teach the PERT scheduling method to civil engineering students. The work was carried out with students from the Construction Planning and Scheduling course, who were given face-to-face classes on Game Theory applied to teaching the PERT method. This proposal considers that gamification plays a role in user engagement, while Game Theory contributes to data analysis and decision strategies. It began with an initial pre-test evaluation consisting of questions classified by items and evaluation criteria to teach the PERT method using Game Theory, followed by a post-test and a quantitative evaluation that allowed to know the perception and interests of the students. Using Game Theory principles in teaching the PERT method demonstrated improvements in students. In addition, the results presented statistically significant differences in favor of using Game Theory, which would allow it to be proposed as a viable option for teaching PERT and other scheduling methods.

This study presents a novel approach that integrates Discrete Event Simulation (DES) with Design of Experiments (DOE) techniques, framed within a stochastic optimization context and guided by a multi-objective goal programming methodology. The focus is on enhancing the operational efficiency of an emergency department (ED), illustrated through a real-world case study conducted in a Chilean hospital. The methodology employs Response Surface Methodology (RSM) to explore and optimize the impact of four critical resources: physicians, nurses, rooms, and radiologists. The response variable, formulated as a goal programming function, captures the aggregated patient flow time across four representative care tracks. The optimization process proceeded iteratively: early stages relied on linear approximations to identify promising improvement directions, while later phases applied a central composite design to model nonlinear interactions through a quadratic response surface. This progression revealed complex interdependencies among resources, ultimately leading to a local optimum. The proposed approach achieved a 50% reduction in the aggregated objective function and improved individual patient flow times by 7% to 26%. Compared to traditional metaheuristic methods, this simulation–optimization framework offers a computationally efficient alternative, particularly valuable when the simulation model is complex and resource-intensive. These findings underscore the value of combining simulation, RSM, and multi-objective optimization to support data-driven decision-making in complex healthcare settings. The methodology not only improves ED performance but also offers a flexible and scalable framework adaptable to other clinical environments seeking resource optimization and operational improvement.