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.

In the face of the high variability in the completion of construction projects, the following research is generated. In the literature we can find several proposals to program projects, however, the variability of the activities causes high variability in the completion date of the projects. We hope that the proposed method, by controlling the start of activities, will ensure the completion date of the projects, by fixing the start of every activity with a high level of probabilistic confidence for the planned project duration. The proposed fixed start method (FSM) was tested in two case studies by using discrete event simulation. Project completion duration results were compared with the critical path method (CPM) and the program evaluation and review technique (PERT). Project completion was evaluated in the case studies by the coefficient of variance (COV), mean, and variance. The new method decreased the scheduled duration variability while meeting project completion times.

The construction industry is continuously affected by external and internal agents that modify and define its competitiveness. Changing markets, currency fluctuations, and tremendous competition have made the issue of competitiveness increasingly important. The literature highlights factors that determine the competitiveness of construction firms, including easy-to-measure elements, such as financial, sales, return on investment, etc., while other elements are not as easily measurable, such as customer satisfaction, employee satisfaction, loyalty, and leadership, among many others. Within these factors, Human Resources (HR) has occupied a central place in the last decade, due to the enormous impact that labor has on project execution. This research mainly focuses on the study of HR and its components to reach a deeper understanding of the impact that HR has on the competitiveness of construction companies. After defining the most relevant variables, an interrelationships model was proposed with the Partial Least Square (PLS) technique. These HR variables impact the competitiveness variables: cost, utility, time, quality, Staff retention, and Health & Safety. The PSL analyses met the evaluation criteria using the structural model, confirming its viability. It was found that there are three important variables related to competitiveness: (i) leadership, (ii) innovation, and (iii) competitiveness.

This paper presents the results of a simulation model regarding the productivity and safety working space for construction workers through the floors of a building using swarm intelligence (SI), a field of artificial intelligence (AI), and specifically using artificial bee colony (ABC) optimization. After designing the algorithm used to build the simulation model, the simulation was used in an actual building project by comparing the travel times of workers conventionally transporting material with another group working on routes optimized by the algorithm. Thus, the proposed algorithm provides routes combining shorter travel times and correct distances between workers when transporting materials in a construction site, handling the interference between crews. After validating the algorithm on-site, no statistically significant differences were found between the travel times of workers and the times delivered by the algorithm. Additionally, the travel times using the routes obtained through the algorithm were significantly lower than those made by workers who moved freely without a predefined route. In summary, the algorithm proposed may help construction practitioners maintain safe movements that respond to hazard contexts imposed by any restriction that demands a safety distance.

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.