Assembly project management of a suspended conveyor

The aim of the paper is to present the investigation results of way in which the planning and implementation of large production and service projects was conducted. The studies have been carried out on the basis of selected enterprises implementing large building and assembly projects, construction projects, electrical installation and industrial insulation assembly work. The project team size was the main criterion of the selection for this study. In the analyzed cases, this quantity exceeded 20 team members. Due to the specifics of the research was used multiple case study method. The analysis allowed to note that the surveyed production and service companies don’t use well-known project management methodologies, which could have a direct impact on the effectiveness of the large projects management.

The conceptual and theoretical basis of systems engineering and project management are still in their early days. For example, almost all literature written in recent years in these areas tend to assume that all projects are fundamentally similar and their management consists of a universal set of functions and activities. Systems engineering books usually describe a system's lifecycle (phases) and typical systems engineering process activities along these phases. In practice however, there are salient differences among systems according to size, industry, customer type, contractor's organization, level of technology and other parameters.Based on an exploratory, on‐going field study we suggest in this paper a conceptual, two dimensional framework for the classification of projects and systems and show how this framework can be used to distinguish among systems engineering styles and practices for various types of engineering projects. Projects are classified according to their level of technological uncertainty at the moment of project initiation and their system scope which is their location on a hierarchy of systems and subsystems. The technological uncertainty dimension is divided into: low‐tech, medium‐tech, high‐tech, and, super high‐tech projects. System scope is divided into: assembly, system and array projects. Considerable differences were found among systems engineering methods along each one of the model's axes. Differences were also observed for a simultaneous change in both dimensions.

PurposeThis study aims to optimize and solve the prefabricated project scheduling model, which can simultaneously consider the precedence relationship constraints of activities, resource limitations and supply time constraints of prefabricated components. The scheduling plan obtained by the optimization model can guide the project manager to reasonably organize the execution process of the construction project and rationally allocate resources to improve project performance.Design/methodology/approachThe paper proposes a multi-mode construction scheduling model for prefabricated projects, which enriches the key constraints of the scheduling model and reflects the construction characteristics of the off-site production and installation process of assembly projects. In addition, an improved genetic algorithm was designed to solve the model, and its effectiveness was verified using the PSPLIB dataset, followed by the application of the scheduling model to a practical construction project.FindingsThe proposed algorithm, validated using PSPLIB datasets, demonstrates superior accuracy and efficiency than the methods discussed in the literature referenced in this paper. It achieves better results, with average deviation rates of 0.00, 0.10 and 0.31% for the J12, J18 and J20 datasets, respectively. Applied to a real-world case, the model significantly reduces project durations and enhances resource utilization compared to the traditional CPM method. The algorithm also effectively minimizes delay impacts by optimizing activity sequences and resource allocation, ensuring strong practical applicability.Originality/valueThis research tackles the scheduling problem of prefabricated projects by considering multiple execution modes for each activity and the supply time constraints of components based on actual construction conditions. An improved genetic algorithm is used to obtain the shortest duration scheduling scheme that meets process priority and resource feasibility. Numerical experiments with PSPLIB examples show the algorithm’s competitive quality, aiding project managers in developing effective schedules and resource allocation. The study also highlights the algorithm’s ability to optimize resource use and minimize delays, ensuring efficient handling of unexpected postponements and enhancing project efficiency.

The objective of this experience report was to present the solutions used in the Management Process of the Construction and Assembly project of a Solar Photovoltaic Plant in utilitarian scale, in complex, uncertain and conflicting contexts. As a leading issue, it is intended to demonstrate that new approaches adopted in the project reported here – distinct from the traditional model and practices – allowed the mitigation of the main problems common to construction projects, including divergences between executive project and field activity, delays in delivery and deadlines initially contracted, and consequent additional expenses to the base budget. As a methodology, its theoretical foundation was based on bibliographic survey, addressing aspects such as: project management and life cycle ofthem,integration between the various processes and stakeholders, and Solar Energy on a utilitarian scale, in the scope of construction. This experience report aimed to discuss and demonstrate the approach used in the Guañizuil II A project, located in Argentina, in which agility, flexibility and innovation resulted in the mitigation of delays and additional costs, the achievement of notable records and the development of practical alternatives that can be applied to other construction projects. It is concluded that engineering projects, in their executive phase, need to be aligned with reality in the field, where an adaptive approach can bring positive gains and impacts throughout the life cycle.

This study investigated the factors influencing employee turnover in electromechanical assembly projects, focusing on the application of statistical techniques and machine learning to support people management. The research used a dataset of 7,333 terminations that occurred between January 2023 and July 2025 in a Brazilian company from the sector, covering individual, organizational, and work-related variables. After preprocessing and exploratory data analysis, three classification models were applied: binary logistic regression (with stepwise selection), decision tree, and Random Forest (with grid search calibration). The results showed that factors such as percentage of leave days, tenure, project type, and state of residence had greater relevance for position replacement. The Random Forest model achieved the best predictive performance, with an AUC-ROC of 0.878 and a Gini coefficient of 0.756, while logistic regression stood out for the interpretability of its coefficients. The findings indicate that integrating statistical methods and machine learning contributes to anticipating turnover scenarios, supporting more effective retention and allocation strategies in human resources management for industrial projects.

The expert judgment risks respond method used now in aircraft assembly project need to be improved for its subjectivity and complication. To make it more effective and reliable, we proposed an improved method that integrated neural network with framework reasoning. Firstly, basing on frame theory, we stored experts' experiences about risk response in the expert system repository by the rule that there were mapping relations between solutions and risk factors. Secondly, the neural network was trained to obtain the knowledge in the form of weight. When using, we got the response solution for the risk by reasoning and matching the output of the neural network with the knowledge in repository. It gives attention to the coupling of all risk factors to avoid the loss of information and also can simulate the expert thinking exactly to advance the project management ology level of aircraft assembly. This method had been replied in a certain airplane design project, the application illustrated the feasibility and validity of our method.

Research on collaborative scheduling of multiple projects of prefabricated building based on the niche genetic-raccoon family optimization algorithm

Modern manufacturing systems are characterized by the fact that they integrate various machines and work equipment, industrial handling robots, industrial robots performing technological operations (welding, riveting, painting, etc.), transfer systems (conveyors, conveyor belts, automated guided vehicle -AGV), storage systems and inspection stations. The paper presents the way in which the assembly project management of a suspended conveyor system was carried out. Critical path method was used. It has identified the assembly operations, the way they succeed in the technological process of assembly, the duration of the operations and the resources (labor force) necessary for their realization. This information has been processed through Microsoft Project resulting in critical activities (critical path), project duration, the ratio between the available and necessary resources (in the analyzed case a significant deficit of the machinist resource) and the corresponding costs. In order to solve the problem of resource requirements, two solutions were used: leveling resources and supplementing them in order to reduce the duration of critical activities. Finally, a version of the conveyor assembly project management resulted in a balance between costs and duration.