This work presents a metaheuristic with distributed processing that finds solutions for an optimization model of the university course timetabling problem, where collective communication and point-to-point communication are applied, which are used to generate cooperation between processes. The metaheuristic performs the optimization process with simulated annealing within each solution that each process works. The highlight of this work is presented in the algorithmic design for optimizing the problem by applying cooperative processes. In each iteration of the proposed heuristics, collective communication allows the master process to identify the process with the best solution and point-to-point communication allows the best solution to be sent to the master process so that it can be distributed to all the processes in progress in order to direct the search toward a space of solutions which is close to the best solution found at the time. This search is performed by applying simulated annealing. On the other hand, the mathematical representation of an optimization model present in the literature of the university course timing problem is performed. The results obtained in this work show that the proposed metaheuristics improves the results of other metaheuristics for all test instances. Statistical analysis shows that the proposed metaheuristic presents a different behavior from the other metaheuristics with which it is compared.

This paper presents a methodological scheme to obtain the maximum benefit in occupational health by attending to psychosocial risk factors in a company. This scheme is based on selecting an optimal subset of psychosocial risk factors, considering the departments’ budget in a company as problem constraints. This methodology can be summarized in three steps: First, psychosocial risk factors in the company are identified and weighted, applying several instruments recommended by business regulations. Next, a mathematical model is built using the identified psychosocial risk factors information and the company budget for risk factors attention. This model represents the psychosocial risk optimization problem as a Multidimensional Knapsack Problem (MKP). Finally, since Multidimensional Knapsack Problem is NP-hard, one simulated annealing algorithm is applied to find a near-optimal subset of factors maximizing the psychosocial risk care level. This subset is according to the budgets assigned for each of the company’s departments. The proposed methodology is detailed using a case of study, and thirty instances of the Multidimensional Knapsack Problem are tested, and the results are interpreted under psychosocial risk problems to evaluate the simulated annealing algorithm’s performance (efficiency and efficacy) in solving these optimization problems. This evaluation shows that the proposed methodology can be used for the attention of psychosocial risk factors in real companies’ cases.

This study presents an interdisciplinary methodology for the Resource Allocation Problem (RAP) in a textile manufacturing environment, aligned with the principles of Industry 5.0, which emphasizes the convergence of advanced technologies and the human factor. The core of this research lies in the integration of an Artificial Neural Network (ANN) to predict worker performance, coupled with process automation, to optimize the allocation of workers to machines. A key ethical consideration addressed by this methodology is the focus on psychosocial factors and workplace well-being, recognizing their contribution to sustainability and organizational success. The benefits of this interdisciplinary approach to RAP are manifold. By considering psychosocial and productivity factors in the optimization model, the study moves beyond traditional methods that often prioritize efficiency at the expense of human considerations. This human-centered approach leads to a more sustainable and socially responsible production environment. The methodology contributes to the development of new tools for managing production in ways that are not only efficient but also significantly aligned with worker well-being. By embedding human-centered constraints directly into the optimization process, this study offers a pathway to mitigate potential issues such as worker overload and improve overall worker satisfaction. Ultimately, this research underscores the value of interdisciplinarity in operational research and production management by demonstrating a cohesive methodology that balances efficiency with ethical considerations for the workforce.

The concept of Lean Manufacturing arises within the Toyota Production System (TPS) with the aim of eliminating all activities that do not generate value to enhance and increase productivity. Lean Manufacturing is applied in the West, and the concept transforms into Lean Manufacturing (LM). This concept no longer belongs solely to the automotive sector; all its tools have now migrated to all activities of daily life. An important application, particularly in dealing with human lives, is in the healthcare sector, specifically in hospitals. This gives rise to the concept of Lean Healthcare, which has broad applications, often resulting in process improvements, the elimination of non-value-generating activities, and better resource management.

This work presents an optimization proposal to better the computational convergence time in convection-diffusion and driven-cavity problems by applying a simulated annealing (SA) metaheuristic, obtaining optimal values in relaxation factors (RF) that optimize the problem convergence during its numerical execution. These relaxation factors are tested in numerical models to accelerate their computational convergence in a shorter time. The experimental results show that the relaxation factors obtained by the SA algorithm improve the computational time of the problem convergence regardless of user experience in the initial low-quality RF proposal.