Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.

Purpose: Literature shows that the economics of early failures in maintenance and electric utilities have not been deeply analyzed. This study aims to focus on quantifying the economic impact that early failures in current transformers have on total maintenance costs. The empirical study is conducted in a regional transmission division of an electric utility located in Mexico.Design/methodology/approach: The utility's database was accessed to collect 219 maintenance records. Clustering techniques were used to identify early failures from a bimodal distribution of failures. Confirmatory goodness-of-fit procedures followed the analysis, and finally, direct and opportunity costs were estimated by adapting the cost-of-quality (PAF) Model.Findings: Around 11% of all maintenance activities are triggered by early failures, and they account for up to US$2.2m during the eight-year period under study, which represents 16% of total maintenance costs. Additionally, opportunity costs represent close to two-thirds of the total costs due to early failures. This was obtained after finding and validating a clear-cut border of 3.5 months between early failures and the rest.Originality/value: Failures in energy grids and power transmission can have a large economic impact on the power industry and the society in general. Thus, the maintenance function in equipment such as current transformers is a crucial entry of the budget of any electric utility. This study is one of the very few that highlights the magnitude and importance of direct and opportunity costs derived from early failures.

Assignation-sequencing models have played a critical role in the competitiveness of manufacturing companies since the mid-1950s. The historic and constant evolution of these models, from simple assignations to complex constrained formulations, shows the need for, and increased interest in, more robust models. Thus, this paper presents a model to schedule agents in unrelated parallel machines that includes sequence and agent–machine-dependent setup times (ASUPM), considers an agent-to-machine relationship, and seeks to minimize the maximum makespan criteria. By depicting a more realistic scenario and to address this $\mathcal{NP}$-hard problem, six mixed-integer linear formulations are proposed, and due to its ease of diversification and construct solutions, two multi-start heuristics, composed of seven algorithms, are divided into two categories: Construction of initial solution (designed algorithm) and improvement by intra (tabu search) and inter perturbation (insertions and interchanges). Three different solvers are used and compared, and heuristics algorithms are tested using randomly generated instances. It was found that models that linearizing the objective function by both job completion time and machine time is faster and related to the heuristics, and presents an outstanding level of performance in a small number of instances, since it can find the optimal value for almost every instance, has very good behavior in a medium level of instances, and decent performance in a large number of instances, where the relative deviations tend to increase concerning the small and medium instances. Additionally, two real-world applications of the problem are presented: scheduling in the automotive industry and healthcare.

Evaluating the effectiveness of social support programs in higher education requires moving beyond homogeneous assessments of student performance. This study integrates intersectionality with dynamic efficiency analysis to examine how academic efficiency evolves across diverse student profiles within the Líderes del Mañana full-scholarship program in Mexico. Using a longitudinal dataset of 1,796 students (22,718 student–term observations), we apply a two-stage approach. First, Window Data Envelopment Analysis (DEA) estimates relative academic efficiency over time. Second, Gaussian Mixture Modeling identifies intersectional student profiles based on efficiency trajectories and contextual characteristics. Results reveal five distinct efficiency trajectories. While most students converge toward high-efficiency levels, one cluster exhibits a clear negative efficiency slope, greater variability, and limited institutional alignment, indicating it is a priority for intervention. Other clusters display stable high performance, continuous improvement, or moderate but non-accelerating trajectories. Findings demonstrate that efficiency differences are not explained by single demographic factors but by configurations of social background and institutional context. This study provides a scalable, data-driven framework for aligning equity and efficiency objectives in higher education policy and scholarship programs.