The Vehicle-Routing Problem (VRP) represents a critical challenge in logistics, encompassing numerous variations, such as time window considerations, multi-depot systems, two-echelon routing aspects, and Satellite Locations (SL). SLs are intermediate facilities that support cross-docking, storage, and transshipment operations. However, inconsistencies in defining “satellite” have hindered precise research and implementation. This study presents a systematic review of the use of satellites for VRP, employing the PRISMA methodology to ensure a comprehensive and reproducible analysis. The findings indicate that about 50% of the reviewed papers include a path-splitting variant. At the same time, there is a notable gap in addressing random demands and pickup and delivery within cross-docking environments. A major limitation is the lack of a well-known public dataset, as about 50% of the datasets are created or adapted for specific studies. Additionally, the analysis reveals significant gaps in dataset standardization and the integration of dynamic routing under uncertainty. These findings underscore the potential of satellite-based systems to optimize urban logistics and supply chains while pointing to critical avenues for future research.

Resumen El tema del liderazgo no deja indiferente a nadie; éste constituye un pilar fundamental para alcanzar apropiadamente las metas organizacionales; razón más que suficiente para estudiarlo profusamente. En esta investigación se plantea como objetivo identificar las diferencias en el estilo de liderazgo (transformacional, transaccional o laissez faire) que se atribuyen un conjunto de dirigentes sindicales pertenecientes a la Confederación de Trabajadores Metalúrgicos, de la Industria y Servicio “Industrial Chile Constramet” de la Región Metropolitana, estudio que se pretende realizar bajo la perspectiva de género. Tomando en cuenta dicha variable, se llevaron a cabo análisis estadísticos descriptivos (frecuencia, porcentaje, media aritmética, desviación estándar) y se realizaron análisis estadísticos inferenciales utilizando la prueba t para muestras independientes. En términos generales, se comprueba que las mujeres se autoperciben como líderes transformacionales en mayor medida que sus colegas hombres.

The Vehicle Routing Problem (VRP) is central to last-mile logistics, yet a gap remains when products have late release times and vehicles can be reloaded en route via mobile satellites that rendezvous with reloading vehicles at customer locations. We propose the VRP with Release Times and Reloading at Mobile Satellites (VRP-RT-RMS) and develop two mixed-integer linear programming formulations: a three-index (MILP-3) and a two-index (MILP-2). The objective minimizes total distance subject to capacity, route duration, synchronization, and time constraints. We generated 40 instances from real data (10 per size $N\in \{10,15,20,25\}$). En-route reloads simultaneously reduce distance and fleet size and can restore feasibility when the classical VRP is infeasible. To contrast the classical VRP with our VRP-RT-RMS, we analyzed a particular instance with $N=10$ customers: total distance decreased by 7.26% and the number of vehicles fell from 5 to 3. As instance size grows, MILP-2 shows superior scalability and efficiency compared with MILP-3. Beyond the technical scope, coordinating reloads is pertinent to urban operations with late product releases, lowering kilometers traveled and delivery times.

This study proposes two optimization models and a column-generation algorithm, applied at the root node, to support tactical planning in sawmills by dynamically generating log cutting patterns aligned with sustainability and efficiency objectives. Starting from an industrial dataset containing 160 cutting patterns, the methodology iteratively incorporates new geometrically feasible configurations guided by the dual prices of a primary model, explicitly considering log supply, product demand, and alternative tactical criteria. Three computational experiments were conducted. The first assesses the convergence behavior of the algorithm and shows reductions in total log consumption of up to 31% as new patterns are generated. The second demonstrates that strategies aimed at minimizing log usage and residues can achieve near-optimal solutions with only 20–25 patterns, since additional configurations provide marginal improvements while increasing setup time and operational complexity. The third experiment confirms that near-optimal performance can be reached with a moderate number of active patterns, facilitating practical implementation in industrial settings. Overall, the proposed methodology offers a flexible and sustainability-oriented decision-support tool for sawmill tactical planning, improving raw-material utilization, reducing residues, and enhancing alignment between supply and demand while maintaining operational feasibility.

Personal Comfort Systems (PCS) have emerged as a flexible alternative to address the diversity of environmental perceptions in office environments. Unlike conventional HVAC systems, PCSs allow users to improve their satisfaction and comfort by exercising individualized control over their immediate environment without interfering with others around them. This study evaluated the use of machine learning models generated by H2O AutoML to predict the use of three PCSs in four office buildings with effective occupancy. These were a thermal wristband, a desk fan, and an adjustable lamp. Data collected through environmental sensors, perception surveys, and spatial and personal attributes were used. Synthetic data augmentation and automated variable selection were also used to optimize the models’ performance. The predictive models had a robust performance, with R² values in the test set of 0.86 for the wristband, 0.84 for the fan, and 0.52 for the lamp. The most influential variables included the BMI, CO₂ level, and thermal satisfaction, highlighting the importance of physiological and subjective factors. The results confirm that the models allow anticipating the use of PCS with high precision in most cases, laying the foundations for the future implementation of user-oriented adaptive systems. This preliminary approach contributes to the design of healthier, more personalized, and more energy-efficient work environments.