Abstract
The Vehicle Routing Problem with Synchronization (VRPSync) aims to minimise the total routing costs while considering synchronization requirements that must be fulfilled between tasks of different routes. These synchronization requirements are especially relevant when it is necessary to have tasks being performed by vehicles within given temporal offsets, a frequent requirement in applications where multiple vehicles, crews, materials, or other resources are involved in certain operations. Although several works in the literature have addressed this problem, mainly the deterministic version has been tackled so far. This paper presents a robust optimization approach for the VRPSync, taking into consideration the uncertainty in vehicle travel times between customers. This work builds on existing approaches in the literature to develop mathematical models for the Robust VRPSync, as well as a branch-and-cut algorithm to solve more difficult problem instances. A set of computational experiments is also devised and presented to obtain insights regarding key performance parameters of the mathematical models and the solution algorithm. The results suggest that solution strategies where certain standard problem constraints are only introduced if a candidate solution violates any of those constraints provide more consistent improvements than approaches that rely on tailor-made cutting planes, added through separation routines. Furthermore, the analysis of the Price of Robustness indicators shows that the adoption of robust solutions can have a significant increase in the total costs, however, this increase quickly plateaus as budgets of uncertainty increase.

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Abstract
This paper addresses a job shop scheduling problem with peak power constraints, in which jobs can be processed once or multiple times on either all or a subset of the machines. The latter characteristic provides additional flexibility, nowadays present in many manufacturing systems. The problem is complicated by the need to determine both the operation sequence and starting time as well as the speed at which machines process each operation. Due to the adherence to renewable energy production and its intermittent nature, manufacturing companies need to adopt power-flexible production schedules. The proposed power control strategies, that is, adjusting processing speed and timing to reduce peak power requirements may impact production time (makespan) and energy consumption. Therefore, we propose a bi-objective approach that minimizes both objectives. A linear programming model is developed to provide a formal statement of the problem, which is solved to optimality for small-sized instances. We also proposed a multi-objective biased random key genetic algorithm framework that evolves several populations in parallel. Computational experiments provide decision and policymakers with insights into the implications of imposing or negotiating power consumption limits. Finally, the several trade-off solutions obtained show that as the power limit is lowered, the makespan increases at an increasing rate and a similar trend is observed in energy consumption but only for very small makespan values. Furthermore, peak power demand reductions of about 25% have a limited impact on the minimum makespan value (4-6% increase), while at the same time allowing for a small reduction in energy consumption.

Abstract
Students spend 30 % of their lives indoors; therefore, a healthy indoor air quality (IAQ) is crucial for their well-being and academic performance in Higher Education Institutions. This review highlights the interventions for improving Indoor Enviclassrooms considering climate change by discussing ventilation techniques, phytoremediation, and building features designed to improve noise levels, thermal comfort, lighting and to reduce odor. Awareness and literacy are enhanced through the student's engagement by offering real-time monitoring knowledge of Indoor Environmental Quality using inexpensive smart sensors combined with IoT technology. Eco-friendly strategies are also highlighted to promote sustainability.

Abstract
Numerous Robotic Process Automation (RPA) market solutions with wildly disparate capabilities and business models are being put forth. RPA is still in its infancy, and its technology framework is continually evolving. There are very few comparative studies of RPA systems, and they do not make it simple to tailor the solution to the needs of the business choosing it. Thus, the research question is that it feasible to design a procedure that enables the choice of the most appropriate RPA tool while accounting for a particular business domain, reality, and set of requirements? In order to accomplish this, this study builds an artifact that comprises a collection of indicators to enable the long-term selection of the best RPA solution for each organization and/or business process using the methodological approach of Design Science Research. The artifact offers a methodology to categorize the level of adaptability of each solution for automating business processes, performs a comparative analysis of existing RPA solutions using a particular framework, and provides an overview of the features of currently available solutions on the market. The viability of the artifact is demonstrated using a real-world case situation. This test demonstrated the artifact’s capacity to meet the goals. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.