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.

Abstract
The cost of transportation of raw materials is a significant part of the procurement costs in the forestry industry. As a result, routing and scheduling techniques were introduced to the transportation of raw materials from extraction sites to transformation mills. However, little to no attention has been given to date to the material reception process at the mill. Another factor that motivated this study was the formation of large waiting queues at the mill gates and docks. Queues increase the reception time and associated costs. This work presents the development of a scheduling and reception system for deliveries at a mill. The scheduling system is based on Trucking Appointment Systems (TAS), commonly used at maritime ports, and on revenue management concepts. The developed system allocates each delivery to a timeslot and to an unloading dock using revenue management concepts. Each delivery is segmented according to its priority. Higher-segment deliveries have priority when there are multiple candidates to be allocated for one timeslot. The developed scheduling system was tested on a set of 120 daily deliveries at a Portuguese paper pulp mill and led to a reduction of 66% in the daily reception cost when compared to a first-in, first-out (FIFO) allocation approach. The average waiting time was also significantly reduced, especially in the case of high-priority trucks.

Abstract
The textile industry is a complex and dynamic system where structured decision-making processes are essential for efficient supply chain management. In this context, mathematical programming models offer a powerful tool for modeling and optimizing the textile supply chain. This systematic review explores the application of mathematical programming models, including linear programming, nonlinear programming, stochastic programming, robust optimization, fuzzy programming, and multi-objective programming, in optimizing the textile supply chain. The review categorizes and analyzes 163 studies across the textile manufacturing stages, from fiber production to integrated supply chains. Key results reveal the utility of these models in solving a wide range of decision-making problems, such as blending fibers, production planning, scheduling orders, cutting patterns, transportation optimization, network design, and supplier selection, considering the challenges found in the textile sector. Analyzing those models, we point out that sustainability considerations, such as environmental and social aspects, remain underexplored and present significant opportunities for future research. In addition, this study emphasizes the importance of incorporating multi-objective approaches and addressing uncertainties in decision-making to advance sustainable and efficient textile supply chain management.

Abstract
Port wine stocks ameliorate during storage, facilitating product differentiation according to age. This induces a trade-off between immediate revenues and further maturation. Varying climate conditions in the limited supply region lead to stochastic purchase prices for wine grapes. Decision makers must integrate recurring purchasing, production, and issuance decisions. Because stocks from different age classes can be blended to create final products, the solution space increases exponentially in the number of age classes. We model the problem of managing port wine inventory as a Markov decision process, considering decay as an additional source of uncertainty. For small problems, we derive general management strategies from the long-run behavior of the optimal policy. Our solution approach for otherwise intractable large problems, therefore, first aggregates age classes to create a tractable problem representation. We then use machine learning to train tree-based decision rules that reproduce the optimal aggregated policy and the enclosed management strategies. The derived rules are scaled back to solve the original problem. Learning from the aggregated optimum outperforms benchmark rules by 21.4% in annual profits (while leaving a 2.8%-gap to an upper bound). For an industry case, we obtain a 17.4%-improvement over current practices. Our research provides distinct strategies for how producers can mitigate climate risks. The purchasing policy dynamically adapts to climate-dependent price fluctuations. Uncertainties are met with lower production of younger products, whereas strategic surpluses of older stocks ensure high production of older products. Moreover, a wide spread in the age classes used for blending reduces decay risk exposure.

Abstract
Uncertainty is pervasive in modern manufacturing settings. In order to cope with unexpected events, scheduling decisions are commonly taken resorting to dispatching rules, which are reactive in nature. However, rule performance varies according to shop utilisation and due date allowance, which often change in dynamic real-world job shops. Therefore, this paper explores systems that select dispatching rules as conditions change over time, namely periodic and real-time dispatching rule selection systems, which are based on supervised learning and reinforcement learning algorithms, respectively. These types of systems have been proposed in the past but have been further improved in this work by carefully selecting the most relevant state features and dispatching rules. Moreover, by testing both approaches on the same instances, it was possible to compare them and determine the most advantageous one. After the tests, which included a wide array of job shop instances, both periodic and real-time systems outperformed state-of-the-art dispatching rules by over 10% tardiness-wise. Nonetheless, the periodic rule selection approach was more robust across all tests than the real-time approach. These results demonstrate that there is a real incentive for managers to adopt dispatching rule selection systems.

Abstract
This study seeks to comprehensively analyze the multidimensional determinants underlying perceptions of safety, security, and comfort in transport mode choice, specifically focusing on private transport, public transport and walking. The research begins with an extensive literature review to identify and delve into the factors influencing perceptions of safety, security, and comfort across various transport modes. This inquiry is further enhanced by organizing two focused group sessions. A total of 35 key factors were identified, forming the basis for subsequent investigation. The study then progressed to the development and administration of a survey aimed at capturing responses from a diverse audience, with the goal of exploring the factors influencing perceptions related to different transport modes. A total of 302 responses were collected and meticulously analyzed to discern the factors impacting various relationships and to identify consistent perceptions across diverse transport modes. Additionally, a factor analysis was conducted to validate the findings derived from the data. The outcomes of this research constitute a significant contribution to the existing literature, offering valuable insights that pave the way for a more holistic understanding of the factors guiding transport mode choices.