Abstract
Today, logistics activities are driven by the pressing need to simultaneously increase efficiency, reduce costs, and promote sustainability. In our research, we tackle this challenge by adapting a general vehicle routing problem with deliveries and pickups to accommodate different types of customers. Customers requiring both delivery and pickup services are mandatory, while those needing only a pickup service (backhaul customers) are optional and are only visited if profitable. A mixed-integer linear programming model is formulated to minimize fuel consumption. This model can address various scenarios, such as allowing mandatory customers to be served with combined or separate delivery or pickup visits, and visiting optional customers either during or only after mandatory customer visits. An adaptive large neighborhood search is developed to solve instances adapted from the literature as well as to solve a real-case study of a beverage distributor. The results show the effectiveness of our approach, demonstrating the potential to utilize the available capacity on vehicles returning to the depot to create profitable and environmentally friendly routes, and so enhancing efficient, cost-effective, and sustainable logistics activities. © 2024 International Federation of Operational Research Societies.

Abstract
The expansion of ethanol production in Brazil sparks several sustainability concerns, including debates on “food versus fuel”, the environmental impacts of monocultures, and indirect land-use change. Since livestock farming occupies a significantly greater area than sugarcane for ethanol production in Brazil and has a large yield gap, sugarcane-livestock integration can be a promising alternative. This integrated system considers crop production systems, biorefinery processing and meat production in both intensive and extensive livestock farming. Optimizing this system for both economic and environmental aspects can be challenging to implement and computationally expensive as this system's complexity arises from nonlinear subsystems and their intertwining input-output flows. For these reasons, this paper develops metamodels from detailed models to: (i) Optimize the extensive livestock farming, (ii) Optimize the confined animal feeding, and (iii) Optimize the integrated system. The main objective is to maximize the Net Present Value relative to investment. This study contributes to the literature by developing innovative models for ethanol-beef integrated production systems and methods for optimizing such systems to avoid negative externalities on food security and environmental impacts. © 2024 Elsevier B.V.

Abstract
The efficient management of assets delivers value and is essential for achieving service objectives, managing risks, and reducing costs. This paper proposes decision-support methods to help capital-intensive industries manage their assets and optimise their life cycle. Optimisation approaches were developed to support longterm investment planning by maximising the value created and minimising the budget used. Also, the trade-off for both objectives was analysed. Using the proposed models will lead to efficient management of available capital and excellent service delivery. Thus, water companies will fulfil the regulator's requirements and present well-founded decision-making. This study was applied to a Portuguese water utility.

Abstract
Manufacturing industry is experiencing another revolution towards the digitalization of industrial processes. Different value chain actors must share specific and sensitive data according to business and data requirements. Digital architectures must ensure seamless and comprehensive communications between actors according to agreed-upon vocabularies. The digital representation of machines and other types of equipment, including crucial information about their static and dynamic operational data, is made possible by the ontological modelling of Asset Administration Shells (AAS), which is proposed in this paper as modular and semantically interoperable resources. These Cognitive Digital Twins are herein defined with de facto domain ontologies that model the semantics of the current operation, status and configurations of assets. This paper reports a proof-of-concept technical implementation that demonstrates an innovative digital architecture that connects and communicates active and modular Digital Twin of a machine in a bi-directional, connecting this asset to a digital manufacturing service provider.

Abstract
European industrial value chains and manufacturing companies have recently faced critical challenges imposed by disruptive events related to the pandemic and associated social/political problems. Many European manufacturing industries have already recognized the importance of digitalization to increase manufacturing systems’ autonomy and, consequently, become more resilient to adapt to new contexts and environments. Augmented reality (AR) is one of the emerging technologies associated with the European Industry 5.0 initiative, responsible for increasing human-machine interactions, promoting resilience through decision-making, and flexibility to deal with variability and unexpected events. However, the application and benefits of AR in increasing manufacturing resilience are still poorly perceived by academia and by European Manufacturing companies. Thus, the purpose of this paper is to contribute to the state of the art by relating the application of AR with current industrial processes towards manufacturing systems resilience. In order to cope with this objective, the industrial resilience and augmented human worker concepts are first presented. Then, through an exploratory study involving different manufacturing companies, a list of relevant disruptive events is compiled, as well as a proposal with specific ideas and functionalities on how AR can be applied to address them. In conclusion, this research work highlights the importance of AR in coping mainly with disruptive events related to Human Workforce Management and Market/Sales Management. The AR application ideas shared a common thread of availability and delivery of information to the worker at the right time, place, and format, acting on the standardization and flexibility of the work to support manufacturing resilience.

Abstract
In today's volatile, uncertain, and complex business environments, manufacturing companies must not only adapt to market demands but also minimize the time between problem occurrence and resolution. The implementation of lean manufacturing systems has been crucial in this regard. However, traditional approaches have shown notable inefficiencies that can be effectively addressed through digitalization. By embracing digital solutions, manufacturing companies can ensure efficient continuous improvement, driving performance to higher levels. This study aims to find a digital solution for a specific company that faces daily challenges associated with low visibility into production. An investigation revealed that the Lean tools used by the company were outdated, directly affecting the generated information and consequently, decision-making. The integration of a Manufacturing Execution System into the factory's dynamics was the solution found. In this context, a step-by-step methodology is proposed to guide the implementation. As a result, a prototype of the system was designed. The validation of the system by end-users demonstrates the success of the proposed methodology.