Abstract
Container terminals are of pivotal importance to global trade, as they act as a bridge between maritime and land transport. However, inefficiencies in operations, such as long waiting times and high emissions, continue to challenge the industry. Current practices, including first-come-first-served (FCFS) berth allocation, often result in ships arriving too early and idling at anchorage, leading to increased fuel consumption and negative environmental impacts. Just-in-Time (JIT) strategies have been identified as a potentially effective approach to address these issues by aligning ship arrivals with berth availability, thus optimising speed and reducing emissions. In this work, we present a simulation-based decision-support tool to evaluate JIT strategies in container terminal operations. By analysing scenarios involving speed optimisation and resource investments, the tool provides insights into key performance metrics, including waiting times, emissions, and resource utilisation. A case study designed around a large Portuguese seaport was used to validate the approach, with significant reductions in emissions and operational inefficiencies. These findings highlight the potential of JIT operations to enhance sustainability and efficiency in the maritime sector. Copyright © 2025. Published by Elsevier B.V.

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.

Abstract
PurposeIn an increasingly competitive market, equipment availability is a strategic variable for the competitiveness and success of companies. The objective of the research in this article is to present contributions to reduce unplanned production stoppages and optimise the operational efficiency of an injection moulding machine. This will be achieved by developing a systematic strategy to integrate predictive and condition-based maintenance systems with maintenance management software.Design/methodology/approachThe model developed is based on the continuous monitoring of electrical signals and vibrations, with the processing of data collected in real time through a script developed in Python. This integrates the information into the maintenance management software, facilitating a quick and accurate response to component wear conditions. The methodology employed was action research, as it was a case study developed in a real context, with active participation in development and implementation, with the aim of continuous improvement.FindingsIn August, a substantial increase was observed in the primary indicators: The mean time between failures (MTBF) increased by 97.36%, the mean time to repair (MTTR) increased by 313.31%, and the downtime was reduced by 65.04%. In December, although the figures were more moderate, significant improvements were maintained: The MTBF increased by 20%, the MTTR increased by 84%, and the downtime was reduced by 79%.Originality/valueThe findings of the study indicated that the implementation of a structured approach for the acquisition and monitoring of electrical signals and vibration data was imperative to achieve substantial gains.

Abstract
The implementation of Onshore Power Supply (OPS) systems is recognised as an effective strategy to reduce air emissions and noise pollution from ships at berth, thereby supporting decarbonization goals established by the European Union. Despite its environmental advantages, the economic viability of OPS remains one of the main barriers to its widespread adoption in ports. In particular, limited research attention has paid to the extent to which the service level provided by OPS infrastructure affects the financial performance of such investments.This paper addresses this gap by analysing the relationship between service level and economic viability through a case study of the Southern Container Terminal at the Port of Leixões (Portugal). Three investment scenarios were evaluated, corresponding to the installation of one, two, and three OPS systems, delivering service levels of 65%, 94%, and 100%, respectively. A 20-year financial analysis (2025–2044) was conducted using Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period as key indicators, under discount rates of 3% and 5%.The results demonstrate that service level significantly influences investment feasibility. The scenario with a 65% service level is economically unviable, presenting negative NPVs and an IRR below the cost of capital. In contrast, the 94% and 100% service level scenarios generate positive NPVs (at 3%) and IRRs slightly above 5%, indicating moderate but acceptable profitability. However, increasing the service level from 94% to 100% does not produce a proportional improvement in financial returns, revealing diminishing marginal gains relative to the intermediate scenario.The findings indicate that maximising service level does not necessarily maximise economic attractiveness. Instead, an optimal balance between infrastructure capacity and expected demand is essential to ensure financial sustainability while delivering substantial environmental benefits. These findings offer practical guidance for port authorities and policymakers seeking to design economically viable OPS systems.

Abstract
The forest-to-bioenergy supply chain is significantly vulnerable to natural disruptions, including wildfires, heavy snowfall, and windstorms. The increased occurrence of these disruptive events has caused severe challenges in forest biomass harvesting and transportation processes, which are difficult to manage. With the need to support decision-makers in designing resilient supply chains (SCs), we propose a Decision Support System (DSS) combining a two-stage stochastic programming framework with various flexibility mechanisms, such as dynamic network reconfiguration and operations postponement. The DSS incorporates an AI-based methodology to identify the most appropriate datasets and resilience metrics, capturing different supply chain dimensions (supply, demand, and operations). This integrated framework supports the selection of effective resilience-enhancing strategies to mitigate large-scale disruptions, with a particular focus on wildfires. The proposed approach is applied in a real case study in Portugal, where the most significant risk factor is wildfires. We perform computational studies and sensitivity analysis to evaluate the applicability and performance of the model and to drive managerial insights. The results show that adopting the model solutions can significantly reduce supply chain logistics and operational costs under more severe disruptive scenarios. Moreover, the results indicate up to a 60% increase in the tons of forest residues that can be removed and processed.

Abstract
Circular Economy is based on implementation of R-strategies to narrow or close the loop of material flows and to minimize raw material consumption by extending the life cycle of materials. Since this approach is expanding from individual organizational actions to a collaborative approach, the objective of this paper is to analyse the role of digital technologies such as AI and cloud platforms in facilitating and changing the collaboration between stakeholders to improve sustainability. This study adopts a qualitative multi case study methodology, using surveys, interviews and document analysis from 10 new ventures in the agri-food ecosystem supported by the cascade funding programme. The results show that collaboration among actors is changed by the different technologies and strategic drivers of circular economy in the considered ecosystem.