Abstract
We show how (well-established) type systems based on non-idempotent intersection types can be extended to characterize termination properties of functional programming languages with pattern matching features. To model such programming languages, we use a (weak and closed) lambda-calculus integrating a pattern matching mechanism on algebraic data types (ADTs). Remarkably, we also show that this language not only encodes Plotkin's CBV and CBN lambda-calculus as well as other subsuming frameworks, such as the bang-calculus, but can also be used to interpret the semantics of effectful languages with exceptions. After a thorough study of the untyped language, we introduce a type system based on intersection types, and we show through purely logical methods that the set of terminating terms of the language corresponds exactly to that of well-typed terms. Moreover, by considering non-idempotent intersection types, this characterization turns out to be quantitative, i.e. the size of the type derivation of a term t gives an upper bound for the number of evaluation steps from t to its normal form.

Abstract
This paper examines the role of digital twins (DTs) in promoting sustainability within seaport operations and logistics. DTs have emerged as promising tools for enhancing seaport performance. Despite the recognized potential of DTs in seaports, there is a paucity of research on their practical implementation and impact on seaport sustainability. Through a systematic literature review, this study seeks to elucidate how DTs contribute to the sustainability of seaports and to identify future research and practical applications. We reviewed and categorized 68 conceptual and practical digital applications into ten core areas that effectively support economic, social, and environmental objectives in seaports. Furthermore, this paper proposes five preliminary potential applications for DTs where practical implementations are currently lacking. The primary findings indicate that DTs can enhance seaport sustainability by facilitating real-time monitoring and decision-making, improving safety and security, optimizing resource utilization, enhancing collaboration and communication, and supporting the development of the seaport ecosystem. Additionally, this study addresses the challenges associated with DT implementation, including high costs, conflicting stakeholder priorities, data quality and availability, and model validation. The paper concludes with a discussion of the implications for seaport managers and policymakers.

Abstract
Purpose: This study addresses the development of a variant process planning tool, following the Knowledge-Based Variant Process Planning methodology, applied in a case study and presents the gains achieved. Methodology/Approach: Case study supported by six steps: (1) Feature Analysis, (2) Knowledge Retrieval, (3) Inference, (4) Plan Adaptation, (5) Knowledge Update, and (6) Plan Validation/Optimisation. Findings: The implementation of the Knowledge-Based Variant Process Planning tool led to significant improvements: planner time reduced by 70%, analyst workload by 90%, and process plan errors to 0%. Results show this approach significantly improves process planning in customised production. Research Limitation/Implication: The limitations are associated with the specificity of the case study problem-the electric engine production systems. Originality/Value of paper: This study helps fill the gap in case studies on the Variant Process Planning approach, specifically for electric engine production systems, paving the way for similar companies to adopt Knowledge-Based Variant Process Planning.

Abstract
Demand-Driven Material Requirements Planning (DDMRP) has been proposed as a solution for managing uncertainty and variability in supply chains by combining decoupling, buffer management and demand-driven planning principles. A key element of DDMRP is its inventory replenishment model, which relies on dynamically adjusted inventory buffers rather than fixed stock levels. However, parameterization of these buffers often involves subjective choices, raising concerns about consistency and performance. This paper assesses the DDMRP replenishment model through discrete-event simulation of a multi-echelon, capacity-constrained production system. Two alternative formulations of the safety stock term in the red zone are compared: the original factor-based approach and a revised formula that incorporates measurable variability coefficients. While both safety stock formulations yield similar numerical results, the revised formula enhances transparency and reduces subjectivity. Assessing the impact of introducing a buffer for components in addition to a finished goods buffer further shows that the components buffer can reduce finished goods inventory requirements while maintaining service levels. These findings contribute to a better understanding of the DDMRP replenishment model, offering practical insights for parameter selection and supply chain design.

Abstract

Abstract
E-commerce popularity has increased the challenges of parcel deliveries, highlighting the urgency in addressing the sustainability of the last-mile, which is considered the most ineffective part of the supply chain and a source of environmental and social issues. Although different alternative last-mile technologies have already been proposed, they all imply complex trade-offs. This study explores how synergies between delivery technologies could improve last-mile logistics. It evaluates the trade-offs between electric vans, cargo bikes, and parcel lockers, and takes into account factors such as package size, consumer requirement, and urban context. The adoption of a combined delivery strategy suggests environmental and social benefits like the mitigation of congestion and emissions while ensuring advantages for companies due to streamlined operations and offering consumers a broader range of choices.