Abstract
The specialization of Health Human Resources (HHR) is increasingly recognized as essential for addressing evolving healthcare demands. This paper presents a comprehensive policy framework for assisting with the implementation of Clinical Nurse Specialist (CNS) roles at the national or regional level, integrating key dimensions including barriers and enablers, regulation and governance, education and training requirements, career development, workforce planning, and economic analysis. The framework was applied to the implementation of CNS roles in Portugal, resulting in the issuance of a decree-law by the government. Our findings demonstrate that the economic analysis step was critical in addressing concerns from government authorities and health system funders regarding the potential budgetary impact of CNS implementation. By providing evidence-based projections of costs and benefits, the economic analysis facilitated smoother negotiations and consensus-building among stakeholders, including nursing unions. Furthermore, the integration of workforce planning ensured the alignment of educational capacity with workforce needs, thus avoiding potential implementation bottlenecks. The application of the framework also revealed important feedback relationships between its dimensions, highlighting the interdependent nature of the implementation process. This dynamic approach, which adapts to real-time feedback and stakeholder input, underscores the necessity of a holistic and iterative strategy for successful CNS role integration. The insights gained from the Portuguese case underscore the utility of this policy framework in guiding the implementation of advanced nursing roles in diverse healthcare contexts.

Abstract
Ensuring process quality in modern manufacturing is increasingly challenging due to the complexity of production processes and reliance on skilled operators, which can lead to suboptimal solutions and poor quality. To address these challenges, we introduce a novel, unsupervised, robust, nonparametric control chart for Phase II monitoring. This chart tracks the degradation of a quality characteristic using a condition index that captures mean and scale shifts without relying on assumptions, offering high flexibility and adaptability. Comparative studies with state-of-the-art nonparametric schemes demonstrate faster detection capabilities and competitive accuracy across various scenarios. We validate our approach through its application in the glass container production process, showcasing its effectiveness in monitoring multiple defective rates. Although tested on defective rates, the methodology is adaptable to any quantifiable quality characteristic.

Abstract
This paper presents an optimisation approach based on mixed-integer programming for tactical planning decisions within fresh fruit processing industries. It applies to fruits such as oranges, tomatoes, guavas and others, where diluted fruit juice needs to be concentrated in evaporators to produce semi-finished or finished products. It considers agricultural and industrial activities, integrating them to address complex and interconnected decisions. Agricultural tasks include planting, harvesting, and transporting fruits from fields to processing plants, while industrial activities involve the production, inventory, and transportation of semi-finished and final products. This approach accommodates multiple agricultural regions, fruit varieties, processing plants, and products, operating on a weekly basis within a one-year planning horizon. It offers a detailed solution for harvesting, the fruit juice concentration process, inventory management for the products produced, and transportation of raw materials and products among processing plants. Production of semi-finished products is modelled using the Proportional Lot-Sizing and Scheduling Problem and the production of finished products is modelled adopting a blending lot-sizing problem. The results were validated through computational experiments using a dataset from a company that processes tomatoes and guavas. Scenario analyses were conducted to evaluate the solution's consistency and real-world applicability. The findings indicate that the approach can support decision making in practice, highlighting its potential as a valuable managerial, analytical, and optimisation tool for some agri-food industries. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Abstract
Vertical pulp and paper production is challenging from a process point of view. Managers must deal with floating bottlenecks, intermediate storage levels, and by-product production to control the whole process while reducing unexpected downtimes. Thus, this paper aims to address the integrated lot sizing and scheduling problem considering continuous digester production, multiple paper machines, and a chemical recovery line to treat by-products. The aim is to minimize the total production cost to meet customer demands, considering all productive resources and encouraging steam production (which can be used in power generation). Production planning should define the sizes of production lots, the sequence of paper types produced in each machine, and the digester working speed throughout the planning horizon. Furthermore, it should indicate the rate of byproduct treatment at each stage of the recovery line and ensure the minimum and maximum storage limits. Due to the difficulty of exactly solving the mixed integer programming model representing this problem for realworld instances, mainly with planning horizons of over two weeks, constructive and improvement heuristics are proposed in this work. Different heuristic combinations are tested on hundreds of instances generated from data collected from the industry. Comparisons are made with a commercial Mixed-Integer and Linear Programming solver and a hybrid metaheuristic. The results show that combining the greedy constructive heuristic with the new variation of a fix-and-optimize improvement method delivers the best performance in both solution quality and computational time and effectively solves realistic size problems in practice. The proposed method achieved 69.41% of the best solutions for the generated set and 55.40% and 64.00% for the literature set for 1 and 2 machines, respectively, compared with the best solution method from the literature and a commercial solver.

Abstract
In the original version of this article, "Data availability" statement was mistakenly inserted. The following data availability statement should be removed. As a final point, while the traditional independent demand model involves comparing unconstrained bookings with unconstrained demand forecasts to assess prediction accuracy, handling dependent demand is more complex, since the availability of a class affects the demand for other classes. Therefore, it is essential to have forecast data for all control policies, as advocated by Fiig et al. (2014), to establish a standardized method for computing forecast errors. This ensures the accurate functionality of the predictive model for optimal margin correction. The original article has been corrected. © The Author(s), under exclusive licence to Springer Nature Limited 2024.