Workforce planning in home social care services is becoming increasingly complex due to increasing demand, limited budgets, and the need for personalized and geographically distributed care. An emerging strategy involves the territorial organization of care delivery, where cities are partitioned into subareas managed by local caregiver teams. Although this model fosters continuity and familiarity between caregivers and users, it introduces operational challenges, particularly in managing fluctuating demand and ensuring efficient staff allocation. This paper addresses these challenges by proposing the Home Care Islands Optimization framework, which supports tactical decision-making in home care planning. The approach integrates two allocation mechanisms: (i) exclusive assignments of caregivers to specific subareas (referred to as islands) and (ii) a flexible floating team capable of operating across different islands. The model also allows for strategic merging of islands to improve resource utilization. This work is the first to integrate tactical workforce planning and territory merging under demand variability in home care. We develop a single-period Integer Linear Programming model that determines optimal hiring strategies, contract types (full-time vs. part-time), and team distribution and allocation while minimizing both non-served demand and cost. The methodology is applied to a real-world case study based on Barcelona’s public home care system, with data provided by the City Council. Through extensive computational experiments, we explore the impact of key parameters on the quality and structure of the solution. We show that non-served demand is eliminated when the tolerance parameter γ ≥ 30 , while the budget variability stabilizes only when γ ≥ 40 . Regarding the composition of the workforce, the share of part-time contracts decreases once α ≥ 10 , and the size of the floating team never exceeds 6–7 caregivers. Moreover, under higher demand variability, the minimum γ required for system stability rises, and the model tends to rely more heavily on part-time and floating caregivers; however, setting α = 11 effectively controls this effect and promotes employment stability. The framework offers a robust and flexible planning tool for public service managers, enabling data-driven decisions that improve service continuity, workforce stability, and system adaptability in the face of uncertain and variable demand. • Optimization model for tactical workforce planning in home social care services. • Integrates fixed local teams and a flexible floating team for better adaptability. • Supports territory merging to improve staff allocation and resource use. • Applied to real data from Barcelona’s public home care system. • Reduces unmet demand and costs through data-driven planning.

Additive manufacturing (AM) technologies continuously evolve in materials and operational processes. However, challenges related to energy consumption, material reuse efficiency, and the integration of Circular Economy (CE) principles may impede the overall sustainability of AM processes. As many industrial sectors adopt AM technologies, there is a growing need for innovative tools, methods, and frameworks that guide companies toward more sustainable and circular production practices. This shift involves improving resource efficiency and fostering new Circular Business Models that emphasize material recovery, product lifecycle extension, and waste minimization. This study assesses current and future trends in the development of sustainable AM technologies for processing metal feedstock, based on a comprehensive patent analysis covering the period from 2004 to 2024. We specifically focus on the sustainability impacts of Electron and Laser Beam Powder Bed Fusion (PBF-EB, PBF-LB) and Direct Energy Deposition (DED), given their widespread industrial applications. Patents were categorized into three groups: materials, processes, and components. Furthermore, AM technologies were analyzed according to their role within the production cycle: materials preparation, pre-processing, manufacturing, and post-processing. This categorization allows for a detailed understanding of how innovations in AM contribute to more sustainable production and consumption practices by improving energy efficiency, material usage, circularity, and overall environmental impact.