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- Systems, economics and neoliberal politics: theories to understand missed nursing carePublication . Jones, Terry; Drach‐Zahavy, Anat; Amorim-Lopes, Mário; Willis, EileenThe phenomenon of missed nursing care is endemic across all sectors. Nurse leaders have drawn attention to the implications of missed care for patient outcomes, with calls to develop clear political, methodological, and theoretical approaches. As part of this call, we describe three structural theories that inform frameworks of missed care: systems theory, economic theory, and neoliberal politics. The final section provides commentary on the strengths and limitations of these three theories, in the light of structuration theory and calls to balance this research agenda by reinstating nurse agency and examining the interactions between nurses as agents and the health systems as structures. The paper argues that a better understanding of variations in structure–agency interaction across the healthcare system might lead to more effective interventions at strategic leverage points.
- Improving picking performance at a large retailer warehouse by combining probabilistic simulation, optimization, and discrete‐event simulationPublication . Amorim-Lopes, Mário; Guimarães, Luís; Alves, João; Almada-Lobo, BernardoDistribution warehouses are a critical part of supply chains, representing a nonnegligible share of the operating costs. This is especially true for unautomated, labor‐intensive warehouses, partially due to time‐consuming activities such as picking up items or traveling. Inventory categorization techniques, as well as zone storage assignment policies, may help in improving operations, but may also be short‐sighted. This work presents a three‐step methodology that uses probabilistic simulation, optimization, and event‐based simulation (SOS) to analyze and experiment with layout and storage assignment policies to improve the picking performance. In the first stage, picking performance is estimated under different storage assignment policies and zone configurations using a probabilistic model. In the second stage, a mixed integer optimization model defines the overall warehouse layout by selecting the configuration and storage assignment policy for each zone. Finally, the optimized layout solution is tested under demand uncertainty in the third, final simulation phase, through a discrete‐event simulation model. The SOS methodology was validated with three months of operational data from a large retailer's warehouse, successfully illustrating how it may be successfully used for improving the performance of a distribution warehouse.