1,721,008 research outputs found
An implementor-adversary approach for uncertain and time-correlated service times in the nurse-to-patient assignment problem
Get PDFParameter uncertainty is common to several optimization problems, especially in health care where patients’ conditions and service times are highly uncertain. Moreover, in Home Health Care services, where patients are treated over a long time horizon, their clinical condition may change and result in uncertain but also highly time-correlated service times. To include time-correlated uncertain parameters is a difficult task in robust optimization. It has been only marginally addressed, especially in the health care management literature. In this paper, we address the nurse-to-patient assignment problem in Home Health Care services under three types of continuity of care, focusing on uncertain and time-correlated service times. In the literature, this problem has been tackled with stochastic programming, robust optimization, and heuristics; however, service times at different time periods have been treated as independent so far. We propose a robust assignment model that accounts for the time-dependency of patients’ service times, with either constant or increasing overtime cost, and an approach based on the implementor-adversary framework to solve it. Service times are modeled as stochastic parameters and time-correlation is included by limiting the difference in the service time requested by the same patient in two consecutive time periods. The goals are to minimize the cost of staff overtime and the number of reassignments that impair patients’ continuity of care. Results from a relevant realistic case show that the approach does not always converge, but the robust solutions provided, under suitable parameters for the uncertainty description, always outperform the deterministic ones even without full convergence
Evaluating the Impact of the Level of Robustness in Operating Room Scheduling Problems
No full textManaging uncertainty in surgery times presents a critical challenge in operating room (OR) scheduling, as it can have a significant impact on patient care and hospital efficiency. Objectives: By incorporating robustness into the decision-making process, we can provide a more reliable and adaptive solution compared to traditional deterministic approaches. Materials and methods: In this paper, we consider a cardinality-constrained robust optimization model for OR scheduling, addressing uncertain surgery durations. By accounting for patient waiting times, urgency levels and delay penalties in the objective function, our model aims to optimise patient-centred outcomes while ensuring operational resilience. However, to achieve an appropriate balance between resilience and robustness cost, the robustness level must be carefully tuned. In this paper, we conduct a comprehensive analysis of the model’s performance, assessing its sensitivity to robustness levels and its ability to handle different uncertainty scenarios. Results: Our results show significant improvements in patient outcomes, including reduced waiting times, fewer missed surgeries and improved prioritisation of urgent cases. Key contributions of this research include an evaluation of the representativeness and performance of the patient-centred objective function, a comprehensive analysis of the impact of robustness parameters on OR scheduling performance, and insights into the impact of different robustness levels. Conclusions: This research offers healthcare providers a pathway to increase operational efficiency, improve patient satisfaction, and mitigate the negative effects of uncertainty in OR scheduling
A robust optimization approach for the Operating Room Planning Problem with uncertain surgery durations.
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A cardinality-constrained robust approach for the Stochastic Surgical Case Assignment Problem.
No full textAn optimization tool to dimension innovative home health care services with devices and disposable materials
No full textHome health care (HHC) consists of care services provided to patients at their domicile rather than in hospitals or other health facilities. HHC human resources are largely studied in the optimization literature, to improve service quality and efficiency. However, the growth of complex HHC services that include the delivery of devices and disposable materials makes it necessary to include them in the decision-making process together with human resources. In fact, they may represent a high cost item, and their release may affect the scheduling of HHC visits. Unfortunately, as far as our knowledge, devices and materials are not considered together with the technical staff required to support their utilization. In this paper, we address the dimensioning problem for new HHC services that also involve devices and materials, considering the joint dimensioning of human and material resources. We include three categories of staff (nurses, physicians and technicians), a set of devices and a set of materials; also, we assume that the requirements from patients are in terms of frequencies, i.e., the maximum number of days between two consecutive visits, two supplies of a material or two uses of a device. We propose a linear programming model and a matheuristic approach for solving instances of realistic dimension. The model allows determining the number of nurses, physicians, and technicians to be hired, as well as the number of devices to be acquired to meet the demand. As for the matheuristics, we combine a decomposition step and a heuristics inspired by the Local Branching idea. Results show the capability of the approach to solve the problem and provide good dimensioning solutions, which can be actually adopted in real-life problems. Moreover, the matheuristic approach performs well in a variety of instances
A method for determining the minimum cost installation for apparatuses of a fixed telecommunication network
No full textMaster chemotherapy planning and clinicians rostering in a hospital outpatient cancer centre
No full textIn the past years, the number of patients in need of chemotherapy treatments has been constantly increasing. Chemotherapy treatments must be carefully planned to provide a suitable and timely care. They are often provided within an outpatient setting. Clinicians and nurses staff must face the increasing demand for chemotherapy treatment with limited resources, such as exam rooms, beds, and seats. In this work, we consider a cancer centre shared among different oncologist specialties, as suggested by the Organisation of European Cancer Institutes. We focus on the oncologist visit that each patient must undergo before the drug infusion, to check if the patient’s conditions are compatible with the drug infusion. We consider the problem of planning the weekly assignment of consultation rooms to cancer pathologies, referred to as Master Chemotherapy Planning. Further, we jointly address the problem of selecting a clinician with suitable skills to cover each consultation room in the weekly schedule, given the cliniciansávailability over a month. Several criteria are considered, such as the number of visits in overtime, the amount of met demand and the clinicians’ workload. The problem is formulated as a lexicographic multiobjective optimisation problem and solved using a sequence of MIP models. Further, we propose a rolling horizon approach to tackle a long planning horizon up to one year, aiming also at keeping the changes of weekly plans from one month to the other as small as possible. The models and rolling horizon procedure are tested on real data from an Italian hospital
A disruption-restoration-based ILP model for surgical scheduling in a Children’s Hospital
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