An efficient matheuristic for offline patient-to-bed assignment problems

Guido, Rosita; Groccia, Maria Carmela; Conforti, Domenico

doi:10.1016/j.ejor.2018.02.007

Cited by 36 publications

(21 citation statements)

References 39 publications

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“…The method was tested on small short families from data set. This method was developed based on previous work of the same author [29] which proposed three optimization models based matheuristic called (FiNeMat) for solving patient to bed assignment which considered it as a sub-task from PASP [17]. In this work the author gives a guideline on how to set up the penalty values for the soft constraints.…”

Section: Pasp-based Optimization Methodsmentioning

confidence: 99%

Healthcare scheduling in optimization context: a review

et al. 2021

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This paper offers a summary of the latest studies on healthcare scheduling problems including patients’ admission scheduling problem, nurse scheduling problem, operation room scheduling problem, surgery scheduling problem and other healthcare scheduling problems. The paper provides a comprehensive survey on healthcare scheduling focuses on the recent literature. The development of healthcare scheduling research plays a critical role in optimizing costs and improving the patient flow, providing prompt administration of treatment, and the optimal use of the resources provided and accessible in the hospitals. In the last decades, the healthcare scheduling methods that aim to automate the search for optimal resource management in hospitals by using metaheuristics methods have proliferated. However, the reported results are disintegrated since they solved every specific problem independently, given that there are many versions of problem definition and various data sets available for each of these problems. Therefore, this paper integrates the existing results by performing a comprehensive review and analyzing 190 articles based on four essential components in solving optimization problems: problem definition, formulations, data sets, and methods. This paper summarizes the latest healthcare scheduling problems focusing on patients’ admission scheduling problems, nurse scheduling problems, and operation room scheduling problems considering these are the most common issues found in the literature. Furthermore, this review aims to help researchers to highlight some development from the most recent papers and grasp the new trends for future directions.

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Section: Pasp-based Optimization Methodsmentioning

confidence: 99%

Healthcare scheduling in optimization context: a review

et al. 2021

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“…Sundaramoorthi et al, (2009) used a tree-based model and kernel density function to study patient assignments. While patient-bed assignment has been studied to address workload issues and cost (Guido, Groccia, & Conforti, 2018;Mullinax & Lawley, 2002;Rosenberger, Green, Keeling, Turpin, & Zhang, 2004), there is a lack of focus on the development 9 of a tool that can proactively quantify the impact of changing geographical bed assignments on nurse workload and quality of care. This gap is addressed in this thesis where the impacts of geographical patient bed assignment are quantified.…”

Section: Healthcare System Designmentioning

confidence: 99%

Developing an Approach to Quantify Nurse Workload and Quality of Care Using Discrete Event Simulation

Qureshi¹

2021

Preprint

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Intensive workload for nurses due to high demands directly impacts the quality of care and nurses’ health. To better manage workload, it is necessary to understand the drivers of workload. This multidisciplinary research provides an adaptable nurse-focused approach to discrete event simulation (DES) modelling that can quantify the effects of changing technical design and operational policies in terms of nurse workload and quality of care. In the first phase of this research, a demonstrator model was developed that explored the impact of nurse-patient ratios. As the number of patients per nurse (nurse-patient ratio) increased, nurse workload increased, and the quality of care deteriorated. In the second phase of this research, the DES model tested the interaction of patient acuity and nurse-patient ratios. As the levels of patient acuity and number of patients per nurse increased, nurse workload increased, and quality of care deteriorated – a result that was not surprising but an ability to quantify this proactively, was conceived. In the third phase of this research, the DES model was validated by means of an external field validation study by adapting the model to a real-world unit. The DES model showed excellent consistency between modelling and real-world outcomes (Intraclass iv Correlation Coefficient = 0.85 to 0.99; Spearman Rank-order Correlation Coefficient = 0.78). The fourth phase of this research used the validated simulation model to test the design implication of geographical patient bed assignment. As nurses were assigned to patient beds further away from the center of the unit or spread further apart, nurse workload increased as the nurse had to walk more leading to a deterioration in the quality of care. The DES modelling capability showed that both aspects of assignment were important for patient bed assignment. The fifth phase of this research combined Digital Human Modelling (DHM) and DES to produce a time-trace of biomechanical load and peak biomechanical load (‘activity’) for a full shift of nursing work. As the nurse was assigned to beds further away from the center of the unit, the cumulative biomechanical load decreased as the nurse spent more time walking yielding a reduced biomechanical load in comparison to the task group ‘activity’. As patient acuity is increased, a decrease in L4/L5 moment is observed as the task duration and frequency of most care task increase. Due to increased care demands, nurses must now spend more time delivering care. Since the care demands are much higher than the current capability of nurses, quality of care is deteriorated. As number of patients per nurse, increased a ‘ceiling’ effect on biomechanical load can be observed as nurses do not have the time to attend to this extra demand for care. The use of this adaptable DES modeling approach can assist decision makers by providing quantifiable information on the potential impact of these decisions on nurse workload and quality of care. Thereby, assisting decision makers to create technical design and operational policies for hospital units that do not compromise patient safety and health of nurses. Keywords: Behavioural operations research; Discrete Event Simulation; Nurse Workload; Quality of care; Healthcare Ergonomics; Human Factors Engineering; Nurses; Healthcare policy

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“…Since then, matheuristic techniques have been applied to an even wider range of applications. Two recent examples being Guido et al (2018), who uses MIP models to explore large neighbourhoods in a metaheuristic search for an offline patient-to-bed assignment problem, and Framinan and Perez-Gonzalez (2018), who use a succession of MIP models in an approximation algorithm for an order scheduling problem. The matheuristic to be presented in this paper is based on an ALNS that uses restricted MIP models to explore large neighbourhoods efficiently.…”

Section: Related Work On Matheuristics and Alnsmentioning

confidence: 99%

A matheuristic approach to large-scale avionic scheduling

et al. 2020

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Pre-runtime scheduling of avionic systems is used to ensure that the systems provide the desired functionality at the correct time. This paper considers scheduling of an integrated modular avionic system which from a more general perspective can be seen as a multiprocessor scheduling problem that includes a communication network. The addressed system is practically relevant and the computational evaluations are made on large-scale instances developed together with the industrial partner Saab. A subset of the instances is made publicly available. Our contribution is a matheuristic for solving these large-scale instances and it is obtained by improving the model formulations used in a previously suggested constraint generation procedure and by including an adaptive large neighbourhood search to extend it into a matheuristic. Characteristics of our adaptive large neighbourhood search are that it is made over both discrete and continuous variables and that it needs to balance the search for feasibility and profitable objective value. The repair operation is to apply a mixed-integer programming solver on a model where most of the constraints are treated as soft and a violation of them is instead penalised in the objective function. The largest solved instance, with respect to the number of tasks, has 54,731 tasks and 2530 communication messages.

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An efficient matheuristic for offline patient-to-bed assignment problems

Cited by 36 publications

References 39 publications

Healthcare scheduling in optimization context: a review

Healthcare scheduling in optimization context: a review

Developing an Approach to Quantify Nurse Workload and Quality of Care Using Discrete Event Simulation

A matheuristic approach to large-scale avionic scheduling

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