Hierarchical Method For Nurse Rostering Based On Granular Pre-Processing Of Constraints

Baskaran, Geetha; Bargieła, Andrzej; Qu, Ronghai

doi:10.7148/2009-0855-0860

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…The degree of satisfaction of additional constraints reflecting staff preferences for allocation of specific shifts provides a measure of the quality of the schedule. We produce feasible scheduling by generating pattern sequences [14]. The scheduling problem is now changed to a problem of scheduling patterns.…”

Section: Domain Transformation Using Greedymentioning

confidence: 99%

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Domain Transformation using Greedy Algorithm in Nurse Scheduling

2015

International Conference on Artificial Intelligence, Energy and Manufacturing Engineering (ICAEME'2015) Jan. 7-8, 2015 Dubai (U

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The nurse scheduling problem (NSP) is a complex optimisation problem of allocating nurses to duty rosters in hospitals. The objective is usually to ensure that there are always sufficient nurses on duty, while taking into account individual preferences with respect to work patterns, requests for leave and financial restrictions, in such a way that all employees are treated considerately and fairly. In this paper, we extend our domain transformation-based approach to nurse scheduling. Domain transformation is an approach to solving complex problems that involves a judicious simplification of the original problem, a solution to the simplified problem and finally a transformation of this solution to compensate for the introduced simplifications. The approach we use involves information granulation of shift types that transforms the problem into a smaller solution domain. Next, schedules derived from smaller problem domains are then converted into the original problem domain. This includes a consideration of the constraints that were not represented in the smaller domain. The problem is solved using a greedy algorithm. The approach is general and can be applied within a wide range of benchmark instances. These benchmark instances have been derived from published datasets as well as from our industry collaborators. The greedy algorithm outperforms in most cases when tested with the different demands and number of nurses. The results facilitated the development of a cost-benefit analysis across different levels of staffing.

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Section: Domain Transformation Using Greedymentioning

confidence: 99%

“…Once the patterns are generated [14], we use them to schedule the one week in the DNR domain. We group the patterns in three categories: cost 0, cost 5 and cost 10 according to the full-time and part-time nurses' patterns.…”

Section: A Get the First Week Cost Groupsmentioning

confidence: 99%

Domain Transformation using Greedy Algorithm in Nurse Scheduling

2015

International Conference on Artificial Intelligence, Energy and Manufacturing Engineering (ICAEME'2015) Jan. 7-8, 2015 Dubai (U

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“…Gallivan et al [7] and Shahani et al [8]) look to improve workflow and decision making processes by mining data from existing CCUs. Indeed, the general problem of designing nurse scheduling algorithms has received considerable attention, including hierarchical [9], greedy [10], genetic, and simulate annealing approaches [11]. Here we connect the important problem of nurse scheduling to the practical implications of device heterogeneity and non-interoperability.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization of Caregiver Schedules Based on Vital Sign Streams

Saad¹,

Khan²

2013

ETSN

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Hospital facilities use a collection of heterogeneous devices, produced by many different vendors, to monitor the state of patient vital signs. The limited interoperability of current devices makes it difficult to synthesize multivariate monitoring data into a unified array of real-time information regarding the patients state. Without an infrastructure for the integrated evaluation, display, and storage of vital sign data, one cannot adequately ensure that the assignment of caregivers to patients reflects the relative urgency of patient needs. This is an especially serious issue in critical care units (CCUs). We present a formal mathematical model of an operational critical care unit, together with metrics for evaluating the systematic impact of caregiver scheduling decisions on patient care. The model is rich enough to capture the essential features of device and patient diversity, and so enables us to test the hypothesis that integration of vital sign data could realistically yield a significant positive impact on the efficacy of critical care delivery outcome. To test the hypothesis, we employ the model within a computer simulation. The simulation enables us to compare the current scheduling processes in widespread use within CCUs, against a new scheduling algorithm that makes use of an integrated array of patient information collected by an (anticipated) vital sign data integration infrastructure. The simulation study provides clear evidence that such an infrastructure reduces risk to patients and lowers operational costs, and in so doing reveals the inherent costs of medical device non-interoperability.

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“…We produce an efficient scheduling system by generating pattern sequences (Baskaran, et al 2009). The scheduling problem is then changed to a problem of scheduling patterns.…”

Section: Nurse Scheduling Problem Using Domain Transformationmentioning

confidence: 99%