Enhanced Greedy Optimization Algorithm with Data Warehousing for Automated Nurse Scheduling System

Ratnayaka, R. M. Kapila Taranga; Wang, Zhongjun; Anamalamudi, Satish; Cheng, Siyue

doi:10.4236/etsn.2012.14007

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…In the proposed solution for the NSP, nurses are distributed randomly into sets (fulfilling hospital requirements). Second an incidence matrix containing nurses data is inputted into Java Program [15] (JP), as well as number of vertices (based on the first step). JP output of colored vertices is used to construct shift groups depending on colors.…”

Section: Methodsmentioning

confidence: 99%

“…Graphs can be used to model many types of relations and processes in physical, biological, social and information systems. Many practical problems can be represented by graphs [15,17,19]. Proper coloring of a graph in the simplest form is an assignment of colors either to vertices of the graphs, or to its edges, in such a way that adjacent vertices/edges are colored differently [21,22].…”

Section: Background Of Studymentioning

confidence: 99%

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Study of nurses’ roster by coloring algorithm parsus manually

Toufic

Alqhtani

2019

Filomat

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Scheduling shifts is a tiresome and time consuming task in any business, and particularly in hospitals where errors are costly, rules are plentiful and changes are rapid. The person performing this function (Head Nurse) will have to keep track of all the employees concerned, distributing hours fairly and avoiding collisions. Rules regulating working hours and breaks have to be followed and the qualifications of individual employees need to be considered. Hours are spent every day on this task in every ward. The goal of this paper is to solve Nurses Scheduling Problem (NSP) and initialize a fair roster for Pediatric Department (PD) in a local hospital in Riyadh Saudi Arabia.

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Section: Methodsmentioning

confidence: 99%

Section: Background Of Studymentioning

confidence: 99%

Study of nurses’ roster by coloring algorithm parsus manually

Toufic

Alqhtani

2019

Filomat

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“…Various approximation methods are available, such as simulated annealing, genetic algorithms, and so on. A standard “greedy” optimization algorithm is used (Ratnayaka, Wang, Anamalamudi, & Cheng, ). The hierarchical clustering method also enables us to define the community structure according to the required granularity level.…”

Section: Connectivity Index: Identifying Widely Accepted Journalsmentioning

confidence: 99%

A profile‐boosted research analytics framework to recommend journals for manuscripts

Silva

Yang

et al. 2014

Asso for Info Science & Tech

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With the increasing pressure on researchers to produce scientifically rigorous and relevant research, researchers need to find suitable publication outlets with the highest value and visibility for their manuscripts. Traditional approaches for discovering publication outlets mainly focus on manually matching research relevance in terms of keywords as well as comparing journal qualities, but other research-relevant information such as social connections, publication rewards, and productivity of authors are largely ignored. To assist in identifying effective publication outlets and to support effective journal recommendations for manuscripts, a threedimensional profile-boosted research analytics framework (RAF) that holistically considers relevance, connectivity, and productivity is proposed. To demonstrate the usability of the proposed framework, a prototype system was implemented using the ScholarMate research social network platform. Evaluation results show that the proposed RAF-based approach outperforms traditional recommendation techniques that can be applied to journal recommendations in terms of quality and performance. This research is the first attempt to provide an integrated framework for effective recommendation in the context of scientific item recommendation.

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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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Enhanced Greedy Optimization Algorithm with Data Warehousing for Automated Nurse Scheduling System

Cited by 11 publications

References 8 publications

Study of nurses’ roster by coloring algorithm parsus manually

Study of nurses’ roster by coloring algorithm parsus manually

A profile‐boosted research analytics framework to recommend journals for manuscripts

Dynamic Optimization of Caregiver Schedules Based on Vital Sign Streams

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