Waiting time computation for blood collection sites

Brummelen, S.P.J. van; Kort, W. L. de; Dijk, N.M. van

doi:10.1016/j.orhc.2015.09.008

Cited by 12 publications

(11 citation statements)

References 21 publications

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“…In this model, formula (6) is the main objective function, formula 7is the secondary objective function, and the rest of formulas from formula (8) to formula (14) are constraints. Formula (8) measures the delay time compared with expected waiting time of patient .…”

Section: Model Designmentioning

confidence: 99%

“…In many literatures, patients' waiting time is a major factor to be considered because it is closely linked with the quality of hospital operation. Now the hospital aims to deliver the highest quality of care, so reducing waiting time of patient is important [7][8][9]. These papers gave a function to compute waiting time or gave a new concept called "breakin-moments" (BIMs) to reduce waiting time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Scheduling of Doctors Outpatient Departments Based on Patients’ Behavior

Ren

Guo

Bai

2016

Scientific Programming

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The low operational efficiency in the field of medical and health care has become a serious problem in China; the long time that the patients have to wait for is the main phenomenon of difficult medical service. Medical industry is service-oriented and its main purpose is to make profits, so the benefits and competitiveness of a hospital depend on patient satisfaction. This paper makes a survey on a large hospital in Harbin of China and collects relevant data and then uses the prospect theory to analyze patients’ and doctors’ behavioral characteristics and the model of patient satisfaction is established based on fuzzy theory with a tripletα/β/γ. The optimal scheduling of clinic is described as a problem with the rule of first come, first served which maximizes patient satisfaction for the main goal and minimizes operating costs for the secondary goal. And the corresponding mathematical model is established. Finally, a solution method named plant growth simulation algorithm (PGSA) is presented. Then, by means of calculating of the example and comparing with genetic algorithm, the results show that the optimum can be reached; meanwhile the efficiency of the presented algorithm is better than the genetic algorithm.

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Section: Model Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Doctors Outpatient Departments Based on Patients’ Behavior

Ren

Guo

Bai

2016

Scientific Programming

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“…[4][5][6] In general, basic queueing models are applied as approximations for complex computer and telecommunication networks, 7,8 manufacturing and service systems, [9][10][11][12] and, more recently, health care systems, [13][14][15] among others. [4][5][6] In general, basic queueing models are applied as approximations for complex computer and telecommunication networks, 7,8 manufacturing and service systems, [9][10][11][12] and, more recently, health care systems, [13][14][15] among others.…”

Section: Introductionmentioning

confidence: 99%

“…The use of queueing models has been the subject of a number of research studies, 1-3 mainly due to their ability to approximately represent industrial systems. [4][5][6] In general, basic queueing models are applied as approximations for complex computer and telecommunication networks, 7,8 manufacturing and service systems, [9][10][11][12] and, more recently, health care systems, [13][14][15] among others. In particular, M∕M∕s queues are one of the most basic queueing models, 16 which, in Kendall notation, stand for Markovian arrivals with rate , exponential service times with average 1∕ , and s parallel identical servers.…”

Section: Introductionmentioning

confidence: 99%

Control charts for traffic intensity monitoring of Markovian multiserver queues

Cruz

Quinino

2019

Quality & Reliability Eng

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A number of recent research studies have applied queueing theory as an approximate modeling tool to mathematically describe industrial systems, which include manufacturing, distribution, and service, for instance. Among the main observable characteristics in queues, the number of users in the system can be controlled to keep waiting times as minimal as possible. The design of efficient control charts is an attempt to monitor and control such systems. Control charts are proposed to monitor infinite queues with Markovian arrivals, exponential service times, and s identical parallel servers. The proposed charts monitor traffic intensities, which are the ratio between the arrival rate and the service rate, estimated through the number of users in the queueing system at random epochs. The effectiveness and efficiency of the proposed approaches in terms of the average run lengths are established by a comprehensive set of Monte Carlo simulations. KEYWORDS attribute control charts, average run length, Markovian queues, quality control wileyonlinelibrary.com/journal/qre [Colour figure can be viewed at wileyonlinelibrary.com] [Colour figure can be viewed at wileyonlinelibrary.com] Frederico R. B. Cruz holds a bachelor's degree in electrical engineering (1988) as well as a master's degree (1991) and a doctorate (1997) in computer science from the Universidade Federal de Minas Gerais, where he is a full professor in the Department of Statistics and conducts research in operations research.Roberto C. Quinino has a PhD in production engineering from the Universidade de São Paulo (1998). He is currently a full professor at the Universidade Federal de Minas Gerais. He develops research on quality control and marketing engineering with an emphasis on stochastic modeling applications acting on the following subjects: Taguchi online, Markov chains, misclassification, optimization, control chart planning, regression analysis, planning of experiments, and conjoint analysis. He supervises undergraduate, specialization, master's, and doctoral students. He has published papers in several international journals.

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“…One of the classical basic queuing models [1], M/M/s queues have many applications and consequently great necessity of inference. In fact, many real-life systems are approximately described by queuing models, including computer and telecommunication networks [2][3][4], manufacturing and service systems [5][6][7], and heathcare systems [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Simple and Yet Efficient Estimators for Markovian Multiserver Queues

Suyama

Quinino

Cruz

2018

Mathematical Problems in Engineering

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Estimators for the parameters of the Markovian multiserver queues are presented, from samples that are the number of clients in the system at arbitrary points and their sojourn times. As estimation in queues is a recognizably difficult inferential problem, this study focuses on the estimators for the arrival rate, the service rate, and the ratio of these two rates, which is known as the traffic intensity. Simulations are performed to verify the quality of the estimations for sample sizes up to 400. This research also relates notable new insights, for example, that the maximum likelihood estimator for the traffic intensity is equivalent to its moment estimator. Some limitations of the results are presented along with a detailed numerical example and topics for future developments in this research area.

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Waiting time computation for blood collection sites

Cited by 12 publications

References 21 publications

Optimal Scheduling of Doctors Outpatient Departments Based on Patients’ Behavior

Optimal Scheduling of Doctors Outpatient Departments Based on Patients’ Behavior

Control charts for traffic intensity monitoring of Markovian multiserver queues

Simple and Yet Efficient Estimators for Markovian Multiserver Queues

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