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Автор: Grafiati
Опубліковано: 13 вересня 2022

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Статті в журналах з теми "Healthcare scheduling"

1

An, Y.-J., Y.-D. Kim, B. J. Jeong, and S.-D. Kim. "Scheduling healthcare services in a home healthcare system." Journal of the Operational Research Society 63, no. 11 (November 2012): 1589–99. http://dx.doi.org/10.1057/jors.2011.153.

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2

Ariff, Hajar, M. Ghazali Kamardan, Suliadi Sufahani, and Maselan Ali. "Review on Queueing Problem in Healthcare." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 304. http://dx.doi.org/10.14419/ijet.v7i4.30.22291.

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Анотація:
This article shows the application of queueing, simulation and scheduling used in the field of healthcare. A summary of queueing, simulation and scheduling theory used in waiting time, appointment system and patient flow are summarised in this article. Different departments in the healthcare system are also considered in this article such as emergency department, outpatient department and the pharmacy. The aim is to provide the reader a general background into queueing, simulation and scheduling in the healthcare.
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3

Restrepo, María I., Louis-Martin Rousseau, and Jonathan Vallée. "Home healthcare integrated staffing and scheduling." Omega 95 (September 2020): 102057. http://dx.doi.org/10.1016/j.omega.2019.03.015.

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4

Barg-Walkow, Laura H., and Wendy A. Rogers. "Modeling Task Scheduling in Complex Healthcare Environments: Identifying Relevant Factors." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 772–75. http://dx.doi.org/10.1177/1541931213601677.

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Multiple task coordination involves scheduling tasks, completing tasks, and integrating tasks into a workflow. Task scheduling can influence outcomes of safety, satisfaction, and efficiency when completing tasks. This is especially important in complex life-critical environments such as healthcare, which incurs many situations where there are multiple tasks and limited resources for addressing all tasks. One approach for understanding tasks coordination is the Strategic Task Overload Management (STOM) model, which is a model for task scheduling behavior. In this theoretical paper, we discuss how this model can be extended to a complex healthcare environment. There are additional considerations (e.g., time) which must be considered when applying this model to healthcare. Ultimately, understanding how emergency physicians make multiple task scheduling decisions will advance theories and models, such as STOM, which can then in turn be implemented to improve scheduling behaviors in complex healthcare environments.
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5

Lakhan, Abdullah, Ali Hassan Sodhro, Arnab Majumdar, Pattaraporn Khuwuthyakorn, and Orawit Thinnukool. "A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks." Sensors 22, no. 6 (March 19, 2022): 2379. http://dx.doi.org/10.3390/s22062379.

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Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications’ execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.
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6

Benila S, Benila S., and Usha Bhanu N. Benila S. "Fog Managed Data Model for IoT based Healthcare Systems." 網際網路技術學刊 23, no. 2 (March 2022): 217–26. http://dx.doi.org/10.53106/160792642022032302003.

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<p>In Internet of things enabled healthcare system, sensors create vast volumes of data that are analyzed in the cloud. Transferring data from the cloud to the application takes a long time. An effective infrastructure can reduce latency and costs by processing data in real-time and close to the user devices. Fog computing can solve this issue by reducing latency by storing, processing, and analyzing patient data at the network edge. Placing the resources at fog layer and scheduling tasks is quite challenging in Fog computing. This paper proposes a Fog Managed Data Model (FMDM) with three layers namely Sensor, Fog and cloud to solve the aforementioned issue. Sensors generate patient data and that are managed and processed by Fog and cloud layers. Tasks are scheduled using a Weighted Fog Priority Job Scheduling algorithm (WFPJS) and fog nodes are allocated based on Priority based Virtual Machine Classification Algorithm (PVCA). The performance of this model is validated with static scheduling techniques with variable patient counts and network configurations. The proposed FMDM with WFPJS reduces response time, total execution cost, network usage, network latency, computational latency and energy consumption.</p> <p>&nbsp;</p>
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7

Zhang, Xiaojin, Shuang Ma, and Songlin Chen. "Healthcare service configuration based on project scheduling." Advanced Engineering Informatics 43 (January 2020): 101039. http://dx.doi.org/10.1016/j.aei.2020.101039.

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8

Abdalkareem, Zahraa A., Amiza Amir, Mohammed Azmi Al-Betar, Phaklen Ekhan, and Abdelaziz I. Hammouri. "Healthcare scheduling in optimization context: a review." Health and Technology 11, no. 3 (April 10, 2021): 445–69. http://dx.doi.org/10.1007/s12553-021-00547-5.

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9

Che, Haiying, Xiaolong Wang, Hong Wang, Zixing Bai, and Honglei Li. "Scheduling with Deadline Constraint of Healthcare Applications on Cloud-Based Workflow." Journal of Medical Imaging and Health Informatics 10, no. 10 (October 1, 2020): 2430–38. http://dx.doi.org/10.1166/jmihi.2020.3290.

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Cloud-based workflow technology has played an important role in the development of large scale healthcare applications with high flexibility to meet variety of healthcare process requirements. Among all the factors affecting the healthcare applications on cloud-based workflow, the tasks scheduling is the crucial one. This paper aims at the cloud-based workflow tasks scheduling with deadline constraints and its implementation in two approaches: heuristic scheduling algorithm (HSA) and meta heuristic scheduling algorithm (HSA-ACO). HSA decomposes the workflow according to its structure and divide the deadline into the level deadlines. Tasks in each level get scheduling priority according to the earliest start time under the constraint of level deadline. In another method, HSA-ACO integrates HSA with ant colony algorithm to achieve better performance. In the last part, we launch the experiment to compare HSA and HSA-ACO with algorithms like Prolis, LACO and ICPCP in three types of workflow with different scales. The experiment results show that HSA-ACO is better than the other algorithms.
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10

Iqbal, Naeem, Imran, Shabir Ahmad, Rashid Ahmad, and Do-Hyeun Kim. "A Scheduling Mechanism Based on Optimization Using IoT-Tasks Orchestration for Efficient Patient Health Monitoring." Sensors 21, no. 16 (August 11, 2021): 5430. http://dx.doi.org/10.3390/s21165430.

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Анотація:
Over the past years, numerous Internet of Things (IoT)-based healthcare systems have been developed to monitor patient health conditions, but these traditional systems do not adapt to constraints imposed by revolutionized IoT technology. IoT-based healthcare systems are considered mission-critical applications whose missing deadlines cause critical situations. For example, in patients with chronic diseases or other fatal diseases, a missed task could lead to fatalities. This study presents a smart patient health monitoring system (PHMS) based on an optimized scheduling mechanism using IoT-tasks orchestration architecture to monitor vital signs data of remote patients. The proposed smart PHMS consists of two core modules: a healthcare task scheduling based on optimization and optimization of healthcare services using a real-time IoT-based task orchestration architecture. First, an optimized time-constraint-aware scheduling mechanism using a real-time IoT-based task orchestration architecture is developed to generate autonomous healthcare tasks and effectively handle the deployment of emergent healthcare tasks. Second, an optimization module is developed to optimize the services of the e-Health industry based on objective functions. Furthermore, our study uses Libelium e-Health toolkit to monitors the physiological data of remote patients continuously. The experimental results reveal that an optimized scheduling mechanism reduces the tasks starvation by 14% and tasks failure by 17% compared to a conventional fair emergency first (FEF) scheduling mechanism. The performance analysis results demonstrate the effectiveness of the proposed system, and it suggests that the proposed solution can be an effective and sustainable solution towards monitoring patient’s vital signs data in the IoT-based e-Health domain.
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Дисертації з теми "Healthcare scheduling"

1

Heasley, McKay N. "Dynamic Appointment Scheduling in Healthcare." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/3176.

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In recent years, healthcare management has become fertile ground for the scheduling theory community. In addition to an extensive academic literature on this subject, there has also been a proliferation of healthcare scheduling software companies in the marketplace. Typical scheduling systems use rule-based analytics that give schedulers advisory information from programmable heuristics such as the Bailey-Welch rule cite{B,BW}, which recommends overbooking early in the day to fill-in potential no-shows later on. We propose a dynamic programming problem formulation to the scheduling problem that maximizes revenue. We formulate the problem and discuss the effectiveness of 3 different algorithms that solve the problem. We find that the 3rd algorithm, which has smallest amount of nodes in the decision tree, has an upper bound given by the Bell numbers. We then present an alternative problem formulation that includes stochastic appointment lengths and no shows.
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2

Banerjea-Brodeur, Monica. "Selection hyper-heuristics for healthcare scheduling." Thesis, University of Nottingham, 2013. http://eprints.nottingham.ac.uk/14395/.

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A variety of approaches have been used to solve a variety of combinatorial optimisation problems. Many of those approaches are tailored to the particular problem being addressed. Recently, there has been a growing number of studies towards providing more general search methodologies than currently exist which are applicable to different problem domains without requiring any algorithmic modification. Hyper-heuristics represent a class of such general methodologies which are capable of automating the design of search process via generating new heuristics and/or mixing existing heuristics to solve hard computational problems. This study focuses on the design of selection hyper-heuristics which attempt to improve an initially created solution iteratively through heuristic selection and move acceptance processes and their application to the real-world healthcare scheduling problems, particularly, nurse rostering and surgery admission planning. One of the top previously proposed general hyper-heuristic methodology was an adaptive hyper-heuristic consisting of many parameters, although their values were either fixed or set during the search process, with a complicated design. This approach ranked the first at an international cross-domain heuristic search challenge among twenty other competitors for solving instances from six different problem domains, including maximum satisfiability, one dimensional bin packing, permutation flow shop, personnel scheduling, travelling salesman, vehicle routing problems. The hyper-heuristics submitted to the competition along with the problem domain implementations can now be considered as the benchmark for hyper-heuristics. This thesis describes two new easy-to-implement selection hyper-heuristics and their variants based on iterated and greedy search strategies. A crucial feature of the proposed hyper-heuristics is that they necessitate setting of less number of parameters when compared to many of the existing approaches. This entails an easier and more efficient implementation, since less time and effort is required for parameter tuning. The empirical results show that our most efficient and effective hyper-heuristic which contains only a single parameter outperforms the top ranking algorithm from the challenge when evaluated across all six problem domains. Moreover, experiments using additional nurse rostering problems which are different than the ones used in the challenge and surgery scheduling problems show that the results found by the proposed hyper-heuristics are very competitive, yielding with the best known solutions in some cases.
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3

Villarreal, Monica Cecilia. "Capacity planning and scheduling with applications in healthcare." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54855.

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In this thesis we address capacity planning problems with different demand and service characteristics, motivated by healthcare applications. In the first application, we develop, implement, and assess the impact of analytical models, accompanied by a decision-support tool, for operating room (OR) staff planning decisions with different service lines. First, we propose a methodology to forecast the staff demand by service line. We use these results in a two-phase mathematical model that defines the staffing budget for each service line, and then decides how many staff to assign to each potential shift and day pair while considering staff overtime and pooling policies and other staff planning constraints. We also propose a heuristic to solve the model's second phase. We implement these models using historical data from a community hospital and analyze the effect of different model parameters and settings. Compared with the current practice, we reduce delays and staff pooling at no additional cost. We validate these conclusions through a simulation model. In the second application, we consider the problem of staff planning and scheduling when there is an accepted time window between each order's arrival and fulfillment, with the goal of obtaining a balanced schedule that focuses on on-time demand fulfillment but also considers staff characteristics and operational practices. Hence, solving this problem requires simultaneously scheduling the staff and the forecasted demand. We propose, implement, and analyze the results of a model for staff and demand scheduling under this setting, accompanied by a decision-support tool. We implement this model in a company that offers document processing and other back-office services to healthcare providers. We provide details on the model validation, implementation, and results, including a 25\% increase in the company's staff productivity. Finally, we provide insights on the effects of some of the model's parameters and settings, and assess the performance of a proposed heuristic to solve this problem. In the third application, we consider a non-consumable resource planning problem. Demand consists of a set of jobs, each job has a scheduled start time and duration, and belongs to a particular demand class that requires a subset of resources. Jobs can be `accepted' or `rejected,' and the service level is measured by the (weighted) percentage of accepted jobs. The goal is to find the capacity level that minimizes the total cost of the resources, subject to global and demand-class-based service level constraints. We first analyze the complexity of this problem and several of its special cases, and then we propose a model to find the optimal inventory for each type of resource. We show the convergence of the sample average approximation method to solve a stochastic extension of the model. This problem is motivated by the inventory planning decisions for surgical instruments for ORs. We study the effects of different model parameters and settings on the cost and service levels, based on surgical data from a community hospital.
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4

Demirbilek, Mustafa. "Dynamically accepting and scheduling patients for home healthcare." Thesis, University of Warwick, 2018. http://wrap.warwick.ac.uk/110628/.

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Importance of home healthcare is growing rapidly since populations of developed and even developing countries are getting older quickly and the number of hospitals, retirement homes, and medical staff do not increase at the same rate. We present Scenario Based Approach (SBA) for the Home Healthcare Nurse Scheduling Problem. In this problem, arrivals of patients are dynamic and acceptance and appointment time decisions have to be made as soon as patients arrive. The primary objective is to maximise the average number of daily visits. For the sake of service continuity, patients have to be visited at the same days and times each week during their service horizon. SBA is basically a simulation procedure based on generating several scenarios and scheduling new customers with a simple but fast heuristic. Then results are analysed to decide whether to accept the new patient and at which appointment day/time. First, two different versions of SBA, Daily and Weekly SBA are developed and analysed for a single nurse. We compare Daily SBA to two greedy heuristics from the literature, distance and capacity based, and computational studies show that Daily SBA makes significant improvements compared to these other two methods for a single nurse. Next, we extend SBA for a multi-nurse case. SBA is compared to a greedy heuristic under different conditions such as same depot case where nurses start their visits from and return to same place, clustered service area, and nurses with different qualification level. SBA gives superior results under all experiment conditions compared to the greedy heuristic.
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5

Gurumurthy, Prakash. "Dynamic stochastic vehicle routing model in home healthcare scheduling /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1426064.

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6

Elsaeiby, Aber. "Healthcare Operations Management: Models for Improving Productivity, Scheduling and Quality." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1449421673.

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7

Dehnoei, Sajjad. "A Stochastic Optimization Approach for Staff Scheduling Decisions at Inpatient Clinics." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40925.

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Staff scheduling is one of the most important challenges that every healthcare organization faces. Long wait times due to the lack of care providers, high salary costs, rigorous work regulations, decreasing workforce availability, and other similar difficulties make it necessary for healthcare decision-makers to pay special attention to this crucial part of their management activities. Staff scheduling decisions can be very difficult. At inpatient clinics, there is not always a good estimate of the demand for services and patients can be discharged at any given time, consequently affecting staff requirements. Moreover, there are many other unpredictable factors affecting the decision process. For example, various seasonal patterns or possible staff leaves due to sickness, vacations, etc. This research describes a solution approach for staff scheduling problems at inpatient clinics where demand for services and patient discharges are considered to be stochastic. The approach is comprehensive enough to be generalizable to a wide range of different inpatient settings with different staff requirements, patient types, and workplace regulations. We first classify patients into a number of patient groups with known care-provider requirements and then develop a predictive model that captures patients’ flow and arrivals for each patient category in the inpatient clinic. This model provides a prediction of the number of patients of each type on each specific day of the planning horizon. Our predictive modelling methodology is based on a Discrete Time Markov model with the number of patients of different types as the state of the system. The predictive model generates a potentially large set of possible scenarios for the system utilization over the planning horizon. We use Monte Carlo Simulation to generate samples of these scenarios and a well known Stochastic Optimization algorithm, called the Sample Average Approximation (SAA) to find a robust solution for the problem across all possible scenarios. The algorithm is linked with a Mixed-Integer Programming (MIP) model which seeks to find the optimal staff schedule over the planning horizon while ensuring maximum demand coverage and cost efficiency are achieved. To check the validity of the proposed approach, we simulated a number of scenarios for different inpatient clinics and evaluated the model’s performance for each of them.
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8

Curtois, Timothy. "Novel heuristic and metaheuristic approaches to the automated scheduling of healthcare personnel." Thesis, University of Nottingham, 2008. http://eprints.nottingham.ac.uk/28309/.

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This thesis is concerned with automated personnel scheduling in healthcare organisations; in particular, nurse rostering. Over the past forty years the nurse rostering problem has received a large amount of research. This can be mostly attributed to its practical applications and the scientific challenges of solving such a complex problem. The benefits of automating the rostering process include reducing the planner’s workload and associated costs and being able to create higher quality and more flexible schedules. This has become more important recently in order to retain nurses and attract more people into the profession. Better quality rosters also reduce fatigue and stress due to overwork and poor scheduling and help to maximise the use of leisure time by satisfying more requests. A more contented workforce will lead to higher productivity, increased quality of patient service and a better level of healthcare. Basically stated, the nurse rostering problem requires the assignment of shifts to personnel to ensure that sufficient employees are present to perform the duties required. There are usually a number of constraints such as working regulations and legal requirements and a number of objectives such as maximising the nurses working preferences. When formulated mathematically this problem can be shown to belong to a class of problems which are considered intractable. The work presented in this thesis expands upon the research that has already been conducted to try and provide higher quality solutions to these challenging problems in shorter computation times. The thesis is broadly structured into three sections. 1) An investigation into a nurse rostering problem provided by an industrial collaborator. 2) A framework to aid research in nurse rostering. 3) The development of a number of advanced algorithms for solving highly complex, real world problems.
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9

Iezzi, Jana. "Multi-criteria decision making in outpatient scheduling." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001817.

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White, Denise L. "Operational Planning and Scheduling in the Outpatient Clinic Environment." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1276527552.

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Книги з теми "Healthcare scheduling"

1

Hall, Randolph, ed. Handbook of Healthcare System Scheduling. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1734-7.

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2

Ferguson, Linda. Scheduling: The job nobody wants but everybody thinks they can do better : a guide to scheduling healthcare professionals. [New Hope, MN: Wheatridge Press, 1995.

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3

Samuel, Hohmann, ed. Increasing patient satisfaction with statistical correlation: A step-by-step guide to JCAHO's staffing effectiveness standards. Marblehead, MA: HCPro, 2003.

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4

Mutingi, Michael, and Charles Mbohwa. Healthcare Staff Scheduling. CRC Press, 2015. http://dx.doi.org/10.1201/b18889.

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5

Handbook Of Healthcare System Scheduling. Springer, 2011.

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6

Hall, Randolph. Handbook of Healthcare System Scheduling. Springer, 2014.

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7

Mbohwa, Charles, and Michael Mutingi. Healthcare Staff Scheduling: Emerging Fuzzy Optimization Approaches. Taylor & Francis Group, 2015.

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8

Lirov, Yuval. Practicing Profitability - Billing Network Effect for Revenue Cycle Control in Healthcare Clinics and Chiropractic Offices: Collections, Audit Risk, SOAP Notes, Scheduling, Care Plans, and Coding. Affinity Billing, Inc, 2007.

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9

Ronen, Boaz, Joseph S. Pliskin, and Shimeon Pass. Constraint Management in a Bottleneck Environment (DRAFT). Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190843458.003.0005.

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This chapter introduces steps 4 through 7 of the theory of constraints—that, respectively, decide how to exploit and utilize the constraint, subordinate the system to the constraint, elevate and break the constraint, and do not let inertia become the system constraint. The chapter shows how to achieve more using the existing resources by focusing on the bottleneck. For example, reducing waste (“garbage time”) of the bottleneck can quite quickly increase the system’s throughput. The subordination of the rest of the system to the bottleneck is then discussed. For this purpose, the scheduling mechanism of drum–buffer–rope can be implemented in some areas of healthcare systems, like operating rooms, leading to increased throughput and reduction of waiting times as well as improved clinical quality.
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Частини книг з теми "Healthcare scheduling"

1

Frezza, Eldo E. "Scheduling." In Patient-Centered Healthcare, 87–92. Boca Raton : Routledge/Taylor & Francis, 2020.: Productivity Press, 2019. http://dx.doi.org/10.4324/9780429032226-12.

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Lim, Gino J., Arezou Mobasher, Laleh Kardar, and Murray J. Cote. "Nurse Scheduling." In Handbook of Healthcare System Scheduling, 31–64. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1734-7_3.

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Dhru, Nidhish. "Overcome Scheduling Challenges." In Office 365 for Healthcare Professionals, 125–44. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3549-2_6.

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Hans, Erwin W., and Peter T. Vanberkel. "Operating Theatre Planning and Scheduling." In Handbook of Healthcare System Scheduling, 105–30. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1734-7_5.

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Hall, Randolph. "Matching Healthcare Resources to Patient Needs." In Handbook of Healthcare System Scheduling, 1–9. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1734-7_1.

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Milburn, Ashlea Bennett. "Operations Research Applications in Home Healthcare." In Handbook of Healthcare System Scheduling, 281–302. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1734-7_11.

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Hans, Erwin W., Mark van Houdenhoven, and Peter J. H. Hulshof. "A Framework for Healthcare Planning and Control." In Handbook of Healthcare System Scheduling, 303–20. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1734-7_12.

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Rossetti, Manuel D., Nebil Buyurgan, and Edward Pohl. "Medical Supply Logistics." In Handbook of Healthcare System Scheduling, 245–80. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1734-7_10.

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Utley, Martin, and Dave Worthington. "Capacity Planning." In Handbook of Healthcare System Scheduling, 11–30. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1734-7_2.

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Gupta, Diwakar, and Wen-Ya Wang. "Patient Appointments in Ambulatory Care." In Handbook of Healthcare System Scheduling, 65–104. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1734-7_4.

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Тези доповідей конференцій з теми "Healthcare scheduling"

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Stan, Ovidiu, Camelia Avram, Iulia Stefan, and Adina Astilean. "Integrated innovative solutions to improve healthcare scheduling." In 2016 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR). IEEE, 2016. http://dx.doi.org/10.1109/aqtr.2016.7501301.

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Kim, Joongheon, and Sungrae Cho. "Queue-aware learning for scheduling in healthcare clouds." In 2017 International Conference on Information and Communications (ICIC). IEEE, 2017. http://dx.doi.org/10.1109/infoc.2017.8001684.

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Pascaru, Cosmin, and Paul Diac. "Vehicle Routing and Scheduling for Regular Mobile Healthcare Services." In 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2018. http://dx.doi.org/10.1109/ictai.2018.00080.

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Masmoudi, Malek, and Racem Mellouli. "MILP for Synchronized-mTSPTW: Application to Home HealthCare Scheduling." In 2014 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2014. http://dx.doi.org/10.1109/codit.2014.6996910.

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Jamthe, Anagha, Amitabh Mishra, and Dharma P. Agrawal. "Scheduling schemes for interference suppression in healthcare sensor networks." In ICC 2014 - 2014 IEEE International Conference on Communications. IEEE, 2014. http://dx.doi.org/10.1109/icc.2014.6883350.

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Reeves, Joseph, and Ming Li. "Context-Aware Analysis Scheduling in Wireless Body Area Networks." In 2018 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2018. http://dx.doi.org/10.1109/ichi.2018.00049.

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Ala, Ali, and Feng Chen. "An Appointment Scheduling Optimization Method in Healthcare with Simulation Approach." In 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA). IEEE, 2020. http://dx.doi.org/10.1109/iciea49774.2020.9101995.

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Sehr, Martin A., Robert R. Bitmead, and John Fontanesi. "Multi-class appointments in individualized healthcare: Analysis for scheduling rules." In 2015 European Control Conference (ECC). IEEE, 2015. http://dx.doi.org/10.1109/ecc.2015.7330707.

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Whitehead, N. Peter, Stephen C. Adams, William T. Scherer, Hyojung Kang, and Matthew Gerber. "Systems thinking and predictive analytics to improve veteran healthcare scheduling." In 2017 Annual IEEE International Systems Conference (SysCon). IEEE, 2017. http://dx.doi.org/10.1109/syscon.2017.7934720.

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Borchani, Rahma, Malek Masmoudi, and Bassem Jarboui. "Hybrid Genetic Algorithm for Home Healthcare routing and scheduling problem." In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2019. http://dx.doi.org/10.1109/codit.2019.8820532.

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