Workload Allocation Research Articles

Medical resource allocation planning by integrating machine learning and optimization models

Patients' waiting time is a major issue in the Canadian healthcare system. The planning for resource allocation impacts patients' waiting time in medicare settings. This research focuses on the reduction of patients' waiting time by providing better planning for radiological resource allocation and efficient workload distribution. Resource allocation planning is directly related to the number of patient-arrival and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different modalities and different timeframes which makes the patient-arrival prediction challenging. In this research, a new three-phase solution framework is proposed where a new multi-target machine learning technique is integrated with an optimization model. In the first phase, a novel Ensemble of Pruned Regressor Chain (EPRC) model is developed and trained offline to predict uncertain parameters, such as patients' arrival. The proposed model is then compared with two popular multi-target prediction methods to evaluate the model's accuracy. In the second phase, the trained model is deployed in the real-time environment to forecast patients' arrival, miss Turn Around Time (miss-TAT) rate, and probable workload count. The forecasted data is used in phase three where a new multi-objective optimization model is developed to determine workload allocation. The Weighted-sum method is used to get efficient solutions. The proposed model is deployed in a Canadian healthcare company and evaluated using real-time healthcare data. It is observed in terms of accuracy, the proposed EPRC model performed 10.81 % better compared to the other multi-target models considered in this study. It is also noticed that the forecasting results have a direct impact on the workload distribution, where the proposed model decreases the total workload by approximately 25 %. Besides, the result shows the efficient workload distribution provided by the proposed framework can reduce the average patients' waiting time by 8.17 %.

Quantitative methods to determine the student workload. I. Empirical study based on digital platforms.

We present a quantitative study of an online course developed during COVID19 sanitary emergency in Chile. We reconstruct the teaching-learning process considering the activity logs on digital platforms in order to answer the question of How do our students study? The results from the analysis evidence the complex adaptive character of the academic environment, which exhibits regularities similar to those found in financial markets (e.g., distributions of the daily time devoted to learning activities follow patterns like Pareto's or Zipf's law). Our empirical results illustrate (i) the relevance of economic notions in the understanding of the teaching-learning processes and (ii) the reliability of quantitative methods based on digital platforms to conduct experimental studies in this framework. We introduce in the present work a series of indicators to characterize the performance of professors, students' follow-up of the course, and their learning progress by crossing information with the results of assessments. In this context, the learning rate appears as a key statistical descriptor for the allocation of the student workload.

Impact of Dual Intensive Care Unit and Rapid Response Team Nursing Roles on Service Delivery in the Intensive Care Unit.

Rapid response teams generally consist of multidisciplinary specialist staff members with advanced knowledge and skills to manage deteriorating patients outside of the intensive care unit. These teams consist predominantly of intensive care unit nurses, who often undertake intensive care unit and rapid response team duties concurrently. Little has been published on the impact of these nurses' absence while attending a rapid response call on the nursing workforce, patient care, and resource allocation in the intensive care unit. To explore the impact of nurses' absence due to attending rapid response calls in other areas of the hospital on service delivery and resource allocation in the intensive care unit. A comprehensive search of 3 databases was performed to identify studies on relationships among the rapid response team, the intensive care unit, and nursing staff. The search yielded 6 quantitative studies, 2 qualitative studies, and a mixed-methods study that were included for analysis. Four key themes were identified: (1) workforce, staffing processes, and resource allocation; (2) alterations to workload and resource allocation; (3) adverse events or incidents; and (4) funding variability of rapid response team models. Review of the studies indicated that dual intensive care unit and rapid response team nursing roles have negative effects on nurses' workload, increase the risk of adverse events, and may compromise patient safety. The staffing of both the intensive care unit and the rapid response team should be examined carefully with an eye toward sustainability, cost-effectiveness, and clear outcome measures.

Renewable-Aware Geographical Load Balancing Using Option Pricing for Energy Cost Minimization in Data Centers

It is becoming increasingly difficult to properly control the power consumption of widely dispersed data centers. Energy consumption is high because of the need to run these data centers (DCs) that handle incoming user requests. The rising cost of electricity at the data center is a contemporary problem for cloud service providers (CSPs). Recent studies show that geo-distributed data centers may share the load and save money using variable power prices and pricing derivatives in the wholesale electricity market. In this study, we evaluate the problem of reducing energy expenditures in geographically dispersed data centers while accounting for variable system dynamics, power price fluctuations, and renewable energy sources. We present a renewable energy-based load balancing employing an option pricing (RLB-Option) online algorithm based on a greedy approach for interactive task allocation to reduce energy costs. The basic idea of RLB-Option is to process incoming user requests using available renewable energy sources. In contrast, in the case of unprocessed user requests, the workload will be processed using brown energy or call option contract at each timeslot. We formulate the energy cost minimization in geo-distributed DCs as an optimization problem considering geographical load balancing, renewable energy, and an option pricing contract from the derivative market while satisfying the set of constraints. We prove that the RLB-Option can reduce the energy cost of the DCs close to that of the optimal offline algorithm with future information. Compared to standard workload allocation methods, RLB-Option shows considerable cost savings in experimental evaluations based on real-world data.

Drawing ex-studio: An eccentric case for correspondence courses

Digitally networked distance learning strategies in the virtual learning environment (VLE) have been increasingly mandated in studio drawing courses in the COVID-19 era. This phenomenon of studio teaching in absence of the studio is referred to in this analysis as an ‘ex-studio’ model. This article posits an alternative and a corrective to the failures and limitations of digital ex-studio learning and teaching through the understudied, underappreciated and largely defunct strategy of correspondence teaching via postal networks. A critique of online studio teaching experiences in the field of drawing is contrasted with the potential for correspondence courses, informed by mail art practices, to re-engage distance learning with community building, material knowledge, skill formation, effective formative assessment, coherent workload allocation and teacher–learner rapport.

The influencing factors of clinical nurses’ problem solving dilemma: a qualitative study

ABSTRACT Purpose Problem solving has been defined as “a goal-directed sequence of cognitive and affective operations as well as behavioural responses to adapting to internal or external demands or challenges. Studies have shown that some nurses lack rational thinking and decision-making ability to identify patients’ health problems and make clinical judgements, and have poor cognition and response to some clinical problems, easy to fall into problem-solving dilemma. This study aimed to understand the influencing factors of clinical nurses’ problem solving dilemma, to provide a basis for developing training strategies and improving the ability of clinical nurses in problem solving. Methods A qualitative research was conducted using in-depth interviews from August 2020 to December 2020. A total of 14 participants from a tertiary hospital in Shanghai, China were recruited through purposive sampling combined with a maximum variation strategy. Data were analysed with the conventional content analysis method. Results Three themes and seven subthemes were extracted: nurse’s own factors (differences in knowledge structure and thinking, differences in professional values, poor strain capacity); improper nursing management (low sense of organizational support, contradiction between large workload and insufficient manpower allocation); patient factors (the concept of emphasizing medicine and neglecting to nurse, individual differences of patients). Conclusion The influencing factors of clinical nurses’ problem-solving dilemma are diverse. Hospital managers and nursing educators should pay attention to the problem-solving of clinical nurses, carry out a series of training and counselling of nurses by using the method of situational simulation, optimize the nursing management mode, learn to use new media technology to improve the credibility of nurses to provide guarantee for effective problem-solving of clinical nurses.

A dark and stormy night: Reallocation storms in edge computing

Efficient resource usage in edge computing requires clever allocation of the workload of application components. In this paper, we show that under certain circumstances, the number of superfluous workload reallocations from one edge server to another may grow to a significant proportion of all user tasks—a phenomenon we present as a reallocation storm. We showcase this phenomenon on a city-scale edge server deployment by simulating the allocation of user task workloads in a number of scenarios capturing likely edge computing deployments and usage patterns. The simulations are based on a large real-world data set of city-wide Wi-Fi network connections, with more than 47M connections over ca. 560 access points. We study the occurrence of reallocation storms in three common edge-based reallocation strategies and compare the latency–workload trade-offs related to each strategy. As a result, we find that the superfluous reallocations vanish when the edge server capacity is increased above a certain threshold, unique for each reallocation strategy, peaking at ca. 35% of the peak ES workload. Further, while a reallocation strategy aiming to minimize latency consistently resulted in the worst reallocation storms, the two other strategies, namely a random reallocation strategy and a bottom-up strategy which always chooses the edge server with the lowest workload as a reallocation target, behave nearly identically in terms of latency as well as the reallocation storm in dense edge deployments. Since the random strategy requires much less coordination, we recommend it over the bottom-up one in dense ES deployments. Moreover, we study the conditions associated with reallocation storms. We discover that edge servers with the very highest workloads are best associated with reallocation storms, with other servers around the few busy nodes thus mirroring their workload. Further, we identify circumstances associated with an elevated risk of reallocation storms, such as summertime (ca. 4 times the risk than on average) and on weekends (ca. 1.5 times the risk). Furthermore, mass events such as popular sports games incurred a high risk (nearly 10 times that of the average) of a reallocation storm in a MEC-based scenario.

Effect of interaction network structure in a response threshold model

Task allocation is a characteristic feature of social insects. This phenomenon is self-organized by workers in a colony without central instructions. Workers perform the necessary tasks while communicating and determining their colony’s local situations or outside environments. They can also allocate the workload of the task to finish quickly. To explain the self-organized phenomenon, we develop a theoretical model that includes the local interaction mechanism among the workers. We extend the fixed response threshold model by introducing the interaction network among workers. The new model is called the interaction network response threshold model. This model analyzes the effects of the interaction network structure on the workload allocation mechanism using the Gini coefficient. We find that the network structure affects the information diffusion process, and the network features affect workload allocation. Our results indicate that communication among the workers in a colony is an essential system for workload allocation.

Ambiguity and peripherality in doctoral co-supervision workload allocation

ABSTRACT Doctoral education and supervision have changed in recent decades. The increasing prevalence of co-supervision has been a notable aspect of this, but change also includes stricter accountability and quality assurance measures, such as the quantification of workload allocations in supervision as well as of academic work more broadly. This paper focuses on challenging issues pertaining to such workload allocation in co-supervision, an important and underexplored feature of doctoral supervision, with the aim of informing policy and improving practice. This qualitative study was conducted in a research-intensive university and data sources were semi-structured interviews with 14 academic staff across multiple disciplines plus a short survey comprising mostly of open-ended questions completed by 106 participants. These data were complimented with institutional supervisor workload allocations. Ambiguity was found to be a dominant feature of the co-supervision allocations and this had both advantages and disadvantages. The concept of ‘supervision peripherality’ is introduced to capture the phenomenon of co-supervisors with a very low share of supervision, which we argue is a phenomenon that challenges what constitutes being a doctoral supervisor and thus warrants further exploration. We also argue institutions should examine the adequacy of having such peripheral supervisors in order to determine when these are truly required.

A cluster-based algorithm for home health care planning: A case study in Australia

Home health care (HHC) refers to the delivery of social, medical and paramedical services to people in their homes. Caregivers are assigned and routed to perform various tasks such as personal care and household chores at the client’s homes. Minimising the total cost and satisfying the client requirements and preferences are critical in HHC. In this paper, we present a mixed-integer linear programming model for HHC routing and scheduling problems, which considers fair and balanced workload allocation of caregivers while minimising the total cost and addressing the client’s needs. Due to the complexity of the problem, using a commercial solver is not practical for real size instances. Therefore, we develop a multi-steps clustering approach using Ordering Points and Agglomerative Hierarchical Clustering to solve the model. The proposed solution approach is applied on several test instances to examine its performance. We also apply the proposed model and solution approach on a case study in Australia.

Low-Latency Anomaly Detection on the Edge-Cloud Continuum for Industry 4.0 Applications: the SEAWALL Case Study

Several emerging Industry 4.0 applications related to the monitoring and fault diagnostic of critical equipment introduce strict bounds on the latency of the data processing. Edge computing has emerged as a viable approach to mitigate the latency by offloading tasks to nodes nearby the data sources; at the same time, few industrial case studies have been reported so far. In this paper, we describe the design, implementation and evaluation of the SEAWALL platform for the heterogeneous data acquisition and low-latency processing in Industry 4.0 scenarios. The framework has been developed within the homonymous project founded by the Italian BIREX industrial consortium and involving both academic and industrial partners. The proposed framework supports data collection from heterogeneous production line machines mapped to different IoT protocols. In addition, it enables the seamless orchestration of workloads in the edge-cloud continuum so that the latency of the alerting service is minimized requirement of the processing task is continuously met, while taking into account the constrained resources of the edge servers. We evaluate the SEAWALL framework in a small-case industrial testbed and quantify the performance gain provided by the dynamic workload allocation on the continuum.

The adaptation of academics to the Covid-19 crisis in terms of work time allocation.

Academics have seen their work environment and routines severely affected by the Covid19 pandemic. This topic has been analyzed by the literature, mostly from personal and descriptive perspectives, that highlight the challenging transitions and adaptations that academics have endured concerning their work and life-balance. This research complements those studies, by using a sample of university academics working all around the world in all disciplinary fields and focuses on a longitudinal perspective of workload and task time allocation of academic work. The findings show that academics which in general had long working hours, further increased their time of the week dedicated to work leading possibly to the reported cases in the literature of increasing stress and burnout during the pandemic. These effects were found to be similar to all academics, independently of their gender and disciplinary field. More concerning is that this increased number of hours worked per week appears to have established itself as part of the new normal. The main driver for the increased workload is associated with teaching, and to a lesser extent with administrative duties.

Revisiting Higher Education Business Models and Academic Performance Metrics Times

Performance metrics for academics in higher education have been changing and becoming more refined; focusing on income generation such as grants, high impact publications or the completion of a higher degree by research that the government awards funding. Government funding is often restrictive and highly competitive. Definitions of research active started to dictate workload models and allocations of teaching duties. The updated Tertiary Education Quality and Standards Agency (TEQSA) threshold standard now requires at least one supervisor of a higher degree by research student to be research active. 
 
 However, the entry of private higher education providers into the education sector created many challenges. Private higher education providers in many cases do not receive government funding and focus on popular degrees. This allows for a highly profitable institution. Although, it represents a significant shift between a higher education that strives for research objectives to a sole focus on educating the massive for a profit. 
 
 Performance metrics, the employment modern awards, enterprise agreements currently do not reflect a teaching-only (intensive) institute. Restricting employers to classifications with inherent duties and pay scale that are simply not reflective of an employee's value or worth.
 
 This paper examines the disparity between duties, expectations and limitations of employment, the education minimum standards and makes recommendations on how they can co-exist better. One subject model discussed, has full-time staff become business owners of a degree enabling them to share in the profit in a franchise-education model.

A multi-objective load balancing and power minimization in cloud using bio-inspired algorithms

In Recent days, cloud computing technology is used as a remote-based virtual computer resource utilization to offer consumers quick and accurate massive data services. In the cloud, infrastructure as services have a significant impact on computing efficiency by Infrastructure as a services (IaaS). Cloud computing uses on-demand resource provisioning. In the cloud, a variety of metaheuristic algorithms are helpful for process allocation. Large-scale datacenters utilise a lot of electricity, which has an influence on the environment and the economy. Using a Micro-Genetic Algorithm, a stable combined process workload allocation method with Cat Swarm Optimization (MG-CSO) is introduced by addressing pre-convergence problems and optimal resource management. For optimal computing efficiency, the resources are dynamically consolidated and clustered. Additionally, the MG-CSO Algorithm produces positive outcomes when compared to other well-known algorithms, such as the Genetic Algorithm (GA) and the Bat Algorithm (BA).This research's focus is to reduce cost, time, Service Level Agreement(SLA), energy usage, and afford good Quality of Service(QoS). Our final scientific results were achieved with 91%.

Educator perspectives on implementing the Optometry Aboriginal and Torres Strait Islander health curriculum framework

ABSTRACT Clinical relevance Aboriginal and Torres Strait Islander Peoples have the right to the highest attainable standard of health and access to health services without discrimination. To uphold these rights, it is important that optometry students are educated in Aboriginal and Torres Strait Islander health and culturally safe practice. Background An Aboriginal and Torres Strait Islander health curriculum framework is now mandatory for optometry programs to implement. This study explores the experiences of those involved in implementing this framework and, from their perspectives, what is and is not working. Methods Heads and educators involved in Aboriginal and Torres Strait Islander health curriculum at Australian optometry programs participated in a mixed-methods study. Information about pedagogy and contextual issues impacting curriculum implementation was collected through surveys and interviews. The design and analysis were underpinned by constructivist grounded theory. Results Most programs had at least partially implemented components of the curriculum framework. While partnerships were considered essential to success, this remains a work in progress. Authentic learning through clinical placement was considered a priority. Assessment was described as complex and not yet adequately addressed. Only 37% of educators felt fully prepared to teach and 6% to assess this curriculum. More workload allocation, funding and recruitment of Aboriginal and Torres Strait Islander staff and students through institutional commitment and leadership were perceived requirements. Conclusion Further developing sustainable partnerships where there is reciprocity between optometry programs and Aboriginal and Torres Strait Islander educators and communities is critical to effectively implementing Aboriginal and Torres Strait Islander health curriculum. While clinical placement was considered an important learning experience, the type of experience and whether or not all students should attend require further consideration. Additionally, educators need support for professional development and sharing resources in this emerging field. This will contribute to improving culturally safe practice in optometry.

Covid-19 and orthopaedic trauma: Quantification of orthopaedic trauma workload and staff resource allocation during a global pandemic-related lockdown

IntroductionHealthcare systems across the world have struggled as a result of the Covid-19 pandemic. Most specialties have redeployed their staff and resources to deal with the pandemic whilst ceasing their planned elective activity. However acute specialties such as Trauma and Orthopaedics still have a significant emergency caseload that must be safely managed, even in a pandemic. The aim of this study was to investigate the change in Orthopaedic Trauma caseload in a Trauma Unit and a Major Trauma Centre during a pandemic compared to pre-pandemic levels and the associated staffing requirements. MethodsThe data presented was collected from a Trauma Unit and a Major Trauma Centre in the United Kingdom. We compared the number of accident and emergency referrals, fracture clinic appointments, inpatient admissions and operations during the six weeks of the first lockdown from 23rd March to May 3, 2020, to the same time period in 2019. ResultsThe results showed that the orthopaedic trauma caseload was approximately half that of pre-pandemic levels, reducing by an average of 54.2%. ConclusionA significant orthopaedic trauma caseload still remains to be safely managed during a pandemic and appropriate resources must be allocated. Staff allocation must take into account sick cover and staff wellbeing. A proportion of staff may need to be ring fenced from redeployment to facilitate this.

Community-Engaged Research on Social Capital and Older Adults’ Health: Lessons Learned

Most adults in the United States prefer to age in their own homes and communities. However, many ageing-in-place models rely on expensive external services, negatively affecting access by lower socioeconomic status (SES) and other vulnerable groups. This article documents two pilot projects conducted by a community-academic partnership that examined associations between social capital, ageing in community, and health among older adults. The first project explored the association between social capital and health across community SES levels. The second project explored one type of social capital, timebanking, and its association with health. We highlight here our lessons learned from these community-engaged research (CER) projects: (1) Our partnership needed to improve our study design and data collection by enhancing our recruitment strategies, community site partnerships, survey instrument and data matching, and research team workload allocation issues. (2) We should have validated our instruments for use with older adults who had mild cognitive and visual impairments, acknowledged how community SES differences influenced our data collection, and included more research assistant support during our community meetings. (3) We would have benefited from protocol development for recording and responding to issues raised by participants. Our projects also led us to relational insights, such as reinforcing the need to foster clear communication across team members, involving community advisory boards earlier in the CER process, seeking network input on research strategies to meet older adults’ needs, and developing plans to sustain long-term relationships. We hope these lessons learned are useful to other community-engaged researchers.

A Workload and Machine Categorization-Based Resource Allocation Framework for Load Balancing and Balanced Resource Utilization in the Cloud

This paper proposes a workload and machine categorization based resource allocation framework for balancing the load across active physical machines as well as utilizing their different resource capacities in a balanced manner. The workload, essentially independent and non-preemptive tasks are allocated resources on the physical machines whose resource availability complements the resource requirement of tasks. Simulation based experiments are performed using CloudSim simulator to execute three different set of tasks comprising 10000, 20000, and 30000 number of tasks. The metric of load imbalance across active physical machines and the metric of utilization imbalance among their considered resource capacities (i.e., CPU and RAM) are measured in different scheduling cycles of a simulation run. Simulation results show that the proposed resource allocation method outperforms the compared methods in terms of balancing the load across active physical machines and utilizing their different resource capacities in a balanced manner.

Effective Workload Allocation in Fog Device based on Power Consumption and Delay Tradeoff

Fog computing that emerges as an important paradigm, describes decentralized computing architecture between cloud and devices. It includes potential challenges, such as increase in traffic overhead, since all requests are sent to the main server that causes delay, which cannot be tolerated in delay sensitive applications and the usage of inappropriate scheduling causes high power consumption in fog device. These challenges must be overcome by employing effective workload. In this paper, in order to find effective workload allocation based on power and delay tradeoff various scheduling algorithms like SJF (Shortest Job First), FCFS (First Come First Served) and RR (Round Robin) are implemented in fog device and its power and delay trade off are analyzed in fog computing subsystem.

Academic workloads: what does a manager need to consider?

ABSTRACT Utilising Bacchi’s framework ‘What’s the problem represented to be?’ the literature on managing academic workloads was analysed to explore why workload allocation models are deemed essential in the contemporary university and the assumptions which underpin workload allocations. Whether due to the need for efficient use of scarce resources or ensuring equity for staff, workload allocation models are promoted as an accurate measure of each individual’s workload and as a measure of accountability. The literature privileges the views of staff whose workloads are ‘managed’ but unable to do their work within their contracted hours. Universities justify long hours of unpaid overtime as a norm of professionalism. Critical issues for managers are identified, and suggestions to enable them to be more effective in the process of workload allocation are presented.