Scheduling Tool Research Articles

Physical education teaching scheduling technology based on chaotic genetic algorithm.

In the modern educational environment, rational and efficient course scheduling is of great significance in ensuring teaching quality and improving resource utilization. The traditional sports scheduling methods are faced with the challenges of diversified demands and complex constraints. For this reason, the study proposes a new physical education course scheduling model after mathematically modeling the physical education scheduling model and improving the genetic environment based on chaotic genetic algorithm, and then proposes a new physical education course scheduling model. The experiment outcomes denote that the average computing time of the improved chaotic genetic algorithm is 28s, and when the number of iterations is 175-200, the optimal number of individuals is 35 at most, and the value of the optimal fitness is 9.4. The simulation test outcomes denote that the new scheduling model can reasonably arrange the course in the 5th-6th section, which meets the needs of students. Meanwhile, when the amount of scheduling teachers is 4 and the amount of courses is 5, the average utilization rate of scheduling resources at this time is the highest 82.5%. Compared with the same type of scheduling model, the new model has the highest superiority of 90.7%, the highest stability of 90.1%, and the highest robustness of 91.6%. The proposed method can meet the diversified and dynamically changing teaching needs, and provides an effective optimization tool for physical education scheduling.

Organization of Vaccination Sites in Quebec: Perception of Users, Employees and managers

Abstract Since late 2020, vaccination sites have emerged rapidly in order to protect the population against COVID-19. Health services in Montérégie (Québec) are provided by three organizations. Each adopted a different approach to immunization. The Regional Public Health Department took this opportunity to describe, compare, and explain these three approaches. A mixed methods design through a multiple case study was conducted. Users, employees and managers were interviewed at the end of 2021 on several performance-related topics. Individual interviews with employees and managers in mid-2022 further explored these topics. 38 managers, 360 employees, and 946 users participated in the survey. 9 managers and 10 employees were interviewed. Most participants were satisfied with vaccination organization regardless of the model used, mainly because of high geographic and temporal accessibility of the sites. Further signage to indicate directions could optimize accessibility. High adaptability was essential for managers and employees. Training strategies such as people to refer to and pairing allowed to better deal with frequent changes and staff rotation. The addition of ongoing training could be beneficial. Rapid changes in employee schedules were identified as a challenge. The use of technology-based scheduling tools, accessible to all, brought added flexibility, which was appreciated. Several ideas were used to communicate updates on a daily basis. However, timely implementation of new recommendations remained an ongoing challenge. Dedicated clinics for specific clienteles were successful. Community partnerships were generally beneficial, although establishing more of them could potentially reach other vulnerable communities. To improve accessibility and minimize health social inequities, a greater investment in local partnerships might reach more vulnerable clienteles. Regular communication and innovative employee support are key elements to improve fluidity of operations. Key messages • Reaching out for vulnerable clienteles is essential to minimize health social inequities. • Rapid changes in services needs underlign fluidity communication importance.

Including Lifetime Hydraulic Turbine Cost into Short-Term Hybrid Scheduling of Hydro and Solar

In traditional short-term hydropower scheduling problems, which usually determine the optimal power generation schedules within one week, the off-design zone of a hydraulic turbine is modeled as a forbidden zone due to the significantly increased risk of turbine damage when operating within this zone. However, it is still plausible to occasionally operate within this zone for short durations under real-world circumstances. With the integration of Variable Renewable Energy (VRE) into the power system, hydropower, as a dispatchable energy resource, operates complementarily with VRE to smooth overall power generation and enhance system performance. The rapid and frequent adjustments in output power make it inevitable for the hydraulic turbine to operate in the off-design zone. This paper introduces the operating zones associated with various production costs derived from fatigue analysis of the hydraulic turbine and calculated based on the turbine replacement cost. These costs are incorporated into a short-term hybrid scheduling tool based on Mixed Integer Linear Programming (MILP). Including production costs in the optimization problem shifts the turbine’s working point from a high-cost zone to a low-cost zone. The resulting production schedule for a Hydro-Solar hybrid power system considers not only short-term economic factors such as day-ahead market prices and water value but also lifetime hydraulic turbine cost, leading to a more comprehensive calculation of the production plan. This research provides valuable insights into the sustainable operation of hydropower plants, balancing short-term profits with lifetime hydraulic turbine costs.

{MFPP(R). An R package for matrix-based flexible project planning

Project planning and scheduling are essential parts of project management. While project planning and scheduling tools are already available to support traditional project management approaches, flexible project management approaches, such as agile, extreme, and hybrid project planning, are less well supported by software tools, especially freely available software packages. To our knowledge, no existing R package for project planning and scheduling can support flexible projects. This paper aims to fill this gap by introducing and describing the R package mfpp for matrix-based flexible project planning/scheduling. This package includes a comprehensive set of tools for project managers to schedule both traditional and flexible project plans. The use of the package is illustrated through examples.

Expert System Software Framework for Nutritional Analysis and Diet-Related Disease

Nigeria faces a serious health crisis with diet-related diseases, which calls for creative ways to improve nutritional results and public knowledge. In addressing the challenges, this study introduces an Expert System Software Framework for Nutritional Analysis and Diet-Related Diseases, leveraging advanced technologies to address healthcare and nutrition challenges. Developed through rigorous research, the framework offers dynamic capabilities, personalized recommendations, and a user-friendly interface. Key features include customized algorithms, blockchain authentication for security, dynamic scheduling, and robust administrative tools. It fills critical gaps by providing insights into nutritional contents and health impacts of foods, supporting informed decision-making and proactive management of diet-related disorders. The performance evaluation demonstrates a high accuracy rate of 91.7%, ensuring reliability in dietary recommendations. Future directions include integrating wearable technology and AI to provide real-time health data for personalized dietary recommendations. This framework sets a new standard in nutritional analysis and disease management, contributing to advancements in healthcare and nutrition. Its recommendations for sustained engagement, collaboration with medical professionals, and global expansion aim to improve user outcomes and quality of life beyond the Nigerian setting.

An 18-month Review of ClusterMarket(TM) as an Instrument Scheduling and Project Management Tool for Brigham Young University’s Electron Microscopy Facility

An 18-month Review of ClusterMarket(TM) as an Instrument Scheduling and Project Management Tool for Brigham Young University’s Electron Microscopy Facility

Coupled weather and crop simulation modeling for smart irrigation planning: a review

ABSTRACT Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management.

Inisiasi Gerakan Desa Keluarga Aktif

Family involvement in the mentoring process is a necessity. The family needs to be placed as the actor or subject. Families design forms of assistance because their basic needs are understood by the family. The family should not be placed as an object that merely receives coaching. The purpose of the community service program consists of two, namely first to initiate an active family village, and second to foster a Blessed, Safe, and Happy family based on Islamic psychology and culture. This service program uses the Sustainable Livelihood Approach (SLA) approach. This approach allows the family as an actor to design a service program. Active family involvement in every activity process. Data collection was carried out using the Participatory Rural Appraisal (PRA) method. Data was collected through a Focus Group Discussion (FGD) process and using a family schedule tool. The results of the program show that firstly the formation of four family development cadre structures, namely Old Age cadres, Youth Family Development Cadres, Integrated Service Post Cadres, and Toddler Family Development Cadres, secondly active family coaching through parenting assistance children through discussion of family goals, family and child needs, the child's personality structure in the perspective of Islamic-Bugis psychology.

Pandemia, turismo e educação na Região Norte do Paraná: Análise comparativa com uso do Radar de Inovação

During the pandemic caused by Covid-19, several sectors of the economy went through difficult times in their management, but perhaps the most affected were the tourism and education sectors. In this context, hotel and educational companies were able to count on the transfer of management technologies through the Local Innovation Agents Project, which, in a partnership between the National Council for Research and Technological Development and the Brazilian Micro and Small Business Support Service, used the Innovation Radar methodology as a management diagnosis. This article aims to compare the Education and Tourism sectors to understand which of them obtained the best results during COVID-19 in the northern region of Paraná, between the months of February 2021 and September 2021. The research is descriptive. The sectors analyzed are Education and Tourism, in the northern region of Paraná. The survey construct includes the 6 dimensions contained in the Innovation Radar, which are: Management by Indicators, Operations Management, Marketing, Sustainable Practices, Innovation and Digital Transformation. The instruments used were the Innovation Radar built in Excel, and online tools for scheduling and holding meetings, such as WhatsApp, Google Calendar, Google Meet and Zoom. The sample is intentional and consists of 3 companies from each sector (Education and Tourism). The results showed that when analyzing which dimensions had the highest growth in each of the verticals, it was concluded that the dimension that achieved the most growth in the Tourism sector was the Marketing dimension, with a growth of 31.65%, on average, and as for the Education sector, Innovation was the dimension that presented the highest percentage of growth, with 64.03%. The Tourism sector presented the best result in Marketing and the Education sector presented the best result in innovation.

Artificial intelligence in dermatology: Bridging the gap in patient care and education

The application of artificial intelligence (AI) in education and clinical medicine has shown tremendous growth. The primary explanation for this application is AI's ability to integrate efficient and tailored methods for screening, using diagnostics, and enhancement of patient and medical education. AI's wide scope of utility can be seen through its ability to improve efficiency in clinical settings through scheduling, charting, diagnostics, and screening tools, ultimately allowing physicians to spend more focused time on patient care. AI has also had a tangible impact on promoting patient education through its ability to provide patients with preliminary information regarding their diagnoses before followup and to further discussion with their physician. AI's application in medical education is promising due to its ability to provide immediate and interactive feedback to the learner, which allows for meaningful reinforcement of knowledge. AI can therefore be recognized as a tool that can provide incredible enhancement in the areas of clinical medicine and education, with meaningful opportunities for integration and application.

A PMBOK-based construction cost management framework for BIM integration in construction projects

Cost overruns persist as a critical issue in construction projects. This study aims to explore how the integration of Building Information Modeling (BIM) can enhance project cost management processes aligned with the PMBOK framework, fostering improved construction cost control and management as well as decision-making. The study investigates the applicability of PMBoK cost management procedures within the context of BIM software and its various applications. A comprehensive BIM-cantered cost management framework is introduced to operationalize this synergy, encompassing planning, estimation, budgeting, monitoring, and control stages. This framework’s efficacy is validated through its application to an industrial facility, leveraging integrated BIM, scheduling, and cost tools. The findings revealed BIM’s benefits in visualization, clash detection, automated take-off, and 5D simulation, influencing informed decision-making during the project. However, these advantages coexist with certain limitations, including challenges in establishing bi-directional quantity-cost linkages, generating customizable reports, conducting risk-centric budgeting, and conducting in-depth subcontractor analyses. This research puts forth an applied BIM framework tailored to construction cost management and provides practical insights to guide adoption strategies and technology advancement needs. The study can assist industry practitioners and policymakers in re-evaluating cost management strategies concerning BIM capabilities.

Comparative Analysis of Methods of Evaluating Human Fatigue

AbstractThe present study used four different methods to estimate fatigue. Forty-seven volunteers (45 men and 2 women), 41.3 ± 7.5 years old, truck operators for 11.5 ± 6.0 years, were included. All participants accepted the invitation to be included in the study. Actigraphy and core temperature were evaluated. The 5-minute psychomotor vigilance test, the Karolinksa Sleepiness Scale (KSS), and the postural assessment using the Light Sonometer™ (Belo Horizonte, Minas Gerais, Brazil) were performed. Fatigue prediction was performed using the Fatigue Avoidance Scheduling Tool (FAST) program. In response to the Pittsburgh Sleep Quality Index (PSQI), 51.06% had good sleep quality and 48.94% had poor sleep quality with an average efficiency of 81.6%. In response to the actigraphy, workers slept an average of 7.2 hours a day with 93.5% efficiency. The workers' core body temperature (CBT) cosinor analysis showed a preserved circadian curve. Core body temperature showed differences between the 6 hours worked in each shift. Similarly, the light sound level meter showed lower risk scores for fatigue in day shifts. Only the variable of the fastest 10% of the Psychomotor Vigilance Test (PVT) showed worse results, while no significant differences were observed by the KSS. The risk analysis by FAST showed a strong influence of the circadian factor. In conclusion, each method has positive and negative points, and it is up to the evaluator/manager to identify the method that best suits the purpose of the evaluation, as well as the local culture and conditions. We recommend using different methods of risk assessment and management in combination with fatigue prediction by Sonometer as well as carrying out assessments, which enable researchers to estimate performance and fatigue throughout the working day, since these may change over the duration of the working day.

Predictive Three-Dimensional Printing of Spare Parts with Internet of Things

Industry 4.0 integrates digital and physical technologies to transform work management, where two core enablers are the internet of things (IoT) and three-dimensional printing (3DP). IoT monitors complex systems in real time, whereas 3DP enables agile manufacturing that can respond to real-time information. However, the details of how these two can be integrated are not yet clear. To gain insights, we consider a scenario where a three-dimensional (3D) printer supplies a critical part to multiple machines that are embedded with sensors and connected through IoT. Although the public perception indicates that this integration would enable on-demand printing, our research suggests that this is not necessarily the case. Instead, the true benefit is the ability to print predictively. In particular, it is typically more effective for the 3D printer to predictively print to stock based on a threshold that depends on the system’s status. We also identify a printing mode called predictive print on demand that allows for minimal inventory, and we find the speed of 3DP to be the primary factor that influences its optimality. Furthermore, we assess the value of IoT in cost reductions by separately analyzing the impact of advance information from embedded sensors and the real-time information fusion through IoT. We find that IoT provides significant value in general. However, the conventional wisdom that IoT’s value scales up for larger systems is suitable only when the expansion is paired with appropriate 3DP capacity. Our framework can help inform investment decisions regarding IoT/embedded sensors and support the development of scheduling tools for predictive 3DP. This paper was accepted by David Simchi-Levi, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00978 .

ENHANCING MEDICAL OFFICER SCHEDULING IN HEALTHCARE ORGANIZATIONS: A COMPREHENSIVE INVESTIGATION OF GENETIC AND GOOGLE OR TOOLS ALGORITHMS FOR MULTI-PROJECT RESOURCE-CONSTRAINED OPTIMIZATION

In healthcare organizations, medical staff scheduling is vital to achieving optimal patient care, ensuring the well-being of medical officers, and the efficiency of operations. This research aims to address the challenges of optimizing the scheduling of limited resources for multiple projects for medical staff, through a comparative analysis of Google OR tools and genetic algorithms. We evaluate the performance of these tools in various scenarios, taking into account factors such as overtime, work balance, and scheduling efficiency. This comparative analysis reveals the strengths and weaknesses of each approach, facilitating the development of improved medical staff scheduling solutions. Additionally, we offer algorithmic optimizations tailored to meet the requirements of specific healthcare settings, which contribute to enhancing the adaptability and effectiveness of scheduling tools. The research findings provide valuable insights to guide decision-making in healthcare institutions, ultimately aiming to enhance the quality of care provided by medical officers and improve the overall efficiency of the healthcare system. In conclusion, the results show that the modified Google OR algorithm significantly outperforms the Google OR tools and the regular genetic algorithm in performance.

Application of Improved Association Rule Algorithm in Teaching Management Systems of Colleges and Universities

Teaching management systems (TMS) are comprehensive platforms designed to streamline various aspects of educational administration, instruction, and communication within academic institutions. These systems typically offer features such as course scheduling, grade management, attendance tracking, and communication tools for instructors, students, and administrators. TMS also facilitates content delivery, assessment creation, and student progress monitoring, providing a centralized hub for all aspects of teaching and learning. With the integration of modern technologies like cloud computing and mobile applications, TMS enhance accessibility, efficiency, and collaboration among stakeholders in education. This paper explores the integration of Frequent Pattern Decision Support Systems (FP-DSS) into Teaching Management Systems (TMS) and its impact on teaching and learning practices in higher education. Through a comprehensive experimental analysis, we investigate the effectiveness of FP-DSS in improving learning outcomes, personalization, and student engagement across various teaching methodologies. The findings reveal significant improvements in learning outcomes, with average exam scores increasing by up to 12% when FP-DSS is incorporated into innovative teaching methodologies. Additionally, we observed enhanced personalization of instruction, with a rating of 9 out of 10 for the effectiveness of FP-DSS in tailoring learning experiences to individual student needs. Furthermore, student engagement showed notable improvements across all experiments, with students actively participating in the learning process and demonstrating higher levels of motivation and interest.

Artificial Intelligence for Patient Flow

Why Is This an issue? Inefficient patient flow contributes to the overcrowding of health care settings and negative clinical outcomes and patient experiences downstream. Patient flow management aims to achieve seamless patient movement through the health care system and between acute and long-term settings, ensuring timely access to quality care. What Is the Technology? Artificial intelligence (AI)-based patient flow management tools are interventions designed to forecast and monitor patient movement from admission to discharge as they progress through different care settings. AI-driven tools can leverage big data and digital information systems (e.g., electronic health records) to facilitate effective patient flow. AI-based patient appointment scheduling tools, which can help improve patient flow, are created to automate appointment scheduling and optimize it by minimizing wait times and matching the demand for health services and hospital capacity. What Is the Potential Impact? AI tools for patient flow management can support volume forecasting of patients with various conditions, especially those experiencing chronic conditions that require different types of treatment or care in different settings over a long period of time. These AI tools can predict admissions, patient movement from the emergency department to inpatient beds, discharge, and transfers to different health care settings. Evidence for their effectiveness in patients with emergency admissions and those transferred to tertiary and quaternary care, as well as inpatients from the general, cardiology, and mental health departments, was reported. In addition, evidence suggested that AI tools can optimize appointment scheduling in general outpatient settings and operating rooms. In health care systems in Canada, AI tools are being used or investigated to enhance patient flow by predicting emergency admissions, transfers to alternate levels of care, and general inpatient discharges, as well as optimizing capacity planning for patients receiving oncology care. AI appointment scheduling tools are currently being used in some oncology care settings and operating rooms across Canada. The implementation of AI systems generally requires an upfront investment of time and other resources in addition to the financial cost of the system itself for set-up, integration, and staff training. The goal of these systems is to improve efficiency and save money, time, and human resources in the long run. What Else Do We Need to Know? Patient privacy and data security issues are concerns regarding widespread implementation of AI tools trained on electronic health records systems and patient datasets. AI algorithms trained on datasets lacking adequate representation of all relevant patients may not predict their flow accurately. Training datasets with sufficient data from all relevant patient groups can ensure the inputs and outputs of the algorithms accurately reflect patient care needs and mitigate potential bias. To accurately predict the care needs of local patients, AI algorithms, once deployed, should be retrained on site-specific datasets containing data for the patient populations that are representative of the hospital or health system in which they are being used. Not all institutions have the hardware or computing power available to adequately or efficiently process the large amounts of data that are required by these AI systems, or the infrastructure needed to use big data (e.g., from electronic health records), and may require additional resources for implementation, as well as for ongoing maintenance and updating of the systems.

{MFPP(R). An R package for matrix-based flexible project planning

Project planning and scheduling are essential parts of project management. While project planning and scheduling tools are already available to support traditional project management approaches, flexible project management, such as agile, extreme and hybrid project planning, are only somewhat supported by computer algorithms. To the best of our knowledge, no existing R package for project planning and scheduling can support project planning and scheduling for flexible projects. In this paper, the goal is to fill this gap; to this end, the R package mfpp for matrix-based flexible project planning/scheduling is introduced and described. This package includes a comprehensive set of tools for project managers to schedule both traditional and flexible project plans. The use of the package is illustrated through examples.

IoT CONTROLLED LED NOTICE BOARD

The IoT Controlled LED sign board design represents a slice- edge bid aimed at revolutionizing display results for a multitude of purposes including advertising, information dispersion, and aesthetic improvement. At its core, the sign board features an intricate array of LEDs strictly arranged in a grid pattern, each strictly orchestrated by a central microcontroller unit( MCU) harmoniously integrated with a Bluetooth module for flawless wireless communication. This community enables druggies to apply their smartphones or any Bluetooth- enabled bias as important remote controls, decreeing the content showcased on the LED board with unknown ease and perfection. Through a devoted mobile operation, druggies gain a gateway to a realm of creative expression, empowered to transmit a myriad of content types ranging from brief textbook dispatches to witching, robustness and images for immediate display. specially, the system's hallmark lies in its capability to grease real- time content operation, preventing the need for physical intervention and fostering dynamic rigidity to evolving demands and scripts. likewise, the mobile operation's robust point set extends beyond bare content transmission, offering sophisticated tools for communication scheduling and contemporaneous operation of multiple sign boards, thereby accelerating scalability and usability across different surroundings similar as bustling retail spaces, bulging transportation capitals, and bustling public spaces likewise. With its intimately designed interface and multifaceted functionality, the IoT Controlled LED sign board design emerges as a lamp of invention, promising to review the veritably geography of information dispersion and visual communication in the digital age. KEYWORDS: IoT Controlled LED subscribe Board, Microcontroller Unit( MCU), Wireless Communication, Mobile Application, Management, Remote Control.

A comprehensive study to facilitate Student Space platform with collaborative learning

The Student Space Virtual Study Groups Platform is a groundbreaking solution designed to transform collaborative learning for students, addressing challenges in forming and sustaining study groups. This online platform integrates advanced technology to create a user-friendly environment, featuring an intuitive interface, dynamic group formation with smart algorithms, real-time collaboration through video conferencing and document editing, centralized resource sharing, and smart scheduling tools. Student Space aims to overcome the limitations of traditional study groups, offering an inclusive and adaptable virtual space for students to connect, collaborate, and excel academically. Anticipated impacts include improved academic performance and the creation of a vibrant academic community. Ongoing enhancements, such as integration with Learning Management Systems, a mobile app for on-the-go collaboration, and improved AI-driven matching algorithms, are planned for the future.

Study Habits and Academic Performance of Undergraduate Nursing Students: A Descriptive Study

Students' academic achievement is greatly influenced by their study habits. Academic Success and Study Habits are tied to and dependent upon one another. There are pupils with various degrees of academic achievement who come from various environments, communities, etc. The academic performance of students has been one of the main indicators used to evaluate the quality of education in schools, colleges, universities, and Institutions. It is a complex process that is influenced by several factors, such as study habits. The study aims to study the study habits and Academic performance of undergraduate Nursing students. Materials and Methods: A self-structured questionnaire schedule tool has been prepared to assess the effect of study habits and Academic performance. The tool was given to 100 students of all the 3rd year and 4th year B.Sc. Nursing students in a Google form. Result: In terms of analysis of study habits and academic performance and the Association between Study habits and academic performance, undergraduate Nursing students had above-average study habits, with a median and SD of 66 ± 9.82 and 8 ± 1.69 respectively. The test was done using a chi-square test. There is a statistically significant association between Study habits and academic performance. The p-value=0.000 Conclusion: The study habits of nursing students have a significant impact on their academic performance. The findings underscore the need for educational institutions to provide resources and guidance on developing strong study habits.