Workload Balancing Research Articles

Integration of digital lean principles and line balancing in apparel manufacturing

Purpose: The integration of digitalization into the apparel manufacturing sector has become a strategic imperative, providing companies with a unique competitive edge. This study explores the application of ITEX PMD, an Internet of Things (IoT) data collection device, and evaluates the ITEX digital line balancing program within the context of digital lean management. By leveraging real-time data tracking, big data utilization, and advanced data analysis techniques, companies aim to enhance flexibility, agility, and operational efficiency in their manufacturing processes. Design/methodology/approach: This research delves into operational definitions for men's hoody sweatshirts, detailing the types of machines used, specifying standard task times, and elucidating the precedence relations of operations. The study utilizes ITEX PMD as a real-time production data collection device, generating periodic line reports through the associated software program. Findings: The ITEX digital line balancing program shows promise in assisting shop floor managers to optimize sewing lines with efficiency and seamlessness, contributing to overall operational excellence. The program provides another layer of visibility into the factory, reducing non-value-added activities, and improving efficiency. The ITEX Soft algorithm facilitates an ideal assembly line layout, ensuring a balanced workload among workstations, aligned with the continuous flow principle in the sewing line. Originality/value: This shop floor and software solution not only enhance visibility but also offer a practical means to reduce non-value-added activities, thereby improving overall efficiency. The ITEX Soft algorithm emerges as a valuable tool, contributing to a more balanced workload among workstations and optimizing assembly line layout. This study sheds light on the potential of digital lean principles in reshaping manufacturing processes and fostering operational excellence in the apparel industry.

Optimizing cloud resource allocation: A long short-term memory and DForest-based load balancing approach

Load balancing is an element that must exist for a cloud server to function properly. Without it, there would be substantial lag and the server won’t be able to operate as intended. In a Cloud computing (CC) establishing, load balancing is the process of dividing workloads and computer resources. The distribution of assets from a data centre involves many different factors, including load balancing of workloads in cloud environment. To make best use each virtual machine’s (VM) capabilities, load balancing needs to be done in a way that ensures that all VMs have balanced loads. Both overloading and underloading are examples of load unbalance, and both of these types of load unbalance could be fixed by using techniques created especially for load balancing. The research that has been done on the subject have not attempted to take into account the factors that affect the problem of load unbalancing. Results indicate that the LSTM and DForest-based load balancing approach significantly improves cloud resource utilization, reduces response times, and minimizes the occurrence of overloading or underloading scenarios. To effectively design those programmes, it is essential to first understand the advantages and disadvantages of current methodologies before developing efficient AI-based load balancing programmes. Compared to existing method the proposed method is high accuracy 0.98, KNN accuracy is 0.97, SVM accuracy is 0.99, and DForest accuracy is 0.987.

Joint Optimization of Item and Pod Storage Assignment Problems with Picking Aisles’ Workload Balance in Robotic Mobile Fulfillment Systems

The item and pod storage assignment problems, two critical issues at the strategic level in robotic mobile fulfillment systems, have a strong correlation and should be studied together. Moreover, the workload balance in each picking aisle needs to be considered in the storage assignment problems to avoid robots’ congestion within picking aisles. Motivated by these, the joint optimization of item and pod storage assignment problems (J-IPSAP) with picking aisles’ workload balance is studied. The mixed integer programming model of the J-IPSAP with the workload balance constraint is formulated to minimize the robots’ movement distance. The improved genetic algorithm (IGA) with the decentralized pod storage assignment strategy is designed to solve the J-IPSAP model. The experimental results show that the IGA can obtain high-quality solutions when compared with Gurobi and the two-stage heuristic algorithms. The robots’ movement distance is smallest when the width-to-length ratio of the storage area is close to 1, and the robots’ movement distance will increase with more stringent workload balance constraints.

Between Work and Family: The Impact of Workload on Career Women

The dual role conflict of a career woman occurs due to an imbalance between workload in the office and time with family. This study aimed to determine the effect of workload on work-family balance in career women. The total number of respondents in the study amounted to 200 career women who were married and had children. The measuring instruments used in this study are the workload and work-family balance scales. The result of this study reveals that workload and work-life balance have a negative relationship among career-oriented women. Researchers have found that women who have a high workload find it difficult to strike the right balance between work and family responsibilities. This can lead to an increased sense of stress, which ultimately affects their overall well-being. The implication of this study shows that the ability to manage workload in the office is needed so that the roles of an employee, wife, and housewife can be balanced. Companies should establish a system that offers convenience for women, ensuring that the workload is distributed proportionally. Additionally, companies should provide support through policies and counseling interventions that aid in maintaining a healthy work-life balance, leading to an increase in worker productivity.

Streamlined Algorithms for Large-Scale Matrix Computations in High-Performance Computing

In the realm of high-performance computing (HPC), large-scale matrix computations are fundamental to a myriad of scientific and engineering applications, from simulations in physics and engineering to data analysis in machine learning. This paper addresses the critical need for efficient and scalable algorithms capable of handling the growing computational demands of these applications. This study explores advanced algorithmic strategies that leverage the architecture of modern HPC systems, including parallelism, distributed computing, and optimized memory usage. The proposed algorithms are designed to minimize computational complexity and maximize performance, ensuring that largescale matrix operations can be executed swiftly and accurately. One of the key contributions of this work is the development of parallel algorithms that distribute the computational workload across multiple processors, significantly reducing execution time. By employing techniques such as data partitioning and workload balancing, the algorithms ensure optimal use of available computational resources, thus enhancing scalability. Furthermore, the paper introduces innovative approaches to memory management, which are crucial for handling large matrices that exceed the memory capacity of individual computing nodes. These approaches include efficient data storage formats and dynamic memory allocation strategies that reduce overhead and improve data access speeds. Another critical aspect of this research is the application of these algorithms to real-world problems. The paper presents case studies in fields such as climate modeling, structural analysis, and machine learning, demonstrating the practical benefits and performance gains achieved through the use of streamlined matrix computation algorithms. The results show substantial improvements in computation time and resource utilization, validating the effectiveness of the proposed methods. Additionally, the paper discusses the integration of these algorithms into existing HPC frameworks and libraries, making them accessible to a broader community of researchers and practitioners. By providing detailed implementation guidelines and performance benchmarks, the study ensures that the benefits of these advanced algorithms can be readily harnessed in various applications. The research also delves into the theoretical underpinnings of the proposed algorithms, offering a rigorous analysis of their computational complexity and scalability. In conclusion, this paper makes significant strides in addressing the challenges of large-scale matrix operations. Through the development of efficient, scalable, and practical algorithms, this research enhances the capability of HPC systems to tackle complex computational tasks, paving the way for advancements in various scientific and engineering domains. The proposed methods not only improve performance and resource utilization but also contribute to the broader goal of making high-performance computing more accessible and effective for large-scale data analysis and simulation.

Workload balancing and scheduling in picking tower systems considering different storage strategies

Picking tower systems are particularly suitable in the e-commerce distribution field. They permit to stock and pick a wide variety of items by maximizing storage capacity and space utilization. Such types of systems are comparable to zone picking solutions, since pickers work on one floor during the same working shift. However, differences also exist since, for example, once the picking tower system is designed, it is not possible to improve or reduce the area of each floor. Thus, a proper workload balancing is required to avoid an efficiency reduction of this system. This paper proposes a mixed integer linear programming mathematical model to assign pickers to floors and jointly schedule the picking list of each of them aiming to minimize the completion time of all orders. The model is applied to a real case study and first managerial insights are derived by investigating how the number of floors, the storage strategy and the number of pickers allowed on each floor influence the workload and the completion time. The results show that, for the considered case, the most effective configuration is the brand-based storage strategy with four picking levels.

Navigating Challenges in Teleradiology Implementation: A Case Study from Saudi Arabia's Healthcare System.

Teleradiology is the practice of interpreting medical images acquired in an off-site location. Teleradiology has been utilized widely around the world to address the needs for subspecialty coverage, workload balancing, and as a solution for understaffing. This study aims to assess the perceptions of teleradiology among radiologists in Saudi Arabia, investigate any challenges they might face, and explore strategies that would help mitigate those challenges. A cross-sectional study using a self-administered electronic questionnaire was conducted to collect responses from radiologists practicing or having practiced teleradiology in Saudi Arabia. The questionnaire was conducted from January to June 2023, and 105 responses were included in the analysis. The responses were analyzed using chi-squared testing to investigate factors affecting the radiologists' perceptions. The most common challenges for teleradiology were access to patients' health records, access to prior imaging exams, and concerns about image quality assurance. Around 74% of participants perceived teleradiology to be beneficial for geographic, after-hour, and subspecialties coverage. Teleradiology was also perceived to help reduce the turn-around time of radiology interpretations. Better communication with referring physicians and technologists was seen as a way to help improve teleradiology services. The findings suggest that the perception of teleradiology's challenges and benefits may not be influenced by experience, workplace, or subspecialty. Emphasis should be placed on the importance of quality assurance of images acquired remotely. Addressing the concerns and challenges related to access to patients' health records is also crucial to ensuring the successful implementation of teleradiology in the country.

Streamlined Algorithms for Large-Scale Matrix Computations in High-Performance Computing

In the realm of high-performance computing (HPC), large-scale matrix computations are fundamental to a myriad of scientific and engineering applications, from simulations in physics and engineering to data analysis in machine learning. This paper addresses the critical need for efficient and scalable algorithms capable of handling the growing computational demands of these applications. This study explores advanced algorithmic strategies that leverage the architecture of modern HPC systems, including parallelism, distributed computing, and optimized memory usage. The proposed algorithms are designed to minimize computational complexity and maximize performance, ensuring that largescale matrix operations can be executed swiftly and accurately. One of the key contributions of this work is the development of parallel algorithms that distribute the computational workload across multiple processors, significantly reducing execution time. By employing techniques such as data partitioning and workload balancing, the algorithms ensure optimal use of available computational resources, thus enhancing scalability. Furthermore, the paper introduces innovative approaches to memory management, which are crucial for handling large matrices that exceed the memory capacity of individual computing nodes. These approaches include efficient data storage formats and dynamic memory allocation strategies that reduce overhead and improve data access speeds. Another critical aspect of this research is the application of these algorithms to real-world problems. The paper presents case studies in fields such as climate modeling, structural analysis, and machine learning, demonstrating the practical benefits and performance gains achieved through the use of streamlined matrix computation algorithms. The results show substantial improvements in computation time and resource utilization, validating the effectiveness of the proposed methods. Additionally, the paper discusses the integration of these algorithms into existing HPC frameworks and libraries, making them accessible to a broader community of researchers and practitioners. By providing detailed implementation guidelines and performance benchmarks, the study ensures that the benefits of these advanced algorithms can be readily harnessed in various applications. The research also delves into the theoretical underpinnings of the proposed algorithms, offering a rigorous analysis of their computational complexity and scalability. In conclusion, this paper makes significant strides in addressing the challenges of large-scale matrix operations. Through the development of efficient, scalable, and practical algorithms, this research enhances the capability of HPC systems to tackle complex computational tasks, paving the way for advancements in various scientific and engineering domains. The proposed methods not only improve performance and resource utilization but also contribute to the broader goal of making high-performance computing more accessible and effective for large-scale data analysis and simulation.

Facilitators of and Barriers to Effective Preceptorships for Pre-licensure Nursing Students: What Nursing Faculty Need to Know.

Preceptorships offer prelicensure nursing students the opportunity to meet their clinical objectives by working one-on-one with a registered nurse (RN). Although preceptorships can be provided for any clinical course, most prelicensure nursing programs offer them during the final semester. Preceptorships provide a bridge between academic study and the real world of nursing. By acting as a teacher, role model, evaluator, and person who can help a student to socialize with the profession, a preceptor can reduce the reality shock experienced by new nurses and provide them with realistic expectations about the nursing profession. The recent nursing shortage has inadvertently led to a preceptor shortage and forced the nursing leadership to become dependent on available RNs, including those without preceptor training or experience. This practice update paper aimed to address the facilitators and barriers associated with preceptorships for prelicensure nursing students and discuss the proposed solutions for effective clinical preceptorship based on evidence and the author's personal reflections. The major facilitators were the discussion of nursing program expectations, the selection of teaching strategies with real-world examples, the creation of a structured weekly plan to promote learning, and the consideration of alternative teaching approaches. The major barriers were preceptors' dual responsibilities to students and patients, challenging students, and the lack of experienced preceptors. The proposed solutions included obtaining support from the nursing leadership team to ensure that the preceptor had a balanced workload, developing the emotional competence of the preceptor, addressing challenging students through a three-way conference with the student, the preceptor, and the clinical faculty and a written plan to ensure student progress, having the nursing leadership conduct periodic assessments of the preceptor pool, and ensuring that appropriate training was offered to candidates who were willing to become preceptors.

Deep Learning Approach for Workload Prediction and Balancing in Cloud Computing

Deep Learning Approach for Workload Prediction and Balancing in Cloud Computing

EXPLORING THE SELECTED FACTORS THAT INFLUENCE TEACHER MOTIVATION IN PRIMARY SCHOOLS IN MOROGORO MUNICIPAL COUNCIL, TANZANIA

This study investigates the selected factors that influence teacher motivation in primary schools in Morogoro Municipal Council, Tanzania. The study, grounded in a pragmatic philosophy, employs a convergent design and a mixed research approach to gather quantitative and qualitative data from primary schools in Morogoro Municipality. The target population comprises teachers, heads of schools, and the District Education Officer (DEO), with a sample size of 68 respondents selected through simple random and purposive sampling. Data collection includes self-administered questionnaires for teachers and interviews with heads of schools and the DEO. Validity and reliability were rigorously assessed, yielding a Cronbach’s alpha of 0.778. Ethical considerations, including informed consent, confidentiality, and ethical clearance, were strictly adhered to through approval from Jordan University College and Morogoro Municipal Council. The analysis highlights that teacher motivation in primary schools’ hinges on factors like strong leadership, resource adequacy, and collaborative environments, although challenges such as workload and work-life balance persist. To improve motivation, administrators should prioritize leadership support and resource provision, while policymakers must address workload concerns through supportive policies. Teachers should embrace collaboration and seek recognition, and parents and communities should foster supportive environments. By addressing these aspects, schools can enhance teacher satisfaction and effectiveness, ultimately benefiting the educational environment.

Health Workforce Challenges: Key Findings From the Swiss Cohort of Healthcare Professionals and Informal Caregivers (SCOHPICA).

The Swiss Cohort of Healthcare Professionals and Informal Caregivers (SCOHPICA) was created to study the career trajectories, retention intentions, and wellbeing of healthcare professionals (HCPs), addressing challenges such as staff turnover, low job satisfaction and burnout. SCOHPICA is a prospective open cohort. An electronic questionnaire was used to collect data from HCPs across multiple healthcare settings in Switzerland, encompassing the intention to stay in the profession, wellbeing, and various organizational, psychosocial, occupational and sociodemographic determinants. The first (2022) baseline sample included 1707 HCPs from over 20 professions. Notably, 13% did not intend to stay in their profession, with intermediate caregivers (24%), registered nurses (17%) and pharmacists (17%) reporting the highest rates. Pharmacists scored lowest in wellbeing. Across determinants, pharmacists, physicians, and registered nurses reported worse scores for workload and work-life balance. Nursing professions had lower scores in various determinants, including influence at work, staffing and resource adequacy, and possibilities for development. SCOHPICA will provide critical insights on HCPs' work conditions and experiences, supporting health workforce monitoring and management, and informing policy-making to ensure high-quality healthcare delivery.

Optimal Establishments of Massive Testing Programs to Combat COVID-19: A Perspective of Parallel-Machine Scheduling-Location (ScheLoc) Problem

Massive testing to identify COVID-19-infected people is crucial in combating COVID-19. However, from the perspective of facility location problems, many current massive testing programs are not properly set, leading to unreasonable travelling distances, long makespan, unbalanced workload, and long queues. This article proposes a decision framework for developing massive testing programs. Specifically, a biobjective parallel-testing-site Scheduling-location (ScheLoc) model is formulated, simultaneously minimizing the makespan and total travelling distance. The former can help reduce the time length of potential virus spread, and the latter can help alleviate the risk of virus spread and traveler inconvenience. To solve the proposed biobjective ScheLoc problem, in addition to the standard <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {\epsilon }$</tex-math></inline-formula> -constraint method, we further develop two novel strategies. The first one iteratively solves simpler approximate MIP models (IMIP). The second innovatively extends the classical logic-based Benders decomposition approach to solve biobjective problems (B-LBBD). A Hong Kong-based case study shows that the proposed decision framework can significantly reduce the makespan and travelling distance (with a mean of 13 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 5.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , respectively) and enhance workload balancing. Besides, the developed solution methods, especially the B-LBBD, outperform the adapted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {\epsilon }$</tex-math></inline-formula> -constraint method in various aspects.

Evaluating Investment in Condition Monitoring for Fleet Maintenance

This paper presents an analysis on the value of condition monitoring considering the uncertainties inherent in RUL (Remaining Useful Life) prognosis, the risks entailed by prognostic inaccuracies, and the essential investment prerequisites associated to the implementation of modern digital maintenance approaches. In particularly, we explore this in the context of fleet maintenance using an optimisation model that integrates predictive maintenance with existing preventive maintenance and operational schedules workload balance dynamically. The model presents a novel approach, combining the aforementioned dimensions with a holistic approach, that corresponds to an unsolved gap in literature. The primary objectives of the model application are to ascertain the conditions under which introducing predictive maintenance methodologies generates value, and to identify the junctures where the investment necessary for model implementation becomes economically justifiable. Furthermore, the paper will discuss the long-term benefits that materialise over an extended time horizon. The analysis shows how different levels of RUL prognosis uncertainty affect the fleet allocation to maintenance and operation, and the cost savings that reducing uncertainty means for fleet management. The costs are calculated executing the model in two exemplar cases using Python and Gurobi solver. Based on the savings comparing two uncertainty cases for RUL, companies can estimate the required time to amortise their investment in condition monitoring.

The role of flight dispatchers: Analysis of workload balancing of scheduled domestic flights at the airport in Pasay city

This paper examines the role of flight dispatchers in analyzing the workload balancing of scheduled domestic flights at the airport in Pasay City. Various factors were analyzed to understand if there was a significant effect on how flight dispatchers balance their workload. Using the qualitative and quantitative research design, results were able to be identified. Out of 24 respondents, one factor shows a significant difference in Professional Adaptation in terms of the sex of the flight dispatchers. Using thematic analysis and phenomenology, Experience, and Maternity were the focal points of the informants when gender-related issues impacted the performance of flight dispatchers. The implications of this study are to identify and address issues on how these factors can affect the job performance and workload balance of a Flight Dispatcher.

Spartus: A 9.4 TOp/s FPGA-Based LSTM Accelerator Exploiting Spatio-Temporal Sparsity.

Long short-term memory (LSTM) recurrent networks are frequently used for tasks involving time-sequential data, such as speech recognition. Unlike previous LSTM accelerators that either exploit spatial weight sparsity or temporal activation sparsity, this article proposes a new accelerator called "Spartus" that exploits spatio-temporal sparsity to achieve ultralow latency inference. Spatial sparsity is induced using a new column-balanced targeted dropout (CBTD) structured pruning method, producing structured sparse weight matrices for a balanced workload. The pruned networks running on Spartus hardware achieve weight sparsity levels of up to 96% and 94% with negligible accuracy loss on the TIMIT and the Librispeech datasets. To induce temporal sparsity in LSTM, we extend the previous DeltaGRU method to the DeltaLSTM method. Combining spatio-temporal sparsity with CBTD and DeltaLSTM saves on weight memory access and associated arithmetic operations. The Spartus architecture is scalable and supports real-time online speech recognition when implemented on small and large FPGAs. Spartus per-sample latency for a single DeltaLSTM layer of 1024 neurons averages 1 μ s. Exploiting spatio-temporal sparsity on our test LSTM network using the TIMIT dataset leads to 46 × speedup of Spartus over its theoretical hardware performance to achieve 9.4-TOp/s effective batch-1 throughput and 1.1-TOp/s/W power efficiency.

К ВОПРОСУ ОБ ОЦЕНКЕ ЭФФЕКТИВНОСТИ И РЕЗУЛЬТАТИВНОСТИ ДЕЯТЕЛЬНОСТИ ГОСУДАРСТВЕННЫХ ГРАЖДАНСКИХ СЛУЖАЩИХ: ОПЫТ КРАСНОЯРСКСТАТА

The assessment of the public civil servants’ activities should provide the personnel management decision-making process with the necessary information on the issue of efficiency and effectiveness. The authors conducted a review of domestic and foreign practice of evaluating professional activity in the civil service and concluded that the research problems of the methodological aspect of this topic are relevant. The approaches used by the authorities are not focused on individualising the contribution of a civil servant to the fulfillment of the objectives of the structure in which they hold the position. The authors propose a system for evaluating the effectiveness and efficiency of performance, which includes three aspects. The operational unit measures the current activity according to the indicators of the official regulations using a quantitative scale. The perspective block evaluates the professional and personal development of civil servants. The block for assessing the level of workload according to the responsibility matrix records the participation of a particular employee in achieving collective performance results. The experience of the territorial division of the federal executive authority (Krasnoyarsk State Statistics Service) is described as one of the options for the assessment approach. The article presents the results of a survey of Krasnoyarsk State Statistics Service employees on the implemented tool for identifying the achievements within the perspective block. It is concluded that the possibilities of personnel decisions are expanding in the areas of developing individual trajectories of professional and personal growth of employees, ensuring a balanced workload and building a fair personalised motivation system.

Coalitional Game Strategy and TSSM for Efficient Load Balancing in Software Defined Networking

Using distributed SDN control, software defined networking (SDN) delivers additional flexibility to network management, and it has been a significant breakthrough in network innovation. Switch migration is often used for distributed controller workload balancing. The time-sharing switch migration (TSSM) scheme proposed a strategy in which multiple controllers are allowed to share the workload of a switch via time sharing during an overloaded condition, resulting in reduced ping-pong controller difficulty, fewer overload occurrences, and improved controller efficiency. However, it requires more than one controller to accomplish, it has greater migration costs and higher controller resource usage during the TSSM operating time. As a result, we presented a coalitional game strategy that optimizes controller selection throughout the TSSM phase depending on flow characteristics. The new TSSM method reduces migration costs and controller resource usage while still providing TSSM benefits. For the sake of practicality, the proposed strategy is implemented using an open network operating system. The experimental findings reveal that, as compared to the typical TSSM system, the proposed technique reduces migration costs and controller resource usage by approximately 18%.

A Study on Examining the Factors Causing the Stress Level of Teachers While Shifting Towards Online Mode of Education in Pakistan

This study aims to investigate the factors contributing to the stress levels of teachers in educational institutions of Pakistan during the shift towards the online mode of education. This quantitative study used a survey questionnaire to obtain data from the teachers. The questionnaire included the items on workload, technical hurdles, social contact, and work-life balance of distance learning teachers while transmission towards online mode. A sample size of 75 teachers was selected for the study. The findings of the study showed that technological challenges such as connectivity issues, coping up with new emerging technology, meeting deadlines and multitasking are the major factors of stress for teachers. They find it difficult to somewhat keep the balance between personal and professional life. On the other hand, the teachers are satisfied by the provided institutional support.

Staffing and faculty compensation in associate degree nursing education programs

The nursing shortage is exacerbated by challenges in nursing education programs, notably a lack of faculty. Associate degree programs are crucial in mitigating this shortage, yet their staffing and compensation remains understudied. A national survey reported a growing faculty shortage, impacted by retirements and compensation disparities. Recommendations to address the workforce include equitable salaries, supportive work environments, and workload balance.