Workload Balancing Research Articles

Cooperative scheduling of airport ground electric service vehicles considering workload balance: A column generation approach

Under the trend of electrification in civil aviation, airports are promoting electric ground vehicles to reduce carbon emissions. This paper conducts on electric airport ground service vehicles in scheduling optimization problems. Moreover, driver psychology is also a growing concern as it can affect the efficiency and safety of ground services. Workload balance is one of the major factors affecting driver psychology. In the context of facing challenges for ground vehicle scheduling, fully using electric vehicles and guaranteeing the workload balance are major concerns in our study. This paper investigates the electric shuttle buses and towing tractors cooperative scheduling considering workload balance optimization problem (e-STCSWB). We introduce a mixed integer programming formulation for e-STCSWB that considers cooperative relationships, workload balance, energy consumption and time windows to minimize total energy consumption costs. The e-STCSWB is obtained small scale exact solutions using the CPLEX solver. Furthermore, a column generation approach is developed, which is enhanced through a problem-specific strategy. Extensive computational results using real-world instances from one of China’s largest airports show that it can obtain optimal or near-optimal solutions. The problem-specific strategy significantly improve the performance in terms of solution quality and solution time. Finally, we perform sensitivity analyses to demonstrate the impact under different numbers of vehicles and workload balance requirements parameter settings.

Development of Progressively Earth-Independent Medical Operations to Enable NASA Exploration Missions.

Introduction -The National Aeronautics and Space Administration's (NASA's) transition from operations in low-Earth orbit to long-duration missions to the Moon and Mars necessitates the development of progressively Earth-independent medical operations (EIMO) to support crews and reduce overall mission risk. Previous work has defined and laid the foundation for EIMO, but further development of the concept is required to prepare for future exploration missions. Methods -NASA's Exploration Medical Capability element organized a series of 5 technical interchange meetings from 2023 to 2024, which included internal (NASA) and external subject-matter experts in human spaceflight, health technology, and austere medicine to create a framework for developing the technologies and procedures necessary to maintain human health and performance in a progressively Earth-independent fashion. Results -The EIMO technical interchange meetings provided a forum for a field of experts and stakeholders to better understand gaps between current approaches to medical care in low-Earth orbit and the innovations needed to maintain the health and performance of astronauts on long-duration deep-space missions. These discussions were recorded, analyzed, and collated into reports that can inform the maturation of EIMO concepts. Conclusions -Multidisciplinary input from experts with experience in human spaceflight, health technology, and austere medicine is critical when planning for long-duration exploration missions. Innovations such as probabilistic risk assessment tools, extended reality devices, and advanced clinical artificial intelligence capabilities have been identified as high-value targets that can enhance inflight medical autonomy while maintaining appropriate workload balance and crew safety. By further developing the EIMO paradigm, NASA aims to identify areas of future work, research, and collaboration to reduce overall risk on future human spaceflight missions into deep space.

Dynamic Robot Routing and Destination Assignment Policies for Robotic Sorting Systems

Robotic sorting systems (RSSs) use mobile robots to sort items by destination. An RSS pairs high accuracy and flexible capacity sorting with the advantages of a flexible layout. This is why several express parcel and e-commerce retail companies, who face heavy demand fluctuations, have implemented these systems. To cope with fluctuating demand, temporal robot congestion, and high sorting speed requirements, workload balancing strategies such as dynamic robot routing and destination reassignment may be of benefit. We investigate the effect of a dynamic robot routing policy using a Markov decision process (MDP) model and dynamic destination assignment using a mixed integer programming (MIP) model. To obtain the MDP model parameters, we first model the system as a semiopen queuing network (SOQN) that accounts for robot movement dynamics and network congestion. Then, we construct the MIP model to find a destination reassignment scheme that minimizes the workload imbalance. With inputs from the SOQN and MIP models, the Markov decision process minimizes parcel waiting and postponement costs and helps to find a good heuristic robot routing policy to reduce congestion. We show that the heuristic dynamic routing policy is near optimal in small-scale systems and outperforms benchmark policies in large-scale realistic scenarios. Dynamic destination reassignment also has positive effects on the throughput capacity in highly loaded systems. Together, in our case company, they improve the throughput capacity by 35%. Simultaneously, the effect of dynamic routing exceeds that of dynamic destination reassignment, suggesting that managers should focus more on dynamic robot routing than dynamic destination reassignment to mitigate temporal congestion. Funding: This work was supported by The Fundamental Research Funds for the Central Universities [Grant WK2040000094] and the National Natural Science Foundation of China [Grants 71921001 and 72091215/72091210]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0458 .

Optimized Framework for Composite Cloud Service Selection: A Computational Intelligence‐Driven Approach

ABSTRACTOver the past decade, as demand for cloud services has surged, the strategic selection of these services has become increasingly crucial. The growing complexity within the cloud industry underscores the urgent need for a robust model for choosing cloud services effectively. Users often struggle to make informed decisions due to the dynamic nature and varying quality of available cloud services. In response, this paper introduces a novel decision‐making approach aimed at optimizing the selection process by identifying the most suitable combination of cloud services. The focus is on integrating these services into a cohesive ensemble to better fulfill user requirements. In contrast to existing methodologies, our approach evaluates cloud services on a continuous scale, taking into account critical tasks such as workload balancing, storage management, and network resource handling. We propose a model for selecting optimal composite cloud services, which includes real‐time optimization and addresses the consideration of null values for Quality of Service (QoS)‐based attributes (e.g., response time, cost, availability, and reliability) in the dataset—a factor overlooked by current literature. The proposed algorithm is inspired by computational intelligence and driven by an evolutionary algorithm‐based approach that undergoes evaluation across multiple datasets. The results illustrate its superiority, showcasing its ability to outperform existing optimization‐based methods in terms of execution time.

Mobile clinics routing and scheduling in the Witzenberg region of South Africa.

Despite much literature on operations research applied to various healthcare problems, impactful implementation in public healthcare is limited, which often results in allocative inefficiency. This article uses a mobile clinic routing and scheduling problem in the Witzenberg region of South Africa as a case study to demonstrate the improvement of implementation success through cross-disciplinary collaboration, and also to propose a new three-stage approach for modelling a mobile clinic problem that incorporates continuity of care, fairness, and minimisation of distance travelled. Mobile clinics are used in many countries to improve access to healthcare for rural communities. Decision makers must assign farms or villages to mobile clinics, and determine their monthly visit schedules. To improve implementation success, we follow a collaborative three-phased mixed-methods approach with healthcare professionals to improve workload balance, fairness, and transportation cost. During phase 1, qualitative and quantitative data are gathered through qualitative research methods. In phase 2, fairly distributed optimal routes and schedules are designed using a three-stage model that incorporates a multi-vehicle routing problem to determine daily routes, a knapsack problem to establish a fair allocation of these daily routes between different clinics, and another variation on the vehicle routing problem to determine the monthly visit schedule that minimises the distance between the last farm visited on each consecutive day in the case of having to return to a farm the next day. Different input parameter estimations result in different routes and schedules. In phase 3, AHP is performed with main decision makers to determine their preferred solution. Final routes and schedules are designed based on model results, AHP results, and contextual input from decision makers. In our case study, an improved workload balance, a 23% reduction in total distance travelled, and buy-in to implement the changes, were obtained.

The fusion of Lean Manufacturing with Industry 4.0 technologies towards a new pillar for improving supply chain performance, the case of the automotive industry in Morocco

In response to the COVID-19 pandemic, a Moroccan automotive glass company embarked on a strategic overhaul to improve its supply chain performance and resilience. Faced with excessive working capital tied up in inventory (Working Capital = $200M), the company chose to integrate advanced artificial intelligence (AI) technologies with Lean Manufacturing principles, including Just-In-Time (JIT) production and workload balancing. The goal was to lower inventory levels while maintaining a high service rate to meet customer demands. AI tools have played a crucial role in predicting quality defects and forecasting equipment availability, facilitating streamlined operations and cost reduction. This initiative also aims to enhance the supply chain's ability to withstand and adapt to future disruptions

Dinamika Perubahan Organisasi dan Strategi Pengelolaan Stres Guru di Madrasah Aliyah

Organizational changes in Madrasah Aliyah aim to improve the quality of education, but often give rise to challenges such as increased workload and stress for teachers. Effective stress management strategies, such as training, open communication, psychosocial support, and balanced workload arrangements, are key to helping teachers adapt to these changes. With a holistic and planned approach, Madrasah Aliyah can create a healthy work environment, support teacher well-being, and improve the quality of teaching in a sustainable manner.

Intelligent process migration in heterogeneous distributed systems

In distributed processing environments, multiple groups of processes are found sharing resources and competing for access. These processes may or may not require synchronization and it is essential to reach a consensus to manage access to resources in a way that establishes a strict order, thus ensuring mutual exclusion. The proposal presented is an innovative and effective solution for the management of shared resources in distributed systems, which allows solving problems related to overload and workload balancing. The evaluation of the state of computational loads and the final comparison using standard deviation are useful tools to detect and correct imbalances in the system. In addition, the possibility of establishing initial configurations of the algorithm for each particular situation allows adapting the solution to the specific needs of each system.

A Global Coverage Path Planning Method for Multi-UAV Maritime Surveillance in Complex Obstacle Environments

The study of unmanned aerial vehicle (UAV) coverage path planning is of great significance for ensuring maritime situational awareness and monitoring. In response to the problem of maritime multi-region coverage surveillance in complex obstacle environments, this paper proposes a global path planning method capable of simultaneously addressing the multiple traveling salesman problem, coverage path planning problem, and obstacle avoidance problem. Firstly, a multiple traveling salesmen problem–coverage path planning (MTSP-CPP) model with the objective of minimizing the maximum task completion time is constructed. Secondly, a method for calculating obstacle-avoidance path costs based on the Voronoi diagram is proposed, laying the foundation for obtaining the optimal access order. Thirdly, an improved discrete grey wolf optimizer (IDGWO) algorithm integrated with variable neighborhood search (VNS) operations is proposed to perform task assignment for multiple UAVs and achieve workload balancing. Finally, based on dynamic programming, the coverage path points of the area are solved precisely to generate the globally coverage path. Through simulation experiments with scenarios of varying scales, the effectiveness and superiority of the proposed method are validated. The experimental results demonstrate that this method can effectively solve MTSP-CPP in complex obstacle environments.

Energy Aware Smart Car Parking System Based Internet of Spatial Things

With the advent of smart and green cities, the development of energy-efficient smart parking systems has attracted researchers to reduce environmental pollution in smart cities due to reducing traffic congestion as well as waste of time and fuel consumption. This article investigates how to integrate the Internet of Spatial Things (IoST) with workload balancing and image processing in fog computing to build an energy-efficient smart parking system. The suggested system applies Q-Learning, a reinforcement learning method, to achieve workload balancing and then deploys image processing to detect vacant parking slots. The proposed method’s main objective of load balancing is to reduce energy consumption. The evaluation of the proposed system is done by comparing two case studies, fog-based and cloud-based IoST implementations, in the iFogSim simulator for various scenarios and scales. Moreover, it evaluates and compares the energy consumption of various devices. Experimental findings show that the devised system in fog-based IoST greatly reduces energy consumption with improves parking space availability in smart parking in contrast to the cloud-based deployment of smart car parking.

Assessment of Ophthalmology Residency Training in Switzerland: A Trainee-Based Survey.

This study aimed to evaluate the current status of ophthalmology residency training in Switzerland and provide insights for enhancing training programs. The survey covered demographic data such as gender, age, workplace, and year of residency, as well as working conditions, current practical and diagnostic skills, interest in scientific work, and future plans. It consisted of 16 multiple choice questions (MCQ), 5 multiple select questions (MSQ), 8 free-text, 6 Net Promoter Score (NPS), and 4 Likert scale questions. All Young Swiss Ophthalmologists (YSO) members received an invitation to participate in the survey using an online form. All collected data were anonymized, and participants provided their consent for data collection, analysis, and publication. The survey assessed the perspectives of 63 Swiss ophthalmology residents on their training experiences. Respondents indicated the greatest interest in specializing in cataract and vitreoretinal surgery, followed by cornea, glaucoma, refractive surgery, and lid surgery. They also expressed a great need for improved supervision, structured curricula, and alignment of clinical and research duties. While pursuing additional surgical training and private practice autonomy, residents also expressed a preference for balanced workloads that emphasize patient care over full-time academic work. The survey indicates a need to standardize ophthalmology residency training in Switzerland to ensure comprehensive and uniform education. It also highlights specific areas for improvement, such as the absence of surgical training. Ongoing assessments are necessary to evaluate future developments.

Dampak Beban Kerja Terhadap Kinerja Karyawan Dannis Collection Surabaya

This study aims to analyze the impact of workload on employee performance at Dennis Collection Surabaya. The research approach used is quantitative, with data collected through questionnaires distributed to 62 employees of Dennis Collection, and the data were analyzed using simple linear regression analysis. The results show a positive and significant relationship between workload and employee performance, with a contribution of 37.6 percent. This indicates that the increase in workload at Dennis Collection is significantly related to the improvement in employee performance. These findings highlight the importance of effective workload management in enhancing employee performance and maintaining their well-being, such as through appropriate workload balancing strategies, providing adequate resources, and offering suitable rewards to maintain employee motivation and performance. These findings make an important contribution to the human resource management literature and offer practical recommendations for managing workload in a dynamic work environment. Additionally, they provide a framework for more effective management strategies in the future. Therefore, it is crucial for Dennis Collection to continuously monitor and adjust employee workloads to ensure they remain within reasonable limits and support the balance between work demands and employee well-being.

Factors affecting workload balance and workplace resiliency among teachers in the implementation of special needs education programs

Balancing workloads and fostering workplace resilience among special needs education teachers hinge on several fundamental factors. This research investigates into the critical elements such as effective administrative support, clear workload guidelines, timely feedback, sufficient resources, and fair workload distribution. The confidence in accommodating student needs, feeling well-supported, and comprehensive teacher training further amplify these dynamics. Workplace resilience thrives on adaptable curricula, collaborative stakeholder planning, and personal well-being practices. To uncover these insights, a quantitative study was conducted, distributing thirty (30) questionnaires among public and high school special needs education teachers in Tayabas City, Lucena City, and Sariaya, Quezon in the Philippines. The collected data was thoroughly analyzed using factor analysis and secondary statistical tools which are weighted mean, analysis of variance, and percentages. Factor Analysis was utilized as the main statistical tool, with assumptions verified through Bartlett’s Test of Sphericity, the KMO measure of sampling adequacy, and Eigenvalues, illuminating the key factors influencing workload balance and workplace resilience. This study not only identifies the significant determinants impacting special education teachers but also offers actionable recommendations to enhance their effectiveness and sustainability in their fundamental roles. By addressing these findings, the research paves the way for a more supportive and resilient work environment for special needs education teachers and provides valuable insights for educational institutions, school administrators, and policymakers.

Supporting Tenure-Seeking Faculty in Online Programs: A Critical Discourse Analysis

Very little research has been conducted on the tenure-seeking faculty experience for those teaching in online programs. The majority of the research in this area focuses on their work in the online classroom but not on them as individuals in relation to the academy as a whole. This would include their needed support for tenure and promotion, while also taking into consideration the variation in their workload due to the online modality and interactions with students and colleagues. Online faculty often need a more balanced workload that is thoughtful of the timing of their service placements, office hours, and teaching schedules, as well as mentorship and guidance for professional development. Likewise, these individuals desire greater mentorship to aid in navigating institutional policies and practices. There may be a lack of understanding related to what their typical work week really looks like and the types of support that would best meet their needs. Our work has created a safer space for these individuals who are tenure-seeking faculty in online programs struggling with these exact issues to provide insight into their lived experiences and share with us some of their basic needs to be holistically supported as they move forward in their fields.

A heuristic for task allocation and path planning of multiple tethered underwater robots considering workload balance

A heuristic for task allocation and path planning of multiple tethered underwater robots considering workload balance

Stress and Academic Workload of Teachers: A Descriptive Correlational Study at Mindanao Mission Academy

This study addresses the research gap concerning the relationship between academic workload, demographic factors, and stress among teachers at Mindanao Mission Academy. The study aims to identify the demographic profile of teachers, including age, gender, years of teaching experience, position, academic units, and extracurricular loads; assess teachers' perceived stress levels; determine the correlation between academic workload and perceived stress; examine the influence of academic units and extracurricular loads on stress; and (5) evaluate differences in stress based on demographic factors. Job Demands-Resources (JD-R) Model by Bakker & Demerouti (2007) provides the theoretical framework for understanding stress in the workplace. The study used a census sample technique and a quantitative approach to administer the Professional Life Stress Scale to 60 administrators and employees. Key data show that the majority of participants are female (60.3%), between the ages of 23 and 24 (24.2%), have advisory positions (60.4%), and balance 0–5 extracurricular activities with 21–30 academic units. According to descriptive analysis, the mean stress level is 17.4, which is moderate. While there is a positive connection between stress and extracurricular loads, Pearson's correlation analysis shows no significant relationship between academic stress and workload (p = 0.867). On the other hand, stress is negatively correlated with age, indicating that younger instructors can be more stressed. Gender and years of experience did not significantly affect stress levels. The findings highlight the need for targeted support systems, such as stress management programs and workload balancing strategies, particularly for younger teachers and those with extensive extracurricular responsibilities, to cultivate a supportive and sustainable work environment.

Resilient Multi-Robot Coverage Path Redistribution Using Boustrophedon Decomposition for Environmental Monitoring.

This study introduces a resilient and adaptive multi-robot coverage path planning approach based on the Boustrophedon Cell Decomposition algorithm, designed to dynamically redistribute coverage tasks in the event of robot failures. The proposed method ensures minimal disruption and maintains a balanced workload across operational robots through a propagation-based redistribution strategy. By iteratively reallocating the failed robot's coverage path to neighboring robots, the method prevents any single robot from becoming overburdened, ensuring efficient task distribution and continuous environmental monitoring. Simulations conducted in five distinct environments, ranging from simple open areas to complex, obstacle-rich terrains, demonstrate the method's robustness and adaptability. A key strength of the proposed approach is its fast and efficient task reallocation process, achieved with minimal propagation cycles, making it suitable for real-time applications even in complex scenarios. The approach reduces task variance and maintains balanced coverage throughout the mission.

Workload-based adaptive decision-making for edge server layout with deep reinforcement learning

Mobile edge computing (MEC) is crucial in applications such as intelligent transportation, innovative healthcare, and smart cities. By deploying servers with computing and storage capabilities at the network edge, MEC enables low-latency services close to end users. However, the configuration of edge servers needs to meet the low-latency requirements and effectively balance the servers’ workloads. This paper proposes an adaptive layout and dynamic optimization method, modeling the edge server layout problem as a Markov decision process. It introduces a workload-based server placement rule that adjusts the locations of edge servers according to the load of base stations, enabling the learning of low-latency and load-balanced server layout strategies. Experimental validation on a real dataset from Shanghai Telecom shows that the proposed algorithm improves average latency performance by about 40% compared to existing technologies, and enhances workload balancing performance by about 17%.

PENGARUH BEBAN KERJA DAN DAN WORK-LIFE BALANCE TERHADAP TURNOVER INTENTION PADA KARYAWAN PT KEDIRI INTERMEDIA PERS

Companies that often ignore the needs and rights of human resources, as well as increasing work demands and working conditions that do not meet expectations, will trigger turnover intention in employees. This study aims to determine the effect of workload and work-life balance on PT. Kediri Intermedia Pers’ employees turnover intention. This research employs a quantitative methodology, utilizing a saturated sampling technique to select a sample of 44 respondents. The analysis technique in this study uses the SmartPLS3 program. From the results of statistical testing, it was found that workload has a positive effect on turnover intention, while work-life balance has a negative effect on turnover intention in employees of PT Kediri Intermedia Pers. Keyword : Workload; Turnover Intention; Work-Life Balance

Ergonomic design of Human-Robot collaborative workstation in the Era of Industry 5.0

The increasing adoption of collaborative robots to support job execution in manufacturing has catalyzed companies’ attention to safety and well-being issues. Sharing the human-centric perspective and harmonious human–machine collaboration concepts emphasized by Industry 5.0, the design phase of a collaborative workstation must integrate both psychological and physical risk evaluations to provide a safe and inclusive work environment suitable for a diversified workforce. Accelerating the pre-deployment phase to quickly reconfigure workstation design and assess its impact on workload balancing and task sequencing during the deployment of assembly lines still represents a challenging task considering the available software tools. This research proposes a new mathematical model to accelerate the design of ergonomic human-robot collaborative workstations based on task alternatives and the combined consideration of postural assessment and fatigue analyses for each of them to design an ergo-friendly collaborative environment. Surface electromyography analysis is jointly adopted with postural risk assessment measured with inertial measurement units and developed by a digital ergonomic platform to determine the optimal workplace configuration for tools, equipment, and resources to promote physical well-being while considering station productivity. Experimental tests are performed to investigate arm muscles and postural risk assessment for different configurations of workstation design and collaborative human-robot job progression. Experimental results demonstrate the feasibility, and the advantages of the proposed approach compared to existing simulation software to quickly generate and assess alternative scenarios and find a trade-off between ergo-quality levels and system performance. The final discussion offers valuable information for decision-makers and practitioners to facilitate the integration of human factors throughout the early stages of ergo-friendly workspace design, while effectively managing the complexity generated by resource allocation and collaborative robots.