Task Assignment Research Articles

TAMT: Privacy-Preserving Task Assignment With Multi-Threshold Range Search for Spatial Crowdsourcing Applications

TAMT: Privacy-Preserving Task Assignment With Multi-Threshold Range Search for Spatial Crowdsourcing Applications

How to Stop Delegating and Start Teaching: Developing Future Leaders from Within

This article explores the importance of leadership development as a strategic priority for organizations seeking to cultivate talent and drive long-term success. It highlights research demonstrating the business impact of intentional leadership programs, and outlines practical strategies for leaders to transition from a culture of delegation to one focused on mentorship, empowerment, and skill development. Key approaches include integrating teaching into daily work, providing opportunities for direct reports to lead from any level, exposing them to diverse perspectives, and creating avenues for impact beyond routine responsibilities. The article uses the case study of State Farm's Agent Development Program to illustrate the real-world implementation and benefits of prioritizing leadership development over simple task assignment. By making learning and growth a cultural cornerstone, the article argues, organizations can build internal talent pipelines, foster innovation, and future-proof their strategic direction for sustained competitive advantage.

청년조리사의 지각된 과잉자격과 이직의도간의 관계에서 조직몰입의 매개효과

This study examined the impact of perceived over-qualification on turnover intention among young cooks and verified the mediating effect of organizational commitment. Targeting cooks aged 19 to 34 working in South Korea's food service industry, an online survey was conducted using a convenience sampling method. A total of 402 responses were collected. After 57 responses were excluded, 345 responses were used in our final sample. Empirical analysis was performed using SPSS 22.0. The results of hypothesis testing are as follows: First, perceived over-qualification negatively (-) affects organizational commitment. Second, perceived over-qualification positively (+) affects turnover intention. Third, organizational commitment negatively (-) affects turnover intention. Fourth, organizational commitment partially mediates the relationship between perceived over-qualification and turnover intention. The importance of addressing perceived over-qualification and enhancing organizational commitment to reduce turnover intention and improve human resource management in the food service industry is highlighted by these study findings. These results suggest the need to redesign the roles of young cooks and provide training in advanced culinary techniques and management skills to offer a vision for long-term growth. Furthermore, meaningful task assignments that promote a sense of achievement and the provision of clear feedback are critical. Efforts such as these are expected to contribute to the stable growth of young cooks within organizations and reduce their turnover intentions.

AoI-Minimal Task Assignment and Trajectory Optimization in Multi-UAV-Assisted Wireless Powered IoT Networks

This paper investigates the energy transfer and data collection problem of multiple unmanned aerial vehicle (UAV)-assisted wireless-powered Internet of Things (IoT) networks. To ensure information freshness for IoT devices and reduce UAVs’ energy consumption, we minimize the average Age of Information (AoI) of IoT devices by jointly optimizing the energy harvesting (EH) and data collection time for IoT devices, the selection of data collection points (DCPs), DCP-IoT associations, and task assignment, flight speed, and trajectories of UAVs, subject to the limited endurance of UAVs. As this problem is nonconvex, we propose a novel DCP association and trajectory-planning scheme that seeks age-optimal solutions through an iterative three-step process. First, we calculate the EH and data collection time for IoT devices using Karush–Kuhn–Tucker (KKT) conditions. Then, we introduce an optimal hovering time allocation-based affinity propagation (OHTAP) clustering algorithm to determine optimal DCP locations and establish DCP-IoT associations. Finally, we develop two algorithms to optimize UAVs’ trajectories: an improved partheno-genetic algorithm with enhancement mechanisms (EIPGA) and a hybrid algorithm that combines improved MinMax k-means clustering with EIPGA. Numerical results confirm that our scheme consistently outperforms benchmark schemes in AoI performance and solution stability across diverse scenarios.

Multi-UAV cooperative task assignment based on multi-strategy improved DBO

Effective task assignment technology plays a pivotal role in optimizing Unmanned Aerial Vehicles operations during the collaboration of multiple Unmanned Aerial Vehicles (multi-UAV) in combat scenarios. Therefore, aiming at the cooperative task assignment of multi-UAV, this paper takes the value and time window of the ground target into consideration, takes the total fuel consumption, execution time, and execution cost of all tasks completed by multi-UAV as the objective functions, constructs a multi-objective multi-task assignment mathematical model, and proposes a multi-strategy improved Dung Beetle Optimizer (MIDBO) to solve the model. The MIDBO employs Sinusoidal chaotic mapping to generate the initial population, enhancing population diversity. Additionally, it integrates the nonlinear convergence factor and spiral search factor to augment the exploration capabilities of the rolling dung beetles. Moreover, by incorporating the subtraction average strategy, the algorithm bolsters the prowess of the foraging dung beetles, leading to improved algorithmic performance and attaining high-quality solutions. The experimental results show that the multi-UAV collaborative task assignment based on the MIDBO can enhance the global optimization ability and assign the optimal task sequence to multi-UAV.

Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future Directions

The rapid advancement of unmanned aerial vehicles (UAVs) has transformed a wide range of applications, including military operations, disaster response, agricultural monitoring, and infrastructure inspection. Deploying multiple UAVs to work collaboratively offers significant advantages in terms of enhanced coverage, redundancy, and operational efficiency. However, as UAV missions become more complex and operate in dynamic environments, the task assignment problem becomes increasingly challenging. Multi-UAV dynamic task assignment is critical for optimizing mission success. It involves allocating tasks to UAVs in real-time while adapting to unpredictable changes, such as sudden task appearances, UAV failures, and varying mission requirements. A key contribution of this article is that it provides a comprehensive study of state-of-the-art solutions for dynamic task assignment in multi-UAV systems from 2013 to 2024. It also introduces a comparative framework to evaluate algorithms based on metrics such as responsiveness, robustness, and scalability in handling real-world dynamic conditions. Our analysis reveals distinct strengths and limitations across three major approaches: market-based, intelligent optimization, and clustering-based solutions. Market-based solutions excel in distributed coordination and real-time adaptability, but face challenges with communication overhead. Intelligent optimization solutions, including evolutionary and swarm intelligence, provide high flexibility and performance in complex scenarios but require significant computational resources. Clustering-based solutions efficiently group and allocate tasks geographically, reducing overlap and improving efficiency, although they struggle with adaptability in dynamic environments. By identifying these strengths, limitations, and emerging trends, this article not only offers a detailed comparative analysis but also highlights critical research gaps. Specifically, it underscores the need for scalable algorithms that can efficiently handle larger UAV fleets, robust methods to adapt to sudden task changes and UAV failures, and multi-objective optimization frameworks to balance competing goals such as energy efficiency and task completion. These insights serve as a guide for future research and a valuable resource for developing resilient and efficient strategies for multi-UAV dynamic task assignment in complex environments.

PEMBAGIAN TUGAS APEL PAGI MINGGUAN PEGAWAI INSTANSI XYZ DENGAN MENGGUNAKAN PEWARNAAN GRAF BESERTA ANALISIS BEBAN PENUGASAN

This study addresses the issue of the weekly morning roll call duty assignment at XYZ Agency. The allocation of morning roll call duties at XYZ Agency has been incidental, leading to uneven task assignments for employees. The author proposes a solution to this problem by implementing the Welch-Powell Algorithm on Graph Coloring to determine the allocation of morning roll call duties for XYZ Agency employees. This is achieved by representing each employee as a point, and the edges represent job similarities. The graph coloring results in this study produce 4 colors: red, green, blue, and purple, with connected points having different colors. The findings reveal that 20 employees participate in the roll call duty each month. The duty assignments are then made for six months and compiled into a task assignment table. Based on descriptive statistical results, each employee receives an average assignment of 4.68 times with a standard deviation of 1.38. Furthermore, the Levene's Test is employed to ensure the equality of workload among different employee job groups. The results indicate differences in the assignment workload among job groups

The Effects of Leaders’ Capabilities on the Performance of Self-Help Groups in Suna West Constituency, Migori County, Kenya

Purpose: The purpose of this study was to determine the effect of leadership capabilities on the performance of Self-Help Groups (SHGs) in Suna West Constituency. Methodology: Utilizing a descriptive research design, the study targeted 1,510 members across 80 SHGs, with a sample size of 306 members determined using the Krejcie and Morgan (1970) formula. Data were analyzed using SPSS for quantitative statistics, such as percentages and means, while qualitative data were examined thematically. Findings: Findings reveal that innovativeness is present in self-help groups (SHGs), with 60% acknowledging change management mechanisms. However, opinions are divided on whether leaders value member contributions, as 39.8% agree and an equal percentage disagree. While 81.7% of SHGs have job descriptions, 40% report inconsistencies in task assignments, indicating a need for better delegation. Self-awareness among leaders is also notable, with all members feeling empowered to voice concerns and 80% of leaders welcoming feedback. Unique Contribution to Theory, Practice and Policy: The study concludes that leadership characterized by innovativeness, effective task management, and a clear vision is essential for enhancing SHG performance, recommending ongoing training for leaders to foster growth. This study uniquely contributes to theory by linking leadership dimensions to SHG performance and informs policy by advocating for targeted training programs to enhance leaders' skills.

Fluid Limits and Optimal Task Assignment Policies for Locally Pooled Service Systems

Task assignment policies play a central role in many online applications, where service requests or tasks arrive over time and are distributed across parallel servers in a data center or cloud computing platform. The way in which the tasks are distributed across the servers has a tremendous impact on the performance perceived by users and the efficiency of server usage, which has attracted strong interest from our research community. Most of this interest has been directed to the so-called supermarket model, but real-life systems have features that fall outside of this standard framework, raising significant challenges which have been pursued in my doctoral thesis [3]. In particular, my thesis provides novel modeling frameworks and performance analysis techniques that enable the study of interactive online applications with fluctuating demand patterns and networked systems.

We Are a go for launch! working with NASA to forecast and improve team dynamics in space missions

ABSTRACT This paper outlines a decade-long NASA-Northwestern University collaboration developing predictive models for crew relationships during long-duration space missions. Two computational models were created: CREWS (predicting social relations) and SCALE (forecasting shared cognition), integrated into the TEAMSTaR dashboard for mission support. Research conducted at NASA's HERA and Russia's NEK facilities revealed that while behavioral team performance improves over time, conceptual performance declines with extended isolation. Five critical markers of team relationships were identified: task affect, leadership dynamics, hindrance relationships, team viability, and shared cognition. Simple interventions like strategic task reassignments proved effective in maintaining positive crew relations. Shared-coordinated leadership demonstrated superiority over hierarchical or fragmented structures. These findings inform crew selection, task assignment, and team cohesion maintenance for Mars missions, with potential applications for extreme environment teams on Earth.

A Cost-Minimized Task Migration Assignment Mechanism in Blockchain Based Edge Computing System

Background: Cloud computing is usually introduced to execute computing intensive tasks for data processing and data mining. As a supplement to cloud computing, edge computing is provided as a new paradigm to effectively reduce processing latency, energy consumption cost and bandwidth consumption for time-sensitive tasks or resource-sensitive tasks. To better meet such requirements during task assignment in edge computing systems, an intelligent task migration assignment mechanism based on blockchain is proposed, which jointly considers the factors of resource allocation, resource control and credit degree. Methods: In this paper, an optimization problem is firstly constructed to minimize the total cost of completing all tasks under constraints of delay, energy consumption, communication, and credit degree. Here, the terminal node mines computing resources from edge nodes to complete task migration. An incentive method based on blockchain is provided to mobilize the activity of terminal nodes and edge nodes, and to ensure the security of the transaction during migration. The designed allocation rules ensure the fairness of rewards for successfully mining resource. To solve the optimization problem, an intelligent migration algorithm that utilizes a dual “actor-reviewer” neural network on inverse gradient update is proposed which makes the training process more stable and easier to converge. Results: Compared to the existing two benchmark mechanisms, the extensive simulation results indicate that the proposed mechanism based on neural network can converge at a faster speed and achieve the minimal total cost. Conclusion: To satisfy the requirements of delay and energy consumption for computing intensive tasks in edge computing scenarios, an intelligent, blockchain based task migration assignment mechanism with joint resource allocation and control is proposed. To realize this mechanism effectively, a dual “actor-reviewer” neural network algorithm is designed and executed.

Energy Efficient and Balanced Task Assignment Strategy for Multi-AAV Patrol Inspection System in Mobile Edge Computing Network

Energy Efficient and Balanced Task Assignment Strategy for Multi-AAV Patrol Inspection System in Mobile Edge Computing Network

Charging-Aware Task Assignment for Urban Logistics with Electric Vehicles

Charging-Aware Task Assignment for Urban Logistics with Electric Vehicles

Addressing a Collaborative Maintenance Planning Using Multiple Operators by a Multi-Objective Metaheuristic Algorithm

Selective maintenance has a significant impact on the sustainable management of maintenance operations. The collaboration of multiple maintenance teams/operators is helpful to achieve sustainability for selective maintenance sequence planning. For products with a large number of components, a single maintenance team/operator is inefficient due to a long completion time which is not acceptable for emergency planning. Providing specific and efficient maintenance sequence planning is critical to effectively handle different types of emergencies (e.g., wartime) while avoiding vague task assignments to multiple maintenance teams/operators. For scheduling many maintenance jobs while improving the efficiency and quality of maintenance operations, this study proposes a collaborative maintenance planning based on the concept of imperfect maintenance. In this regard, this study develops a multi-objective optimization model to optimize parallel maintenance sequences considering maintenance profit, maintenance cost, maintenance team, and resource limitations. We show the feasibility of the proposed multi-objective optimization model through a real case of maintenance practice for the components of an assistor device. For analyzing the complexity of the proposed maintenance sequence planning problem, this study introduces a new multi-objective metaheuristic algorithm which is an enhanced multi-objective gravitational search algorithm (EMOGSA) to find high-quality Pareto solutions for the proposed problem. Different multi-objective evaluation metrics are used to study the performance of the proposed algorithm. From the results, the proposed model and developed solution algorithm can help maintenance decision-makers to determine complex maintenance planning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper deals product with a maintenance and proposes gravitational search algorithm based on only maintenance task, which maintenance task. The goal of this paper is to analyze the maintenance problem from the perspective of collaboration of multiple maintenance teams/operators.

DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach With Generative Model

Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach With Generative Model

Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

Investigating EFL students’ perceived values of online cooperative learning in MOOCs

In the rapidly evolving landscape of online education, understanding what drives student satisfaction is crucial for designing effective learning experiences. The study examines the factors influencing English as a foreign language (EFL) students’ satisfaction with online cooperative learning (CL) in massive open online courses (MOOCs). Employing a mixed-methods approach, the research investigates how different aspects of CL contribute to student satisfaction and identifies challenges students face in such environments. Quantitative data were gathered from 374 students through a structured survey, while qualitative insights were derived from semi-structured interviews with 16 participants. The findings suggest that CL enhances academic performance, engagement, and social interaction among students. However, challenges such as language barriers, unequal participation, and technological issues were also highlighted. The study emphasizes the importance of clear task assignments, effective leadership, and structured collaboration to mitigate these challenges. The research underscores the need for further exploration into the nuanced experiences of EFL students in MOOCs, particularly concerning cultural and linguistic factors that may influence their learning outcomes. These insights contribute to the broader understanding of how CL can be optimized in online education settings to enhance student satisfaction.

Scalable and energy-efficient task allocation in industry 4.0: Leveraging distributed auction and IBPSO.

Industry 4.0 has transformed manufacturing with the integration of cutting-edge technology, posing crucial issues in the efficient task assignment to multi-tasking robots within smart factories. The paper outlines a unique method of decentralizing auctions to handle basic tasks. It also introduces an improved variant of the improved Binary Particle Swarm Optimization (IBPSO) algorithm to manage complicated tasks that require multi-robot collaboration. The main contributions we make are: the design of an auction decentralization algorithm (AOCTA) which allows for an efficient and flexible task distribution in dynamic contexts, the optimization of coalition formation in complex jobs by using IBPSO and improves the efficiency of energy and decreases the cost of computation as well as thorough simulations that show that our proposed method significantly surpasses conventional methods for efficiency, task completion rates in terms of energy usage, task completion rate, and scaling of the system. This research contributes to the development of smart manufacturing through providing an effective solution that aligns with the sustainability objectives and addresses operational efficiency as well as environmental impacts. Addressing the challenges posed by dynamic task allocation in distributed multi-robot systems, these advanced technologies provide a comprehensive solution, facilitating the evolution of innovative manufacturing systems.

A Stable Task Assignment Mechanism for Multi-Platform Mobile Crowdsensing

A Stable Task Assignment Mechanism for Multi-Platform Mobile Crowdsensing