Task Assignment Research Articles

Fusion of cluster analysis and differential particle swarm optimisation for multi-UAV task assignment on power transmission line

PurposeMulti-unmanned aerial vehicle (UAV) missions aim to optimize the execution of multiple missions using limited resources, making it possible to balance the objectives of each mission while minimizing the time to completion.Design/methodology/approachAn algorithm combining cluster analysis and differential evolution particle swarm optimization (DE-PSO) is proposed to solve this problem.FindingsThe investigative study is based on the homogenization of multi-UAV missions in multi-objective task distribution to reduce the total elapsed time.Practical implicationsThis method effectively reduces task time and provides a solution for multi-UAV operations in transmission line cooperation.Originality/valueA novel heuristic algorithm is proposed, and the algorithm fully considers the clustering characteristics under multi-region and the positional relationship characteristics of scene target distribution. It also fully considers the physical characteristics of airport location and UAV power to uniformly optimize the time.

A Hybrid Method to Solve the Multi-UAV Dynamic Task Assignment Problem

In the rapidly evolving field of aerial robotics, the coordinated management of multiple unmanned aerial vehicle (multi-UAV) systems to address complex and dynamic environments is increasingly critical. Multi-UAV systems promise enhanced efficiency and effectiveness in various applications, from disaster response to infrastructure inspection, by leveraging the collective capabilities of UAV fleets. However, the dynamic nature of such environments presents significant challenges in task allocation and real-time adaptability. This paper introduces a novel hybrid algorithm designed to optimize multi-UAV task assignments in dynamic environments. State-of-the-art solutions in this domain have exhibited limitations, particularly in rapidly responding to dynamic changes and effectively scaling to large-scale environments. The proposed solution bridges these gaps by combining clustering to group and assign tasks in an initial offline phase with a dynamic partial reassignment process that locally updates assignments in response to real-time changes, all within a centralized–distributed communication topology. The simulation results validate the superiority of the proposed solution and demonstrate its improvements in efficiency and responsiveness over existing solutions. Additionally, the results highlight the scalability of the solution in handling large-scale problems and demonstrate its ability to efficiently manage a growing number of UAVs and tasks. It also demonstrated robust adaptability and enhanced mission effectiveness across a wide range of dynamic events and different scale scenarios.

Web-Based System for Student Performance Tracking and Lab Resource Allocation

This study presents the design and implementation of a web-based academic management system focused on tracking student performance and efficiently allocating lab resources. The system integrates a lightweight Flask backend and a responsive HTML/CSS/JavaScript frontend, delivering role-based access control for students, faculty, and administrators. It enables streamlined academic operations such as mark uploading, attendance management, lab scheduling, and announcement broadcasting. The backend utilizes RESTful APIs for modular functionality and secure session handling. MySQL serves as the primary database for storing structured academic data. The system was tested for authentication, task assignment, and lab resource management functionalities, and results demonstrated reliable performance, scalability, and improved administrative efficiency. By replacing fragmented manual systems with an integrated digital platform, the project contributes to enhancing educational workflows and information accessibility in academic institutions. Key Words: student performance, lab allocation, academic management, Flask, web-based system, education technology

Cooperative-Rationality-Based Multiplatform Task Assignment Mechanisms for Mobile Crowdsensing

Cooperative-Rationality-Based Multiplatform Task Assignment Mechanisms for Mobile Crowdsensing

Pufferfish: Cost-aware based task scheduling algorithm using pufferfish optimization algorithm in cloud computing

Due to the dynamic and diverse nature of workloads on cloud platforms, task scheduling in the cloud computing paradigm presents a substantial challenge to scholars. Effective scheduling of these varied tasks on appropriate virtual resources is a major challenge. Incorrect task assignments can degrade service quality and violate SLA metrics, ultimately increasing costs for cloud providers. To address these issues and improve scheduling efficiency, propose an efficient cost aware based task scheduling algorithm that considers the priorities of tasks and VMs, ensuring accurate assignment of tasks to appropriate VMs. Our scheduling algorithm is based on the Pufferfish Optimization (PFO) algorithm and is implemented using the CloudSim simulation environment. Compare our approach with reference methods, such as SMA, HHO, and HBO. Simulation results demonstrate that our proposed method significantly reduces turnaround time, response time, and processing time compared to baseline approaches.

Gradient-Guided Credit Assignment and Joint Optimization for Dependency-Aware Spatial Crowdsourcing

Dependency-aware spatial crowdsourcing (DASC) addresses the unique challenges posed by subtask dependencies in spatial task assignment. This paper investigates the task assignment problem in DASC and proposes a two-stage Recommend and Match Optimization (RMO) framework, leveraging multi-agent reinforcement learning for subtask recommendation and a multi-dimensional utility function for subtask matching. The RMO framework primarily addresses two key challenges: credit assignment for subtasks with interdependencies and maintaining overall coherence between subtask recommendation and matching. Specifically, we employ meta-gradients to construct auxiliary policies and establish a gradient connection between two stages, which can effectively address credit assignment and joint optimization of subtask recommendation and matching, while concurrently accelerating network training. We further establish a unified gradient descent process through gradient synchronization across recommendation networks, auxiliary policies, and the matching utility evaluation function. Experiments on two real-world datasets validate the effectiveness and feasibility of our proposed approach.

Dynamic UAV Task Allocation and Path Planning with Energy Management Using Adaptive PSO in Rolling Horizon Framework

Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments for applications such as surveillance, delivery, and data collection, where efficient task allocation and path planning are critical to minimizing mission completion time while managing limited energy resources. This paper proposes a novel approach that integrates energy management into a rolling horizon framework for dynamic UAV task allocation and path planning. We introduce an enhanced Particle Swarm Optimization (PSO) algorithm, incorporating adaptive perturbation strategies and a local search mechanism based on simulated annealing, to optimize UAV task assignments and routes. The rolling horizon framework enables the system to adapt to evolving task demands over time. Energy consumption is explicitly modeled, accounting for flight, computation, and recharging at designated stations, ensuring practical applicability. Extensive simulations demonstrate that the proposed method reduces the mission makespan significantly compared to conventional static planning approaches, while effectively balancing energy usage and recharging requirements. These results highlight the potential of our approach for real-world UAV operations in dynamic settings.

A Probabilistic Approach to Load Balancing in Multi-Cloud Environments via Machine Learning and Optimization Algorithms

Efficient load balancing stands out as a crucial challenge in multi-cloud environments, particularly for applications that demand ultra-reliable, low-latency communications (URLLC). This paper proposes a novel approach integrating Decision Functions with Normal Distributions (DFND) for precise probabilistic modeling of task-to-cloud compatibility. Multivariate normal distributions capture interdependencies between resource features such as CPU, memory, bandwidth, and latency, ensuring accurate resource compatibility evaluation. Additionally, the Tasmanian Devil Optimization (TDO) algorithm employs dynamic exploration and exploitation strategies inspired by natural behaviors, providing rigorous optimization to improve task assignment in dynamic, multi-cloud environments. It uses flexible methods to ensure the optimization process is both efficient and scalable. Simulation results using CloudSim demonstrate significant improvements over state-of-the-art methods in terms of makespan reduction, response time minimization, resource utilization, and cost efficiency. The proposed framework effectively supports latency-sensitive, large-scale applications in dynamic, heterogeneous multi-cloud environments.

Improving the cadastral registration system for real estate objects based on their technical condition indicators

The article discusses the development of a systematic approach to creating a Unified Information Database on the technical condition of real estate objects across all urban and rural development areas. The necessity of this solution is justified by the current situation in urban planning, particularly after the enactment of Russian Federal Law No. 494-FZ on the Comprehensive Development of Territories (CDT). Decision-making regarding the renovation of large urban quarter territories has been hindered by the lack of data on the technical condition of most buildings. This situation is further exacerbated by the practice of conducting major repairs (FKR) without preliminary instrumental inspections, thus failing to address the actual causes of structural deterioration. The authors propose a systematic solution to this problem, involving the establishment of professionally trained units equipped with diagnostic instruments, standardized inspection methodologies tailored to building types, specialized software, and precise task assignments that account for the specific characteristics of the territory’s development. All inspections are conducted in accordance with territorial development plans and consolidated into a dedicated GIS-based real estate database in the form of specially developed Real Estate Passports. Data on the technical condition of each object, including key indicators such as structural strength, deformations, and physical-technical characteristics of materials, are aligned with current construction standards (SP and SNiP). This information is used in designing master plans for territorial development, planning major housing repairs, and within the Russian Federal Service for State Registration, Cadastre, and Cartography (Rosreestr) cadastral registration system. The article is based on extensive real-world experience in building inspections and urban renovation planning.

Market-based multiagent system for task allocation and battery charging of AGVs using collision-free path

Automated guided vehicles (AGVs) are responsible for transporting materials within a warehouse. They require continuous task allocation, must perform assigned tasks without collisions and deadlocks, and need to maintain adequate battery charge to improve throughput while preventing depletion. Designing a suitable control architecture and optimising the model for AGV systems in dynamic environments poses an intricate challenge due to the computational demands associated with each flow. We introduce a model that employs a multiagent system to manage the AGV system, incorporating both task assignment and battery charging procedures. Viewed from an algorithmic standpoint, the task allocation procedure mitigates collision and deadlock risks, while the battery charging procedure ascertains the appropriate charging station and timing for each visit. In this model, both task allocation and battery charging operations adopt a market-based approach among multiple agents, enabling each agent to make optimal decisions based on its available data. Consequently, the computational and decision-making burdens are distributed among individual agents. Experimental results indicate that the proposed model is competitive with existing benchmark models, outperforming them by at least 6.7% in dynamic environments with frequent task occurrences. The proposed model holds promise for contributing to the advancement of AGV applications in warehouse management.

Secure Attestation and Dynamic Load Balancing (SALB) for Optimized Container Management: Ensuring Integrity and Enhancing Resource Efficiency

ABSTRACTContainer technology has revolutionized the deployment and management of applications by providing isolated, lightweight environments that simplify scaling and enhance flexibility. In this context, the secure attestation‐based load balancing (SALB) algorithm is proposed to further enhance containerized environments by integrating secure deployment practices with dynamic load balancing. This algorithm addresses two pivotal aspects of modern computing systems: security and resource management. It employs remote attestation to ensure the integrity and trustworthiness of both container images and host environments, thereby safeguarding against potential threats from compromised or malicious components. Concurrently, it manages and allocates resources across containers based on real‐time workload assessments, optimizing performance, and resource utilization. The algorithm follows a structured approach: initializing containers, generating application packages, and defining operational thresholds for CPU and memory. It then performs remote attestation to verify host and container image integrity before deploying the containers. Resource allocation and task assignment are dynamically adjusted based on container usage, with provisions for handling overloaded or failed containers through reassignment and the creation of new containers. The implementation of the SALB algorithm results in a more secure and efficient container management system. It balances workloads effectively while maintaining high levels of security through rigorous attestation processes. The dynamic resource allocation and fault‐tolerance features enhance system performance and reliability, demonstrating the algorithm's capability to optimize containerized environments and ensure robust, reliable operations.

Psychological distress in health care workers during the beginning, the middle, and the last part of the COVID-19 pandemic

The COVID-19 pandemic placed significant psychological strain on healthcare workers. Our study tracked health care workers in the Czech Republic throughout the pandemic to examine the impact of stressors on psychological distress over time and explore gender differences in these associations. We studied health care workers from the Czech arm of the international COVID-19 HEROES Study who took part in all three waves of data collection in 2020, 2021 and 2022 (n = 264). We employed a path model to examine the relationships among six stressors (low trust in workplace, assignment of new tasks, prioritization of patients, experience of death due to COVID-19, experience of discrimination or violence, contact with COVID-19 patients), psychological distress (measured by General Health Questionnaire), and covariates (age, gender, occupation). We incorporated autoregressive paths and interactions to assess the longitudinal impact of stressors. Prior levels of distress predicted subsequent distress, with significant carry-over effects observed between each wave. Stressors had direct association to distress in the same wave of data collection (2020 and 2021), but not in subsequent waves, and stressors in 2022 did not have a significant direct effect to distress. Men reported lower distress in 2020 and 2021, but not in 2022. Furthermore, no significant interactions between stressors and gender were found. Age and occupation were not related to distress levels. There is a need for adaptable mental health support that addresses current stressors through targeted interventions, while also providing ongoing monitoring beyond crises, particularly for individuals with high distress levels.

Design and Develop Mining Maintenance Mobile Application

The design and development of a mobile application system especially suited for mining industry maintenance management. The system was created to solve typical issues in mining operations, like equipment failures, ineffective maintenance plans, and poor communication between operators and maintenance crews. This app is designed to streamline the assignment of maintenance tasks within mining operations, enhancing efficiency and communication. Administrators can easily allocate jobs to employees based on skill sets, availability, and location, ensuring optimal resource utilization. The app features real-time notifications, progress tracking, and comprehensive reporting tools to monitor job status and employee performance. By integrating these functionalities, the application aims to minimize downtime, improve safety standards, and increase overall productivity in the mining sector. Chatti, M. A., & Ziegler, J. (2017) [7] The proposed mining maintenance mobile application revolutionizes the traditional maintenance management system by enabling seamless job allocation and tracking for administrative staff. Features including digital work orders, fault diagnostics, predictive and preventative maintenance alerts, and real-time monitoring are all integrated into the mobile application. Through intuitive user interfaces, it facilitates the easy tracking and administration of mobile machinery and equipment while offering extensive information on equipment performance, maintenance history, and downtime. To accommodate distant mining locations with spotty internet, the application also offers offline functionality. Elsayed, T., & Ramadan, H. (2020) [11] The application's efficacy in improving asset reliability and streamlining maintenance work scheduling has been confirmed through case studies and field testing. The study ends by providing insights into the application's potential future developments, such as machine learning-based improvements for predictive maintenance and more features for extensive mining operations. Bello, O., Zeadally, S., & Badra, M. (2017) [5]

Multi‐Criteria Optimization of Scientific Workflow Schedules for Improved Energy Efficiency in Cloud Infrastructures

ABSTRACTRising global dependence on cloud services has become crucial for enterprises, aiming to guarantee continuous data accessibility while pursuing enhanced energy efficiency and minimized carbon emissions from data centers. However, the persistent challenge of high‐energy consumption in these facilities necessitates a concentrated approach toward energy reduction. This paper introduces an innovative multi‐objective scheduling strategy for scientific workflows, tailored for heterogeneous computing environments. Our method employs a hybrid genetic algorithm, incorporating Hill Climbing to generate an initial population of chromosomes. Subsequently, a genetic algorithm optimizes task assignments to the most suitable virtual machines, utilizing a meticulously designed fitness function to evaluate each chromosome's suitability for solving the scheduling problem. Through extensive experimentation, we demonstrate that our proposed algorithm outperforms other scheduling techniques in terms of solution quality, contributing to reduced energy consumption, processing duration, and cost. We contend that this innovative approach holds substantial potential in mitigating the energy consumption and carbon footprint associated with cloud data centers, offering a sustainable and environmentally conscious solution for scientific workflow scheduling.

Optimizing Cyber Protection Team (CPT) Operations: An Analysis of Task Synchronization and Process Agility in Defensive Cyber Operations

The operations process for Cyber Protection Teams (CPTs) outlined in the U.S. Cyber Command’s (USCYBERCOM) Cyber Warfare Publication (CWP) 3-33.4, Cyber Protection Team Organization, Functions, And Employment, is currently hindered by inefficiencies stemming from a lack of synchronization between task assignment and execution. This disjointed approach results in a rigid, linear project lifecycle that fails to adapt to the dynamic tempo and increasing sophistication of adversarial cyber operations. Consequently, critical dependencies are missed, milestones are misaligned, and delays in the execution of defensive cyber operations (DCO) occur, ultimately impeding the CPT's ability to meet the strategic intent of USCYBERCOM, which emphasizes agility, creativity, and rapid deployment. This study critically examines the existing CPT operations process with the goal of identifying the optimal timeline for executing DCO missions while highlighting key inefficiencies that hinder operational effectiveness. By deconstructing the process into discrete work packages and analyzing task dependencies through Gantt chart modeling, the critical path is identified, and a best-case scenario is simulated. Even under ideal conditions, however, the process remains excessively rigid, resulting in prolonged timelines that undermine mission success. To address these shortcomings, this research advocates for the integration of agile project management methodologies into the CPT operations framework. By promoting flexibility, real-time feedback loops, and dynamic task sequencing, this approach would enhance the synchronization between planning and execution phases, enabling timelier and more effective DCO. The findings offer a pathway toward aligning the CPT operations process with USCYBERCOM’s vision of a more adaptive and responsive force capable of meeting the demands of the modern cyber threat landscape.

Exploring Instructor-Led Team Formation and Task Assignment to Enhance Group Dynamics in Pharmacy Problem-Based Learning.

Exploring Instructor-Led Team Formation and Task Assignment to Enhance Group Dynamics in Pharmacy Problem-Based Learning.

Centralized and Distributed Strategies for Handover-Aware Task Allocation in Satellite Constellations

As satellite constellations grow in size, there is an increasing need for autonomous, scalable, and real-time dynamic task assignment to address the unique operational challenges of such distributed systems. In particular, a time-varying task assignment (i.e., for observing various regions of Earth) often means that the corresponding satellite has to reorient itself or its sensors, costing time and energy. However, most assignment algorithms for area requests proposed in the literature do not account for the significant cost associated with task handover in satellite constellations. In this paper, we develop a framework for solving the seemingly non-deterministic polynomial-time (NP)-hard problem of optimal dynamic task allocation while minimizing task handover. In particular, we develop Handover-Aware Assignment with Lookahead (HAAL), an algorithm with centralized and distributed variants, and solutions that provably achieve 50% of the optimal value. We then proceed to show that HAAL significantly outperforms similar heuristic methods proposed in the literature for realistic constellation experiments with up to a thousand satellites. The algorithm scales polynomially in the number of satellites/tasks and offers a smooth tradeoff between computational efficiency and performance, allowing the designer to tune the algorithm based on available computing resources, communication bandwidth, and required performance.

Optimization of task assignment for multi-farm multi-weeding robots based on discrete artificial bee colony algorithm

Optimization of task assignment for multi-farm multi-weeding robots based on discrete artificial bee colony algorithm

Daksh: Local Talent and Task Assistance Platform

द (Daksh) is a localized platform designed to empower skilled individuals by connecting them with task opportunities and users seeking reliable assistance. It aims to address the gap between talent utilization and demand by creating a communitydriven network that simplifies task management, uplifts local workers, and promotes economic growth. With features like skill verification, AIpoweredmatching, offline accessibility, and real-time notifications, Daksh ensures inclusivity and accessibility,particularly for India's vast unorganized workforce. This initiative not only facilitates seamless task assignment but also fosters social and economic development by empowering underrepresented talent

Reinforcement Learning-Based Variable Horizon Model Predictive Control of Multirobot Systems in Dynamic Environments

Abstract Multirobot systems (MRS) consist of multiple autonomous robots that collaborate to perform tasks more efficiently than single-robot systems. These systems enhance flexibility, enabling applications in areas such as environmental monitoring, search and rescue, and agricultural automation while addressing challenges related to coordination, communication, and task assignment. Model predictive control (MPC) stands out as a promising controller for multirobot control due to its preview capability and effective constraint handling. However, MPC's performance heavily relies on the chosen length of the prediction horizon. Extending the prediction horizon significantly raises computation costs, making its tuning time-consuming and task-specific. To address this challenge, we introduce a framework utilizing a Collective Reinforcement Learning strategy to generate the prediction horizon dynamically based on the states of the robots. We propose that the prediction horizon of any robot in MRS depends on the states of all the robots. Additionally, we propose a versatile on-demand collision avoidance (VODCA) strategy to enable on-the-fly collision avoidance for multiple robots operating under varying prediction horizons. This approach establishes a better tradeoff between performance and computation costs, allowing for adaptable prediction horizons for each robot at every time-step. Numerical studies are performed to investigate the scalability of the proposed framework, the stiffness of the learned reinforcement learning (RL) policy, and the comparison with the fixed horizon and existing variable horizon MPC methods. The framework is also implemented on multiple TurtleBot3 Waffle Pi for various multirobot tasks.