Task Assignment Research Articles

An effective knowledge-based evolutionary algorithm for task assignment problem of pollination robots and spraying drones in multi-orchard scenarios

An effective knowledge-based evolutionary algorithm for task assignment problem of pollination robots and spraying drones in multi-orchard scenarios

PDMA: Efficient and privacy-preserving dynamic task assignment with multi-attribute search in crowdsourcing

PDMA: Efficient and privacy-preserving dynamic task assignment with multi-attribute search in crowdsourcing

Dual learning based Pareto evolutionary algorithm for a kind of multi-objective task assignment problem

Dual learning based Pareto evolutionary algorithm for a kind of multi-objective task assignment problem

Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

Activity-based capability updating method for task assignment in mobile crowdsensing

Activity-based capability updating method for task assignment in mobile crowdsensing

Blockchain Based Delay‐Tolerant Resource Optimization in Fog and Cloud Layers Utilizing NNGOA and LS2BiOLSTM

ABSTRACTResource Optimization (RO) in fog and cloud layers enhances performance, minimizes costs, and ensures seamless integration of distributed systems. However, prevailing works failed to perform resource optimization in both fog and cloud layers due to their complex and disparate architectures. Therefore, the proposed work performs resource optimization efficiently in both fog and cloud layers by predicting the network traffic congestion using Neuron Northern Goshawk Optimization Algorithm (NNGOA) and Log Sigmoid Softplus Bidirectional Orthogonal Long Short‐Term Memory (LS2BiOLSTM). At first, the Cloud Users are registered and logged in for task assignments. Meanwhile, the Smart Contract (SC) based Service Level Management (SLM) is created for tasks. After that, the signature is created for SLA and is verified during task assignment. For predicting the network traffic congestion in tasks, LS2BiOLSTM is utilized. Then, the predicted congestion tasks are clustered and mapped into a fog layer. Simultaneously, from the Cloud Server (CS), the data center is prioritized using SoftSign Bell‐Fuzzy (SSB‐Fuzzy). Finally, the resources are optimized efficiently with a high accuracy of 98.1259% using NNGOA, which outperforms the existing methodologies.

Hybrid Clustering-Enhanced Brain Storm Optimization Algorithm for Efficient Multi-Robot Path Planning

To address the core challenges in multi-robot path planning (MRPP) within large-scale, complex environments—namely path conflicts, suboptimal task allocation, and computational inefficiency—this paper introduces a Hybrid Clustering-Enhanced Brain Storm Optimization (HC-BSO) algorithm designed to improve both path quality and computational efficiency significantly. For optimizing initial task assignment, the conventional K-Means clustering method is supplanted by a hybrid clustering methodology that integrates Mini-Batch K-Means with Density-Based Spatial Clustering of Applications with Noise (DBSCAN), facilitating an efficient and robust partitioning of task points. Concurrently, we incorporate a two-stage exploration–perturbation evolutionary strategy. This strategy effectively balances global exploration with local exploitation, thereby enhancing solution diversity and search depth. Comparative analyses against the standard Brain Storm Optimization (BSO) and other prominent swarm intelligence algorithms reveal that HC-BSO exhibits significant advantages in terms of total path length, computational time, and path conflict avoidance. Notably, in large-scale, multi-task scenarios, HC-BSO consistently generates high-quality, conflict-free paths, demonstrating superior stability, convergence, and scalability.

Efficient Employee Timesheet Tracker Using Object-Oriented Programming and File Handling in C++

Abstract — The purpose of this project is to design and develop a C++-based Timesheet Management System aimed at improving workforce time tracking and project productivity. Efficient timesheet systems are essential for ensuring accurate record-keeping of employee working hours, task assignments, and project timelines. This system utilizes object-oriented programming principles and efficient data structures in C++ to implement features such as real-time time entry logging, role-based access, and automated report generation. The project supports both daily and weekly entry modes, offering flexibility for various organizational workflows. File handling and data validation techniques are employed to securely store and process timesheet records. Additionally, the system includes features for identifying inconsistencies and generating performance summaries, which aid in effective resource management. By automating and streamlining the timesheet process, this solution reduces manual errors and administrative overhead, providing a reliable foundation for payroll processing and project evaluation.. Keywords — Timesheet Management, C++ Programming, Time Tracking, Project Monitoring, File Handling, Employee Productivity.

Optimization of Tank Cleaning Station Locations and Task Assignments in Inland Waterway Networks: A Multi-Period MIP Approach

Inland waterway transportation is critical for the movement of hazardous liquid cargoes. To prevent contamination when transporting different types of liquids, certain shipments necessitate tank cleaning at designated stations between tasks. This process often requires detours, which can decrease operational efficiency. This study addresses the Tank Cleaning Station Location and Cleaning Task Assignment (TCSL-CTA) problem, with the objective of minimizing total system costs, including the construction and operational costs of tank cleaning stations, as well as the detour costs incurred by ships visiting these stations. We formulate the problem as a mixed-integer programming (MIP) model and prove that it can be reformulated into a partially relaxed MIP model, preserving optimality while enhancing computational efficiency. We further analyze key mathematical properties, showing that the assignment constraint matrix is totally unimodular, enabling efficient relaxation, and that the objective function exhibits submodularity, reflecting diminishing returns in facility investment. A case study on the Yangtze River confirms the model’s effectiveness, where the optimized plan resulted in detour costs accounting for only 5.2% of the total CNY 4.23 billion system cost and achieved an 89.1% average station utilization. Managerial insights reveal that early construction and balanced capacity allocation significantly reduce detour costs. This study provides a practical framework for long-term tank cleaning infrastructure planning, contributing to cost-effective and sustainable inland waterway logistics.

Towards Optimal Guidance of Autonomous Swarm Drones in Dynamic Constrained Environments

ABSTRACTAs autonomous drone swarms become increasingly important for complex missions, there remains a critical need for integrated approaches that can simultaneously handle task allocation and safe navigation in dynamic environments. This paper addresses the challenge of optimally allocating tasks and generating collision‐free trajectories for drone swarms operating in obstacle‐rich settings. Our proposed Swarm Allocation and Route Generation (SARG) framework integrates optimal task assignment with dynamically feasible trajectory planning, enabling efficient mission completion while ensuring safe navigation through complex 3D workspaces. Using quadrotors as our experimental platform, the framework incorporates both Drone‐to‐Obstacle and Drone‐to‐Drone collision avoidance algorithms, alongside a modified path planning algorithm that enhances simultaneous graph search efficiency. Our extensive experiments demonstrate that the SARG framework significantly improves performance over existing approaches. The SARG framework, while maintaining a 100% collision avoidance rate in dense environments, achieves a 21.6% reduction in the computation time of the simultaneous graph searching phase compared to conventional methods, contributing to overall system efficiency. These results establish SARG as a viable solution for real‐world autonomous drone swarm applications in complex, dynamic settings. Supporting Information, including animated simulations, are available at https://youtu.be/56oabPTUz4g.

Reptile Honey Badger Optimization Algorithm‐Based Deep Quantum Neural Network for Task Allocation in Multi‐Robot Systems

ABSTRACTTask allocation in multi‐robot systems has been a critical area of research, with applications spanning various industries, such as logistics, agriculture, and manufacturing. The allocation of tasks to multi‐robots improves the system performance, which generally minimizes total resource consumption or cost needed for performing a group of tasks. In dynamic multi‐robot systems, efficient task allocation is critical for optimizing system performance, especially in response to environmental changes like faults or the actions of other robots. Therefore, a new approach called reptile honey badger optimization algorithm_deep quantum neural network (RHBA_DQNN) is framed for task allocation in multi‐robot systems. At first, the tasks are grouped utilizing the fuzzy local information C‐means (FLICM) clustering model. Then, the assignment of tasks for the group of robots is conducted using the devised RHBA, where monetary cost, distance, time, and completion time are considered objective functions. The proposed RHBA is the combination of the reptile search algorithm (RSA) and honey badger algorithm (HBA). Finally, the penalty cost is decided based on the deep quantum neural network (DQNN). Moreover, the RHBA_DQNN has obtained a minimum overall cost, execution time, distance, and monetary cost of 81.251, 9.99, 1.600, and 0.249, respectively.

Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks.

Low Earth orbit (LEO) satellite networks have shown extensive application in the fields of navigation, communication services in remote areas, and disaster early warning. Inspired by multi-access edge computing (MEC) technology, satellite edge computing (SEC) technology emerges, which deploys mobile edge computing on satellites to achieve lower service latency by leveraging the advantage of satellites being closer to users. However, due to the limitations in the size and power of LEO satellites, processing computationally intensive tasks with a single satellite may overload it, reducing its lifespan and resulting in high service latency. In this paper, we consider a scenario of multi-satellite collaborative offloading. We mainly focus on computation offloading in the satellite edge computing network (SECN) by jointly considering the transmission power and task assignment ratios. A maximum delay minimization problem under the power and energy constraints is formulated, and a distributed balance increasing penalty dual decomposition (DB-IPDD) algorithm is proposed, utilizing the triple-layer computing structure that can leverage the computing resources of multiple LEO satellites. Simulation results demonstrate the advantage of the proposed solution over several baseline schemes.

A cluster-oriented task assignment optimization for green high-performance computing center operations

A cluster-oriented task assignment optimization for green high-performance computing center operations

Switch-Off Policy in Flow Lines with Dynamic Fractional Task Assignation

Switch-off policies are widely used in manufacturing to reduce energy consumption during machine idle times without interfering with production. However, they can negatively impact productivity and customer performance. This study introduces a method that integrates switch-off policies with the dynamic allocation of fractional tasks between adjacent stations in a production line. Simulation models were developed to assess performance against two benchmark scenarios: “always on” and “no fractional tasks”. Results show that the proposed approach significantly reduces energy consumption while mitigating the adverse effects on customer performance. The integration of real-time data processing and adaptive task allocation maintains production efficiency under fluctuating demand and operational disturbances, supporting more sustainable manufacturing operations.

Determining Task Assignments for Candidate Workers Based on Trajectory Prediction

Determining Task Assignments for Candidate Workers Based on Trajectory Prediction

Digitalization and Optimization of University Business Processes through BPM and KPI Integration

Introduction: The digitalization of business processes has become a necessity for organizations to enhance their efficiency and productivity. Higher education institutions, in particular, are increasingly turning to digital solutions to streamline administrative operations and improve service delivery to students. Objective: This study aims to digitalize and improve the business processes of the university by leveraging Business Process Management (BPM) mechanisms and new technologies. The primary focus is on optimizing student support processes to enhance efficiency, reduce administrative delays, and improve communication between students and academic staff. Methods: This research follows a structured approach that includes: First, Business Process Analysis and Design – Identifying users, tasks, and all relevant process elements to design an optimized student support process. Second, Integration of KPIs in BPM – Incorporating Key Performance Indicators (KPIs) into digitalized business processes to improve performance and quality within the digital transformation framework. Results: The paper presents the development of BP-KPI, a solution that integrates BPM and KPIs to automate and enhance student support at Oran University of Science and Technology Mohamed-Boudiaf (USTO). The BP-KPI platform offers functionalities such as automated task assignment, real-time progress tracking, and personalized notifications for students. Discussion: By streamlining student support processes, the proposed solution minimizes administrative delays, improves resource allocation, and fosters efficient communication between students and academic authorities. The integration of BPM and KPIs demonstrates strong potential in addressing challenges within student services. Furthermore, the BP-KPI framework offers a scalable model for digital transformation in higher education, contributing to enhanced institutional efficiency, personalized student assistance, and data-driven decision-making.

Automating ITSM Compliance (GDPR/SOC 2/HIPAA) in Jira Workflows: A Framework for High-Risk Industries

Regulatory compliance is increasingly a fundamental part of a methodology to shield one’s organization from unscrupulous practices in enterprise IT. Organizations are bound by these compliance frameworks, such as GDPR, SOC 2, and the Health Insurance Portability and Accountability Act (HIPAA), to have the most potent data security, privacy, and integrity controls in place as they pertain to data. Organizations can get integrated options for handling workflows and ensuring compliance with the automated options of IT Service Management (ITSM) tools like Jira. With customizable workflows, automated notifications, and task assignments, Jira exposes organizations to powerful and easy-to-enforce compliance with these regulations across large and distributed teams. This study explores ways of automating the compliance workflows using Jira and how it would integrate well with other ITSM tools and perfectly tie with IT service and DevOps processes. It also talks about how complex it is to automate compliance, including configuring workflows and integrating legacy systems. This will help the organization automate compliance tasks, lessen human error risk, accelerate the audit, and stay on track with compliance metrics. Jira case studies are also presented, which explain how Jira is used in high-risk cases, reducing the risk associated with compliance and improving audit and streamlining of workflow. The paper ends by recommending industry organizations that want to utilize the best practices of compliance automation as part of their strategies and predicting trends that will affect compliance automation ITSM practices in the future, including AI and machine learning, blockchain technology.

Store fulfillment with autonomous mobile robots and in-store customers

Omnichannel services, such as buy-online-pickup-in-store, curbside pickup, and ship-from-store, have shifted the order-picking tasks previously completed by in-store customers doing their shopping to the retailer’s responsibility. To fulfill these orders, many retailers have deployed a store fulfillment strategy, where online orders are picked from brick-and-mortar retail store shelves. We focus on the design of operations inside a store where in-store customers collaborate with autonomous mobile robots (AMRs) to pick up online orders. Due to the uncertainty of in-store customers’ availability and their willingness to participate, the problem of synchronizing in-store customers with AMRs is highly stochastic. Thus, we model the stochastic order-picking problem with uncertain synchronization times of in-store customers and AMRs as a Markov Decision Process to determine how a retailer should dynamically assign tasks to a set of AMRs and dedicated pickers. We develop a heuristic solution framework that generates a set of initial assignments and routes for heterogeneous picking resources and dynamically updates them as the actual synchronization times between AMRs and in-store customers unfold. We analyze multiple strategies to generate the initial set of task assignments and routes as well as update such decisions based on the system state. To guide on whether the proposed approach is economically and operationally viable, we conduct extensive computational experiments using actual online grocery orders and empirical shopping behavior data. We illustrate the feasibility of such a policy to achieve similar picking performance as the status quo and through an economic analysis show that deploying dedicated pickers and AMRs aided by in-store customers in a store environment are economically viable.

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.