Task Assignment Research Articles

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

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

Enhancing crowdsourcing through skill and willingness-aligned task assignment with workforce composition balance

Enhancing crowdsourcing through skill and willingness-aligned task assignment with workforce composition balance

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

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.

Optimized Path Planning and Scheduling in Robotic Mobile Fulfillment Systems Using Ant Colony Optimization and Streamlit Visualization

Context: In the age of rapid e-commerce growth; the Robotic Mobile Fulfillment Systems (RMFS) have become the major trend in warehouse automation. These systems involve the use of self- governed mobile chares to collect shelves as well as orders for deliveries with regard to optimization of task allocation and with reduced expenses. However, in a manner to implement such systems, one needs to find enhanced algorithms pertaining to resource mapping and the planning of movement of robots in sensitive environments. Problem Statement: Despite RMFS have certain challenges especially when it comes to the distribution of tasks and the overall distances that employees have to cover. Objective: The main goal of this paper is to propose a new compound optimization model based on RL-ACO to optimize the RMFS’s task assignment and navigation. Also, the direction of the study is to investigate how such methods can be applied to real-life warehouse automation and how effective such methods can be on a large scale. Methodology: This research introduces a new optimization model for RMFS selection which integrates reinforcement learning with Ant Colony Optimization (ACO). Specifically, a real gym environment was created to perform the order assignment and training in the way of robotic movement. Reinforcement Learning (RL) models were trained with Proximal Policy Optimization (PPO) for improving the dynamic control of robots and ACO was used for computing optimal shelf trajectories. The performance was also measured by policy gradient loss, travelled distance and time taken to complete the tasks. Results: The proposed framework showed potential in enhancing the efficiency of tasks required and the travel distances involved. In each of the RL models used the shortest paths were identified and the best route was determined to have a total distance of 102.91 units. Also, other values such as, value function loss and policy gradient loss showed learning and convergence in iterations. To build a global solution, ACO integration went a step forward in enabling route optimization through effective combinatorial problems solving. Implications: This research offers a practical, generalizable and flexible approach for the improvement of the operations of RMFS and thinking for warehouse automation.

Crafting and Evaluating Generative ai Prompts: Insights from Students, Educators, and Marketers8

ABSTRACT The rapid adoption and evolution of generative artificial intelligence (AI) tools like ChatGPT and Gemini have impacted marketing education and practice. This paper introduces a prompt engineering assignment that utilizes generative AI to build critical thinking skills through the development of keyword research strategies, customer profiles, and presentation outlines. Using insights from students, educators, and marketers, the assignment is refined to include (a) reflections about the learning and application of the generative AI tools and (b) evaluations about the accuracy and potential for bias in the generated output. The assignment tasks align with marketing practice, and guidance is provided on how to adapt and update the assignment.

Manajemen Strategi dalam Membangun Brand Image Lembaga Pendidikan di SMK Mambaul Ulum Paiton Jou

The purpose of this research is to describe management functions, supporting factors and inhibiting factors in building the brand image of Mambaul Ulum Paiton Vocational School. This research uses a qualitative approach with the aim of describing the strategy for building the image of Mambaul Ulum Paiton Vocational School through management functions, which are composed of planning, organizing, implementing and evaluating, without the intention of drawing general conclusions or generalizations. Data collection techniques include interviews, observation, and document analysis. The research results show that planning includes setting goals/targets, needs analysis, strategy, determining resources, implementation and evaluation of planning. Organizing includes resource allocation, task assignment, determination procedures, and organizational structure. Direction involves service leadership, verbal motivation, public relations job descriptions, and policies according to planning. Monitoring and evaluation includes evaluation of success, corrective action, and solution action. Supporting factors include the achievements of the principal, staff or employees, as well as the achievements of students. Inhibiting factors include a lack of human staff, a lack of personal computer units, and a lack of budget.

Coupled Optimization of UAV Cluster Path Optimization and Task Assignment on a Mobile Platform

This paper focuses on the coupled optimization problem of path optimization and task assignment for UAVs mounted on mobile platforms. Combining the UAV turning angle, minimum direct flight trajectory and other flight characteristics, the path optimization model on the 3D raster map is established with the objectives of shortest flight time and minimum UAV destruction, and the optimal path between the vertices of each mission is derived by using an improved Gray Wolf Optimization algorithm. Combining the takeoff and landing time-window constraints with the range and mission resource constraints, this mission planning model is established with the objective of maximizing the efficiency ratio of mission revenue–UAV damage consumption. Combining the optimal paths between vertices, a complete UAV flight path is formed, which provides a path optimization and goal assignment method for UAV clusters mounted on mobile platforms to perform multiple tasks cooperatively, and its feasibility and effectiveness are verified through simulation experiments.

Development of a Fleet Management System for Multiple Robots’ Task Allocation Using Deep Reinforcement Learning

This paper presents a fleet management system (FMS) for multiple robots, utilizing deep reinforcement learning (DRL) for dynamic task allocation and path planning. The proposed approach enables robots to autonomously optimize task execution, selecting the shortest and safest paths to target points. A deep Q-network (DQN)-based algorithm evaluates path efficiency and safety in complex environments, dynamically selecting the optimal robot to complete each task. Simulation results in a Gazebo environment demonstrate that Robot 2 achieved a path 20% shorter than other robots while successfully completing its task. Training results reveal that Robot 1 reduced its cost by 50% within the first 50 steps and stabilized near-optimal performance after 1000 steps, Robot 2 converged after 4000 steps with minor fluctuations, and Robot 3 exhibited steep cost reduction, converging after 10,000 steps. The FMS architecture includes a browser-based interface, Node.js server, rosbridge server, and ROS for robot control, providing intuitive monitoring and task assignment capabilities. This research demonstrates the system’s effectiveness in multi-robot coordination, task allocation, and adaptability to dynamic environments, contributing significantly to the field of robotics.

On the issue of a working model for modernizing the Unified State Exam to overcome inequality of access to higher education

The study formulates approaches to determining inequality of access to higher education through regionally determined choice of subjects and Unified State Exam results. The dependence of the choice and results of the Unified State Exam on the quality of education and innovative development of regions is presented. Indicators of inequality are the distribution of students according to “success cohorts” when passing the Unified State Exam and the choice of subject by students to take, and the average score in the region. Quantitative characteristics of the choice of basic and specialized mathematics and other academic subjects by students in the regions are presented. The calculations used data from the entire population of applicants in 2019 and 2020; accordingly, the conclusions of the article relate to the period until 2021. The problem of increasing the efficiency of the Unified State Examination is objective and has matured before the political events associated with the departure from the Bologna system in 2022. It is noted that Olympiads and additional internal university tests are not an alternative to the Unified State Exam. Based on the goals of leveling regional inequality, a variant of the model for modernizing the Unified State Examination with an objective assessment procedure is proposed– a remote creative exam using the existing infrastructure of examination points and video communication. Due to the random assignment of tasks and assessing experts from different regions, universities, organizations, remote creative exam retains the advantages of the Unified State Exam, comparability of results is achieved, corruption and the possibility of “training” for certain types of answers are eliminated. A person assesses the exam results: a teacher at a general education organization or university, the head of a government agency or enterprise. The examination model includes the advantages of Olympiads and internal university examinations without their corruption-prone components. The article notes that any innovation in the field of final/entrance examinations must necessarily go through testing and experimentation with a gradual increase in coverage and adjustment of the applied methods and procedures based on the results of professional discussion.

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.

IoRT-Based Middleware for Heterogeneous Multi-Robot Systems

The concurrence of social robots with different functionalities and cyber-physical systems in indoor environments has recently been increasing in many fields, such as medicine, education, and industry. In such scenarios, the collaboration of such heterogeneous robots demands effective communication for task completion. The concept of the Internet of Robotic Things (IoRT) is introduced as a potential solution, leveraging technologies like Artificial Intelligence, Cloud Computing, and Mesh Networks. This paper proposes an IoRT-based middleware that allows the communication of different types of robot operating systems in dynamic environments, using a cloud-based protocol. This middleware facilitates task assignment, training, and planning for heterogeneous robots, while enabling distributed communication via WiFi. The system operates in two control modes: local and cloud-based, for flexible communication and information distribution. This work highlights the challenges of current communication methods, particularly in ensuring information reach, agility, and handling diverse robots. To demonstrate the middleware suitability and applicability, an implementation of a proof-of-concept is shown in a touristic scenario where several guide robots can collaborate by effectively sharing information gathered from their heterogeneous sensor systems, with the aid of cloud processing or even internal communication processes. Results show that the performance of the middleware allows real-time applications for heterogeneous multi-robot systems in different domains.

Workplace Injury and Death: A National Overview of Changing Trends by Sex, United States 1998-2022.

Women represent a substantial portion of the US workforce. However, injury and fatality rates for female workers have, historically, remained lower than rates for male workers. Fatal occupational data from the Census of Fatal Occupational Injuries (CFOI) and nonfatal injury data from the National Electronic Injury Surveillance System-Occupational Supplement (NEISS-Work) for the years 1998-2022 were examined to produce rate ratios of male to female fatal and nonfatal occupational injury rates for all workers in the United States. Auto-regressive linear models were developed to analyze rate ratios by sex for fatal and nonfatal occupational injuries by age group, injury event, and select industries to determine if female occupational fatal and nonfatal injury rates were following trends comparable to male rates. Over the 25-year study period, male injury and fatality rates were consistently higher than females. Occupational fatality rates for males were more than nine times higher than female rates, and for nonfatal occupational injuries, male rates were 1.4 times higher than female rates. These analyses indicate that the differences in nonfatal injury rates by sex may be attenuating, however, the large gap by sex in workplace fatalities has remained unchanged. Occupational safety and health research with a more specific focus on these sex differences is needed to gain a clearer understanding of how sex differences affect hiring, job training, task assignment and completion, and injury risk, to identify areas where prevention efforts could be most successful.

Feasibility-aware hierarchical task assignment for equipment inspection robots with heterogeneous work efficiency

In this paper, we address optimal task assignments for equipment inspection robots in a factory. In view of the complex work environment and variety of the devices to be inspected, coordination of heterogeneous robots is inevitable for such a system. In this paper, we propose a computationally efficient hierarchical optimization architecture for a robotic team with heterogeneous work efficiency, where the upper layer assigns robots to subareas and the lower assigns them to target devices together with the inspection items. In order to ensure feasibility of the lower-layer optimization, we present novel logical constraints for the upper-layer problem such that enough robotic resources are reserved at each time frame, while showing that the resources used in the past can be kept track of by a dynamic constraint. Finally, we demonstrate the effectiveness of the present solution through simulation that duplicates operations in an actual factory.

Employee Task Assignment Android Application

An Android-based Employee Tracking System was created to monitor all employee activities. This system tracks scheduling and time-off requests, which are crucial for managers to determine when employees are expected to be in the office or working remotely. The system provides a convenient way for managers to oversee their team’s performance via mobile phones. It was developed using the JAVA programming language, with data being stored in an SQLite database. The design followed an Object-Oriented Analysis and Design methodology, incorporating a structured, iterative approach. Data was gathered through document reviews and field methods, with analytical tools like bar charts applied to analyze the findings. The implemented system successfully improved productivity, reduced costs, and enabled instant access to employee attendance records.

Addressing the Return Visit Challenge in Autonomous Flying Ad Hoc Networks Linked to a Central Station.

Unmanned Aerial Vehicles (UAVs) have become essential tools across various sectors due to their versatility and advanced capabilities in autonomy, perception, and networking. Despite over a decade of experimental efforts in multi-UAV systems, substantial theoretical challenges concerning coordination mechanisms still need to be solved, particularly in maintaining network connectivity and optimizing routing. Current research has revealed the absence of an efficient algorithm tailored for the routing problem of multiple UAVs connected to a central station, especially under the constraints of maintaining constant network connectivity and minimizing the average goal revisit time. This paper proposes a heuristic routing algorithm for multiple UAV systems to address the return visit challenge in flying ad hoc networks (FANETs) linked to a central station. Our approach introduces a composite valuation function for target prioritization and a mathematical model for task assignment with relay allocation, allowing any UAV to visit various objectives and gain an advantage or incur a cost for each. We exclusively utilized a simulation environment to mimic UAV operations, assessing communication range, connectivity, and routing performance. Extensive simulations demonstrate that our routing algorithm remains efficient in the face of frequent topological alterations in the network, showing robustness against dynamic environments and superior performance compared to existing methods. This paper presents different approaches to efficiently directing UAVs and explains how heuristic algorithms can enhance our understanding and improve current methods for task assignments.

A decomposition-based dynamic constrained multi-objective task assignment for heterogeneous crowdsensing

Efficient task allocation is a crucial issue of Mobile crowdsensing (MCS). Generally, only homogeneous mobile users like human are selected as the participants, causing a difficulty to meet the spatiotemporal coverage demand on human-unreachable regions. To overcome this drawback, unmanned aerial vehicles are introduced to form heterogeneous MCS, which can be formulated into a dynamic constrained multi-objective task allocation model. Taking the maximum average sensing quality of all tasks and the maximum average remaining budget for each subtask as the optimization objectives, an improved decomposition-based multi-objective evolutionary algorithm is presented to find the optimal allocation scheme. Specifically, the problem is first decomposed into a set of dynamic constrained scalar subproblems. For each subproblem, a stochastic configuration network (SCN)-based initialization is developed to produce the promising population, in which SCNs learn the probabilities of mobile users being allocated to each task. Following that, a reinforcement learning-based autonomous evolutionary strategy is adopted to recommend the most appropriate solvers in terms of the state of current population. A hybrid population update mechanism is then employed to form the high-quality offspring, with the purpose of balancing the feasibility, convergence and diversity. The extensive experiments on 20 dynamic instances are conducted to demonstrate the effectiveness of proposed algorithm compared to other task allocation algorithms.

A systematic modeling and effectiveness evaluation framework for heterogeneous missile swarm systems

Abstract Heterogeneous missile swarm employs different kinds of missiles to work cooperatively for systematic warfare, which attract much attention in recent years. In order to effectively calculate and evaluate the systematic fighting capacity of heterogenous missile swarm in different scenarios, a systematic modeling and effectiveness evaluation framework is proposed in this paper. Firstly, the combat loop is defined, where the task assignment between missiles and targets is carried out using the contract net auction algorithm. Then the systematic model of heterogeneous missile swarm is constructed in terms of Observation-Orientation-Decision Action(OODA) loop network. Finally, the uncertain self-information method is used to calculate the system’s effectiveness. It can be seen from the results of the engineering example that the framework fully considers the whole process of the combat system, which can comprehensively and effectively evaluate the system effectiveness of the heterogeneous missile swarm system.