Task Allocation Research Articles

Developing a cross-functional team coordination framework: A model for optimizing business operations

In today's complex business environment, effective cross-functional team coordination is crucial for optimizing operations and achieving organizational goals. This study introduces a comprehensive framework designed to enhance cross-functional team coordination through structured operational management strategies. By integrating key principles of collaboration, communication, and accountability, the framework aims to bridge departmental silos and promote a culture of teamwork. The proposed model is structured around three core components: communication channels, collaborative tools, and performance metrics. First, it emphasizes the establishment of robust communication channels that facilitate real-time information sharing and feedback among team members from diverse functions. This fosters transparency and aligns objectives, ensuring that all team members are on the same page. Second, the framework advocates for the implementation of collaborative tools that streamline project management and task allocation. These tools enable team members to track progress, manage workloads, and share resources effectively, thereby enhancing overall productivity. By utilizing technology that supports remote collaboration, organizations can also adapt to the increasing trend of virtual teams, ensuring that geographical barriers do not hinder coordination. Finally, the model incorporates performance metrics that allow teams to evaluate their effectiveness and make data-driven decisions. By establishing clear key performance indicators (KPIs) related to team outcomes, organizations can continuously monitor progress and identify areas for improvement. This iterative approach not only optimizes current operations but also fosters a culture of continuous improvement within cross-functional teams. The framework is designed for applicability across various industries, demonstrating its versatility in addressing the unique challenges faced by organizations. By adopting this structured approach to cross-functional coordination, businesses can enhance their operational efficiency, reduce project delays, and improve overall team morale. This study contributes to the existing literature on operational management by providing a practical model that organizations can implement to optimize their business operations through improved cross-functional teamwork.

A qualitative case study on the development of the scholarship of teaching and learning: Centred on primary and secondary school teachers

This study aims to explore the perceptions of the Scholarship of Teaching and Learning (SoTL) of primary and secondary school teachers in C City, China, as well as the challenges they face in developing these abilities. Through narrative inquiry involving five current teachers, the research collected their personal experiences in the development of teaching and academic abilities, with data gathered through semi-structured interviews. The findings reveal that teachers are primarily driven by external forces, professional identity, personal growth, and the need to improve teaching quality in their efforts to enhance teaching and academic abilities. However, they also encounter challenges such as teaching pressures, time management difficulties, insufficient school support, and declining energy. To overcome these obstacles, teachers have adopted strategies such as time management, task allocation, and cognitive enhancement. The study concludes by recommending that through the combined efforts of teachers, schools, and society, a strong professional belief system should be established, and a supportive environment should be created to collaboratively promote the development of teaching and academic abilities among primary and secondary school teachers, thereby fostering their professional growth.

The meaning of organizational culture and structure for interprofessional collaboration

Abstract In Germany, early neurological rehabilitation (ENR) acts as a link between acute inpatient care and rehabilitation, aiming to restore patients’ ability to rehabilitate amid their complex clinical conditions. Interprofessional cooperation (IPC) is crucial for harmonized treatment due to patients’ vulnerability. An interprofessional team comprising medical, nursing, and various therapy professionals is essential for this purpose. To explore the meaning of organizational culture and structure to IPC, 15 group discussions in 5 clinics were conducted with n = 76 participants. 1. Weekly team meetings are used to coordinate treatment goals and outcomes, but their implementation varies from clinic to clinic. Two types of meetings were identified: those that focus primarily on billing compliance and others that address patients’ long-term perspectives. However, both require additional social service involvement and are not sufficient to adequately address daily tasks, requiring additional IPC coordination. Some clinics have therefore introduced daily meetings to improve the coordination of IPC tasks. 2. Therapy planning and therapist assignment shape IPC and task fulfillment. Three therapy planning methods were observed: centralized, block-based, and flexible, each with different therapist allocation strategies. Centralized planning with flexible allocation leads to appointment pressure and interprofessional communication challenges. Block therapy reduces therapist turnover and promotes better interprofessional coordination in patient care. Flexible therapy planning is conducted through daily interprofessional meetings with shared task allocation and planning leads to a shared understanding. Initial analysis underscores the meaning of organizational structure on interprofessional task fulfillment in ENR, emphasizing the importance of enhancing different cooperation formats which can balance stability and flexibility and integrate social services effectively in patientcare. Key messages • Morning interprofessional meetings on the ward facilitate flexible responses to daily changes in patients’ health conditions and promote interprofessional collaboration. • The consideration of occupational group-specific processes in ward organization is a necessary structural prerequisite for successful interprofessional collaboration.

Toward a Generic Framework for Mission Planning and Execution with a Heterogeneous Multi-Robot System

This paper presents a comprehensive framework for mission planning and execution with a heterogeneous multi-robot system, specifically designed to coordinate unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) in dynamic and unstructured environments. The proposed architecture evaluates the mission requirements, allocates tasks, and optimizes resource usage based on the capabilities of the available robots. It then executes the mission utilizing a decentralized control strategy that enables the robots to adapt to environmental changes and maintain formation stability in both 2D and 3D spaces. The framework’s architecture supports loose coupling between its components, enhancing system scalability and maintainability. Key features include a robust task allocation algorithm, and a dynamic formation control mechanism, using a ROS 2 communication protocol that ensures reliable information exchange among robots. The effectiveness of this framework is demonstrated through a case study involving coordinated exploration and data collection tasks, showcasing its ability to manage missions while optimizing robot collaboration. This work advances the field of heterogeneous robotic systems by providing a scalable and adaptable solution for multi-robot coordination in challenging environments.

Beyond the school: principals’ insights on expanded roles in times of crisis. The case of COVID-19 in Turkey

ABSTRACT This study examined the perspectives of school principals on the workload of the additional administrative responsibilities they were assigned during COVID-19 in Turkey. The purpose of this study was to gain insight into the nature of these additional responsibilities and to assess their impact on the principals who took them on. The research group consisted of 62 school administrators (principals and vice principals), from public elementary schools. The research data were collected through semi-structured interviews. The collected data were analyzed using content analysis. The results showed that principals were assigned tasks without regard to their specific roles, with no differentiation based on their positions. The study also revealed a variety of emotional experiences among the participants, ranging from fear and anxiety to feelings of satisfaction and commitment. Overall, this study provided valuable insights into the complex dynamics of principals’ experiences during COVID-19 and highlighted the need for transparent communication, equitable task allocation, and adequate support mechanisms to maintain their well-being and commitment.

Mired in tasks: how daily workplace multitasking shapes employee service innovation behavior

Purpose Drawing on conservation of resources theory, this paper aims to propose that daily multitasking increases employees’ daily role pressure, ultimately hindering their service innovation behavior. Furthermore, we suggest that trait mindfulness may mitigate this negative impact. Design/methodology/approach To empirically test the study hypotheses, this paper conducted a multilevel diary field study, collecting data from 64 employees over 10 working days to comprehensively validate the theoretical model. Findings The findings indicate that daily multitasking indirectly reduces daily service innovation behavior by increasing employee role pressure. Notably, this indirect effect is more pronounced among employees with lower trait mindfulness, while it is less significant for those with higher trait mindfulness. Practical implications Hotels should manage multitasking, reduce employee stress and promote service innovation by optimizing task allocation and redesigning work processes. Additionally, hotel management should adopt a multifaceted and innovative approach to foster mindfulness traits. Originality/value This study contributes to the literature on multitasking and innovation by examining how and when daily multitasking influences employee service innovation behavior.

Power Optimized Task Scheduling using Genetic Algorithm (POTS-GA) in Cloud Environment

In a cloud environment, the allocation of tasks has become pivotal on account of rapid growth of user requests. The processing of user requests leads to a significant execution time, and a huge amount of power is also consumed. Consequently, task scheduling for optimizing makes pan and power usage has become critical, particularly in a heterogeneous environment. This research work proposes Power-Optimized Task Scheduling using Genetic Algorithm (POTS-GA) that aims to minimize execution time and power consumption. The proposed strategy employs genetic algorithm to take scheduling decision while taking into consideration the execution time and overall power consumption of resources. The fitness computation considering both objectives and the customized genetic operators ensure to search for a better scheduling solution. The experiments performed on a large number of tasks and virtual machines show that the proposed POTS-GA approach outperforms other task scheduling strategies including Efficient Task Allocation using Genetic Algorithm (ETA-GA), Round Robin algorithm (RRA), and First Come First Serve (FCFS) and Greedy algorithm in terms of makes pan and power consumption.

Sequential Task Allocation of More Scalable Artificial Dragonfly Swarms Considering Dubins Trajectory

With the rapid advancement of UAV technology and the increasing complexity of tasks, multi-UAV systems face growing challenges in task execution. Traditional task allocation algorithms often perform poorly when dealing with issues such as local optima, slow convergence speed, and low convergence accuracy, making it difficult to meet the demands for efficiency and practicality in real-world applications. To address these problems, this paper focuses on collaborative task allocation technology for multi-UAV. It proposes a collaborative task allocation strategy for multi-UAV in a multi-target environment, which comprehensively considers various complex constraints in practical application scenarios. The strategy utilizes Dubins curves for trajectory planning and constructs a multi-UAV collaborative task allocation model, with targets including the shortest total distance index, the minimum time index, and the trajectory coordination index. Each UAV is set as an artificial dragonfly by modifying the traditional dragonfly algorithm, incorporating differential evolution algorithms and their crossover, mutation, and selection operations to bring UAV swarms closer to the characteristics of biological dragonflies. The modifications can enhance the global scalability of artificial dragonfly swarms (ADSs), including wider search capacity, wider speed range, and more diverse search accuracy. Meanwhile, potential solutions with global convergence properties are stored to better support real-time adjustments to task allocation. The simulation results show that the proposed strategy can generate a conflict-free task execution scheme and plan the trajectory, which has advantages in changing the data scale of the UAV and the target and improves the reliability of the system to a certain extent.

Heterogeneous multi-task allocation algorithm based on multi-round distributed auction

Aiming at the distributed decision-making needs of different combat tasks such as multi-machine collaborative detection, attack, and evaluation, a multi-round distributed auction algorithm for heterogeneous tasks with time constraints is established. The algorithm flexibly solves the distributed collaborative task allocation problem with complex constraints between drones with different capabilities. In the distributed task allocation framework, each drone can realize distributed asynchronous computing and asynchronous communication, and can ensure good solution efficiency and accuracy. The task allocation principle is mainly based on the nearest allocation, while taking into account the target value and time constraints, realizing the priority allocation of important targets under limited resources and making the total journey as short as possible. Simulation results show that: given any number of drones and targets and the configuration of drone combat capabilities, the heterogeneous multi-round distributed auction algorithm can obtain good allocation results.

A distributed task allocation method for heterogeneous UAVs in dynamic and communication-constrained environments

A distributed task allocation method for heterogeneous UAVs in dynamic and communication-constrained environments

A Systematic Literature Review on Multi-Robot Task Allocation

Muti-Robot system is gaining attention and is one of the critical areas of research when it comes to robotics. Coordination among multiple robots and how different tasks are allocated to different system agents are being studied. The objective of this Systematic Literature Review (SLR) is to provide insights on the recent advancement in Multi Robot Task Allocation(MRTA) problems emphasizing promising approaches for task allocation. In this study, we collected scientific papers from 5 different databases for MRTA. We outline the different approaches for task allocation algorithms, classifying them according to the methods, and emphasizing recent advances. In addition, we discuss the function of uncertainty in task allocation and typical coordination techniques utilized in task allocation to identify gaps in the literature and suggest the most promising ones.

Fault tolerant & priority basis task offloading and scheduling model for IoT logistics

The Internet of Things (IoT) has connected millions of devices to the internet for communication and computation purposes. Due to constraints such as limited battery power, processing capabilities, and storage capacity, IoT devices often rely on remote fog nodes for task execution, a practice known as task offloading. The NP-hard nature of offloading and scheduling IoT tasks onto fog nodes poses significant challenges. Current IoT task offloading often relies on single parameters like device MIPS, RAM, or battery power. Similarly, task allocation on fog nodes frequently prioritizes VM MIPS. To address energy consumption and task failures in IoT systems, robust fault tolerance, efficient task offloading, and effective scheduling are crucial. This paper introduces a novel Fault-Tolerant, Priority-Based Task Offloading and Scheduling Model (FP-TOSM) for IoT logistics. Utilizing the Analytic Hierarchy Process (AHP) for multi-criteria decision-making, IoT tasks are prioritized. Tasks below a specified threshold are executed locally, while those within a defined range are offloaded to a fog node. Tasks exceeding this range are delegated to the cloud. The Euclidean formula is used to determine the proximity between the IoT logistics vehicle and fog nodes for offloading decisions. Dynamic re-clustering fault tolerance mechanisms are utilized to manage task failures after offloading. Successful offloaded tasks are allocated to suitable VMs in the cloud and fog nodes. The proposed strategy selects the most suitable VM for offloaded tasks using a multi-criteria decision-making process to reduce energy consumption, SLA violations, and execution costs. The model's performance is evaluated through simulations in iFogSim2. Results demonstrate reductions in energy consumption by up to 5.8 % and 10.4 %, decreases in SLA violations by up to 25.2 %, and a 16.28 % improvement in response time compared to an ACO-based model with a response time of 17.8 %. Additionally, the task failure ratio shows a significant reduction of up to 22.1 %. These findings highlight the effectiveness of FP-TOSM in enhancing efficiency and reliability within fog computing environments.

Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction

Generative AI (GenAI) systems offer opportunities to increase user productivity in many tasks, such as programming and writing. However, while they boost productivity in some studies, many others show that users are working ineffectively with GenAI systems and losing productivity. Despite the apparent novelty of these usability challenges, these ‘ironies of automation’ have been observed for over three decades in Human Factors research on the introduction of automation in domains such as aviation, automated driving, and intelligence. We draw on this extensive research alongside recent GenAI user studies to outline four key reasons for productivity loss with GenAI systems: a shift in users’ roles from production to evaluation, unhelpful restructuring of workflows, interruptions, and a tendency for automation to make easy tasks easier and hard tasks harder. We then suggest how Human Factors research can also inform GenAI system design to mitigate productivity loss by using approaches such as continuous feedback, system personalization, ecological interface design, task stabilization, and clear task allocation. Thus, we ground developments in GenAI system usability in decades of Human Factors research, ensuring that the design of human-AI interactions in this rapidly moving field learns from history instead of repeating it.

Multi-UAVs task allocation method based on MPSO-SA-DQN

Multi-UAVs play an important role in the battlefield. Although many methods are proposed to solve the Multi-UAV task allocation, there still existing the problems of complex time constraints and uncertain solution space. The reason is that multi-UAVs usually face changing environmental factors. Aiming at solving such problem, this paper proposes a multi-UAV task assignment method based on Deep Q-based evolutionary reinforcement learning algorithms (MPSO-SA-DQN). Specifically, this method builds a multi-agent training framework based on the deep evolutionary reinforcement learning mechanism and SA-DQN. Its aim is to improve the global exploration and optimization capabilities of multi-agents. At the same time, the multi-dimensional particle swarm optimization algorithm is introduced to optimize the state space. Based on task priority mapping, the MPSO-SA-DQN algorithm framework is proposed. As a result, multi-agents can optimize the execution state in real time in the environment interaction. Besides, it also has the ability to reach optimal state and maximum reward. According to the characteristics of multi-UAV global task assignment, this paper designs a priority state space autoencoder strategy and global task feature. A multi-UAVs tasks allocation and iterative optimization method based on MPSO-SA-DQN algorithm is proposed, so as to continuously optimize the task allocation scheme. The simulation results show that the multi-UAV task allocation method based on MPSO-SA-DQN can effectively solve the problem of uncertainty in the optimal solution space of task allocation. At the same time, the algorithm achieves faster convergence result, and a good prospect of promotion in the field of UAV swarm cooperative task planning.

Real-time reactive task allocation and planning of large heterogeneous multi-robot systems with temporal logic specifications

Existing methods for the task allocation and planning (TAP) of multi-robot systems with temporal logic specifications mainly rely on optimization-based approaches or graph search techniques applied to the product automaton. However, these methods suffer from high computational cost and scale poorly with the number of robots and the complexity of temporal logic tasks, thus limiting the applicability in real-time implementation, especially for large multi-robot systems. To address these challenges, this work develops a novel TAP framework that can solve reactive temporal logic planning problems for large-scale heterogeneous multi-robot systems (HMRS) in real time. Specifically, we develop a planning decision tree (PDT) to represent the task progression and task allocation specialized for HMRS with temporal logic specifications. Based on the PDT, we develop two key search algorithms—the planning decision tree search (PDTS) and the interactive planning decision tree search (IPDTS)—where PDTS generates an offline plan which will be modified online by PDTS and IPDTS jointly to enable fast reactive planning if environmental changes or temporary tasks occur. Such a design can generate satisfying plan for HMRS with multiple orders of magnitude more robots than those that existing methods can manipulate. Rigorous analysis shows that the PDT-based planning is feasible (i.e., the generated plan is applicable) and complete (i.e., a feasible plan, if exits, is guaranteed to be found). The algorithm complexity further indicates that the solution time is only linearly proportional to the robot numbers and types. Simulation and experiment results demonstrate that reactive plan can be generated for large HMRS in real-time, which outperforms the state-of-the-art methods.

Human leading or following preferences: Effects on human perception of the robot and the human–robot collaboration

Achieving effective and seamless human–robot collaboration requires two key outcomes: enhanced team performance and fostering a positive human perception of both the robot and the collaboration. This paper investigates the capability of the proposed task planning framework to realize these objectives by integrating human leading/following preferences and performance into its task allocation and scheduling processes. We designed a collaborative scenario wherein the robot autonomously collaborates with participants. The outcomes of the user study indicate that the proactive task planning framework successfully attains the aforementioned goals. We also explore the impact of participants’ leadership and followership styles on their collaboration. The results reveal intriguing relationships between these factors which warrant further investigation in future studies.

Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm

ABSTRACT An efficient resource utilization method can greatly reduce expenses and unwanted resources. Typical cloud resource planning approaches lack support for the emerging paradigm regarding asset management speed and optimization. The use of cloud computing relies heavily on task planning and allocation of resources. The task scheduling issue is more crucial in arranging and allotting application jobs supplied by customers on Virtual Machines (VM) in a specific manner. The task planning issue needs to be specifically stated to increase scheduling efficiency. The task scheduling in the cloud environment model is developed using optimization techniques. This model intends to optimize both the task scheduling and VM placement over the cloud environment. In this model, a new hybrid-meta-heuristic optimization algorithm is developed named the Hybrid Lemurs-based Gannet Optimization Algorithm (HL-GOA). The multi-objective function is considered with constraints like cost, time, resource utilization, makespan, and throughput. The proposed model is further validated and compared against existing methodologies. The total time required for scheduling and VM placement is 30.23%, 6.25%, 11.76%, and 10.44% reduced than ESO, RSO, LO, and GOA with 2 VMs. The simulation outcomes revealed that the developed model effectively resolved the scheduling and VL placement issues.

Large language models recover scientific collaboration networks from text

Science is a collaborative endeavor. Yet, unlike co-authorship, interactions within and across teams are seldom reported in a structured way, making them hard to study at scale. We show that Large Language Models (LLMs) can solve this problem, vastly improving the efficiency and quality of network data collection. Our approach iteratively applies filtering with few-shot learning, allowing us to identify and categorize different types of relationships from text. We compare this approach to manual annotation and fuzzy matching using a corpus of digital laboratory notebooks, examining inference quality at the level of edges (recovering a single link), labels (recovering the relationship context) and at the whole-network level (recovering local and global network properties). Large Language Models perform impressively well at each of these tasks, with edge recall rate ranging from 0.8 for the highly contextual case of recovering the task allocation structure of teams from their unstructured attribution page to 0.9 for the more explicit case of retrieving the collaboration with other teams from direct mentions, showing a 32% improvement over a fuzzy matching approach. Beyond science, the flexibility of LLMs means that our approach can be extended broadly through minor prompt revision.

Marginal Effect-aware Multiple-Vehicle Scheduling for Road Data Collection: A Near-optimal Result

Vehicles equipped with abundant sensors offer a promising way for large-scale, low-cost road data collection. To realize this potential, a well-designed vehicle scheduling scheme is essential for deploying the recruited drivers efficiently. Nevertheless, existing works fail to consider the marginal effect among drivers’ collections. Different from them, this article introduces a, to the best of our knowledge, new multiple-vehicle scheduling problem that jointly optimizes task allocation and vehicle trajectory planning to maximize the overall collection utility by accounting for the marginal effect in drivers’ data collections. However, solving this problem is non-trivial due to its involvement with multiple coupled NP-hard problems. To this end, we propose MeSched, a marginal effect-aware multiple-vehicle scheduling scheme designed for road data collection. Specifically, we first present a greedy-based auxiliary graph construction method to disentangle the initial problem into multiple independent single-vehicle scheduling subproblems. Furthermore, we build an approximate surrogate function that transforms each subproblem into a tractable form involving only a single variable. The theoretical analysis proves that MeSched can achieve a 1-(1/ e ) ¼ -approximation ratio in polynomial time. Comprehensive evaluations based on a real-world trajectory dataset of 12,493 vehicles demonstrate that MeSched can significantly improve the collection utility by 104.5% on average compared with four baselines.

Optimization of office process task allocation based on deep reinforcement learning

In office platforms, we often need to face the situation of a large number of parallel heterogeneous process tasks. This not only tests the ability of the task executors themselves, but also puts forward requirements for the performance of the collaborative scheduling system. Using the reinforcement learning method, combined with quantitative analysis such as collaborative coordination and slackness, and based on the Markov Bollinger theory, a multi-agent Bollinger model is proposed to realize the optimization scheduling system with overall process coordination and maximum completion time as the optimization objectives, thereby improving the overall execution efficiency. Taking the real business system process as the test scenario, under the same optimization objective, the reinforcement learning algorithm based on D3QN and DRL and the meta-heuristic algorithm based on ant colony are compared to verify the effectiveness of the proposed method.