Task Priority Research Articles

Supplier Involvement and Firm Financial Performance: Bringing Strategic Fit to the Fore

AbstractWhile supplier involvement is largely considered an industry best practice, its financial performance implications are far from clear and remain equivocal. These disparate findings led us to shift the conversation to exploring the strategic conditions that may enhance the value of the practice. We do so by drawing on the strategic fit perspective, an underexplored perspective in supplier involvement research. We argue that the value of supplier involvement is shaped by the task priority associated with a firm's strategic orientation. Accounting for the multidimensional nature of strategic fit, we further investigate whether strategic fit is likely to be impacted by a firm's level of market competition. Using a longitudinal survey of automotive manufacturers in Brazil, we demonstrate that supplier involvement fits well with a low‐cost strategy as opposed to a differentiation strategy. However, the degree of fit varies across levels of market competition. The study's overarching contribution is that it develops a theory of how strategic factors interact to impact the financial performance implications of supplier involvement, stressing the significance of strategic fit as the underlying mechanism.

Distinct Neural Mechanisms Underlying Dual-Task Priority During Gait Across Cognitive and Motor Networks.

Background: Prioritization strategy during gait significantly influences gait performance and successful gait relies on interactions between cognitive and motor functions. This study aimed to examine the within- and between-network connectivities of cognitive and motor networks associated with dual-task priority during gait. Methods: Twenty-nine healthy individuals (66.86 ± 8.53 years) underwent the timed-up-and-go (TUG) test alone, TUG with a cognitive task, and the cognitive task alone. The cognitive task involved sequentially subtracting three from a random number between 50 and 100. The resting-state functional magnetic resonance imaging was acquired on the same day. Using independent component analysis, the dorsal attention network (DAN), frontoparietal network (FPN), primary motor network (PM), and lateral motor network were assessed. The participants were divided into cognitive and motor priority groups based on the modified attention allocation index (mAAI). Group comparisons of within- and between-network connectivity were conducted using permutation tests. Additionally, correlation analysis was employed to investigate the association between-network connectivity and task priority. Results: The cognitive priority group showed cognitive dual-task facilitation. In comparison to the motor priority group, they also showed comparable motor dual-task costs and lower combined dual-task costs. They exhibited increased within-network connectivity in the left FPN and enhanced between-network connectivity between the right FPN and both the DAN and PM. These between-network connectivities were negatively correlated with mAAI scores. Conclusion: The results suggest distinct neural mechanisms across cognitive and motor networks based on individuals' dual-task strategies. This may have implications for understanding gait performance in complex contexts.

Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations

Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT. First, we introduce an LAPS-aided edge computing architecture for the agricultural IoT, in which each task is segmented into several interdependent subtasks for processing. Next, we formulate a total task processing delay minimization problem, taking into account constraints related to task dependency and priority, as well as equipment energy consumption. Then, by treating the task dependencies as directed acyclic graphs, a heuristic task processing algorithm with priority selection is developed to solve the formulated problem. Finally, the numerical results show that the proposed edge computing scheme outperforms state-of-the-art works and the local computing scheme in terms of the total task processing delay.

Multiobjective Hybrid Al-Biruni Earth Namib Beetle Optimization and Deep Learning based Task Scheduling in Cloud Computing

With the rapid development of computing networks, cloud computing (CC) enables the deployment of large-scale applications and meets the increased rate of computational demands. Moreover, task scheduling is an essential process in CC. The tasks must be effectually scheduled across the Virtual Machines (VMs) to increase resource usage and diminish the makespan. In this paper, the multi-objective optimization called Al-Biruni Earth Namib Beetle Optimization (BENBO) with the Bidirectional-Long Short-Term Memory (Bi-LSTM) named as BENBO+ Bi-LSTM is developed for Task scheduling. The user task is subjected to the multi-objective BENBO, in which parameters like makespan, computational cost, reliability, and predicted energy are used to schedule the task. Simultaneously, the user task is fed to Bi-LSTM-based task scheduling, in which the VM parameters like average computation cost, Earliest Starting Time (EST), task priority, and Earliest Finishing Time (EFT) as well as the task parameters like bandwidth and memory capacity are utilized to schedule the task. Moreover, the task scheduling outcomes from the multi-objective BENBO and Bi-LSTM are fused for obtaining the final scheduling with less makespan and resource usage. Moreover, the predicted energy, resource utilization and makespan are considered to validate the BENBO+ Bi-LSTM-based task scheduling, which offered the optimal values of 0.669J, 0.535 and 0.381.

Heuristic approaches for a multi-mode resource availability cost problem in aircraft manufacturing

A multi-mode time-constrained project scheduling problem with generalized temporal constraints arising in aircraft manufacturing is studied in the paper. We propose a priority rule-based heuristic (PRH) and a biased random-key genetic algorithm (BRKGA) for its solution. A serial generation scheme (SGS) is used for computing schedules from a priority order of the tasks with given resource capacities and mode assignments. The SGS cannot guarantee that the maximum project duration and maximum time lags are respected. Starting with the highest possible resource capacities, the PRH performs the SGS in a repeated manner, reducing the least used resource capacity by one unit until the schedule becomes infeasible. Different priority rules are used for determining both mode assignments and task priority orders. We encode these two decisions as well as the resource capacities in the BRKGA and apply the SGS for decoding. Project duration and maximum time lag violations are penalized in the fitness function. Extensive computational experiments based on problem instances motivated by settings found at a large aircraft manufacturer demonstrate that the BRKGA outperforms the PRH under almost all experimental conditions, especially for problem instances with more complex networks and shorter maximum project durations.

User Task Priority Based Resource Allocation with Multi Class Task Scheduling Strategy and Load Balancing in Cloud Environment

User Task Priority Based Resource Allocation with Multi Class Task Scheduling Strategy and Load Balancing in Cloud Environment

Steps of Transformational Leadership by School Principals to Enhance Educator Quality in Early Childhood Education Institutions

PAUD educators hold professional positions, which means they must maintain a high level of professionalism due to their constant interaction with students. The challenges faced by PAUD (Early Childhood Education) educators can hinder the achievement of quality education. The leadership of the PAUD principal plays a significant role in enhancing the quality of educators. This study aims to identify the steps taken by the principal’s transformational leadership to improve educator quality. The research was conducted at Al Irsyad Islamic Kindergarten in Madiun. A qualitative approach was employed, using a case study method. Data collection involved observations, interviews, and documentation. The survey results indicate that enhancing the quality of educators through transformational leadership requires specific steps in leadership transformation. Key actions include determining task priorities, assessing educator readiness, reviewing performance, and formulating a vision. Quality PAUD educators are essential for producing quality students, ultimately contributing to the development of a glorious generation in the future.

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.

Methodology for identifying reference architectural and spatial solutions of universities using a graphic matrix

The present study considers architectural and spatial development of the existing universities and campuses as relevant issue. Administrative and academic buildings, libraries, laboratories, student dormitories and sports facilities, medical centers, pedestrian zones, green spaces, parks and sports grounds, green area recreations contribute to strengthening social ties between students and teachers, providing a beneficial effect on learning and development. The present paper analyzes examples of successful development of student campuses and identifies the factors of their effectiveness. The study findings may serve as a foundation for creating architectural and spatial solutions aimed at comprehensive development of a comfortable educational environment in modern universities of the Volga region. Furthermore, the results of the conducted analysis will assist in identifying priorities of architectural development tasks in areas at different levels — from urban planning to the installation of engineering and automation systems. Well-maintained green spaces, convenient areas for relaxation and socialization, as well as a variety of sports and cultural facilities, will contribute to the creation of comfortable and stimulating atmosphere, which, in turn, will enhance students' motivation for learning and develop their social skills. Effective planning, consideration of the needs and preferences of students, and their involvement in the decision-making process are indicated as the key factors for successful architectural and spatial solutions of university campuses. In addition, the maintenance and regular updating of green spaces and campus infrastructure are found significant.

Dynamic scheduling of workshop resource in cloud manufacturing environment

In the cloud manufacturing environment, workshop resource scheduling serves as a pivotal component, characterized by increased dynamics and complexities. Nevertheless, existing dynamic scheduling methods are often limited to solving specific dynamic events. Thus, considering the actual workshop resource scheduling in a cloud manufacturing environment, this article examines the methods to address unexpected events including randomly arriving tasks, resource breakdown, as well as resource maintenance. Besides, a dynamic scheduling method based on the Game Theory, considering workshop capacity in cloud manufacturing, was developed. In the first place, the priority of workshop tasks was evaluated by Game Theory, and the optimal task processing sequence in the workshop was determined to maximize benefits. Secondly, to verify the dynamic regulation performance of the method, it was combined with the particle swarm optimization (PSO) algorithm considering multi-objective factors to obtain an ameliorated PSO algorithm addressing the challenge of resource optimization scheduling in a genuinely dynamic workshop environment. Finally, this method was tested through a case study, and the results demonstrate that it can achieve superior dynamic and static performance compared to alternative algorithms.

Heterogeneous system list scheduling algorithm based on improved optimistic cost matrix

In heterogeneous computing systems, efficient task-scheduling methods are paramount for enhancing computational performance. However, the existing algorithm exhibits certain deficiencies, notably its oversight of load balancing concerns and inadequate emphasis on the out-degree property of tasks. To address these issues, a novel list scheduling algorithm is proposed, Average Earliest Finish Time (AEFT), which proficiently allocates task flows onto heterogeneous processors. The AEFT algorithm primarily consists of two key stages: (1) prioritizing tasks to determine the distribution of task priorities and (2) assigning optimal processors for tasks with given priorities. By leveraging its specific topology, the AEFT algorithm minimizes the scheduling length of task flows. Simultaneously, a prediction mechanism in determining task prioritization and selecting processors stages is proposed to reduce the scheduling time of task flows. In addition, in the processor selection stage, AEFT algorithm considers the out-degree characteristics of tasks, ameliorating situations of processor load imbalance. The AEFT algorithm demonstrates superior performance compared to prior list scheduling algorithms concerning makespan, speedup, and the percentage of occurrences of better solutions, as evidenced by experiments conducted on randomly generated and real-application graphs. Specifically, for t tasks and p processors, the AEFT algorithm achieves a time complexity of O(t2p).

Resource Allocation Based on Task Priority and Resource Consumption in Edge Computing

The computational power of Internet of Things (IoT) devices is usually low, which makes it necessary to process data and extract relevant information on devices with higher processing capacity. Edge Computing emerged as a complementary solution to cloud computing, providing devices at the network edge with computational resources to handle the data processing and analysis that constrained IoT devices eventually cannot perform. This solution allows data processing closer to the IoT devices, reducing latency for IoT applications. However, the resource constraints of edge nodes, which have lower computational power than the cloud nodes, make resource allocation and processing massive requests challenging. This study proposes an edge resource allocation mechanism based on task priority and machine learning. The proposed approach efficiently allocates resources for IoT requests based on their task priorities while monitoring the resource consumption of edge nodes. This study evaluates the performance of different classification algorithms by using well-known metrics for classifying models. The most efficient classifier achieved an accuracy of 92% and a precision of 90%. The results indicate good performance when using this classifier in the evaluated approach. The proposed mechanism demonstrated that resource management can be done more efficiently with significantly lower resource utilization when compared to an allocation method based only on distance. The study tested different scenarios regarding the number of requests, edge nodes, and a proposed failure mechanism to schedule failed node tasks to functional nodes. This failure control mechanism is a significant contribution of the proposal. Therefore, the proposed method in this study can become a valuable tool for efficient resource management with reduced computational cost and efficient resource allocation.

The Multiple Resource Theory and Model. Some Misconceptions in Data Interpretations

Multiple Resource Theory, and one particular instantiation of the theory in the 4-Dimensional Multiple Resource Model have often been invoked as mechanisms to account for dual task interference. Here we present the data and analyses requirements necessary to conclude that the multiple resource model either does or does not predict an increase in the dual task decrement; and in particular we describe the need to consider and control both task difficulty and task priority in comparing conditions that are argued to contrast shared versus separate resources. We then consider one common misconception regarding what the model predicts regarding perfect time sharing, and address the importance of considering alternative theory-based mechanisms of task switching and of confusion and crosstalk, to account for patterns of multi-task interference.

Joint Task Offloading, Resource Allocation, and Trajectory Design for Multi-UAV Cooperative Edge Computing With Task Priority

Joint Task Offloading, Resource Allocation, and Trajectory Design for Multi-UAV Cooperative Edge Computing With Task Priority

Improving AI-assisted video editing: Optimized footage analysis through multi-task learning

In recent years, AI-assisted video editing has shown promising applications. Understanding and analyzing camera language accurately is fundamental in video editing, guiding subsequent editing and production processes. However, many existing methods for camera language analysis overlook computational efficiency and deployment requirements in favor of improving classification accuracy. Consequently, they often fail to meet the demands of scenarios with limited computing power, such as mobile devices. To address this challenge, this paper proposes an efficient multi-task camera language analysis pipeline based on shared representations. This approach employs a multi-task learning architecture with hard parameter sharing, enabling different camera language classification tasks to utilize the same low-level feature extraction network, thereby implicitly learning feature representations of the footage. Subsequently, each classification sub-task independently learns the high-level semantic information corresponding to the camera language type. This method significantly reduces computational complexity and memory usage while facilitating efficient deployment on devices with limited computing power. Furthermore, to enhance performance, we introduce a dynamic task priority strategy and a conditional dataset downsampling strategy. The experimental results demonstrate that achieved a comprehensive accuracy surpassing all previous methods. Moreover, training time was reduced by 66.33%, inference cost decreased by 59.85%, and memory usage decreased by 31.95% on the 2-task dataset MovieShots; on the 4-task dataset AVE, training time was reduced by 95.34%, inference cost decreased by 97.23%, and memory usage decreased by 61.21%.

A stable method for task priority adaptation in quadratic programming via reinforcement learning

In emerging manufacturing facilities, robots must enhance their flexibility. They are expected to perform complex jobs, showing different behaviors on the need, all within unstructured environments, and without requiring reprogramming or setup adjustments. To address this challenge, we introduce the A3CQP, a non-strict hierarchical Quadratic Programming (QP) controller. It seamlessly combines both motion and interaction functionalities, with priorities dynamically and autonomously adapted through a Reinforcement Learning-based adaptation module. This module utilizes the Asynchronous Advantage Actor–Critic algorithm (A3C) to ensure rapid convergence and stable training within continuous action and observation spaces. The experimental validation, involving a collaborative peg-in-hole assembly and the polishing of a wooden plate, demonstrates the effectiveness of the proposed solution in terms of its automatic adaptability, responsiveness, flexibility, and safety.

Deep learning and optimization enabled multi-objective for task scheduling in cloud computing

ABSTRACT In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling utilizing the multi-objective optimization and deep learning (DL) model. Initially, the multi-objective task scheduling is carried out by the incoming user utilizing the proposed hybrid fractional flamingo beetle optimization (FFBO) which is formed by integrating dung beetle optimization (DBO), flamingo search algorithm (FSA) and fractional calculus (FC). Here, the fitness function depends on reliability, cost, predicted energy, and makespan, the predicted energy is forecasted by a deep residual network (DRN). Thereafter, task scheduling is accomplished based on DL using the proposed deep feedforward neural network fused long short-term memory (DFNN-LSTM), which is the combination of DFNN and LSTM. Moreover, when scheduling the workflow, the task parameters and the virtual machine’s (VM) live parameters are taken into consideration. Task parameters are earliest finish time (EFT), earliest start time (EST), task length, task priority, and actual task running time, whereas VM parameters include memory utilization, bandwidth utilization, capacity, and central processing unit (CPU). The proposed model DFNN-LSTM+FFBO has achieved superior makespan, energy, and resource utilization of 0.188, 0.950J, and 0.238, respectively.

Online dual robot–human collaboration trajectory generation by convex optimization

For dynamic collision-free trajectory planning in dual-robot and human collaborative tasks, this paper develops an online dual-robot Mutual Collision Avoidance (MCA) scheme based on convex optimization. A novel convex optimization formulation model, named Disciplined Convex programming by Shifting reference paths (DCS), is proposed for solving the single-robot trajectory optimization problem. Furthermore, a new dual-robot trajectory convex optimization algorithm is presented for online adjustment of the dual-robot trajectories according to the collaborative task priority. The overall pipeline, named DCS-MCA, generates collision-free and time-optimal dual-robot trajectories, while prioritizing the task accessibility of the high-priority robot. Simulation experiments demonstrate that DCS exhibits comparable performance to the current state-of-the-art single-robot motion planner, while the DCS-MCA outperforms common algorithms by up to 30% in time optimality for dual-robot collaborative tasks. The feasibility and dynamic performance of the proposed approach are further validated in a real collaborative cell, illustrating its suitability for collaborative dual-robot tasks in moderately dynamic environments.

Exploring the affordances of service robots and the effects on frontline employees in China: a qualitative research in hotels

Purpose This study aims to investigate the affordances of service robots (SRs) in hotels and their effects on frontline employees (FLEs). Design/methodology/approach Purposive and referral samplings methods were used to conduct 28 semistructured interviews with hotel FLEs, and the transcribed manuscript was analyzed based on grounded theory. Findings The study identifies six dimensions of SR affordances: physical, sensory, task, safety, social and emotional affordances. The main effects of SR affordances on FLEs involve reducing work stress and mental fatigue and increasing positive emotions in the psychological aspects of FLEs. In terms of behavioral aspects, shifts in task priorities and enhancements in SR usage behaviors were observed. Accordingly, a mechanistic framework was revealed through which SR affordances influence FLEs via direct and indirect interactions between FLEs and SRs. Originality/value This paper expands robotics research from a supply-side perspective and is one of the few studies to investigate SR affordances in the field of hospitality research. Findings of this study provide practical guidelines for designing and implementing SRs to support hotel FLEs in their daily work.

Multi-area collision-free path planning and efficient task scheduling optimization for autonomous agricultural robots

Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access paths in diverse and complex multi-region planning scenarios.