Self-adapting task allocation approach for software outsourcing services

In this paper, we consider the task allocation problem for autonomous agents where the distances between any two agents should always be less than a predetermined value. To easily tackle the problem, we introduce a novel concept referred to as a virtual task that helps to decouple the task allocation from the path planning in the joint task allocation and path planning problem. An overall task allocation procedure with two steps is proposed. After the virtual tasks are generated with preliminary clustering in the first step, tasks are clustered into groups and allocated to agents in the second step. To reduce the computational complexity of optimal clustering and task allocation, we propose a suboptimal method called the cluster-level traveling salesman problem (TSP). Numerical results show that the cluster-level TSP suffers only little performance degradation compared to the optimal method, even though its computational costs are dramatically reduced.

The author describes the implementation of a task allocation algorithm for multiprocessing systems, providing a simulation package to allocate tasks among identical processors. The proposed model provides an optimal task-assignment strategy for distributed programs by using the graph theoretical approach proposed by W. Chu et al. (1980). This approach discusses the interconnections between the modules, which are distributed over many processors to take advantage of parallel processing. The simulation package that is used to simulate the task allocation is written in APL. It generates run-times, communication delays, and execution times for the tasks to be distributed. It then optimizes the system execution time and recommends a task allocation scheme based on these values. In addition, the author provides an allocation strategy based on simulation results. In particular, he focuses on the optimal values of the system run-time and communication delay, and subsequently the system execution time.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In radar networks, research on the task allocation strategy for multi-target imaging is essential, especially under limited radar resources. From the targets' perspective, the goal is to acquire enough resources to obtain high-quality inverse synthetic aperture radar (ISAR) images. Then there is competition among targets for radar resources. Therefore, the task allocation optimization problem is considered as a cooperative game, specifically a non-transferable coalitional game when the sparse ISAR imaging algorithm is adopted. A stable coalition structure, which allows cooperation among targets to achieve satisfactory image resolution using the least amount of time, represents an optimal task allocation scheme. Then, the corresponding algorithm based on coalition-forming rules is proposed. Through the framework of cooperative game theory, a stable network structure (i.e., the task allocation strategy) is formed while minimizing the radar time resource cost. Finally, experiments confirm that the proposed algorithm can achieve an optimal task allocation strategy with a lower computation time cost.

How to guarantee the data rate and latency requirement for an application with limited energy is an open issue in wireless virtualized sensor networks. In this paper, we integrate the wireless energy transfer technology into the wireless virtualized sensor network and focus on the stochastic performance guarantee. Firstly, a joint task and resource allocation optimization problem are formulated. In order to characterize the stochastic latency of data transmission, effective capacity theory is resorted to study the relationship between network latency violation probability and the transmission capability of each node. The performance under the FDMA mode and that under the TDMA mode are first proved to be identical. We then propose a bisection search approach to ascertain the optimal task allocation with the objective to minimize the application latency violation probability. Furthermore, a one-dimensional searching scheme is proposed to find out the optimal energy harvesting time in each time block. The effectiveness of the proposed scheme is finally validated by extensive numerical simulations. Particularly, the proposed scheme is able to lower the latency violation probability by 11.6 times and 4600 times while comparing with the proportional task allocation scheme and the equal task allocation scheme, respectively.

Integrating robotics in construction works has the potential to address persistent challenges such as labor shortages, health risks, and low productivity. This paper proposes a systematic task planning approach for human-robot collaboration (HRC) in structure assembly, comprising a robotic potential scoring-based task classification system and a constraint programming-based task allocation and sequencing optimization model. Based on key factors such as component properties, connection methods, robotic payload capacities, material storage layouts, and workspace safety, the task classification system provides a systematic approach to classifying construction tasks. To obtain optimal task allocation and sequencing, an extended flexible job shop scheduling problem (FJSSP) based optimization model integrates human fatigue into the objective function to balance operational efficiency and worker well-being. A timber frame assembly case study with different HRC configurations and storage arrangements was conducted to evaluate the performance in enhancing makespan and reducing human fatigue. This work establishes a robust foundation for optimizing HRC in structure assembly, paving the way for more effective and human-centric practices in the industry. • Proposed a task classification system based on robotic potential evaluation • Developed a constraint programming model optimizing efficiency and human fatigue • Found high-capability robots enhance flexibility in collaboration with human worker • Guided design and application of robot deployment in structural assembly • Provided task sequencing and allocation combinations with varying emphases

Continuous timely repair and replacement of infrastructures, equipment and utilities play an important role in maintaining the smooth-running of a city or local community. Thereby, to help individuals and businesses go about their daily activities with ease, it is vital to develop a proper method for automatically identifying and assigning capable workers for tasks. This paper defines the community management service task allocation problem as CMS-TAP and hence an end-to-end “recommendation + allocation” network, i.e. a task recommender and allocation optimization network (denoted as TROpt-NET), is then developed for handling such problem. TROpt-NET consists of two layers, namely one for predicting worker ability and the other for allocating tasks which are TR Layer and TA Layer, corresponding to “recommendation” and “allocation” of tasks. Different from operations research approaches where workers are assigned to jobs based on their pre-labelled skills and fixed locations, we propose a task recommender and allocation optimization network. The TR layer is a task recommender system designed to learn implicit worker abilities for different tasks using Neural Collaborative Filtering (NCF) by mining a historical dataset of worker task completion. Whereas in the TA layer a differential optimization approach for allocation is used because of its differentiable property and ability to allow for backpropagation to the prediction layer. In this study, we first formulate the CMS-TAP problem as a recommendation +optimization problem and then propose and end-to-end network architecture that tackles the problem in a real-world setting. TROpt-NET curbs uncertainty and assumptions in optimization by learning to more accurately approximate worker ability across different tasks. Additionally, the network can learn implicit worker abilities enabling optimal utilization of workers across a wide range of tasks, which is often ignored in task allocation problems. We find that normalizing worker ability across all tasks improves the implicit learning capability of the network and that good approximations don’t always lead to optimal allocation but learning allocations by backpropagating through recommendations improves the allocation objective. Offline experiments on a real-world large-scale dataset demonstrate the effectiveness of our proposed TROpt-NET.

In the rapidly evolving field of unmanned aerial vehicle (UAV) applications, the complexity of task planning and trajectory optimization, particularly in high-dimensional operational environments, is increasingly challenging. This study addresses these challenges by developing the Adaptive Distortion Suppression Correlation Filter Cooperative Optimization (ARCF-ICO) algorithm, designed for high-dimensional UAV task allocation and trajectory planning. The ARCF-ICO algorithm combines advanced correlation filter technologies with multi-objective optimization techniques, enhancing the precision of trajectory planning and efficiency of task allocation. By incorporating weather conditions and other environmental factors, the algorithm ensures robust performance at low altitudes. The ARCF-ICO algorithm improves UAV tracking stability and accuracy by suppressing distortions, facilitating optimal path selection and task execution. Experimental validation using the UAV123@10fps and OTB-100 datasets demonstrates that the ARCF-ICO algorithm outperforms existing methods in Area Under the Curve (AUC) and Precision metrics. Additionally, the algorithm’s consideration of battery consumption and endurance further validates its applicability to current UAV technologies. This research advances UAV mission planning and sets new standards for UAV deployment in both civilian and military applications, where adaptability and accuracy are critical.

A typical mission consists of several deadline-driven, inter-dependent tasks which need to adhere to specific resource constraints. This makes Optimal Task Allocation (OTA) of a particular mission a daunting task, even for simple and less-challenging environments. The inherent challenges of Mission Critical Environment (MCE) (e.g., Disconnected, intermittent, and limited communication among nodes, high error rate, node mobility, etc.) make OTA even harder to accomplish. To address this problem, earlier, we proposed an automated approach, namely, Centralized Optimal Task Allocation Algorithm (COTAA). While COTAA performs OTA in an efficient manner, it is based on some assumptions (e.g., nodes must follow publish/subscribe-based communication model, there is no inter-dependency among tasks, and static central unit is solely responsible for task allocation) that make COTAA applicable only to specific MCEs (e.g., post-disaster recovery) and do not work well for other MCEs such as group-based UAV (Unmanned Aerial Vehicle) operation, robot-driven mission, etc. In this paper, we propose another novel automated approach, Decentralized Optimal Task Allocation Algorithm (DOTAA), which relaxes the above-mentioned assumptions and performs OTA in an efficient manner. In DOTAA, we have exploited the concept of application-layer hash and bidding approach to perform the OTA for larger classes of MCEs. We have also evaluated our solution using ns-2 simulator and our results show that DOTAA outperforms COTAA in terms of scalability, task allocation time, and bandwidth consumption.

SUMMARYThis paper investigates task allocation for multiple robots by applying the game theory-based negotiation approach. Based on the initial task allocation using a contract net-based approach, a new method to select the negotiation robots and construct the negotiation set is proposed by employing the utility functions. A negotiation mechanism suitable for the decentralized task allocation is also presented. Then, a game theory-based negotiation strategy is proposed to achieve the Pareto-optimal solution for the task reallocation. Extensive simulation results are provided to show that the task allocation solutions after the negotiation are better than the initial contract net-based allocation. In addition, experimental results are further presented to show the effectiveness of the approach presented.

In the cloud computing environment, task scheduling with multiple objectives optimization becomes a highly challenging problem in such a dynamic and bursty environment. Previous studies have mostly emphasized assigning the incoming tasks in a specific scenario, with a weak generalization ability to various objectives automatically. Thus, they suffer the inefficient issue under large-scale and heterogeneous cloud workloads. To address this issue, we propose a deep reinforcement learning (DRL)-based intelligent cloud task scheduler, which makes the optimal scheduling decision only dependent on learning directly from its experience without any prior knowledge. We formulate task scheduling as a dynamical optimization problem with constraints and then adopt the deep deterministic policy gradients (DDPG) network to find the optimal task assignment solution while meeting the performance and cost constraints. We propose a correlation-aware state representation method to capture the inherent characteristics of demands, and a dual reward model is designed to learn the optimal task allocation strategy. Extensive experimental results on Alibaba cloud workloads show that compared with other existing solutions, our proposed DDPG-based task scheduler enjoy superiority and effectiveness in performance and cost optimization.

ABSTRACT: This project aims to develop a comprehensive system that facilitates seamless access and management of various business modules, including Sales, Design, Plan Procurement, Project Management, Billing, and Service. The system is built with a secure login page and a verification code mechanism to ensure that only authorized personnel can access specific modules. Each module is designed to interact with data in Excel format, enabling users to convert and export information efficiently. The Sales, Design, Project Procurement, Project, Billing, and Service modules each contain functionality that is critical for business operations, while the Excel conversion feature provides a powerful tool for exporting, analyzing, and reporting data. The system ensures data integrity and security while offering an intuitive interface for users to manage operations across different departments. Ultimately, the project streamlines business processes, improves data management, and enhances productivity by integrating these functionalities into a unified platform.The Sales module allows for managing customer interactions, tracking sales performance, and generating sales reports. The Design module supports project design activities, including resource planning and task allocation. The Project Procurement module streamlines the procurement process by managing supplier data, purchase orders, and inventory management. The Project Management module tracks the progress of ongoing projects, monitors milestones, and generates project-related documentation. The Billing module handles invoicing, payment tracking, and financial reporting, while the Access Control module restricts access to sensitive information based on roles, ensuring that only authorized users can view or modify critical data.

We propose a game-theoretic multi-robot task allocation framework that enables a large team of robots to optimally allocate tasks in dynamically changing environments. As our main contribution, we design a decision-making algorithm that defines how the robots select tasks to perform and how they repeatedly revise their task selections in response to changes in the environment. Our convergence analysis establishes that the algorithm enables the robots to learn and asymptotically achieve the optimal stationary task allocation. Through experiments with a multi-robot trash collection application, we assess the algorithm’s responsiveness to changing environments and resilience to failure of individual robots.

Task allocation is a critical aspect of teamwork, especially in complex projects such as auditing. In audit task allocation, collaboration among team members is essential, as the success of the project depends not only on individual expertise but also on efficient teamwork. This process is closely related to the GMRA problem, a complex optimization challenge. This study tackles the challenges of task allocation in phased tasks and multitasking within auditing. We propose a sequential and parallel assignment method based on tree-structured tasks, formalized as the TSTA<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sp</sub> problem. Through both theoretical and experimental analyses, we demonstrate its feasibility and effectiveness. Additionally, we introduce improvements using the Gurobi solver and validate their impact. The results show significant optimization in audit task allocation.

Multi-Robot Multi-Station Cooperative Spot Welding Task Allocation Based on Stepwise Optimization: An Industrial Case Study

Distributed agile software development is a promising paradigm, addressing the necessities of emergent software application markets that are described by huge user base and small time to market characteristics. A key decision involved during the development process, is task allocation to team members. An appropriate task-member assignment facilitates project management, lessens the complexities and influences chances of project success. Task allocation becomes a more challenging activity in a distributed agile software development due to insufficient understanding of different factors and dependencies involved. We propose a task allocation framework comprising of two phases: one, identifying factors and dependencies that strongly influence the task allocation decision; two, proposing a quantitative method that allocates tasks to team members who best match the task requirements. Task requirements are expressed as capabilities, catering for different aspects, such as technical, personal, and environment. Our method stays transparent to the targeted objectives, in this case the best match. Other objectives, such as quality and cost may be introduced conveniently; even multiple objectives can be addressed. Such a method also allows quality evaluation of task-member assignment during and after the project completion, toward minimizing related risks.