Multiple Task Allocation Research Articles

Research on Multiple AUVs Task Allocation with Energy Constraints in Underwater Search Environment

The allocation of tasks among multiple Autonomous Underwater Vehicles (AUVs) with energy constraints in underwater environments presents an NP-complete problem with far-reaching consequences for marine exploration, environmental monitoring, and underwater construction. This paper critically examines the contemporary methodologies and technologies in the task allocation for multiple AUVs, with a particular focus on strategies that optimize navigation time with energy consumption constraints. By conceptualizing the multiple AUVs task allocation issue as a Capacitated Vehicle Routing Problem (CVRP) and addressing it using the SCIP solver, this study seeks to identify effective task allocation strategies that enhance the operational efficiency and minimize the mission duration in energy-restricted underwater settings. The findings of this research provide valuable insights into efficient task allocation under energy constraints, providing useful theoretical implications and practical guidance for optimizing task planning and energy management in multiple AUVs systems. These contributions are demonstrated through the improved solution quality and computational efficiency.

An iterative two-phase optimization method for heterogeneous multi-drone routing problem considering differentiated demands

Owing to low cost, high flexibility and delivery efficiency, effectively addressing the challenges of “last-mile” delivery. While collaborative truck-drone delivery systems have been proposed to overcome limitations such as limited battery life and payload capacity, they are not well-suited for large and heavy parcel delivery. To solve the issue, a pioneering heterogeneous multi-drone delivery system. In this system, the mother drone handles the delivery of large and heavy parcels, releasing small drones to manage the delivery of smaller and lighter parcels. To address the complexities of this multi-drone delivery system, we introduce a divide-and-conquer framework consisting of two integral phases. The first phase, the task allocation phase, generates multiple task allocation schemes, while the second phase, the single-drone route planning phase, produces high-quality routes for each individual drone. Two phases are performed in an iterative manner until the predefined stopping criteria are satisfied. In the task allocation phase, we propose a simulated annealing algorithm (SA) to facilitate task allocation among multiple drones, utilizing transfer and recombination operators to generate high-quality solutions. After obtaining the task allocation scheme, a satisfactory route of a mother drone is generated by a variable neighborhood descent algorithm (VND). A desirable route for each single small drone is produced by dynamic programming (DP).Extensive experiments are conducted, demonstrating the outstanding optimization and time efficiency of the proposed two-phase optimization method by the fact that it obtains within a 4.89% gap from the optimal solution generated by CPLEX in 15.48 s for instance up to 125 nodes.

Improved grey wolf optimizer for multiple unmanned aerial vehicles task allocation

Grey wolf optimizer (GWO) is a metaheuristic optimization algorithm proposed in 2014, which has already been applied in many fields. However, there are still two problems in GWO: i) during the optimization process, there are three leading wolves to lead the population for search, resulting in poor population diversity and ii) because of its position updated equation which not only brings strong convergence ability but also makes it easily fall into local optimal. In this paper, to overcome this, the following contributions were made: i) an improved GWO (IGWO) with two strategies was proposed to solve the above problems and ii) for verifying the effectiveness of IGWO, it was applied in solving multiple UAVs task allocation problems. The experimental results show that IGWO can solve this problem well and suit for large-scale complex examples.

Multiple Cooperative Task Allocation in Group-Oriented Social Mobile Crowdsensing

Mobile crowdsensing, a new paradigm, has drawn much attention from the online community, in which mobile users are connected by using smartphones with sharing of information via mobile social networks. Multiple cooperative task allocation (MCTA) is a crucial problem in mobile crowdsensing, where each task requires more than one user to cooperatively complete. As more and more users join sensing tasks in groups, it is indispensable to develop a group-oriented crowdsensing mechanism supporting MCTA. However, existing studies generally focus on a group that can provide sufficient users to accomplish a task. Once these groups no longer exist, the corresponding task will be discarded or be performed with compromised quality. In this article, we propose a novel three-phase approach named Group-oriented Cooperative Crowdsensing (GoCC) to tackle the MCTA problem in social mobile crowdsensing. This approach exploits real-life relationships in the social network to form compatible groups, which improves the task coverage via group-oriented cooperation while achieving good task cooperation quality. Specifically, phase 1 selects a subset of users on the social network as initial leaders and directly pushes sensing tasks to them. Phase 2 utilizes the leaders to search for their socially connected users to model groups. Phase 3 presents the process of group-oriented task allocation for solving the MCTA problem. Experiments on the real-world dataset validate that our approach significantly outperforms the representative approaches.

Improved Genetic Algorithm (VNS-GA) using polar coordinate classification for workload balanced multiple Traveling Salesman Problem (mTSP)

The multiple traveling salesman problem (mTSP) is an extension of the traveling salesman problem (TSP), which has wider applications in real life than the traveling salesman problem such as transportation and delivery, task allocation, etc. In this paper, an improved genetic algorithm (VNS-GA) that uses polar coordinate classification to generate the initial solutions is proposed. It integrates the variable neighbourhood algorithm to solve the multiple objective optimization of the mTSP with workload balance. Aiming to workload balance, the first design of this paper is about generating initial solutions based on the polar coordinate classification. Then a distance comparison insertion operator is designed as a neighbourhood action for allocating paths in a targeted manner. Finally, the neighbourhood descent process in the variable neighbourhood algorithm is fused into the genetic algorithm for the expansion of search space. The improved algorithm is tested on the TSPLIB standard data set and compared with other genetic algorithms. The results show that the improved genetic algorithm can increase computational efficiency and obtain a better solution for workload balance and this algorithm has wild applications in real life such as multiple robots task allocation, school bus routing problem and other optimization problems.

Multiple Task Assignment and Path Planning of a Multiple Unmanned Surface Vehicles System Based on Improved Self-Organizing Mapping and Improved Genetic Algorithm

This paper addresses multiple task assignment and path-planning problems for a multiple unmanned surface vehicle (USVs) system. Since it is difficult to solve multi-task allocation and path planning together, we divide them into two sub-problems, multiple task allocation and path planning, and study them separately. First, to resolve the multi-task assignment problem, an improved self-organizing mapping (ISOM) is proposed. The method can allocate all tasks in the mission area, and obtain the set of task nodes that each USV needs to access. Second, aiming at the path planning of the USV accessing the task nodes, an improved genetic algorithm (IGA) with the shortest path is proposed. To avoid USV collision during navigation, an artificial potential field function (APFF) is proposed. A multiple USV system with multi-task allocation and path planning is simulated. Simulation results verify the effectiveness of the proposed algorithms.

Allocating Heterogeneous Tasks in Participatory Sensing with Diverse Participant-Side Factors

This paper proposes a novel task allocation framework, PSTasker, for participatory sensing (PS), which aims to maximize the overall system utility on PS platform by coordinating the allocation of multiple tasks. While existing studies mainly optimize the task allocation from the perspective of the task organizer (e.g., maximizing coverage or minimizing incentive cost), PSTasker further considers diverse factors on the participants’ side, including user work bandwidth, user availability, devices’ sensor configuration, task completion likelihood, and mobility pattern. Furthermore, by considering the heterogeneity in three dimensions (i.e., task, time, and space), it adopts a novel model to measure task sensing quality and overall system utility. In PSTasker, it first calculates the utlity of a given task allocation plan by jointly fusing different participant-side factors into one unified estimation function, and then employs an iterative greedy process to optimize the task allocation. Extensive evaluations based on real-world mobility traces demonstrate that PSTasker outperforms the baseline methods under various settings.

An Efficient Sampling-Based Algorithms Using Active Learning and Manifold Learning for Multiple Unmanned Aerial Vehicle Task Allocation under Uncertainty.

This paper presents a sampling-based approximation for multiple unmanned aerial vehicle (UAV) task allocation under uncertainty. Our goal is to reduce the amount of calculations and improve the accuracy of the algorithm. For this purpose, Gaussian process regression models are constructed from an uncertainty parameter and task reward sample set, and this training set is iteratively refined by active learning and manifold learning. Firstly, a manifold learning method is used to screen samples, and a sparse graph is constructed to represent the distribution of all samples through a small number of samples. Then, multi-points sampling is introduced into the active learning method to obtain the training set from the sparse graph quickly and efficiently. This proposed hybrid sampling strategy could select a limited number of representative samples to construct the training set. Simulation analyses demonstrate that our sampling-based algorithm can effectively get a high-precision evaluation model of the impact of uncertain parameters on task reward.

TANSA: Task Allocation Using Nomadic Soft Agents for Multirobot Systems

The availability of a gamut of ubiquitous and embedded hardware, coupled with rapid advancements in sensing technologies, has thrown open research on a wide range of real-world multirobot applications. Investigating novel mechanisms for the allocation of tasks within such multirobot systems (MRS) forms a prominent area of research. This paper addresses the issue of competing instances of concurrent task allocations occurring within the system, wherein all allocations vie for the same set or subsets of robots. In this paper, we formally describe the problem within an optimization framework and propose an approach for Task Allocation using Nomadic Soft Agents (TANSA) to handle concurrent and multiple task allocation instances in MRS. Nomadic soft agents are autonomous programs (code) that have the ability to migrate and execute within a network. The proposed mechanism uses a novel concept termed mobility resource that aids in regulating multiple instances of task allocations by forcing voluntary and adaptive back-offs on part of the nomadic soft agents. We also discuss a variant of the proposed approach-TANSA* for comparing the significance of adaptive and nonadaptive back-off mechanisms. Extensive experiments have been performed in emulated environments on real networks. The results clearly show that the TANSA-based approach outperforms two other prominent task allocation mechanisms. Further experiments in the real world performed using seven robots fortify the practical viability of this approach.

Task Allocation in Spatial Crowdsourcing: Current State and Future Directions

Spatial crowdsourcing (SC) is an emerging paradigm of crowdsourcing, which commits workers to move to some particular locations to perform spatio-temporal-relevant tasks (e.g., sensing and activity organization). Task allocation or worker selection is a significant problem that may impact the quality of completion of SC tasks. Based on a conceptual model and generic framework of SC task allocation, this paper first gives a review of the current state of research in this field, including single task allocation, multiple task allocation, low-cost task allocation, and quality-enhanced task allocation. We further investigate the future trends and open issues of SC task allocation, including skill-based task allocation, group recommendation and collaboration, task composition and decomposition, and privacy-preserving task allocation. Finally, we discuss the practical issues on real-world deployment as well as the challenges for large-scale user study in SC task allocation.

Two Innovative Coalition Formation Models for Dynamic Task Allocation in Disaster Rescues

This paper addresses the problem of multi-objective coalition formation for task allocation. In disaster rescue, due to the dynamics of environments, heterogeneity and complexity of tasks as well as limited available agents, it is hard for the single-objective and single (task)-to-single (agent) task allocation approaches to handle task allocation in such circumstances. To this end, two multi-objective coalition formation for task allocation models are proposed for disaster rescues in this paper. First, through coalition formation, the proposed models enable agents to cooperatively perform complex tasks that cannot be completed by single agent. In addition, through adjusting the weights of multiple task allocation objectives, the proposed models can employ the linear programming to generate more adaptive task allocation plans, which can satisfy different task allocation requirements in disaster rescue. Finally, through employing the multi-stage task allocation mechanism of the dynamic programming, the proposed models can handle the dynamics of tasks and agents in disaster environments. Experimental results indicate that the proposed models have good performance on coalition formation for task allocation in disaster environments, which can generate suitable task allocation plans according to various objectives of task allocation.

Multiple Unmanned Aerial Vehicles Cooperative Combat Task Allocation Method Based on Receding Horizon Control

Multiple Unmanned Aerial Vehicles Cooperative Combat Task Allocation Method Based on Receding Horizon Control

Multiple Unmanned Aerial Vehicle Task Allocation Based on Cloud Mode

Multiple Unmanned Aerial Vehicle Task Allocation Based on Cloud Mode

Deformation similarity clustering based collision detection in clothing simulation

Purpose – The 3D dynamic clothing simulation is widely used in computer-added garment design. Collision detection and response are the essential component and also the efficiency bottleneck in the simulation. The purpose of this paper is to propose a high efficient collision detection algorithm for 3D clothing-human dynamic simulation to achieve both real-time and virtually real simulation effects. Design/methodology/approach – The authors approach utilizes the offline data learning results to simplify the online collision detection complexity. The approach includes two stages. In the off-line stage, model triangles with most similar deformations are first, partitioned into several near-rigid-clusters. Clusters from the clothing model and the human model are matched as pairs according to the fact that they hold the potential to intersect. For each cluster, a hierarchical bounding box tree is then constructed. In the on-line stage, collision detection is checked and treated parallelly inside each cluster pairs. A multiple task allocation strategy is proposed in parallel computation to ensure efficiency. Findings – Reasonably partitioning the 3D clothing and human model surfaces into several clusters and implementing collision detection on these cluster pairs can efficiently reduce the model primitive amounts that need be detected, consequently both improving the detection efficiency and remaining the simulation virtual effect. Originality/value – The current methods only utilize the dynamic clothing-human status; the authors algorithm furthermore combines the intrinsic correspondence relationship between clothing and human clusters to efficiently shrink the detection query scope to accelerate the detection speed. Moreover, partitioning the model into several independent clusters as detection units is much more profitable for parallel computation than current methods those treat the model entirety as the unit.

Research of Multiple UAVs Task Allocation Based on Improved Contract Net

This paper makes a research on multiple UAVs task allocation in cooperative combat. The control target is presented and the model is built of the problem. By introducing load factor, the traditional task allocation algorithm based on contract net is improved. With considering the mission capability of UAVs, a task allocation algorithm based on improved contract net is given. The improved algorithm can significantly reduce the communication between UAVs, and can optimize the task allocation process. The simulation results show that compared with the traditional task allocation algorithm based on contract net, the improved algorithm can improve the efficiency of UAVs task allocation and balance the task load between the UAVs, it can solve the problem of multiple types of tasks allocation effectively.

Game theory-based negotiation for multiple robots task allocation

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.

Relationships of Multitasking, Physicians’ Strain, and Performance

Simultaneous task performance ("multitasking") is common in hospital physicians' work and is implicated as a major determinant for enhanced strain and detrimental performance. The aim was to determine the impact of multitasking by hospital physicians on their self reported strain and performance. A prospective observational time-and-motion study in a Community Hospital was conducted. Twenty-seven hospital physicians (surgical and internal specialties) were observed in 40 full-shift observations. Observed physicians reported twice on their self-monitored strain and performance during the observation time. Associations of observed multitasking events and subsequent strain and performance appraisals were calculated. About 21% of the working time physicians were engaged in simultaneous activities. The average time spent in multitasking activities correlated significantly with subsequently reported strain (r = 0.27, P = 0.018). The number of instances of multitasking activities correlated with self-monitored performance to a marginally significant level (r = 0.19, P = 0.098). Physicians who engage in multitasking activities tend to self-report better performance but at the cost of enhanced psychophysical strain. Hence, physicians do not perceive their own multitasking activities as a source for deficient performance, for example, medical errors. Readjustment of workload, improved organization of work for hospital physicians, and training programs to improve physicians' skills in dealing with multiple clinical demands, prioritization, and efficient task allocation may be useful avenues to explore to reduce the potentially negative impact of simultaneous task performance in clinical settings.

An architecture for P2P bag-of-tasks execution with multiple task allocation policies in desktop grids

In this paper, we propose and evaluate a flexible architecture for desktop grids that supports multiple task allocation policies on top of a structured P2P overlay. In our proposal, a Bag-of-Tasks application is submitted to random nodes and placed in their local queue, that is processed in a FIFO way. When a node becomes idle, a task allocation policy is executed that fetches tasks from remote nodes. The proposed architecture is flexible since it is decoupled from both the P2P middleware and the P2P overlay. A prototype of the proposed architecture was implemented on top of the JXTA middleware, using the Chord P2P search overlay. The results obtained in a 16-machine heterogeneous desktop grid show that very good performance gains are obtained with multiple task allocation policies. Also, a speedup of 9.85 was achieved for an application composed of 270 network flow balancing tasks, reducing its wallclock execution time from 32.51 min to 3.3 min.

Skill versus judgement and the architecture of organisations

All activities involve a combination of the exercise of skill and the exercise of judgment. This paper distinguishes these types of talent along three dimensions: the ease with which their exercise can be monitored, the delay before its effects are realised, and codifiability of the tasks involved. It then presents a two-period model of the allocation of multiple tasks within an organization. It shows that the incentives for bundling and separation of tasks within such an organization depend on the relative ease of monitoring of the two tasks, as well as on the extent of correlation between the talents they require. It demonstrates that organizations may rationally place “too much” emphasis on routine tasks, provided these reveal information about talents that may be valuable in non-routine tasks, and in spite of the fact that the incentives to perform the non-routine tasks well will thereby be blunted. The results are applied to the work of aid-giving agencies, to the role of banks in transition economies and to the structure of incentives within the economics profession.