Journal articles: 'Auction-based task allocation'

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Baroudi, Uthman, Mohammad Alshaboti, Anis Koubaa, and Sahar Trigui. "Dynamic Multi-Objective Auction-Based (DYMO-Auction) Task Allocation." Applied Sciences 10, no. 9 (May 8, 2020): 3264. http://dx.doi.org/10.3390/app10093264.

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In this paper, we address the problem of online dynamic multi-robot task allocation (MRTA) problem. In the existing literature, several works investigated this problem as a multi-objective optimization (MOO) problem and proposed different approaches to solve it including heuristic methods. Existing works attempted to find Pareto-optimal solutions to the MOO problem. However, to the best of authors’ knowledge, none of the existing works used the task quality as an objective to optimize. In this paper, we address this gap, and we propose a new method, distributed multi-objective task allocation approach (DYMO-Auction), that considers tasks’ quality requirement, along with travel distance and load balancing. A robot is capable of performing the same task with different levels of perfection, and a task needs to be performed with a level of perfection. We call this level of perfection quality level. We designed a new utility function to consider four competing metrics, namely the cost, energy, distance, type of tasks. It assigns the tasks dynamically as they emerge without global information and selects the auctioneer randomly for each new task to avoid the single point of failure. Extensive simulation experiments using a 3D Webots simulator are conducted to evaluate the performance of the proposed DYMO-Auction. DYMO-Auction is compared with the sequential single-item approach (SSI), which requires global information and offline calculations, and with Fuzzy Logic Multiple Traveling Salesman Problem (FL-MTSP) approach. The results demonstrate a proper matching with SSI in terms of quality satisfaction and load balancing. However, DYMO-Auction demands 20% more travel distance. We experimented with DYMO-Auction using real Turtlebot2 robots. The results of simulation experiments and prototype experiments follow the same trend. This demonstrates the usefulness and practicality of the proposed method in real-world scenarios.

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Zhang, Jiandong, Yuyang Chen, Qiming Yang, Yi Lu, Guoqing Shi, Shuo Wang, and Jinwen Hu. "Dynamic Task Allocation of Multiple UAVs Based on Improved A-QCDPSO." Electronics 11, no. 7 (March 25, 2022): 1028. http://dx.doi.org/10.3390/electronics11071028.

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With the rapid changes in the battlefield situation, the requirement of time for UAV groups to deal with complex tasks is getting higher, which puts forward higher requirements for the dynamic allocation of the UAV group. However, most of the existing methods focus on task pre-allocation, and the research on dynamic task allocation technology during task execution is not sufficient. Aiming at the high real-time requirement of the multi-UAV collaborative dynamic task allocation problem, this paper introduces the market auction mechanism to design a discrete particle swarm algorithm based on particle quality clustering by a hybrid architecture. The particle subpopulations are dynamically divided based on particle quality, which changes the topology of the algorithm. The market auction mechanism is introduced during particle initialization and task coordination to build high-quality particles. The algorithm is verified by constructing two emergencies of UAV sudden failure and a new emergency task.

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Braquet, Martin, and Efstathios Bakolas. "Greedy Decentralized Auction-based Task Allocation for Multi-Agent Systems." IFAC-PapersOnLine 54, no. 20 (2021): 675–80. http://dx.doi.org/10.1016/j.ifacol.2021.11.249.

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Zhenyu Wu, Minhua Xiao, Bo Jin, and Lin Feng. "Dynamic task allocation based on distance of superior probability auction." Journal of Convergence Information Technology 7, no. 2 (February 29, 2012): 10–17. http://dx.doi.org/10.4156/jcit.vol7.issue2.2.

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Fang, Baofu, Lu Chen, Hao Wang, Shuanglu Dai, and Qiubo Zhong. "Research on Multirobot Pursuit Task Allocation Algorithm Based on Emotional Cooperation Factor." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/864180.

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Multirobot task allocation is a hot issue in the field of robot research. A new emotional model is used with the self-interested robot, which gives a new way to measure self-interested robots’ individual cooperative willingness in the problem of multirobot task allocation. Emotional cooperation factor is introduced into self-interested robot; it is updated based on emotional attenuation and external stimuli. Then a multirobot pursuit task allocation algorithm is proposed, which is based on emotional cooperation factor. Combined with the two-step auction algorithm recruiting team leaders and team collaborators, set up pursuit teams, and finally use certain strategies to complete the pursuit task. In order to verify the effectiveness of this algorithm, some comparing experiments have been done with the instantaneous greedy optimal auction algorithm; the results of experiments show that the total pursuit time and total team revenue can be optimized by using this algorithm.

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Liang, Yajie, Kun Zhou, and Caicong Wu. "Dynamic Task Allocation Method for Heterogenous Multiagent System in Uncertain Scenarios of Agricultural Field Operation." Journal of Physics: Conference Series 2356, no. 1 (October 1, 2022): 012049. http://dx.doi.org/10.1088/1742-6596/2356/1/012049.

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This study focuses on the problem of dynamic task allocation for a heterogeneous multiagent system (MAS) in uncertain scenarios and its application in agricultural field operation. Previous studies lacked robustness or efficiency for uncertain environments especially in agricultural field, such as agent removal, agent inclusion, changes of agent capabilities, and task changes. We present herein a novel concept of the potential field of capability influence (PFCI), and based on which, we can estimate potential overloaded tasks. This provides an opportunity to improve the allocation of the remaining tasks. Then, we propose a heuristic-based clustering auction (HBCA) method by introducing PFCI into the auction mechanism to achieve an effective and efficient task allocation. The whole method consists of the three following phases based on the auction mechanism: 1) auctioneer preprocessing phase that adopts a capability priority strategy to select the preferable agent; 2) bidder preprocessing phase that introduces the PFCI to obtain a list of clustered tasks heuristically; and 3) adaptive auction phase that applies the auction mechanism to achieve an appropriate match between agents and tasks. Numerical simulations with different uncertain field operation scenarios validate the effectiveness of the proposed method. The results show that the proposed HBCA can dramatically reduce the total assignment cost and outperform state-of-the-art methods, especially in the case of obvious variances in agent capabilities.

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He, Jianhua, Siqi Tao, Yang Deng, Libin Chen, and Zhiying Mou. "Research on Multi-Sensor Resource Dynamic Allocation Auction Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 2 (April 2019): 330–36. http://dx.doi.org/10.1051/jnwpu/20193720330.

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This paper designs a multi-sensor resource dynamic allocation method based on auction algorithm. Tasks are prioritized according to the needs of the engineering field. Task priority is used as the basis for multi-sensor resource allocation order, taking into account the target's threat value and information needs. The sensor and task pairing function is established and used to measure the sensor resource dynamic allocation, we also use Analytic Hierarchy Process to determine the weight of each performance parameter in the pairing function (such as detection probability, intercept probability, positioning accuracy, tracking accuracy, recognition probability, etc.). The auction algorithm is improved by adding resource dynamic allocation constraints, which not only ensures the continuous execution of the target task, but also improves the dynamic allocation efficiency of multi-sensor resources. The simulation results show that the allocation method in this paper is scientific and reasonable.

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Fazal, Nayyer, Muhammad Tahir Khan, Shahzad Anwar, Javaid Iqbal, and Shahbaz Khan. "Task allocation in multi-robot system using resource sharing with dynamic threshold approach." PLOS ONE 17, no. 5 (May 4, 2022): e0267982. http://dx.doi.org/10.1371/journal.pone.0267982.

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Task allocation is a fundamental requirement for multi-robot systems working in dynamic environments. An efficient task allocation algorithm allows the robots to adjust their behavior in response to environmental changes such as fault occurrences, or other robots’ actions to increase overall system performance. To address these challenges, this paper presents a Task Allocation technique based on a threshold level which is an accumulative value aggregated by a centralized unit using the Task-Robot ratio and the number of the available resource in the system. The threshold level serves as a reference for task acceptance and the task acceptance occurs despite resource shortage. The deficient resources for the accepted task are acquired through an auction process using objective minimization. Despite resource shortage, task acceptance occurs. The threshold approach and the objective minimization in the auction process reduce the overall completion time and increase the system’s resource utilization up to 96%, which is demonstrated theoretically and validated through simulations and real experimentation.

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LIANG, Zhiwei, Jie SHEN, Xiang YANG, Juan LIU, and Songhao ZHU. "Task Allocation Algorithm Based on Auction in RoboCup Rescue Robot Simulation." Robot 35, no. 4 (2013): 410. http://dx.doi.org/10.3724/sp.j.1218.2013.00410.

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Chen, Chao, Weidong Bao, Tong Men, Xiaomin Zhu, Ji Wang, and Rui Wang. "NECTAR-An Agent-Based Dynamic Task Allocation Algorithm in the UAV Swarm." Complexity 2020 (September 16, 2020): 1–14. http://dx.doi.org/10.1155/2020/6747985.

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The advancement of UAV technology makes the use of UAVs more and more widespread, and the swarm is the main mode of UAV applications owing to its robustness and adaptability. Meanwhile, task allocation plays an essential role in a swarm to obtain overall high performance and unleash the potential of each UAVs owing to the complexity of the large-scale swarm. In this paper, we pay attention to the real-time allocation problem of dynamic tasks. We design models for the task assigning problem to construct the constraints model and assigning objectives. In addition, we introduce a novel agent-based allocating mechanism based on the auction process, including the design for three kinds of agents and the cooperation mechanism among different agents. Moreover, we proposed a new algorithm to calculate the bidding values of UAVs, by which the messages passed between UAVs can be reduced. On the basis of the assigning mechanism, we put up with a novel agent-based real-time task allocation algorithm named NECTAR for dynamic tasks in the UAV swarm. Furthermore, we conduct extensive experiments to evaluate the performance of our NECTAR, and the results indicate that NECTAR is able to solve the real-time task allocation for dynamic tasks and achieve high performance of the UAV swarm.

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Deng, Yueyue, Pierre-Philippe J. Beaujean, Edgar An, and Edward Carlson. "Task Allocation and Path Planning for Collaborative Autonomous Underwater Vehicles Operating through an Underwater Acoustic Network." Journal of Robotics 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/483095.

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Dynamic and unstructured multiple cooperative autonomous underwater vehicle (AUV) missions are highly complex operations, and task allocation and path planning are made significantly more challenging under realistic underwater acoustic communication constraints. This paper presents a solution for the task allocation and path planning for multiple AUVs under marginal acoustic communication conditions: a location-aided task allocation framework (LAAF) algorithm for multitarget task assignment and the grid-based multiobjective optimal programming (GMOOP) mathematical model for finding an optimal vehicle command decision given a set of objectives and constraints. Both the LAAF and GMOOP algorithms are well suited in poor acoustic network condition and dynamic environment. Our research is based on an existing mobile ad hoc network underwater acoustic simulator and blind flooding routing protocol. Simulation results demonstrate that the location-aided auction strategy performs significantly better than the well-accepted auction algorithm developed by Bertsekas in terms of task-allocation time and network bandwidth consumption. We also demonstrate that the GMOOP path-planning technique provides an efficient method for executing multiobjective tasks by cooperative agents with limited communication capabilities. This is in contrast to existing multiobjective action selection methods that are limited to networks where constant, reliable communication is assumed to be available.

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Edalat, Neda, Chen-Khong Tham, and Wendong Xiao. "An auction-based strategy for distributed task allocation in wireless sensor networks." Computer Communications 35, no. 8 (May 2012): 916–28. http://dx.doi.org/10.1016/j.comcom.2012.02.004.

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Shi, Jieke, Zhou Yang, and Junwu Zhu. "An auction-based rescue task allocation approach for heterogeneous multi-robot system." Multimedia Tools and Applications 79, no. 21-22 (December 20, 2018): 14529–38. http://dx.doi.org/10.1007/s11042-018-7080-4.

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Rinaldi, Marco, Stefano Primatesta, Giorgio Guglieri, and Alessandro Rizzo. "Auction-based Task Allocation for Safe and Energy Efficient UAS Parcel Transportation." Transportation Research Procedia 65 (2022): 60–69. http://dx.doi.org/10.1016/j.trpro.2022.11.008.

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Arif, Muhammad Usman, and Sajjad Haider. "A Flexible Framework for Diverse Multi-Robot Task Allocation Scenarios Including Multi-Tasking." ACM Transactions on Autonomous and Adaptive Systems 16, no. 1 (March 31, 2021): 1–23. http://dx.doi.org/10.1145/3502200.

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In a multi-robot operation, multi-tasking resources are expected to simultaneously perform multiple tasks, thus, reducing the overall time/energy requirement of the operation. This paper presents a task allocation framework named Rostam that efficiently utilizes multi-tasking capable robots. Rostam uses a task clustering mechanism to form robot specific task maps. The customized maps identify tasks that can be multi-tasked by individual robots and mark them for simultaneous execution. The framework then uses an Evolutionary Algorithm along with the customized maps to make quality task allocations. The most prominent contribution of this work is Rostam's flexible design which enables it to handle a range of task allocation scenarios seamlessly. Rostam's performance is evaluated against an auction-based scheme; the results demonstrate its effective use of multi-tasking robots. The paper also demonstrates Rostam's flexibility towards a number of MRTA scenarios through a case study.

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Kim, Min-Geol, Suk-Hoon Shin, Eun-Bog Lee, and Sung-Do Chi. "Modified Consensus Based Auction Algorithm for Task Allocation of Multiple Unmanned Aerial Vehicle." Journal of the Korea Society for Simulation 23, no. 4 (December 31, 2014): 197–202. http://dx.doi.org/10.9709/jkss.2014.23.4.197.

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Kong, Yan, Minjie Zhang, and Dayong Ye. "An Auction-Based Approach for Group Task Allocation in an Open Network Environment." Computer Journal 59, no. 3 (August 25, 2015): 403–22. http://dx.doi.org/10.1093/comjnl/bxv061.

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Xie, Bing, Shaofei Chen, Jing Chen, and LinCheng Shen. "A mutual-selecting market-based mechanism for dynamic coalition formation." International Journal of Advanced Robotic Systems 15, no. 1 (January 1, 2018): 172988141875584. http://dx.doi.org/10.1177/1729881418755840.

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This article presents a novel market-based mechanism for a dynamic coalition formation problem backgrounded under real-time task allocation. Specifically, we first analyze the main factors of the real-time task allocation problem, and formulate the problem based on the coalition game theory. Then, we employ a social network for communication among distributed agents in this problem, and propose a negotiation mechanism for agents forming coalitions on timely emerging tasks. In this mechanism, we utilize an auction algorithm for real-time agent assignment on coalitions, and then design a mutual-selecting method to acquire better performance on agent utilization rate and task completion rate. And finally, our experimental results demonstrate that our market-based mechanism has a comparable performance in task completion rate to a decentralized approach (within 25% better on average) and a centralized dynamic coalition formation method (within 10% less on average performance).

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Xue, Kai, Zhiqin Huang, Ping Wang, and Zeyu Xu. "An Exact Algorithm for Task Allocation of Multiple Unmanned Surface Vehicles with Minimum Task Time." Journal of Marine Science and Engineering 9, no. 8 (August 22, 2021): 907. http://dx.doi.org/10.3390/jmse9080907.

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Task allocation of unmanned surface vehicles (USVs) with low task cost is an important research area which assigns USVs from starting points to different target points to complete tasks. Most of the research lines of task allocation are using heuristic algorithms to obtain suboptimal solutions to reduce both the max task cost and total task cost. In practice, reducing the maximum is more important to task time, which is from the departure of USVs to the last USV arriving at the designated position. In this paper, an exact algorithm is proposed to minimize the max task time and reduce the total task time based on the Hungarian algorithm. In this algorithm, task time is composed of the travel time along the planned path and the turning time at initial and target points. The fast marching square method (FMS) is used to plan the travel path with obstacle avoidance. The effectiveness and practicability of the proposed algorithm are verified by comparing it with the Hungarian algorithm (HA), the auction algorithm (AA), the genetic algorithm (GA) and the ant colony optimization algorithm (ACO). The results of path planning and task allocation are displayed in the simulation.

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Alhaqbani, Amjaad, Heba Kurdi, and Kamal Youcef-Toumi. "Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions." Remote Sensing 13, no. 1 (December 23, 2020): 27. http://dx.doi.org/10.3390/rs13010027.

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The challenge concerning the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) has significantly spurred research interest due to its contribution to the success of various fleet missions. This challenge becomes more complex in time-constrained missions, particularly if they are conducted in hostile environments, such as search and rescue (SAR) missions. In this study, a novel fish-inspired algorithm for multi-UAV missions (FIAM) for task allocation is proposed, which was inspired by the adaptive schooling and foraging behaviors of fish. FIAM shows that UAVs in an SAR mission can be similarly programmed to aggregate in groups to swiftly survey disaster areas and rescue-discovered survivors. FIAM’s performance was compared with three long-standing multi-UAV task allocation (MUTA) paradigms, namely, opportunistic task allocation scheme (OTA), auction-based scheme, and ant-colony optimization (ACO). Furthermore, the proposed algorithm was also compared with the recently proposed locust-inspired algorithm for MUTA problem (LIAM). The experimental results demonstrated FIAM’s abilities to maintain a steady running time and a decreasing mean rescue time with a substantially increasing percentage of rescued survivors. For instance, FIAM successfully rescued 100% of the survivors with merely 16 UAVs, for scenarios of no more than eight survivors, whereas LIAM, Auction, ACO and OTA rescued a maximum of 75%, 50%, 35% and 35%, respectively, for the same scenarios. This superiority of FIAM performance was maintained under a different fleet size and number of survivors, demonstrating the approach’s flexibility and scalability.

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Jain, Vibha, and Bijendra Kumar. "Combinatorial auction based multi-task resource allocation in fog environment using blockchain and smart contracts." Peer-to-Peer Networking and Applications 14, no. 5 (May 23, 2021): 3124–42. http://dx.doi.org/10.1007/s12083-021-01161-y.

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Elango, Murugappan, Subramanian Nachiappan, and Manoj Kumar Tiwari. "Balancing task allocation in multi-robot systems using K -means clustering and auction based mechanisms." Expert Systems with Applications 38, no. 6 (June 2011): 6486–91. http://dx.doi.org/10.1016/j.eswa.2010.11.097.

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Choi, S. H., and W. K. Zhu. "Performance Optimisation of Mobile Robots for Search-and-Rescue." Applied Mechanics and Materials 232 (November 2012): 403–7. http://dx.doi.org/10.4028/www.scientific.net/amm.232.403.

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This paper presents a team performance optimisation system for multiple mobile robots in search-and-rescue operations, in which refugees are first discovered and subsequently robots are dispatched to transport themto shelters. Coordination of mobile robots involves two fundamental issues, namely task allocation and motion planning. While task allocation assigns jobs to robots, motion planning generates routes for robots to execute the assigned jobs. Task allocation and motion planning together play a pivotal role in optimisation of the robot team performance. These two issues become more challenging in dynamic search-and-rescue environments, where the refugees are unpredictably discovered at different locations and the traffic conditions of rescue zones keep changing. Weaddress these two issues by proposing an auction-based closed-loop module for task allocation and a bio-inspired intelligent module for motion planning. The task allocation module is characterised with a closed-loop bid adjustment mechanism to improve the bid accuracy even in light of stochastic rescue requests. The motion planning module is bio-inspired intelligent in that it features detection of imminent neighbours and responsiveness of virtual force navigation in dynamic traffic conditions. Simulations show that the proposed system is a practical tool to optimise the dynamic operations of search-and-rescue by a team of mobilerobots.

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Wang, Jun, Xiao Lin Lu, Si Yuan Guo, Lu Yu, and Wei Liu. "Network Resource Allocation for Scalable Video Streaming over P2P Networks Based on Game Theory." Applied Mechanics and Materials 687-691 (November 2014): 1974–78. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1974.

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In order to adapt to the heterogeneity of terminals and networks, Scalable Video Coding (SVC) encodes raw video stream with different scales of temporal, spatial and quality into layers. Considering the P2P network characteristic, it is a challenging task to design an appropriate P2P steaming network resource allocation mechanism combining with SVC. In this paper, SVC is applied in P2P streaming based on game theory; considering free-riding, bandwidth conflicts in P2P multi-overlay and one chunk with multiple providers, we design a bidirectional serial auction model that jointly optimize the bandwidth allocation, the data scheduling and the incentive mechanism, then optimized allocation for scalable video streaming over P2P networks is achieved. With extensive theoretical analysis, we show that these games converge to an optimal topology for each overlay.

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Tang, Jian, Kejun Zhu, Haixiang Guo, Chengzhu Gong, Can Liao, and Shuwen Zhang. "Using auction-based task allocation scheme for simulation optimization of search and rescue in disaster relief." Simulation Modelling Practice and Theory 82 (March 2018): 132–46. http://dx.doi.org/10.1016/j.simpat.2017.12.014.

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Wang, Bo, and Mingchu Li. "Resource Allocation Scheduling Algorithm Based on Incomplete Information Dynamic Game for Edge Computing." International Journal of Web Services Research 18, no. 2 (April 2021): 1–24. http://dx.doi.org/10.4018/ijwsr.2021040101.

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With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, making cloud computing face many technical challenges in terms of applicability. In response to cloud computing's shortcomings, edge computing has come into its own. However, many factors affect task offloading and resource allocation in the edge computing environment, such as the task offload latency, energy consumption, smart device mobility, end-user power, and other issues. This paper proposes a dynamic multi-winner game model based on incomplete information to solve multi-end users' task offloading and edge resource allocation. First, based on the history of end-users storage in edge data centers, a hidden Markov model can predict other end-users' bid prices at time t. Based on these predicted auction prices, the model determines their bids. A dynamic multi-winner game model is used to solve the offload strategy that minimizes latency, energy consumption, cost, and to maximizes end-user satisfaction at the edge data center. Finally, the authors designed a resource allocation algorithm based on different priorities and task types to implement resource allocation in edge data centers. To ensure the prediction model's accuracy, the authors also use the expectation-maximization algorithm to learn the model parameters. Comparative experimental results show that the proposed model can better results in time delay, energy consumption, and cost.

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Hooshangi, Navid, Ali Asghar Alesheikh, Mahdi Panahi, and Saro Lee. "Urban search and rescue (USAR) simulation system: spatial strategies for agent task allocation under uncertain conditions." Natural Hazards and Earth System Sciences 21, no. 11 (November 15, 2021): 3449–63. http://dx.doi.org/10.5194/nhess-21-3449-2021.

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Abstract. Task allocation under uncertain conditions is a key problem for agents attempting to achieve harmony in disaster environments. This paper presents an agent-based simulation to investigate task allocation considering appropriate spatial strategies to manage uncertainty in urban search and rescue (USAR) operations. The proposed method is based on the contract net protocol (CNP) and implemented over five phases: ordering existing tasks considering intrinsic interval uncertainty, finding a coordinating agent, holding an auction, applying allocation strategies (four strategies), and implementing and observing the real environment. Applying allocation strategies is the main innovation of the method. The methodology was evaluated in Tehran's District 1 for 6.6, 6.9, and 7.2 magnitude earthquakes. The simulation began by calculating the numbers of injured individuals, which were 28 856, 73 195, and 111 463 people for each earthquake, respectively. Simulations were performed for each scenario for a variety of rescuers (1000, 1500, and 2000 rescuers). In comparison with the CNP, the standard duration of rescue operations with the proposed approach exhibited at least 13 % improvement, with a maximal improvement of 21 %. Interval uncertainty analysis and comparison of the proposed strategies showed that increased uncertainty led to increased rescue time for the CNP and strategies 1 to 4. The time increase was less with the uniform distribution strategy (strategy 4) than with the other strategies. The consideration of strategies in the task allocation process, especially spatial strategies, facilitated both optimization and increased flexibility of the allocation. It also improved conditions for fault tolerance and agent-based cooperation stability in the USAR simulation system.

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Li, Xun, Zhi Zhang, Dan-Dan Wu, Michel Medema, and Alexander Lavozik. "A multi-robot allocation model for multi-object based on Global Optimal Evaluation of Revenue." International Journal of Advanced Robotic Systems 18, no. 6 (November 1, 2021): 172988142110606. http://dx.doi.org/10.1177/17298814211060650.

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The problem of global optimal evaluation for multi-robot allocation has gained attention constantly, especially in a multi-objective environment, but most algorithms based on swarm intelligence are difficult to give a convergent result. For solving the problem, we established a Global Optimal Evaluation of Revenue method of multi-robot for multi-tasks based on the real textile combing production workshop, consumption, and different task characteristics of mobile robots. The Global Optimal Evaluation of Revenue method could traversal calculates the profit of each robot corresponding to different tasks with global traversal over a finite set, then an optimization result can be converged to the global optimal value avoiding the problem that individual optimization easy to fall into local optimal results. In the numerical simulation, for fixed set of multi-object and multi-task, we used different numbers of robots allocation operation. We then compared with other methods: Hungarian, the auction method, and the method based on game theory. The results showed that Global Optimal Evaluation of Revenue reduced the number of robots used by at least 17%, and the delay time could be reduced by at least 16.23%.

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Zhu, Kejun, Jian Tang, Haixiang Guo, Chengzhu Gong, and Jinling Li. "Using a combinatorial auction-based approach for simulation of cooperative rescue operations in disaster relief." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 04 (August 2018): 1850035. http://dx.doi.org/10.1142/s1793962318500356.

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In practice, we experience low efficiency of search and rescue (SAR) frequently in disaster relief. Here, we will optimize the SAR through agent-based simulation. In the kind of cases described here, rescue teams are characterized by different capabilities, and the tasks often require different capabilities to complete. To this end, a combinatorial auction-based task allocation scheme is used to develop a cooperative rescue plan for the heterogeneous rescue teams. Then, we illustrate the proposed cooperative rescue plan in different scenarios with the case of landslide disaster relief. The simulation results indicate that the combinatorial auction-based cooperative rescue plan would increase victims’ relative survival probability by 13.8–16.3%, increase the ratio of survivors getting rescued by 10.7–12.7%, and decrease the average elapsed time for one site getting rescued by 19.0–26.6%. The proposed rescue plan outperforms the rescue plan based on the F-Max-Sum a little bit. The robustness analysis shows that the proposed rescue plan is relatively reliable on condition that both the search radius and scope of cooperation are larger than thresholds. Furthermore, we have investigated how the number of rescue teams influences the rescue efficiency.

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Yu, Wan-Yu, Xiao-Qiang Huang, Hung-Yi Luo, Von-Wun Soo, and Yung-Lung Lee. "Auction-Based Consensus of Autonomous Vehicles for Multi-Target Dynamic Task Allocation and Path Planning in an Unknown Obstacle Environment." Applied Sciences 11, no. 11 (May 30, 2021): 5057. http://dx.doi.org/10.3390/app11115057.

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The autonomous vehicle technology has recently been developed rapidly in a wide variety of applications. However, coordinating a team of autonomous vehicles to complete missions in an unknown and changing environment has been a challenging and complicated task. We modify the consensus-based auction algorithm (CBAA) so that it can dynamically reallocate tasks among autonomous vehicles that can flexibly find a path to reach multiple dynamic targets while avoiding unexpected obstacles and staying close as a group as possible simultaneously. We propose the core algorithms and simulate with many scenarios empirically to illustrate how the proposed framework works. Specifically, we show that how autonomous vehicles could reallocate the tasks among each other in finding dynamically changing paths while certain targets may appear and disappear during the movement mission. We also discuss some challenging problems as a future work.

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Zhao, Yan, Guo Qing Long, and Shi You Dong. "Dynamic Mission Control Method for Multi-UAV System." Applied Mechanics and Materials 490-491 (January 2014): 942–46. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.942.

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On the problem of mission control of the multi-UAV system in a dynamic environment, the method of Dynamic Task Allocation and Coordination (DTAC), based on the combination of market mechanism and alliance recruitment, is proposed. On the basis of the DTAC model of multi-UAV system, constructed with the application of market negotiation, the physical object of the market negotiation mechanism is substituted by that of alliance recruitment. Furthermore, the recruiter is empowered to coordinate within the alliance, which reduces the auction times and increases the efficiency of DTAC. The result of simulation shows that the method can effectively solve the problem of dynamic mission control of the Multi-UAV system.

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Li, Lan, Xiaoyong Zhang, Kaiyang Liu, Fu Jiang, and Jun Peng. "An Energy-Aware Task Offloading Mechanism in Multiuser Mobile-Edge Cloud Computing." Mobile Information Systems 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/7646705.

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Mobile-edge cloud computing, an emerging and prospective computing paradigm, can facilitate the complex application execution on resource-constrained mobile devices by offloading computation-intensive tasks to the mobile-edge cloud server, which is usually deployed in close proximity to the wireless access point. However, in the multichannel wireless interference environment, the competition of mobile users for communication resources is not conducive to the energy efficiency of task offloading. Therefore, how to make the offloading decision for each mobile user and select its suitable channel become critical issues. In this paper, the problem of the offloading decision is formulated as a 0-1 nonlinear integer programming problem under the constraints of channel interference threshold and the time deadline. Through the classification and priority determination for the mobile devices, a reverse auction-based offloading method is proposed to solve this optimization problem for energy efficiency improvement. The proposed algorithm not only achieves the task offloading decision but also gives the facility of resource allocation. In the energy efficiency performance aspects, simulation results show the superiority of the proposed scheme.

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Gao, Li-Ping, Tao Jin, and Chao Lu. "A Long-Term Quality Perception Incentive Strategy for Crowdsourcing Environments with Budget Constraints." International Journal of Cooperative Information Systems 29, no. 01n02 (March 2020): 2040005. http://dx.doi.org/10.1142/s0218843020400055.

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Quality control is a critical design goal for crowdsourcing. However, when measuring the long-term quality of workers, the existing strategies do not make effective use of workers’ historical information, whereas others regard workers’ conditions as fixed values, even if they do not consider the impact of workers’ quality. This paper proposes a long-term quality perception incentive model (called QAI model) in a crowdsourcing environment with budget constraints. In this work, QAI divides the entire long-term activity cycle into multiple stages based on proportional allocation rules. Each stage treats the interaction between the requester and the worker as a reverse auction process. At each stage, a truthful, individually rational, budget feasible, quality-aware task allocation algorithm is designed. At the end of each stage, according to hidden Markov model (HMM), this paper proposes a new framework for quality prediction and parameter learning framework, which can make use of workers’ historical information efficiently. Experiments have verified the feasibility of our algorithm and showed that the proposed QAI model leads to improved results.

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Predescu, Alexandru, Diana Arsene, Bogdan Pahonțu, Mariana Mocanu, and Costin Chiru. "A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support." Applied Sciences 11, no. 4 (February 5, 2021): 1449. http://dx.doi.org/10.3390/app11041449.

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This paper presents the current state of the gaming industry, which provides an important background for an effective serious game implementation in mobile crowdsensing. An overview of existing solutions, scientific studies and market research highlights the current trends and the potential applications for citizen-centric platforms in the context of Cyber–Physical–Social systems. The proposed solution focuses on serious games applied in urban water management from the perspective of mobile crowdsensing, with a reward-driven mechanism defined for the crowdsensing tasks. The serious game is designed to provide entertainment value by means of gamified interaction with the environment, while the crowdsensing component involves a set of roles for finding, solving and validating water-related issues. The mathematical model of distance-constrained multi-depot vehicle routing problem with heterogeneous fleet capacity is evaluated in the context of the proposed scenario, with random initial conditions given by the location of players, while the Vickrey–Clarke–Groves auction model provides an alternative to the centralized task allocation strategy, subject to the same evaluation method. A blockchain component based on the Hyperledger Fabric architecture provides the level of trust required for achieving overall platform utility for different stakeholders in mobile crowdsensing.

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Periyasami, Karthikeyan, Arul Xavier Viswanathan Mariammal, Iwin Thanakumar Joseph, and Velliangiri Sarveshwaran. "Combinatorial Double Auction Based Meta-scheduler for Medical Image Analysis Application in Grid Environment." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 999–1007. http://dx.doi.org/10.2174/2213275911666190320161934.

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Background: Medical image analysis application has complex resource requirement. Scheduling Medical image analysis application is the complex task to the grid resources. It is necessary to develop a new model to improve the breast cancer screening process. Proposed novel Meta scheduler algorithm allocate the image analyse applications to the local schedulers and local scheduler submit the job to the grid node which analyses the medical image and generates the result sent back to Meta scheduler. Meta schedulers are distinct from the local scheduler. Meta scheduler and local scheduler have the aim at resource allocation and management. Objective: The main objective of the CDAM meta-scheduler is to maximize the number of jobs accepted. Methods: In the beginning, the user sends jobs with the deadline to the global grid resource broker. Resource providers sent information about the available resources connected in the network at a fixed interval of time to the global grid resource broker, the information such as valuation of the resource and number of an available free resource. CDAM requests the global grid resource broker for available resources details and user jobs. After receiving the information from the global grid resource broker, it matches the job with the resources. CDAM sends jobs to the local scheduler and local scheduler schedule the job to the local grid site. Local grid site executes the jobs and sends the result back to the CDAM. Success full completion of the job status and resource status are updated into the auction history database. CDAM collect the result from all local grid site and return to the grid users. Results: The CDAM was simulated using grid simulator. Number of jobs increases then the percentage of the jobs accepted also decrease due to the scarcity of resources. CDAM is providing 2% to 5% better result than Fair share Meta scheduling algorithm. CDAM algorithm bid density value is generated based on the user requirement and user history and ask value is generated from the resource details. Users who, having the most significant deadline are generated the highest bid value, grid resource which is having the fastest processor are generated lowest ask value. The highest bid is assigned to the lowest Ask it means that the user who is having the most significant deadline is assigned to the grid resource which is having the fastest processor. The deadline represents a time by which the user requires the result. The user can define the deadline by which the results are needed, and the CDAM will try to find the fastest resource available in order to meet the user-defined deadline. If the scheduler detects that the tasks cannot be completed before the deadline, then the scheduler abandons the current resource, tries to select the next fastest resource and tries until the completion of application meets the deadline. CDAM is providing 25% better result than grid way Meta scheduler this is because grid way Meta scheduler allocate jobs to the resource based on the first come first served policy. Conclusion: The proposed CDAM model was validated through simulation and was evaluated based on jobs accepted. The experimental results clearly show that the CDAM model maximizes the number of jobs accepted than conventional Meta scheduler. We conclude that a CDAM is highly effective meta-scheduler systems and can be used for an extraordinary situation where jobs have a combinatorial requirement.

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Bredin, J. L., D. C. Parkes, and Q. Duong. "Chain: A Dynamic Double Auction Framework for Matching Patient Agents." Journal of Artificial Intelligence Research 30 (September 30, 2007): 133–79. http://dx.doi.org/10.1613/jair.2303.

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In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic, two-sided market. A single commodity, such as a resource or a task, is bought and sold by multiple buyers and sellers that arrive and depart over time. Our algorithm, Chain, provides the first framework that allows a truthful dynamic double auction (DA) to be constructed from a truthful, single-period (i.e. static) double-auction rule. The pricing and matching method of the Chain construction is unique amongst dynamic-auction rules that adopt the same building block. We examine experimentally the allocative efficiency of Chain when instantiated on various single-period rules, including the canonical McAfee double-auction rule. For a baseline we also consider non-truthful double auctions populated with ``zero-intelligence plus"-style learning agents. Chain-based auctions perform well in comparison with other schemes, especially as arrival intensity falls and agent valuations become more volatile.

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Wang, Yu-ge. "Task allocation algorithm based on auction in WSAN." Advanced Journal of Engineering, 2022, 45–51. http://dx.doi.org/10.55571/aje.2022.08009.

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To solve the problem of unreasonable task allocation in WSAN, which leads to uneven energy consumption of actuators and too long task execution time, a task allocation algorithm based on auction is proposed. Task decomposition model, task efficiency model and task allocation model were established. The auction actuator node decomposes the task into a plurality of task elements according to the task decomposition model; The actuator node inputs the task element into the task efficiency model, and calculates the efficiency of the completed task element; The auction actuator node comprehensively selects the best task allocation scheme according to the task allocation model. Simulation results show that compared with other algorithms, this algorithm reduces the task completion time, balances the energy consumption of actuators and prolongs the network life.

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Edalat, Neda, Wendong Xiao, Mehul Motani, Nirmalya Roy, and Sajal K. Das. "Auction-based task allocation with trust management for shared sensor networks." Security and Communication Networks, September 2012, n/a. http://dx.doi.org/10.1002/sec.631.

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Li, Hongli, Hongrui Zhu, Dongming Xu, Xuanyao Lin, Guoshuai Jiao, Yang Song, and Min Huang. "Dynamic task allocation based on auction in robotic mobile fulfilment system." Journal of Industrial and Management Optimization, 2023, 0. http://dx.doi.org/10.3934/jimo.2023010.

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EIJYNE, TAN, RISHWARAJ G, and PONNAMBALAM S G. "Development of a task-oriented, auction-based task allocation framework for a heterogeneous multirobot system." Sādhanā 45, no. 1 (May 9, 2020). http://dx.doi.org/10.1007/s12046-020-01330-4.

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Alshaboti, Mohammed, and Uthman Baroudi. "Multi-robot Task Allocation System: Fuzzy Auction-Based and Adaptive Multi-threshold Approaches." SN Computer Science 2, no. 2 (February 10, 2021). http://dx.doi.org/10.1007/s42979-021-00479-x.

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Moussa G., Souleymane, Belgacem Bettayeb, M’hammed Sahnoun, Fabrice Duval, and Abdelaziz Bensrhair. "Modular mobile manipulators coalition formation through distributed transportation tasks allocation." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, October 4, 2020, 095440542096122. http://dx.doi.org/10.1177/0954405420961225.

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The use of mobile manipulators for transportation tasks has provided solutions to several flexibility problems in manufacturing systems. Mobile manipulators are mobile entities equipped with robotic arm for loading and unloading of parts and an Automated Guided Vehicle (AGV) for their transport. In order to increase further the flexibility of these systems, the mobile manipulators could be modular, where their two entities are able to work together or separately. The assignment of transportation operations to different smart entities working together is a complex problem, which has not been addressed sufficiently in the literature. This paper proposes a two-stage decision approach for transportation task assignment, which is based on auction mechanism and a coalition formation process modeled with integer linear programming. A real use case has been implemented to test the efficiency of the proposed method. The proposed approach gives promising results that are discussed.

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K, Rajchandar, Baskaran R, Padmanabhan Panchu K, and Rajmohan M. "A novel fuzzy and reverse auction‐based algorithm for task allocation with optimal path cost in multi‐robot systems." Concurrency and Computation: Practice and Experience 34, no. 5 (November 18, 2021). http://dx.doi.org/10.1002/cpe.6716.

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Arif, Muhammad Usman. "Robot coalition formation against time-extended multi-robot tasks." International Journal of Intelligent Unmanned Systems ahead-of-print, ahead-of-print (August 16, 2021). http://dx.doi.org/10.1108/ijius-12-2020-0070.

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PurposeMulti-robot coalition formation (MRCF) refers to the formation of robot coalitions against complex tasks requiring multiple robots for execution. Situations, where the robots have to participate in multiple coalitions over time due to a large number of tasks, are called Time-extended MRCF. While being NP-hard, time-extended MRCF also holds the possibility of resource deadlocks due to any cyclic hold-and-wait conditions among the coalitions. Existing schemes compromise on solution quality to form workable, deadlock-free coalitions through instantaneous or incremental allocations.Design/methodology/approachThis paper presents an evolutionary algorithm (EA)-based task allocation framework for improved, deadlock-free solutions against time-extended MRCF. The framework simultaneously allocates multiple tasks, allowing the robots to participate in multiple coalitions within their schedule. A directed acyclic graph–based representation of robot plans is used for deadlock detection and avoidance.FindingsAllowing the robots to participate in multiple coalitions within their schedule, significantly improves the allocation quality. The improved allocation quality of the EA is validated against two auction schemes inspired by the literature.Originality/valueTo the best of the author's knowledge, this is the first framework which simultaneously considers multiple MR tasks for deadlock-free allocation while allowing the robots to participate in multiple coalitions within their plans.

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Chandra, Praphul, Yadati Narahari, Debmalya Mandal, and Prasenjit Dey. "Novel Mechanisms for Online Crowdsourcing with Unreliable, Strategic Agents." Proceedings of the AAAI Conference on Artificial Intelligence 29, no. 1 (February 16, 2015). http://dx.doi.org/10.1609/aaai.v29i1.9340.

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Motivated by current day crowdsourcing platforms and emergence of online labor markets, this work addresses the problem of task allocation and payment decisions when unreliable and strategic workers arrive over time to work on tasks which must be completed within a deadline. We consider the following scenario: a requester has a set of tasks that must be completed before a deadline; agents (aka crowd workers) arrive over time and it is required to make sequential decisions regarding task allocation and pricing. Agents may have different costs for providing service and these costs are private information of the agents. We assume that agents are not strategic about their arrival times but could be strategic about their costs of service. In addition, agents could be unreliable in the sense of not being able to complete the assigned tasks within the allocated time; these tasks must then be reallocated to other agents to ensure ontime completion of the set of tasks by the deadline. For this setting, we propose two mechanisms: a DPM (DynamicPrice Mechanism) and an ABM (Auction Based Mechanism). Both mechanisms are dominant strategy incentive compatible, budget feasible, and also satisfy ex-post individual rationality for agents who complete the allocated tasks. These mechanisms can be implemented in current day crowdsourcing platforms with minimal changes to the current interaction model.

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Kang, Kai, Su Xiu Xu, Ray Y. Zhong, Bing Qing Tan, and George Q. Huang. "Double Auction-Based Manufacturing Cloud Service Allocation in an Industrial Park." IEEE Transactions on Automation Science and Engineering, 2020, 1–13. http://dx.doi.org/10.1109/tase.2020.3029081.

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Journal articles on the topic 'Auction-based task allocation'

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