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Articoli di riviste sul tema "Multiagent scheduling"

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Autore: Grafiati

Pubblicato: 6 luglio 2024

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1

Zhang, Jie, Gang Wang, Yafei Song, Fangzheng Zhao e Siyuan Wang. "Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure". Mathematical Problems in Engineering 2019 (6 novembre 2019): 1–14. http://dx.doi.org/10.1155/2019/4259649.

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For task planning of the command and control structure, the existing algorithms exhibit low efficiency and poor replanning quality under abnormal conditions. Given the requirements of the current accusation architecture, a distributed command and control structure model is built in this paper based on multiagents, which exploits the superiority of multiagents in achieving complex tasks. The concept of MultiAgent-HTN is proposed based on the framework. The original hierarchical task network planning algorithm is optimized, the multiagent collaboration framework is redefined, and the coordination mechanism of local conflict is developed. With the classical resource scheduling problem as the experimental background, the proposed algorithm compared with the classical HTN algorithm is drawn. According to the experimental results, the proposed algorithm exhibits higher quality and higher efficiency than the existing algorithm and the space anomaly is significant in the course of processing. The planning is more efficient and the time is more complicated and superior in solving the same problem, and the algorithm exhibits good convergence and adaptability. In the conclusion, it is proved that the distributed command and control structure proposed in this paper exhibits high practicability in relevant fields and can solve the problem of distributed command and control structure in a multiagent scenario.

2

Li, Zhipeng, Xiumei Wei, Xuesong Jiang e Yewen Pang. "A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling". Mathematical Problems in Engineering 2021 (12 gennaio 2021): 1–12. http://dx.doi.org/10.1155/2021/1796296.

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It is difficult to coordinate the various processes in the process industry. We built a multiagent distributed hierarchical intelligent control model for manufacturing systems integrating multiple production units based on multiagent system technology. The model organically combines multiple intelligent agent modules and physical entities to form an intelligent control system with certain functions. The model consists of system management agent, workshop control agent, and equipment agent. For the task assignment problem with this model, we combine reinforcement learning to improve the genetic algorithm for multiagent task scheduling and use the standard task scheduling dataset in OR-Library for simulation experiment analysis. Experimental results show that the algorithm is superior.

3

Boerkoel Jr., James, e Edmund Durfee. "Decoupling the Multiagent Disjunctive Temporal Problem". Proceedings of the AAAI Conference on Artificial Intelligence 27, n. 1 (30 giugno 2013): 123–29. http://dx.doi.org/10.1609/aaai.v27i1.8583.

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The Multiagent Disjunctive Temporal Problem (MaDTP) is a general constraint-based formulation for scheduling problems that involve interdependent agents. Decoupling agents' interdependent scheduling problems, so that each agent can manage its schedule independently, requires agents to adopt additional local constraints that effectively subsume their interdependencies. In this paper, we present the first algorithm for decoupling MaDTPs. Our distributed algorithm is provably sound and complete. Our experiments show that the relative efficiency of using temporal decoupling to find solution spaces for MaDTPs, compared to algorithms that find complete solution spaces, improves with the interconnectedness between agents schedules, leading to orders of magnitude relative speeedup. However, decoupling by its nature restricts agents' scheduling flexibility; we define novel flexibility metrics for MaDTPs, and show empirically how the flexibility sacrificed depends on the degree of coupling between agents' schedules.

4

Zhou, Yi, e Weili Xia. "Optimization Algorithm and Simulation of Public Resource Emergency Scheduling Based on Wireless Sensor Technology". Journal of Sensors 2021 (8 ottobre 2021): 1–10. http://dx.doi.org/10.1155/2021/2450346.

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Public resource scheduling refers to the rational allocation and effective use of resources, while public emergency scheduling refers to the rational allocation and effective use of resources in the context of emergencies. Its main purpose is to reduce casualties and property losses caused by emergencies. This paper mainly studies the emergency scheduling of public resources based on line sensing technology and solves the scheduling problem of public resources through algorithm optimization. Firstly, combined with the positioning algorithm of wireless sensor, this paper optimizes the positioning and detection technology of wireless sensor technology. Then, we design an improved multiagent genetic algorithm (MAGA-MTERS) using natural number coding and design a penalty function to solve the model. Then, the algorithm is compared with the traditional genetic algorithm. The results show that the accurate positioning of wireless sensor technology can improve the efficiency of public resource scheduling and save the scheduling cost. The multiagent genetic algorithm optimizes the positioning function of wireless sensor. Compared with the traditional genetic algorithm, MAGA-MTERS algorithm can obtain a better solution.

5

Boerkoel Jr., J. C., e E. H. Durfee. "Distributed Reasoning for Multiagent Simple Temporal Problems". Journal of Artificial Intelligence Research 47 (28 maggio 2013): 95–156. http://dx.doi.org/10.1613/jair.3840.

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This research focuses on building foundational algorithms for scheduling agents that assist people in managing their activities in environments where tempo and complex activity interdependencies outstrip people's cognitive capacity. We address the critical challenge of reasoning over individuals' interacting schedules to efficiently answer queries about how to meet scheduling goals while respecting individual privacy and autonomy to the extent possible. We formally define the Multiagent Simple Temporal Problem for naturally capturing and reasoning over the distributed but interconnected scheduling problems of multiple individuals. Our hypothesis is that combining bottom-up and top-down approaches will lead to effective solution techniques. In our bottom-up phase, an agent externalizes constraints that compactly summarize how its local subproblem affects other agents' subproblems, whereas in our top-down phase an agent proactively constructs and internalizes new local constraints that decouple its subproblem from others'. We confirm this hypothesis by devising distributed algorithms that calculate summaries of the joint solution space for multiagent scheduling problems, without centralizing or otherwise redistributing the problems. The distributed algorithms permit concurrent execution to achieve significant speedup over the current art and also increase the level of privacy and independence in individual agent reasoning. These algorithms are most advantageous for problems where interactions between the agents are sparse compared to the complexity of agents' individual problems.

6

Montana, David, Jose Herrero, Gordon Vidaver e Garrett Bidwell. "A multiagent society for military transportation scheduling". Journal of Scheduling 3, n. 4 (2000): 225–46. http://dx.doi.org/10.1002/1099-1425(200007/08)3:4<225::aid-jos44>3.0.co;2-r.

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7

Chien, Steve, Minh Do, Alan Fern e Wheeler Ruml. "Preface". Proceedings of the International Conference on Automated Planning and Scheduling 24 (21 maggio 2014): xi—xiii. http://dx.doi.org/10.1609/icaps.v24i1.13611.

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Abstract (sommario):

The papers in this proceedings present the latest advances in the field of automated planning and scheduling, ranging in scope from theoretical analyses of planning and scheduling problems and processes, to new algorithms for planning and scheduling under various constraints and assumptions, and the empirical evaluation of planning and scheduling techniques. They reflect recent research trends in subareas such as optimal planning, probabilistic and nondeterministic planning, path planning, multiagent planning, and new developments in heuristics and their analysis for planning algorithms.

8

Frankoviè, B., Labátová S., Budinská e I. "Approach to Scheduling Problem Solution in Production Systems Using the Multiagent System". Journal of Advanced Computational Intelligence and Intelligent Informatics 4, n. 4 (20 luglio 2000): 263–67. http://dx.doi.org/10.20965/jaciii.2000.p0263.

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This paper considers job-shop-scheduling problem in multimachine multipart manufacturing systems. The purpose of the article is to contribute to the decision on scheduling rules for job-shop problems. The paper also describes the possibility of utilization of MAS formalism to represent different parts of the production systems and their mutual relations. The interrelations in the multiagent world are examined.

9

Rabelo, Ricardo J. "Interoperating standards in multiagent agile manufacturing scheduling systems". International Journal of Computer Applications in Technology 18, n. 1/2/3/4 (2003): 146. http://dx.doi.org/10.1504/ijcat.2003.002134.

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10

Walker, S. S., R. W. Brennan e D. H. Norrie. "Holonic Job Shop Scheduling Using a Multiagent System". IEEE Intelligent Systems 20, n. 1 (gennaio 2005): 50–57. http://dx.doi.org/10.1109/mis.2005.9.

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11

Taghaddos, Hosein, Ulrich Hermann, Simaan AbouRizk e Yasser Mohamed. "Simulation-Based Multiagent Approach for Scheduling Modular Construction". Journal of Computing in Civil Engineering 28, n. 2 (marzo 2014): 263–74. http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000262.

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12

Berger, T., Y. Sallez, D. Trentesaux e C. Tahon. "Two Heterarchical Multiagent Approaches for FMS Dynamic Scheduling". Systems Analysis Modelling Simulation 42, n. 5 (gennaio 2002): 757–68. http://dx.doi.org/10.1080/716067181.

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13

Guo, Qing-lin, e Ming Zhang. "Multiagent-based scheduling optimization for Intelligent Manufacturing System". International Journal of Advanced Manufacturing Technology 44, n. 5-6 (12 dicembre 2008): 595–605. http://dx.doi.org/10.1007/s00170-008-1858-x.

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14

He, Jianjia, Jian Wu, Ye Zhang, Yaopeng Wang e Hua He. "Large-Scale Customized Production Scheduling of Multiagent-Based Medical 3D Printing". Computational Intelligence and Neuroscience 2022 (18 luglio 2022): 1–13. http://dx.doi.org/10.1155/2022/6557137.

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Abstract (sommario):

Three-dimensional (3D) printing, also known as additive manufacturing, has unique advantages over traditional manufacturing technologies; thus, it has attracted widespread attention in the medical field. Especially in the context of the frequent occurrence of major public health events, where the medical industry’s demand for large-scale and customized production is increasing, traditional 3D printing production scheduling methods take a long time to handle large-scale customized medical 3D printing (M-3DP) production and have weak intelligent collaboration ability in the face of job-to-device matching under multimaterial printing. Given the problem caused by M-3DP large-scale customized production scheduling, an intelligent collaborative scheduling multiagent-based method is proposed in this study. First, a multiagent-based optimization model is established. On this basis, an improved genetic algorithm embedded with the product mix strategy and the intelligent matching mechanism is designed to optimize the completion time and load balance between devices. Finally, the effectiveness of the proposed method is evaluated using numerical simulation. The simulation results indicated that compared with the simple genetic algorithm, particle swarm optimization, and snake optimizer, the improved genetic algorithm could better reduce the M-3DP mass customization production scheduling time, optimize the load balance between devices, and promote the “intelligent manufacturing” process of M-3DP mass customization.

15

Boerkoel Jr., James, e Edmund Durfee. "A Comparison of Algorithms for Solving the Multiagent Simple Temporal Problem". Proceedings of the International Conference on Automated Planning and Scheduling 20 (25 maggio 2021): 26–33. http://dx.doi.org/10.1609/icaps.v20i1.13420.

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The Simple Temporal Problem (STP) is a popular representation for solving centralized scheduling and planning problems. When scheduling agents are associated with different users who need to coordinate some of their activities, however, considerations such as privacy and scalability suggest solving the joint STP in a more distributed manner. Building on recent advances in STP algorithms that exploit loosely-coupled problem structure, this paper develops and evaluates algorithms for solving the multiagent STP. We define a partitioning of the multiagent STP with provable privacy guarantees, and show that our algorithms can exploit this partitioning while still finding the tightest consistent bounds on timepoints that must be coordinated across agents. We also demonstrate empirically that our algorithms can exploit concurrent computation, leading to solution time speed-ups over state-of-the-art centralized approaches, and enabling scalability to problems involving larger numbers of loosely-coupled agents.

16

Kuhnimhof, Tobias, e Christoph Gringmuth. "Multiday Multiagent Model of Travel Behavior with Activity Scheduling". Transportation Research Record: Journal of the Transportation Research Board 2134, n. 1 (gennaio 2009): 178–85. http://dx.doi.org/10.3141/2134-21.

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17

Veit, Andreas, Ying Xu, Ronghuo Zheng, Nilanjan Chakraborty e Katia Sycara. "Multiagent Coordination for Energy Consumption Scheduling in Consumer Cooperatives". Proceedings of the AAAI Conference on Artificial Intelligence 27, n. 1 (29 giugno 2013): 1362–68. http://dx.doi.org/10.1609/aaai.v27i1.8482.

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A key challenge to create a sustainable and energy-efficient society is in making consumer demand adaptive to energy supply, especially renewable supply. In this paper, we propose a partially-centralized organization of consumers, namely, a consumer cooperative for purchasing electricity from the market. We propose a novel multiagent coordination algorithm to shape the energy consumption of the cooperative. In the cooperative, a central coordinator buys the electricity for the whole group and consumers make their own consumption decisions based on their private consumption constraints and preferences. To coordinate individual consumers under incomplete information, we propose an iterative algorithm in which a virtual price signal is sent by the coordinator to induce consumers to shift demand. We prove that our algorithm converges to the central optimal solution. Additionally we analyze the convergence rate of the algorithm via simulations on randomly generated instances. The results indicate scalability with respect to the number of agents and consumption slots.

18

Ntuen, Celestine A., E. H. Park, Y.-M. Wang e William P. Byrd. "The top architecture for multiagent task planning and scheduling". Computers & Industrial Engineering 23, n. 1-4 (novembre 1992): 153–56. http://dx.doi.org/10.1016/0360-8352(92)90086-y.

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19

Li, Yinong, Jianbo Li e Junjie Pang. "A Graph Attention Mechanism-Based Multiagent Reinforcement-Learning Method for Task Scheduling in Edge Computing". Electronics 11, n. 9 (24 aprile 2022): 1357. http://dx.doi.org/10.3390/electronics11091357.

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Multi-access edge computing (MEC) enables end devices with limited computing power to provide effective solutions while dealing with tasks that are computationally challenging. When each end device in an MEC scenario generates multiple tasks, how to reasonably and effectively schedule these tasks is a large-scale discrete action space problem. In addition, how to exploit the objectively existing spatial structure relationships in the given scenario is also an important factor to be considered in task-scheduling algorithms. In this work, we consider indivisible, time-sensitive tasks under this scenario and formalize the task-scheduling problem to minimize the long-term losses. We propose a multiagent collaborative deep reinforcement learning (DRL)-based distributed scheduling algorithm based on graph attention neural networks (GATs) to solve task-scheduling problems in the MEC scenario. Each end device creates a graph representation agent to extract potential spatial features in the scenario and a scheduling agent to extract the timing-related features of the tasks and make scheduling decisions using a gated recurrent unit (GRU). The simulation results show that, compared with several baseline algorithms, our proposed algorithm can take advantage of the spatial positional relationship of devices in the environment, significantly reduce the average delay and drop rate, and improve link utilization.

20

Weng, Yu, Haozhen Chu e Zhaoyi Shi. "An Intelligent Offloading System Based on Multiagent Reinforcement Learning". Security and Communication Networks 2021 (24 marzo 2021): 1–13. http://dx.doi.org/10.1155/2021/8830879.

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Intelligent vehicles have provided a variety of services; there is still a great challenge to execute some computing-intensive applications. Edge computing can provide plenty of computing resources for intelligent vehicles, because it offloads complex services from the base station (BS) to the edge computing nodes. Before the selection of the computing node for services, it is necessary to clarify the resource requirement of vehicles, the user mobility, and the situation of the mobile core network; they will affect the users’ quality of experience (QoE). To maximize the QoE, we use multiagent reinforcement learning to build an intelligent offloading system; we divide this goal into two suboptimization problems; they include global node scheduling and independent exploration of agents. We apply the improved Kuhn–Munkres (KM) algorithm to node scheduling and make full use of existing edge computing nodes; meanwhile, we guide intelligent vehicles to the potential areas of idle computing nodes; it can encourage their autonomous exploration. Finally, we make some performance evaluations to illustrate the effectiveness of our constructed system on the simulated dataset.

21

Rubrico, Jose Ildefonso U., Toshimitsu Higashi, Hirofumi Tamura, Makoto Nikaido e Jun Ota. "A Fast Scheduler for Multiagent in a Warehouse". International Journal of Automation Technology 3, n. 2 (5 marzo 2009): 165–73. http://dx.doi.org/10.20965/ijat.2009.p0165.

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A major goal in scheduling multiagent for warehouse picking is to decrease operating cost by minimizing makespan among transport agents. Computational time must be within ten seconds for real-sized instances. Orders are initially batched by solving the split delivery vehicle routing problem, resulting trips are assigned to agents to balance their picking time, and trips are assigned to minimize blocking delays among agents. Simulation results confirmed that our proposal reduces picking time an average of 11.48% over conventional approaches.

22

Xu, Yunting, Haibo Zhou, Ting Ma, Jiwei Zhao, Bo Qian e Xuemin Shen. "Leveraging Multiagent Learning for Automated Vehicles Scheduling at Nonsignalized Intersections". IEEE Internet of Things Journal 8, n. 14 (15 luglio 2021): 11427–39. http://dx.doi.org/10.1109/jiot.2021.3054649.

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23

Sim, Kwang Mong, Minjie Zhang e Takayuki Ito. "Special issue on negotiation and scheduling mechanisms for multiagent systems". Multiagent and Grid Systems 4, n. 1 (8 maggio 2008): 1–3. http://dx.doi.org/10.3233/mgs-2008-4101.

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24

Mouaddib, Abdel-illah. "Co-operative scheduling for a resource-bounded multiagent planning system". Journal of Experimental & Theoretical Artificial Intelligence 16, n. 2 (aprile 2004): 57–71. http://dx.doi.org/10.1080/09528130412331282763.

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25

Kalyaev, A. I., e I. A. Kalyaev. "Method of multiagent scheduling of resources in cloud computing environments". Journal of Computer and Systems Sciences International 55, n. 2 (marzo 2016): 211–21. http://dx.doi.org/10.1134/s1064230716010081.

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26

Whitbrook, Amanda, Qinggang Meng e Paul W. H. Chung. "Reliable, Distributed Scheduling and Rescheduling for Time-Critical, Multiagent Systems". IEEE Transactions on Automation Science and Engineering 15, n. 2 (aprile 2018): 732–47. http://dx.doi.org/10.1109/tase.2017.2679278.

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27

Crawford, Elisabeth, e Manuela Veloso. "An experts approach to strategy selection in multiagent meeting scheduling". Autonomous Agents and Multi-Agent Systems 15, n. 1 (15 giugno 2006): 5–28. http://dx.doi.org/10.1007/s10458-006-0010-2.

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28

Shou, Yongyi, Wenwen Xiang, Ying Li e Weijian Yao. "A Multiagent Evolutionary Algorithm for the Resource-Constrained Project Portfolio Selection and Scheduling Problem". Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/302684.

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Abstract (sommario):

A multiagent evolutionary algorithm is proposed to solve the resource-constrained project portfolio selection and scheduling problem. The proposed algorithm has a dual level structure. In the upper level a set of agents make decisions to select appropriate project portfolios. Each agent selects its project portfolio independently. The neighborhood competition operator and self-learning operator are designed to improve the agent’s energy, that is, the portfolio profit. In the lower level the selected projects are scheduled simultaneously and completion times are computed to estimate the expected portfolio profit. A priority rule-based heuristic is used by each agent to solve the multiproject scheduling problem. A set of instances were generated systematically from the widely used Patterson set. Computational experiments confirmed that the proposed evolutionary algorithm is effective for the resource-constrained project portfolio selection and scheduling problem.

29

Wang, Guofeng, Kangli Zhao, Yu Yang, Junjie Lu e Youbing Zhang. "A Decentralized Energy Flow Control Framework for Regional Energy Internet". Complexity 2019 (28 ottobre 2019): 1–10. http://dx.doi.org/10.1155/2019/3928268.

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As a new form of smart grid, the energy transmission mode of the Energy Internet (EI) has changed from one direction to the interconnected form. Centralized scheduling of traditional power grids has the problems of low communication efficiency and low system resilience, which do not contribute to long-term development in the future. Owing to the fact that it is difficult to achieve an optimal operation for centralized control, we propose a decentralized energy flow control framework for regional Energy Internet. Through optimal scheduling of regional EI, large-scale utilization and sharing of distributed renewable energy can be realized, while taking into consideration the uncertainty of both demand side and supply side. Combing the multiagent system with noncooperative game theory, a novel electricity price mechanism is adopted to maximize the profit of the regional EI. We prove that Nash equilibrium of theoretical noncooperative game can realize consensus in the multiagent system. The numerical results of real-world traces show that the regional EI can better absorb the renewable energy under the optimized control strategy, which proves the feasibility and economy of the proposed decentralized energy flow control framework.

30

Feldman, M., e T. Tamir. "Approximate Strong Equilibrium in Job Scheduling Games". Journal of Artificial Intelligence Research 36 (30 novembre 2009): 387–414. http://dx.doi.org/10.1613/jair.2892.

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A Nash Equilibrium (NE) is a strategy profile resilient to unilateral deviations, and is predominantly used in the analysis of multiagent systems. A downside of NE is that it is not necessarily stable against deviations by coalitions. Yet, as we show in this paper, in some cases, NE does exhibit stability against coalitional deviations, in that the benefits from a joint deviation are bounded. In this sense, NE approximates strong equilibrium. Coalition formation is a key issue in multiagent systems. We provide a framework for quantifying the stability and the performance of various assignment policies and solution concepts in the face of coalitional deviations. Within this framework we evaluate a given configuration according to three measures: (i) IR_min: the maximal number alpha, such that there exists a coalition in which the minimal improvement ratio among the coalition members is alpha, (ii) IR_max: the maximal number alpha, such that there exists a coalition in which the maximal improvement ratio among the coalition members is alpha, and (iii) DR_max: the maximal possible damage ratio of an agent outside the coalition. We analyze these measures in job scheduling games on identical machines. In particular, we provide upper and lower bounds for the above three measures for both NE and the well-known assignment rule Longest Processing Time (LPT). Our results indicate that LPT performs better than a general NE. However, LPT is not the best possible approximation. In particular, we present a polynomial time approximation scheme (PTAS) for the makespan minimization problem which provides a schedule with IR_min of 1+epsilon for any given epsilon. With respect to computational complexity, we show that given an NE on m >= 3 identical machines or m >= 2 unrelated machines, it is NP-hard to determine whether a given coalition can deviate such that every member decreases its cost.

31

Zhou, Bowen, Zhibo Zhang, Chao Xi e Boyu Liu. "A Novel Two-Stage, Dual-Layer Distributed Optimization Operational Approach for Microgrids with Electric Vehicles". Mathematics 11, n. 21 (6 novembre 2023): 4563. http://dx.doi.org/10.3390/math11214563.

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As the ownership of electric vehicles (EVs) continues to rise, EVs are becoming an integral part of urban microgrids. Incorporating the charging and discharging processes of EVs into the microgrid’s optimization scheduling process can serve to load leveling, reducing the reliance of the microgrid on external power networks. This paper proposes a novel two-stage, dual-layer distributed optimization operational approach for microgrids with EVs. The lower layer is a distributed control layer, which ensures, through consensus control methods, that every EV maintains a consistent charging/discharging and state of charge (SOC). The upper layer is the optimization scheduling layer, determining the optimal operational strategy of the microgrid using the multiagent reinforcement learning method and providing control reference signals for the lower layer. Additionally, this paper categorizes the charging process of EVs into two stages based on their SOC: the constrained scheduling stage and the free scheduling stage. By employing distinct control methods during these two stages, we ensure that EVs can participate in the microgrid scheduling while fully respecting the charging interests of the EV owners.

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Ezugwu, Absalom E., Marc E. Frincu, Afolayan A. Obiniyi, Seyed M. Buhari e Sahalu B. Junaidu. "Multiagent-based approach for scheduling meta-applications in heterogeneous grid environments". Multiagent and Grid Systems 11, n. 2 (17 agosto 2015): 59–79. http://dx.doi.org/10.3233/mgs-150229.

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33

Balasubramanian, S., e D. H. Norrie. "A Multiagent Architecture for Concurrent Design, Process Planning, Routing, and Scheduling". Concurrent Engineering 4, n. 1 (marzo 1996): 7–16. http://dx.doi.org/10.1177/1063293x9600400102.

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34

Brazier, Frances M. T., Catholijn M. Jonker, Frederik Jan Jungen e Jan Treur. "Distributed scheduling to support a call center: A cooperative multiagent approach". Applied Artificial Intelligence 13, n. 1-2 (gennaio 1999): 65–90. http://dx.doi.org/10.1080/088395199117496.

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35

Xu, Rui, ZhaoYu Li e PingYuan Cui. "Geometry-based distributed arc-consistency method for multiagent planning and scheduling". Science China Technological Sciences 62, n. 1 (6 settembre 2018): 133–43. http://dx.doi.org/10.1007/s11431-017-9197-3.

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36

Feng, Zhidong, Ge Liu, Luofeng Wang, Qinghua Gu e Lu Chen. "Research on the Multiobjective and Efficient Ore-Blending Scheduling of Open-Pit Mines Based on Multiagent Deep Reinforcement Learning". Sustainability 15, n. 6 (16 marzo 2023): 5279. http://dx.doi.org/10.3390/su15065279.

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Abstract (sommario):

In order to solve the problems of a slow solving speed and easily falling into the local optimization of an ore-blending process model (of polymetallic multiobjective open-pit mines), an efficient ore-blending scheduling optimization method based on multiagent deep reinforcement learning is proposed. Firstly, according to the actual production situation of the mine, the optimal control model for ore blending was established with the goal of minimizing deviations in ore grade and lithology. Secondly, the open-pit ore-matching problem was transformed into a partially observable Markov decision process, and the ore supply strategy was continuously optimized according to the feedback of the environmental indicators to obtain the optimal decision-making sequence. Thirdly, a multiagent deep reinforcement learning algorithm was introduced, which was trained continuously and modeled the environment to obtain the optimal strategy. Finally, taking a large open-pit metal mine as an example, the trained multiagent depth reinforcement learning algorithm model was verified via experiments, with the optimal training model displayed on the graphical interface. The experimental results show that the ore-blending optimization model constructed is more in line with the actual production requirements of a mine. When compared with the traditional multiobjective optimization algorithm, the efficiency and accuracy of the solution have been greatly improved, and the calculation results can be obtained in real-time.

37

Khoukhi, Amar, e Adlene Moualek. "Multiagent Architecture Combined with a Multicontract Protocol for FMS Control". Journal of Advanced Computational Intelligence and Intelligent Informatics 5, n. 4 (20 luglio 2001): 201–12. http://dx.doi.org/10.20965/jaciii.2001.p0201.

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This paper describes a new Multiagent architecture for control of flexible manufacturing. In this architecture, agents coordinate their actions following a new negotiation protocol used for scheduling and rescheduling of tasks. The proposed protocol, MultiContract-Net, is an innovation of Contract-Net protocol enabling several tasks to be negotiated concurrently in real time with optimal results. Thus, the multicontract-Net protocol enables both dynamic task allocation and optimization of opportunities provided by manufacturing flexibility by handling knowledge uncertainty characterizing the negotiation process. This paper stresses the efficiency of distributed implementation.

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Hu, Jiangping, Yulong Zhou e Yunsong Lin. "Second-Order Multiagent Systems with Event-Driven Consensus Control". Abstract and Applied Analysis 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/250586.

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Event-driven control scheduling strategies for multiagent systems play a key role in future use of embedded microprocessors of limited resources that gather information and actuate the agent control updates. In this paper, a distributed event-driven consensus problem is considered for a multi-agent system with second-order dynamics. Firstly, two kinds of event-driven control laws are, respectively, designed for both leaderless and leader-follower systems. Then, the input-to-state stability of the closed-loop multi-agent system with the proposed event-driven consensus control is analyzed and the bound of the inter-event times is ensured. Finally, some numerical examples are presented to validate the proposed event-driven consensus control.

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Zhu, Cheng, Jiangfeng Luo, Weiming Zhang e Zhong Liu. "OL-DEC-MDP Model for Multiagent Online Scheduling with a Time-Dependent Probability of Success". Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/753487.

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Focusing on the on-line multiagent scheduling problem, this paper considers the time-dependent probability of success and processing duration and proposes an OL-DEC-MDP (opportunity loss-decentralized Markov Decision Processes) model to include opportunity loss into scheduling decision to improve overall performance. The success probability of job processing as well as the process duration is dependent on the time at which the processing is started. The probability of completing the assigned job by an agent would be higher when the process is started earlier, but the opportunity loss could also be high due to the longer engaging duration. As a result, OL-DEC-MDP model introduces a reward function considering the opportunity loss, which is estimated based on the prediction of the upcoming jobs by a sampling method on the job arrival. Heuristic strategies are introduced in computing the best starting time for an incoming job by each agent, and an incoming job will always be scheduled to the agent with the highest reward among all agents with their best starting policies. The simulation experiments show that the OL-DEC-MDP model will improve the overall scheduling performance compared with models not considering opportunity loss in heavy-loading environment.

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Zou, Qijie, Youkun Hu, Dewei Yi, Bing Gao e Jing Qin. "Cooperative Multiagent Attentional Communication for Large-Scale Task Space". Wireless Communications and Mobile Computing 2022 (24 gennaio 2022): 1–13. http://dx.doi.org/10.1155/2022/4401653.

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With the rapid development of mobile robots, they have begun to be widely used in industrial manufacturing, logistics scheduling, intelligent medical, and other fields. For large-scale task space, the communication between multiagents is the key to affect cooperation productivity, and agents can coordinate more effectively with the help of dynamic communication. However, the traditional communication mechanism uses simple message aggregation and broadcast and, in some cases, lacks the distinction of the importance of information. Multiagent deep reinforcement learning (MDRL) is valid to solve the problem of informational coordination strategies. However, how different messages affect each agent’s decision-making process remains a challenging task for large-scale task. To solve this problem, we propose IMANet (Import Message Attention Network). It divides the decision-making process into two substages: communication and action, where communication is considered to be part of the environment. First, an attention mechanism based on query vectors is introduced. The correlation between the query vector agent’s own information and the current state information of other agents is estimated, and then, the results are used to distinguish the importance of information from other agents. Second, the LSTM network is used as the unit controller for each agent, and individual rewards are used to guide the agent training after communication. Finally, IMANet is evaluated on tasks on challenging multi-agent platforms, Predator and Prey (PP), and traffic junction. The results show that IMANet can improve the efficiency of learning and training, especially when applied to large-scale task space, with a success rate 12% higher than CommNet in baseline experiments.

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Wu, Z., e M. X. Weng. "Multiagent Scheduling Method With Earliness and Tardiness Objectives in Flexible Job Shops". IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 35, n. 2 (aprile 2005): 293–301. http://dx.doi.org/10.1109/tsmcb.2004.842412.

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Liu, Ning, Mohamed A. Abdelrahman e Srini Ramaswamy. "A Complete Multiagent Framework for Robust and Adaptable Dynamic Job Shop Scheduling". IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 37, n. 5 (settembre 2007): 904–16. http://dx.doi.org/10.1109/tsmcc.2007.900658.

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Wu, Wen-Hsiang. "A Two-Agent Single-Machine Scheduling Problem with Learning and Deteriorating Considerations". Mathematical Problems in Engineering 2013 (2013): 1–18. http://dx.doi.org/10.1155/2013/648082.

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Recently, interest in scheduling with deteriorating jobs and learning effects has kept growing. However, research in this area has seldom considered the multiagent setting. Motivated by these observations, we consider two-agent scheduling on a single machine involving the learning effects and deteriorating jobs simultaneously. In the proposed model, we assume that the actual processing time of a job of the first (second) agent is a decreasing (increasing) function of the total processing time of the jobs already processed in a schedule. The objective is to minimize the total weighted completion time of the jobs of the first agent with the restriction that no tardy job is allowed for the second agent. We develop a branch-and-bound and a simulated annealing algorithms for the problem. We perform extensive computational experiments to test the performance of the algorithms.

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Cheng, Haoyu, Ruijia Song, Linpeng Xu, Di Zhang e Shengli Xu. "H ∞ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning". International Journal of Aerospace Engineering 2022 (15 marzo 2022): 1–15. http://dx.doi.org/10.1155/2022/2650632.

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This paper is devoted to H ∞ consensus design and online scheduling for homogeneous multiagent systems (MASs) with switching topologies via deep reinforcement learning. The model of homogeneous MASs with switching topologies is established based on switched systems theory, in which the switching of topologies is viewed as the switching among subsystems. By employing linear transformation, the closed-loop systems of MASs are converted into reduced-order systems. The problem of H ∞ consensus design can be transformed to the issue of H ∞ control. It is supposed that the consensus protocol is composed of two parts: dynamics-based protocol and learning-based protocol, where dynamics-based protocol is provided to guarantee the convergence and weighted attenuation and learning-based protocol is proposed to improve the transient performance. Then, the multiple Lyapunov function (MLF) method and mode-dependent average dwell time (MDADT) method are combined to ensure the stability and weighted H ∞ disturbance attenuation index of reduced-order systems. The sufficient existing conditions of dynamics-based protocol are given through the feasible solutions of linear matrix inequalities (LMIs). Moreover, the online scheduling is formulated as a Markov decision process, and the deep deterministic policy gradient (DDPG) algorithm in the framework of actor-critic is proposed for the compensation of disturbance to explore optimal control policy. The online scheduling of parameters of MASs is viewed as bounded compensation of dynamics-based protocol, whose stability can be guaranteed by nonfragile control theory. Finally, simulation results are provided to illustrate the effectiveness and superiority of the proposed method.

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Bukhvalov, O., V. Gorodetsky, O. Karsaev, G. Kudryavtsev e V. Samoylov. "Privacy-Preserved Distributed Coordination of Production Scheduling in B2B Networks: A Multiagent Approach". IFAC Proceedings Volumes 46, n. 9 (2013): 2122–27. http://dx.doi.org/10.3182/20130619-3-ru-3018.00453.

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Kalyaev, I. A., e A. I. Kalyaev. "Method and Algorithms for Adaptive Multiagent Resource Scheduling in Heterogeneous Distributed Computing Environments". Automation and Remote Control 83, n. 8 (agosto 2022): 1228–45. http://dx.doi.org/10.1134/s0005117922080069.

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Kanellos, Fotios D. "Multiagent-System-Based Operation Scheduling of Large Ports’ Power Systems With Emissions Limitation". IEEE Systems Journal 13, n. 2 (giugno 2019): 1831–40. http://dx.doi.org/10.1109/jsyst.2018.2850970.

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Kung, Jan-Yee, Yuan-Po Chao, Kuei-I. Lee, Chao-Chung Kang e Win-Chin Lin. "Two-Agent Single-Machine Scheduling of Jobs with Time-Dependent Processing Times and Ready Times". Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/806325.

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Abstract (sommario):

Scheduling involving jobs with time-dependent processing times has recently attracted much research attention. However, multiagent scheduling with simultaneous considerations of jobs with time-dependent processing times and ready times is relatively unexplored. Inspired by this observation, we study a two-agent single-machine scheduling problem in which the jobs have both time-dependent processing times and ready times. We consider the model in which the actual processing time of a job of the first agent is a decreasing function of its scheduled position while the actual processing time of a job of the second agent is an increasing function of its scheduled position. In addition, each job has a different ready time. The objective is to minimize the total completion time of the jobs of the first agent with the restriction that no tardy job is allowed for the second agent. We propose a branch-and-bound and several genetic algorithms to obtain optimal and near-optimal solutions for the problem, respectively. We also conduct extensive computational results to test the proposed algorithms and examine the impacts of different problem parameters on their performance.

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Hanif, Shaza, Shahab Ud Din, Ning Gui e Tom Holvoet. "Multiagent Coordination and Teamwork: A Case Study for Large-Scale Dynamic Ready-Mixed Concrete Delivery Problem". Mathematics 11, n. 19 (29 settembre 2023): 4124. http://dx.doi.org/10.3390/math11194124.

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The ready-mixed concrete delivery (RMC) problem is a scheduling problem, where multiple trucks deliver concrete to order sites abiding by hard constraints in a dynamic environment. It is an NP-hard problem, impractical to solve using exhaustive methods. Thus, it requires heuristic-based approaches for generating sub-optimal schedules. Due to its distributed nature, we address this problem using a decentralised, scalable, cooperative MAS (multiagent system) that dynamically generates schedules. We explore the impact of teamwork by trucks on schedule optimisation. This work illustrates two novel approaches that address the dynamic RMC problem; a Delegate MAS approach and a team-extended approach. We present an empirical study, comparing our novel approaches with existing ones. The evaluation is performed by classifying the RMC case study scenarios into unique stress, scale, and dynamism characteristics. With 40% to 70% improvement over different metrics, the results show that both approaches generate better schedules, and using agent teams augments the performance. Thus, such decentralized MAS with the appropriate coordination approach and teamwork can be used for solving constrained dynamic scheduling problems.

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Rajeswari, M., J. Amudhavel, Sujatha Pothula e P. Dhavachelvan. "Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem". Computational Intelligence and Neuroscience 2017 (2017): 1–26. http://dx.doi.org/10.1155/2017/6563498.

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The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria.