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Journal articles on the topic 'Multiagent scheduling'

Author: Grafiati

Published: 6 July 2024

Last updated: 31 July 2025

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1

Zhang, Jie, Gang Wang, Yafei Song, Fangzheng Zhao, and Siyuan Wang. "Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure." Mathematical Problems in Engineering 2019 (November 6, 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 coordinatio

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Li, Zhipeng, Xiumei Wei, Xuesong Jiang, and Yewen Pang. "A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling." Mathematical Problems in Engineering 2021 (January 12, 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

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Boerkoel Jr., James, and Edmund Durfee. "Decoupling the Multiagent Disjunctive Temporal Problem." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (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

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Zhou, Yi, and Weili Xia. "Optimization Algorithm and Simulation of Public Resource Emergency Scheduling Based on Wireless Sensor Technology." Journal of Sensors 2021 (October 8, 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 positioni

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Boerkoel Jr., J. C., and E. H. Durfee. "Distributed Reasoning for Multiagent Simple Temporal Problems." Journal of Artificial Intelligence Research 47 (May 28, 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 schedu

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6

Montana, David, Jose Herrero, Gordon Vidaver, and Garrett Bidwell. "A multiagent society for military transportation scheduling." Journal of Scheduling 3, no. 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, and Wheeler Ruml. "Preface." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 21, 2014): xi—xiii. http://dx.doi.org/10.1609/icaps.v24i1.13611.

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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.

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8

Frankoviè, B., Labátová S., Budinská, and I. "Approach to Scheduling Problem Solution in Production Systems Using the Multiagent System." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 4 (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.

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9

Rabelo, Ricardo J. "Interoperating standards in multiagent agile manufacturing scheduling systems." International Journal of Computer Applications in Technology 18, no. 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, and D. H. Norrie. "Holonic Job Shop Scheduling Using a Multiagent System." IEEE Intelligent Systems 20, no. 1 (2005): 50–57. http://dx.doi.org/10.1109/mis.2005.9.

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11

Taghaddos, Hosein, Ulrich Hermann, Simaan AbouRizk, and Yasser Mohamed. "Simulation-Based Multiagent Approach for Scheduling Modular Construction." Journal of Computing in Civil Engineering 28, no. 2 (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, and C. Tahon. "Two Heterarchical Multiagent Approaches for FMS Dynamic Scheduling." Systems Analysis Modelling Simulation 42, no. 5 (2002): 757–68. http://dx.doi.org/10.1080/716067181.

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13

Guo, Qing-lin, and Ming Zhang. "Multiagent-based scheduling optimization for Intelligent Manufacturing System." International Journal of Advanced Manufacturing Technology 44, no. 5-6 (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, and Hua He. "Large-Scale Customized Production Scheduling of Multiagent-Based Medical 3D Printing." Computational Intelligence and Neuroscience 2022 (July 18, 2022): 1–13. http://dx.doi.org/10.1155/2022/6557137.

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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

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15

Boerkoel Jr., James, and Edmund Durfee. "A Comparison of Algorithms for Solving the Multiagent Simple Temporal Problem." Proceedings of the International Conference on Automated Planning and Scheduling 20 (May 25, 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

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16

Olayinka Akinbolajo, Olayinka Akinbolajo. "Enhancing Job Scheduling Efficiency through Multi-Agent Systems in Distributed Computing Environments." International Journal of Advances in Engineering and Management 7, no. 3 (2025): 706–11. https://doi.org/10.35629/5252-0703706711.

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The rapid growth of distributed computing environments has necessitated the development of efficient job scheduling mechanisms to optimize resource utilization and minimize latency. MultiAgent Systems (MAS) have emerged as a promising approach to address the complexities of job scheduling in such environments. This paper explores the integration of MAS into distributed computing systems to enhance job scheduling efficiency. We propose a novel framework that leverages the autonomous, collaborative, and adaptive capabilities of agents to improve scheduling decisions. Through extensive simulation

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17

Kuhnimhof, Tobias, and Christoph Gringmuth. "Multiday Multiagent Model of Travel Behavior with Activity Scheduling." Transportation Research Record: Journal of the Transportation Research Board 2134, no. 1 (2009): 178–85. http://dx.doi.org/10.3141/2134-21.

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18

Veit, Andreas, Ying Xu, Ronghuo Zheng, Nilanjan Chakraborty, and Katia Sycara. "Multiagent Coordination for Energy Consumption Scheduling in Consumer Cooperatives." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (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 pref

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19

Ntuen, Celestine A., E. H. Park, Y.-M. Wang, and William P. Byrd. "The top architecture for multiagent task planning and scheduling." Computers & Industrial Engineering 23, no. 1-4 (1992): 153–56. http://dx.doi.org/10.1016/0360-8352(92)90086-y.

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20

Li, Yinong, Jianbo Li, and Junjie Pang. "A Graph Attention Mechanism-Based Multiagent Reinforcement-Learning Method for Task Scheduling in Edge Computing." Electronics 11, no. 9 (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

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21

Weng, Yu, Haozhen Chu, and Zhaoyi Shi. "An Intelligent Offloading System Based on Multiagent Reinforcement Learning." Security and Communication Networks 2021 (March 24, 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

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22

Rubrico, Jose Ildefonso U., Toshimitsu Higashi, Hirofumi Tamura, Makoto Nikaido, and Jun Ota. "A Fast Scheduler for Multiagent in a Warehouse." International Journal of Automation Technology 3, no. 2 (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.

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23

Wang, Guofeng, Kangli Zhao, Yu Yang, Junjie Lu, and Youbing Zhang. "A Decentralized Energy Flow Control Framework for Regional Energy Internet." Complexity 2019 (October 28, 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

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24

Xu, Yunting, Haibo Zhou, Ting Ma, Jiwei Zhao, Bo Qian, and Xuemin Shen. "Leveraging Multiagent Learning for Automated Vehicles Scheduling at Nonsignalized Intersections." IEEE Internet of Things Journal 8, no. 14 (2021): 11427–39. http://dx.doi.org/10.1109/jiot.2021.3054649.

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25

Sim, Kwang Mong, Minjie Zhang, and Takayuki Ito. "Special issue on negotiation and scheduling mechanisms for multiagent systems." Multiagent and Grid Systems 4, no. 1 (2008): 1–3. http://dx.doi.org/10.3233/mgs-2008-4101.

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26

Mouaddib, Abdel-illah. "Co-operative scheduling for a resource-bounded multiagent planning system." Journal of Experimental & Theoretical Artificial Intelligence 16, no. 2 (2004): 57–71. http://dx.doi.org/10.1080/09528130412331282763.

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27

Kalyaev, A. I., and I. A. Kalyaev. "Method of multiagent scheduling of resources in cloud computing environments." Journal of Computer and Systems Sciences International 55, no. 2 (2016): 211–21. http://dx.doi.org/10.1134/s1064230716010081.

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28

Whitbrook, Amanda, Qinggang Meng, and Paul W. H. Chung. "Reliable, Distributed Scheduling and Rescheduling for Time-Critical, Multiagent Systems." IEEE Transactions on Automation Science and Engineering 15, no. 2 (2018): 732–47. http://dx.doi.org/10.1109/tase.2017.2679278.

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29

Crawford, Elisabeth, and Manuela Veloso. "An experts approach to strategy selection in multiagent meeting scheduling." Autonomous Agents and Multi-Agent Systems 15, no. 1 (2006): 5–28. http://dx.doi.org/10.1007/s10458-006-0010-2.

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30

Shou, Yongyi, Wenwen Xiang, Ying Li, and 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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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 expecte

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31

Feldman, M., and T. Tamir. "Approximate Strong Equilibrium in Job Scheduling Games." Journal of Artificial Intelligence Research 36 (November 30, 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

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32

Zhou, Bowen, Zhibo Zhang, Chao Xi, and Boyu Liu. "A Novel Two-Stage, Dual-Layer Distributed Optimization Operational Approach for Microgrids with Electric Vehicles." Mathematics 11, no. 21 (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/dischargi

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Feng, Zhidong, Ge Liu, Luofeng Wang, Qinghua Gu, and Lu Chen. "Research on the Multiobjective and Efficient Ore-Blending Scheduling of Open-Pit Mines Based on Multiagent Deep Reinforcement Learning." Sustainability 15, no. 6 (2023): 5279. http://dx.doi.org/10.3390/su15065279.

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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 p

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34

Ezugwu, Absalom E., Marc E. Frincu, Afolayan A. Obiniyi, Seyed M. Buhari, and Sahalu B. Junaidu. "Multiagent-based approach for scheduling meta-applications in heterogeneous grid environments." Multiagent and Grid Systems 11, no. 2 (2015): 59–79. http://dx.doi.org/10.3233/mgs-150229.

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35

Balasubramanian, S., and D. H. Norrie. "A Multiagent Architecture for Concurrent Design, Process Planning, Routing, and Scheduling." Concurrent Engineering 4, no. 1 (1996): 7–16. http://dx.doi.org/10.1177/1063293x9600400102.

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36

Brazier, Frances M. T., Catholijn M. Jonker, Frederik Jan Jungen, and Jan Treur. "Distributed scheduling to support a call center: A cooperative multiagent approach." Applied Artificial Intelligence 13, no. 1-2 (1999): 65–90. http://dx.doi.org/10.1080/088395199117496.

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37

Xu, Rui, ZhaoYu Li, and PingYuan Cui. "Geometry-based distributed arc-consistency method for multiagent planning and scheduling." Science China Technological Sciences 62, no. 1 (2018): 133–43. http://dx.doi.org/10.1007/s11431-017-9197-3.

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38

Khoukhi, Amar, and Adlene Moualek. "Multiagent Architecture Combined with a Multicontract Protocol for FMS Control." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 4 (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 n

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39

Hu, Jiangping, Yulong Zhou, and 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 th

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40

Zhu, Cheng, Jiangfeng Luo, Weiming Zhang, and 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 als

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Zou, Qijie, Youkun Hu, Dewei Yi, Bing Gao, and Jing Qin. "Cooperative Multiagent Attentional Communication for Large-Scale Task Space." Wireless Communications and Mobile Computing 2022 (January 24, 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

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Wu, Z., and 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, no. 2 (2005): 293–301. http://dx.doi.org/10.1109/tsmcb.2004.842412.

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Liu, Ning, Mohamed A. Abdelrahman, and 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, no. 5 (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 completio

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Cheng, Haoyu, Ruijia Song, Linpeng Xu, Di Zhang, and Shengli Xu. "H ∞ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning." International Journal of Aerospace Engineering 2022 (March 15, 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 comp

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Bukhvalov, O., V. Gorodetsky, O. Karsaev, G. Kudryavtsev, and V. Samoylov. "Privacy-Preserved Distributed Coordination of Production Scheduling in B2B Networks: A Multiagent Approach." IFAC Proceedings Volumes 46, no. 9 (2013): 2122–27. http://dx.doi.org/10.3182/20130619-3-ru-3018.00453.

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Kalyaev, I. A., and A. I. Kalyaev. "Method and Algorithms for Adaptive Multiagent Resource Scheduling in Heterogeneous Distributed Computing Environments." Automation and Remote Control 83, no. 8 (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, no. 2 (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, and 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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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 j

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Rajeswari, M., J. Amudhavel, Sujatha Pothula, and 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 Optimi

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