Semi-Online Scheduling Algorithm of Multi-Agent in Network Management

Agent technology has played an important role in distributed network management, and agent scheduling is an inevitable problem in a multi-agent system. This chapter introduces a network management scenario to support dynamic scheduling decisions. Some algorithms are proposed to decompose the whole network management task into several groups of sub-tasks. During the course of decomposition, different priorities are assigned to sub-tasks. Then, based on the priorities of these sub-tasks, a dynamic multi-agent scheduling algorithm based on dependences of sub-tasks is proposed. An experiment has been done with the decomposition algorithms, the results of which demonstrate the advantage of the algorithms. The performance test demonstrates that the competitive ratio of the dynamic scheduling algorithm is always smaller than that of the existing online scheduling algorithm, which indicates that the performance of the dynamic scheduling algorithm is better than the existing online scheduling algorithm. Finally, as an application example, the process of network stream management is presented. The authors hope that this scheduling method can give a new approach or suggestion for studying dynamic agents scheduling technology.

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Multi-Agent Based Autonomic Network Management Architecture

IEEE Transactions on Network and Service Management ◽

10.1109/tnsm.2021.3059752 ◽

2021 ◽

pp. 1-1

Author(s):

Sisay Tadesse Arzo ◽

Riccardo Bassoli ◽

Fabrizio Granelli ◽

Frank H.P. Fitzek

Keyword(s):

Network Management ◽

Agent Based ◽

Management Architecture ◽

Multi Agent ◽

Network Management Architecture

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Multi-agent reinforcement learning for online scheduling in smart factories

Robotics and Computer-Integrated Manufacturing ◽

10.1016/j.rcim.2021.102202 ◽

2021 ◽

Vol 72 ◽

pp. 102202

Author(s):

Tong Zhou ◽

Dunbing Tang ◽

Haihua Zhu ◽

Zequn Zhang

Keyword(s):

Reinforcement Learning ◽

Online Scheduling ◽

Multi Agent ◽

Smart Factories

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Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints

Applied Sciences ◽

10.3390/app9102117 ◽

2019 ◽

Vol 9 (10) ◽

pp. 2117

Author(s):

Ming Chong Lim ◽

Han-Lim Choi

Keyword(s):

Task Allocation ◽

Random Sampling ◽

Scheduling Algorithm ◽

Stochastic Algorithm ◽

Speed Of Convergence ◽

Temporal Dependence ◽

Real World Applications ◽

Coupled Constraints ◽

Multi Agent ◽

Feasible Solutions

Multi-agent task allocation is a well-studied field with many proven algorithms. In real-world applications, many tasks have complicated coupled relationships that affect the feasibility of some algorithms. In this paper, we leverage on the properties of potential games and introduce a scheduling algorithm to provide feasible solutions in allocation scenarios with complicated spatial and temporal dependence. Additionally, we propose the use of random sampling in a Distributed Stochastic Algorithm to enhance speed of convergence. We demonstrate the feasibility of such an approach in a simulated disaster relief operation and show that feasibly good results can be obtained when the confirmation and sample size requirements are properly selected.

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A simulation-driven online scheduling algorithm for the maintenance and operation of wind farm systems

SIMULATION ◽

10.1177/00375497211028605 ◽

2021 ◽

pp. 003754972110286

Author(s):

Eduardo Pérez

Keyword(s):

Wind Turbines ◽

Wind Farm ◽

Computational Study ◽

Scheduling Algorithm ◽

Discrete Event ◽

Wind Farms ◽

Online Scheduling ◽

Maintenance Scheduling ◽

Lead Times ◽

System Specification

Wind turbines experience stochastic loading due to seasonal variations in wind speed and direction. These harsh operational conditions lead to failures of wind turbines, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions that will avoid failures. In this article, a simulation-driven online maintenance scheduling algorithm for wind farm operational planning is derived. Online scheduling is a suitable framework for this problem since it integrates data that evolve over time into the maintenance scheduling decisions. The computational study presented in this article compares the performance of the simulation-driven online scheduling algorithm against two benchmark algorithms commonly used in practice: scheduled maintenance and condition-based monitoring maintenance. An existing discrete event system specification simulation model was used to test and study the benefits of the proposed algorithm. The computational study demonstrates the importance of avoiding over-simplistic assumptions when making maintenance decisions for wind farms. For instance, most literature assumes maintenance lead times are constant. The computational results show that allowing lead times to be adjusted in an online fashion improves the performance of wind farm operations in terms of the number of turbine failures, availability capacity, and power generation.

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ONLINE SCHEDULING OF DYNAMIC TREES

Parallel Processing Letters ◽

10.1142/s0129626495000564 ◽

1995 ◽

Vol 05 (04) ◽

pp. 635-646 ◽

Cited By ~ 9

Author(s):

MICHAEL A. PALIS ◽

JING-CHIOU LIOU ◽

SANGUTHEVAR RAJASEKARAN ◽

SUNIL SHENDE ◽

DAVID S.L. WEI

Keyword(s):

Lower Bound ◽

Competitive Ratio ◽

Scheduling Algorithm ◽

Optimal Schedule ◽

Online Scheduling ◽

The Other ◽

Static Case ◽

Scheduling Problem ◽

Dynamic Trees ◽

Dynamic Tree

The scheduling problem for dynamic tree-structured task graphs is studied and is shown to be inherently more difficult than the static case. It is shown that any online scheduling algorithm, deterministic or randomized, has competitive ratio Ω((1/g)/ log d(1/g)) for trees with granularity g and degree at most d. On the other hand, it is known that static trees with arbitrary granularity can be scheduled to within twice the optimal schedule. It is also shown that the lower bound is tight: there is a deterministic online tree scheduling algorithm that has competitive ratio O((1/g)/ log d(1/g)). Thus, randomization does not help.

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Multi-Agent Interactions for a Network Management System (Tele-MACS Approach)

Software Agents for Future Communication Systems ◽

10.1007/978-3-642-58418-3_6 ◽

1999 ◽

pp. 160-182 ◽

Cited By ~ 1

Author(s):

Alex L. G. Hayzelden ◽

John Bigham ◽

Zhiyuan Luo

Keyword(s):

Network Management ◽

Management System ◽

Network Management System ◽

Agent Interactions ◽

Multi Agent

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A Multi-Agent System for Optimal Supply Chain Management

Architectural Design of Multi-Agent Systems ◽

10.4018/978-1-59904-108-7.ch015 ◽

2011 ◽

pp. 281-304

Author(s):

Hyung Rim Choi ◽

Hyun Soo Kim

Keyword(s):

Supply Chain ◽

Supply Chain Management ◽

Environmental Changes ◽

Scheduling Algorithm ◽

Dynamic Environment ◽

Multi Agent System ◽

Production Environment ◽

Agent System ◽

Chain Management ◽

Multi Agent

Supply chain management recently has been developing into a dynamic environment that has to accept the changes in the formation of the supply chain. In other words, the supply chain is not static but varies dynamically according to the environmental changes. Therefore, under this dynamic supply chain environment, the priority is given not to the management of the existing supply chain but to the selection of new suppliers and outsourcing companies in order to organize an optimal supply chain. The objective of this research is to develop a multi-agent system that enables the effective formation and management of an optimal supply chain. The multi agent system for optimal supply chain management developed in this research is a multi agent system based on the scheduling algorithm, a cooperative scheduling methodology, which enables the formation of an optimal supply chain and its management. By means of active communications among internal agents, a multi-agent system for optimal supply chain management makes it possible to quickly respond to the production environment changes such as the machine failure or outage of outsourcing companies and the delivery delay of suppliers. This research has tried to suggest a new direction and new approach to the optimal supply chain management by means of a multi-agent system in dynamic supply chain environment

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