Reinforcement learning in a distributed market-based production control system

Csáji, Balázs Csanád and Monostori, László and Kádár, Botond Géza (2006) Reinforcement learning in a distributed market-based production control system. ADVANCED ENGINEERING INFORMATICS, 20 (3). pp. 279-288. ISSN 1474-0346 10.1016/j.aei.2006.01.001

[img] Text
Restricted to Registered users only

Download (531kB) | Request a copy


The paper presents an adaptive iterative distributed scheduling algorithm that operates in a market-based production control system. The manufacturing system is agentified, thus, every machine and job is associated with its own software agent. Each agent learns how to select presumably good schedules, by this way the size of the search space can be reduced. In order to get adaptive behavior and search space reduction, a triple-level learning mechanism is proposed. The top level of learning incorporates a simulated annealing algorithm, the middle (and the most important) level contains a reinforcement learning system, while the bottom level is done by a numerical function approximator, such as an artificial neural network. The paper suggests a cooperation technique for the agents, as well. It also analyzes the time and space complexity of the solution and presents some experimental results. (C) 2006 Elsevier Ltd. All rights reserved.

Item Type: Article
Subjects: Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions: Research Laboratory on Engineering & Management Intelligence
SWORD Depositor: MTMT Injector
Depositing User: MTMT Injector
Date Deposited: 19 Jan 2022 07:28
Last Modified: 19 Jan 2022 07:28

Update Item Update Item