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Enhancing the performance of an agent-based manufacturing system through learning and forecasting
Authors:Weiming Shen  Francisco Maturana  Douglas H Norrie
Affiliation:(1) Division of Manufacturing Engineering, The University of Calgary, 2500 University Dr. NW, Calgary, AB, Canada, T2N 1N4;(2) Division of Manufacturing Engineering, The University of Calgary, 2500 University Dr. NW, Calgary, AB, Canada, T2N 1N4;(3) Division of Manufacturing Engineering, The University of Calgary, 2500 University Dr. NW, Calgary, AB, Canada, T2N 1N4
Abstract:Agent-based technology has been identified as an important approach for developing next generation manufacturing systems. One of the key techniques needed for implementing such advanced systems will be learning. This paper first discusses learning issues in agent-based manufacturing systems and reviews related approaches, then describes how to enhance the performance of an agent-based manufacturing system through ldquolearning from historyrdquo (based on distributed case-based learning and reasoning) and ldquolearning from the futurerdquo (through system forecasting simulation). ldquoLearning from historyrdquo is used to enhance coordination capabilities by minimizing communication and processing overheads. ldquoLearning from the futurerdquo is used to adjust promissory schedules through forecasting simulation, by taking into account the shop floor interactions, production and transportation time. Detailed learning and reasoning mechanisms are described and partial experimental results are presented.
Keywords:Multi-agent learning  multi-agent systems  intelligent manufacturing  distributed manufacturing systems  case-based learning and reasoning  forecasting
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