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Decentralized manufacturing management by a multi-agent optimal control method
A supply chain management system is defined as communications among suppliers, plants, distribution centres, retailers and demand stimulus. These systems are large scale and multi-agent, and therefore a decentralized control method must be used. Also, demand forecasting, as a challenge in production...
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Published in: | Transactions of the Institute of Measurement and Control 2014-12, Vol.36 (8), p.935-945 |
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description | A supply chain management system is defined as communications among suppliers, plants, distribution centres, retailers and demand stimulus. These systems are large scale and multi-agent, and therefore a decentralized control method must be used. Also, demand forecasting, as a challenge in production management, can be estimated by advanced methods or modelled using demand forecasting functions. In this paper, a new decentralized receding horizon control method is used to achieve customer contentment and low-cost inventory in a complete chain of supply, manufacture, assembly, warehouse, distribution and retail units. The main novelty of the method returns to the use of both the move suppression term and the look-ahead idea to increase robustness and smoothness in a supply chain containing assembly units. Also, a Kalman filter estimator is applied to estimate states and output variables. For this purpose, a suitable model and appropriate optimal control method are developed. Finally, the efficiency is indicated regarding simulation results. |
doi_str_mv | 10.1177/0142331214525799 |
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These systems are large scale and multi-agent, and therefore a decentralized control method must be used. Also, demand forecasting, as a challenge in production management, can be estimated by advanced methods or modelled using demand forecasting functions. In this paper, a new decentralized receding horizon control method is used to achieve customer contentment and low-cost inventory in a complete chain of supply, manufacture, assembly, warehouse, distribution and retail units. The main novelty of the method returns to the use of both the move suppression term and the look-ahead idea to increase robustness and smoothness in a supply chain containing assembly units. Also, a Kalman filter estimator is applied to estimate states and output variables. For this purpose, a suitable model and appropriate optimal control method are developed. Finally, the efficiency is indicated regarding simulation results.</description><identifier>ISSN: 0142-3312</identifier><identifier>EISSN: 1477-0369</identifier><identifier>DOI: 10.1177/0142331214525799</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Assembly ; Control algorithms ; Decentralized ; Demand ; Forecasting ; Manufacturing ; Mathematical models ; Multiagent systems ; Optimal control ; Optimization techniques ; Supply chains</subject><ispartof>Transactions of the Institute of Measurement and Control, 2014-12, Vol.36 (8), p.935-945</ispartof><rights>The Author(s) 2014</rights><rights>SAGE Publications © Dec 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c342t-2c7a3b8d6a9d68ae39efa48882fdbe456d1bf3204cd58c37b6bca131a57eb7d83</citedby><cites>FETCH-LOGICAL-c342t-2c7a3b8d6a9d68ae39efa48882fdbe456d1bf3204cd58c37b6bca131a57eb7d83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27906,27907,79114</link.rule.ids></links><search><creatorcontrib>Miranbeigi, M</creatorcontrib><creatorcontrib>Moshiri, B</creatorcontrib><creatorcontrib>Rahimi-Kian, A</creatorcontrib><title>Decentralized manufacturing management by a multi-agent optimal control method</title><title>Transactions of the Institute of Measurement and Control</title><description>A supply chain management system is defined as communications among suppliers, plants, distribution centres, retailers and demand stimulus. These systems are large scale and multi-agent, and therefore a decentralized control method must be used. Also, demand forecasting, as a challenge in production management, can be estimated by advanced methods or modelled using demand forecasting functions. In this paper, a new decentralized receding horizon control method is used to achieve customer contentment and low-cost inventory in a complete chain of supply, manufacture, assembly, warehouse, distribution and retail units. The main novelty of the method returns to the use of both the move suppression term and the look-ahead idea to increase robustness and smoothness in a supply chain containing assembly units. Also, a Kalman filter estimator is applied to estimate states and output variables. For this purpose, a suitable model and appropriate optimal control method are developed. Finally, the efficiency is indicated regarding simulation results.</description><subject>Assembly</subject><subject>Control algorithms</subject><subject>Decentralized</subject><subject>Demand</subject><subject>Forecasting</subject><subject>Manufacturing</subject><subject>Mathematical models</subject><subject>Multiagent systems</subject><subject>Optimal control</subject><subject>Optimization techniques</subject><subject>Supply chains</subject><issn>0142-3312</issn><issn>1477-0369</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp1kLtPwzAQxi0EEqWwM0ZiYQn4bWdE5SlVsMAc-ZWSyomLnQzlr8dRGFAlptPd9_tOdx8AlwjeICTELUQUE4IwogwzUVVHYIGoECUkvDoGi0kuJ_0UnKW0hRBSyukCvN474_ohKt9-O1t0qh8bZYYxtv1m6tTGdVkv9L5QRTf6oS3zKA_Cbmg75QsTsjv4onPDZ7Dn4KRRPrmL37oEH48P76vncv329LK6W5eGUDyU2AhFtLRcVZZL5UjlGkWllLix2lHGLdINwZAay6QhQnNtFCJIMeG0sJIswfW8dxfD1-jSUHdtMs571bswphpxySlFBLKMXh2g2zDGPl-XKYw5Z5DxTMGZMjGkFF1T72L-L-5rBOsp4Pow4GwpZ0vKifxZ-h__AymkeuY</recordid><startdate>20141201</startdate><enddate>20141201</enddate><creator>Miranbeigi, M</creator><creator>Moshiri, B</creator><creator>Rahimi-Kian, A</creator><general>SAGE Publications</general><general>Sage Publications Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SP</scope><scope>7U5</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>L7M</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>S0W</scope><scope>7TB</scope></search><sort><creationdate>20141201</creationdate><title>Decentralized manufacturing management by a multi-agent optimal control method</title><author>Miranbeigi, M ; 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subjects | Assembly Control algorithms Decentralized Demand Forecasting Manufacturing Mathematical models Multiagent systems Optimal control Optimization techniques Supply chains |
title | Decentralized manufacturing management by a multi-agent optimal control method |
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