The Selection of the Best Control Rule for a Multiple-Load AGV System Using Simulation and Fuzzy MADM in a Flexible Manufacturing System
Pick up-dispatching problem together with delivery-dispatching problem of a multiple-load automated guided vehicle (AGV) system have been studied. By mixing different pick up-dispatching rules, several control strategies (alternatives) have been generated and the best control strategy has been deter...
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| Format: | Article |
| Language: | English |
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Wiley
2010-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2010/821701 |
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| author | Parham Azimi Hasan Haleh Mehran Alidoost |
| author_facet | Parham Azimi Hasan Haleh Mehran Alidoost |
| author_sort | Parham Azimi |
| collection | DOAJ |
| description | Pick up-dispatching problem together with delivery-dispatching problem of a multiple-load automated guided vehicle (AGV) system have been studied. By mixing different pick up-dispatching rules, several control strategies (alternatives) have been generated and the best control strategy has been determined considering some important criteria such as System Throughput (ST), Mean Flow Time of Parts (MFTP), Mean Tardiness of Parts (MFTP), AGV Idle Time (AGVIT), AGV Travel Full (AGVTF), AGV Travel Empty (AGVTE), AGV Load Time (AGVLT), AGV Unload Time (AGVUT), Mean Queue Length (MQL) and Mean Queue Waiting (MQW). For ranking the control strategies, a new framework based on MADM methods including fuzzy MADM and TOPSIS method were developed. Then several simulation experiments which had been based on a flow path layout to find the results were conducted. Finally, by using TOPSIS method, the control strategies were ranked. Furthermore, a similar approach was used for determining the optimal fleet size. The main contribution of this paper is developing a new approach combining the top managers' views in selecting the best control strategy for AGV systems while trying to optimize the fleet size at the mean time by combining MADM, MCDM and simulation methods. |
| format | Article |
| id | doaj-art-a265d7ed0a9e4b4d97ec1971ed3ebc88 |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-a265d7ed0a9e4b4d97ec1971ed3ebc882025-08-20T03:38:44ZengWileyModelling and Simulation in Engineering1687-55911687-56052010-01-01201010.1155/2010/821701821701The Selection of the Best Control Rule for a Multiple-Load AGV System Using Simulation and Fuzzy MADM in a Flexible Manufacturing SystemParham Azimi0Hasan Haleh1Mehran Alidoost2Islamic Azad University (Qazvin Branch), Department of Mechanical and Industrial Engineering, P.O. Box 341851416, Qazvin, IranIslamic Azad University (Qazvin Branch), Department of Mechanical and Industrial Engineering, P.O. Box 341851416, Qazvin, IranIslamic Azad University (Qazvin Branch), Department of Mechanical and Industrial Engineering, P.O. Box 341851416, Qazvin, IranPick up-dispatching problem together with delivery-dispatching problem of a multiple-load automated guided vehicle (AGV) system have been studied. By mixing different pick up-dispatching rules, several control strategies (alternatives) have been generated and the best control strategy has been determined considering some important criteria such as System Throughput (ST), Mean Flow Time of Parts (MFTP), Mean Tardiness of Parts (MFTP), AGV Idle Time (AGVIT), AGV Travel Full (AGVTF), AGV Travel Empty (AGVTE), AGV Load Time (AGVLT), AGV Unload Time (AGVUT), Mean Queue Length (MQL) and Mean Queue Waiting (MQW). For ranking the control strategies, a new framework based on MADM methods including fuzzy MADM and TOPSIS method were developed. Then several simulation experiments which had been based on a flow path layout to find the results were conducted. Finally, by using TOPSIS method, the control strategies were ranked. Furthermore, a similar approach was used for determining the optimal fleet size. The main contribution of this paper is developing a new approach combining the top managers' views in selecting the best control strategy for AGV systems while trying to optimize the fleet size at the mean time by combining MADM, MCDM and simulation methods.http://dx.doi.org/10.1155/2010/821701 |
| spellingShingle | Parham Azimi Hasan Haleh Mehran Alidoost The Selection of the Best Control Rule for a Multiple-Load AGV System Using Simulation and Fuzzy MADM in a Flexible Manufacturing System Modelling and Simulation in Engineering |
| title | The Selection of the Best Control Rule for a Multiple-Load AGV System Using Simulation and Fuzzy MADM in a Flexible Manufacturing System |
| title_full | The Selection of the Best Control Rule for a Multiple-Load AGV System Using Simulation and Fuzzy MADM in a Flexible Manufacturing System |
| title_fullStr | The Selection of the Best Control Rule for a Multiple-Load AGV System Using Simulation and Fuzzy MADM in a Flexible Manufacturing System |
| title_full_unstemmed | The Selection of the Best Control Rule for a Multiple-Load AGV System Using Simulation and Fuzzy MADM in a Flexible Manufacturing System |
| title_short | The Selection of the Best Control Rule for a Multiple-Load AGV System Using Simulation and Fuzzy MADM in a Flexible Manufacturing System |
| title_sort | selection of the best control rule for a multiple load agv system using simulation and fuzzy madm in a flexible manufacturing system |
| url | http://dx.doi.org/10.1155/2010/821701 |
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