Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm

被引：0
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作者：
Jerald, J. [1 ]
Asokan, P. [2 ]
Saravanan, R. [3 ]
Rani, A. Delphin Carolina [4 ]
机构：
[1] School of Mechanical Engineering, SASTRA (Deemed University), Thanjavur 613402, India
[2] Department of Production Engineering, National Institute of Technology, Trichy 625015, India
[3] Department of Mechanical Engineering, JJ College of Engg. and Technology, Trichy 625009, India
[4] Department of Computer Science and Engg., PR Engg. College, Thanjavur 613403, India
来源：
International Journal of Advanced Manufacturing Technology | 2006年 / 29卷 / 5-6期
关键词：
Automated Guided Vehicles (AGVs) are among various advanced material handling techniques that are finding increasing applications today. They can be interfaced to various other production and storage equipment and controlled through an intelligent computer control system. Both the scheduling of operations on machine centers as well as the scheduling of AGVs are essential factors contributing to the efficiency of the overall flexible manufacturing system (FMS). An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs an integral part of the overall scheduling activity. In this paper; simultaneous scheduling of parts and AGVs is done for a particular type of FMS environment by using a non-traditional optimization technique called the adaptive genetic algorithm (AGA). The problem considered here is a large variety problem (16 machines and 43 parts) and combined objective function (minimizing penalty cost and minimizing machine idle time). If the parts and AGVs are properly scheduled; then the idle time of the machining center can be minimized; as such; their utilization can be maximized. Minimizing the penalty cost for not meeting the delivery date is also considered in this work. Two contradictory objectives are to be achieved simultaneously by scheduling parts and AGVs using the adaptive genetic algorithm. The results are compared to those obtained by conventional genetic algorithm. © 2006 Springer-Verlag London Limited;
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页码：584 / 589
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