Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm |
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Authors: | J Jerald P Asokan R Saravanan A Delphin Carolina Rani |
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Affiliation: | (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. & Technology, Trichy, 625009, India;(4) Department of Computer Science & Engg., PR Engg. College, Thanjavur, 613403, India |
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Abstract: | 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. |
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Keywords: | Adaptive genetic algorithm Automatic guided vehicles Flexible manufacturing system Genetic algorithm and scheduling |
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