Scheduling of Machines and AGVs Simultaneously in FMS through Hybrid Teaching Learning Based Optimization Algorithm
Kanakavalli Prakash Babu1, Vommi Vijaya Babu2, Medikondu Nageswara Rao3
1Kanakavalli Prakash Babu*, Department of Mechanical V. R. Siddhartha Engineering, College Vijayawada, Visakhapatnam, Andhra Pradesh. India.
2Vommi Vijaya Babu, Department of Mechanical A.U. College of Engineering, (A), Andhra University, Visakhapatnam, Andhra Pradesh. India.
3Medikondu Nageswara Rao, Department of Mechanical KLEF University, Vaddeswaram , India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2048-2055 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3318129219/2019©BEIESP | DOI: 10.35940/ijeat.B3318.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The most complex problem in FMS is scheduling task, due to this complexity it has created interest among many researchers. Even though FMS scheduling problem was considered earlier, material handling systems like (AGVs) scheduling was not done effectively. As transportation times cannot be neglected in an FMS, a carefully managed and designed material handling system is important in achieving the required integration in flexible manufacturing environment. Hence there is a need for scheduling both the machines and material handling system simultaneously for the successful implementation of an FMS, which makes the scheduling of FMS more complex. Metaheuristic Algorithms are mostly received by the researchers, because of their capability to tackle more complex problems. Hybridization of the metaheuristics may further improve their performance. In the present work a new hybrid metaheuristic Teaching Learning based optimization(HTLBO) is proposed to solve simultaneous scheduling problems.
Keywords: AGVs, FMS, Operational Completion Time (makespan), Metaheuristic algorithms, , NP-hard problems