Open AccessScheduling of Machines and AGVs Simultaneously in FMS through Hybrid Teaching Learning Based Optimization Algorithm
Author(s) -
K. Prakash Babu,
Vommi Vijaya Babu,
M. Nageswara Rao
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b3318.129219
Subject(s) - metaheuristic , computer science , scheduling (production processes) , flexible manufacturing system , job shop scheduling , two level scheduling , fair share scheduling , dynamic priority scheduling , distributed computing , mathematical optimization , algorithm , embedded system , mathematics , computer network , routing (electronic design automation) , quality of service , schedule , operating system
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.