Abstract—Analysis and modeling of flexible manufacturing system (FMS) includes priority analysis of machining jobs and machining routing for efficient profit and production. Flexible manufacturing system (FMS) job Priority calculation and alternate routing problems become exceptionally complex when it comes to contain frequent variations in the part designs of incoming jobs. This paper is focused on priority analysis and routing of variety of incoming jobs into the system efficiently and maximizing system utilization and throughput of system where machines are equipped with different tools and tool magazines but multiple machines can be assigned to single operation. For the complete analysis of the proposed work, a cloud of four incoming jobs have been considered. The Jobs have been assigned the priority according to Slack per Remaining Operations. Usually the probability of incoming job priority is calculated based on three parameters based strategy. In this work an adaptive Neuro fuzzy inference system (ANFIS) is developed to calculate the priority of incoming jobs based on Slack per Remaining Operations (S/RO) parameter. The next part of this work deals with the development of fuzzy inference system (FIS) for the efficient machine job routing. The job of fuzzy inference system generated is to select the best alternative route with multi-criteria scheduling. There are three criteria for routing with 27 rules. With the help of the rules the best route is selected. Four horizontal CNC lathe machines have been utilized for this work. Therefore, in this paper, an ANFIS system is developed to generate best priority of incoming jobs and a FIS system is designed to generate best alternative routes.

Index Terms—Adaptive Neuro fuzzy inference system (ANFIS), Flexible manufacturing system (FMS), Incoming job priority, Slack per Remaining Operations (S/RO)

Cite: Rajkiran Bramhane, Arun Arora, and H Chandra, "Simulation of Flexible Manufacturing System Using Adaptive Neuro Fuzzy Hybrid Structure for Efficient Job Sequencing and Routing," International Journal of Mechanical Engineering and Robotics Research, Vol.3, No. 4, pp. 33-48, October 2014.