Simulation modelling and analysis of part and tool flow control decisions in a flexible manufacturing system

Authors:
N. Suresh Kumar;R. Sridharan
Affiliations:
Mechanical Engineering Department, College of Engineering, Adoor 691551, Kerala, India;Mechanical Engineering Department, National Institute of Technology, Calicut 673601, Kerala, India
Venue:
Robotics and Computer-Integrated Manufacturing
Year:
2009

Citing 6
Cited 1

A branch and bound approach for machine load balancing in flexible manufacturing systems

Management Science
A synthesis of decision models for tool management in automated manufacturing

Management Science
Analysis of tool sharing in an FMS: a simulation study

Computers and Industrial Engineering
Scheduling in job shops with machine breakdowns: an experimental study

Computers and Industrial Engineering
Simulation Modeling and Analysis

Simulation Modeling and Analysis
Operation sequence and tool selection in flexible manufacturing systems under dynamic tool allocation

Computers and Industrial Engineering

Recency-based storage assignment and warehouse configuration for recurrent demands

Computers and Industrial Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents the details of a simulation study carried out for analyzing the impact of scheduling rules that control part launching and tool request selection decisions of a flexible manufacturing system (FMS) operating under tool movement along with part movement policy. Two different scenarios have been investigated with respect to the operation of FMS. In scenario 1, the facilities such as machines, tool transporter and part transporter are assumed to be continuously available without breakdowns, whereas in scenario 2, these facilities are prone to failures. For each of these scenarios, a discrete-event simulation model is developed for the purpose of experimentation. A number of scheduling rules are incorporated in the simulation models for the part launching and tool request selection decisions. The performance measures evaluated are mean flow time, mean tardiness, mean waiting time for tool and percentage of tardy parts. The results obtained through the simulation have been statistically analyzed. The best possible scheduling rule combinations for part launching and tool request selection have been identified for the chosen FMS.