Total Performance Analysis of a Downsized Manufacturing System

2008 ◽  
pp. 61-64
Author(s):  
Nozomu Mishima ◽  
Shinsuke Kondou ◽  
Keijiro Masui
Keyword(s):  
Performance Analysis ◽  
Manufacturing System ◽  
Total Performance

1106 A Study on Manufacturing Process Design Using Total Performance Analysis Method

2007 ◽  
Vol 2007.17 (0) ◽  
pp. 25-26
Author(s):  
Nozomu Mishima ◽  
Shinsuke Kondoh ◽  
Keijiro Masui ◽  
Tsuneo Kurita ◽  
Mitsutaka Mastumoto
Keyword(s):  
Performance Analysis ◽  
Process Design ◽  
Manufacturing Process ◽  
Analysis Method ◽  
Total Performance

scholarly journals Performance Analysis of a Flexible Manufacturing System (FMS)

Procedia CIRP ◽  
2017 ◽  
Vol 63 ◽  
pp. 424-429 ◽  
Author(s):  
Kashif Mahmood ◽  
Tatjana Karaulova ◽  
Tauno Otto ◽  
Eduard Shevtshenko
Keyword(s):  
Performance Analysis ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System

Simulation and Performance Analysis of a FMS/CIM Using Stochastic Timed Petri Nets

2013 ◽  
Vol 837 ◽  
pp. 322-327
Author(s):  
Daniela Coman ◽  
Adela Ionescu
Keyword(s):  
Performance Analysis ◽  
Petri Nets ◽  
Petri Net ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Utilization Rate ◽  
Deadlock Detection ◽  
Timed Petri Nets ◽  
Graphic Construction ◽  
And Performance

This paper focuses on the modelling, simulation and the performance analysis of a flexible manufacturing system using stochastic timed Petri nets so as to evaluate various performance parameters such as utilization rate of machines, deadlock detection, cycle time, and throughput rate of system in order to obtain the optimum productivity. The simulation of the manufacturing system using Petri nets provides the possibility to view the manufacturing process in time. Petri net model is implemented in Petri Net Toolbox under MATLAB environment. It is achieved the graphic construction of the net. Then, transporting it into a specific mathematical formalism it is made, so that the fulfiled structure to be fully retrieved and used to bring out the internal dynamics of the model. It is validated in this way the net topology, the evolution of (their dynamics), as well as the structural and behavioral properties (corresponding to checking if resources usage is stable and the model have no deadlocks). Some global performance indicators are determined in order to evaluate the performance of the proposed manufacturing system.

Performance Analysis of a Flexible Manufacturing System

2009 ◽  
Vol 10 (3) ◽  
pp. 23-34 ◽  
Author(s):  
S. Wadhwa ◽  
Yves Ducq ◽  
Mohammed Ali ◽  
Anuj Prakash
Keyword(s):  
Performance Analysis ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System

Modified Krill Herd Swarm Optimization (MKHSO) Based Optimal Neural Network Model for Analysis Multi-Performance Parameters in Manufacturing System

2018 ◽  
Vol 17 (02) ◽  
pp. 197-212
Author(s):  
R. Prasanna Lakshmi ◽  
P. Nelson Raja
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Preventive Maintenance ◽  
Manufacturing Industry ◽  
Manufacturing System ◽  
Mean Square ◽  
Operational Cost ◽  
Performance Parameters ◽  
Swarm Optimization ◽  
Krill Herd

Develop a multi-target exhibit by considering the workstation reliability for preventive maintenance perspective, the general availability of the framework for production purposes, and total operational expenses for both preventive support and production arranging decisions. Despite that, the greater parts of the reviews in upkeep optimization do not consider the creation necessities experienced eventually. In this paper, hybrid inspired optimization model for the performance analysis in the manufacturing industry is utilized. This forecast investigation neural Network considered for weight streamlining procedure alongside parameters, for example, Total Operational Cost (TOC), availability and reliability of assembling framework. Weight examination krill and swarm intelligence are used to limit Mean Square Error (MSE) for all parameters. All the perfect outcomes show the way that the refined slip-up qualities between the output of the trial values and the foreseen qualities are solidly proportionate to zero in the arranged framework. From the results, the proposed Modified Krill herd Swarm Optimization (MKHSO) based perfect neural framework exhibits a precision of 98.23%, which diverges from the existing methodology.

Performance analysis and measurement of reconfigurable manufacturing systems

2014 ◽  
Vol 25 (7) ◽  
pp. 934-957 ◽  
Author(s):  
Ibrahim H. Garbie
Keyword(s):  
Performance Analysis ◽  
Performance Measurement ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Relative Weight ◽  
Reconfigurable Manufacturing Systems ◽  
Reconfigurable Manufacturing ◽  
Market Demand ◽  
System Productivity ◽  
Content Type

Purpose – The purpose of this paper is to propose a new performance analysis and measurement regarding reconfigurable manufacturing systems (RMS) taken into consideration new circumstances which include changes in the market demand, changes in a product design, and/or introduction of a new product. As the reconfiguration process is applied to a manufacturing system to improve the system's performance due to new circumstances, the RMS process has potential quantitative and qualitative measures. Design/methodology/approach – The manufacturing system has a great impact on the performance measurement and the selection of the objectives to measure the performance is very important. These objectives include the critical requirements for a RMS and they are as follows: product cost, manufacturing response, system productivity, people behavior, inventory, and quality of the finished products. Because each criterion measure in a RMS is a potential source of evaluation, it should have a relative weight with respect to the other measures. First, each criterion will be measured individually. Second, these measures need to be evaluated through an aggregate quantitative metric because there is a lack of analytical techniques to analyze and evaluate both qualitative and quantitative measures. Findings – Performance evaluation of a RMS from one circumstance to another is highly desired by using the new quantitative metric regarding updating (upgrading) the system for the next period based on the previous one. The results show that the applicable of using this new technique in evaluating the RMS. The results also support the new quantitative metric. Originality/value – The suggestion of a new aggregate performance measurement metric including the all potential objectives is highly considered. This paper provides an insight into each objective individually to measure it. It is also used from 0 to 1 as range of measure to evaluate the potential and aggregate metrics toward next reconfiguration with respect to the existing one.

scholarly journals Performance Analysis of Complex Manufacturing System using Petri Nets Modeling Method

2021 ◽  
Vol 1950 (1) ◽  
pp. 012061
Author(s):  
Amit Kumar ◽  
Vinod Kumar ◽  
Vikas Modgil ◽  
Ajay Kumar ◽  
Anita Sharma
Keyword(s):  
Performance Analysis ◽  
Petri Nets ◽  
Manufacturing System ◽  
Modeling Method
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