Simulation in Production Planning: An Overview with Emphasis on Recent Developments in Cycle Time Estimation

2010 ◽  
pp. 565-591 ◽  
Author(s):  
Bruce E. Ankenman ◽  
Jennifer M. Bekki ◽  
John Fowler ◽  
Gerald T. Mackulak ◽  
Barry L. Nelson ◽  
...  
Keyword(s):  
Production Planning ◽  
Cycle Time ◽  
Time Estimation ◽  
Recent Developments

Cycle time reduction by improved MRP-based production planning

2000 ◽  
Vol 38 (18) ◽  
pp. 4823-4841 ◽  
Author(s):  
Ashutosh Agrawal ◽  
Ioannis Minis ◽  
Rakesh Nagi
Keyword(s):  
Production Planning ◽  
Cycle Time ◽  
Cycle Time Reduction ◽  
Time Reduction

A FUZZY-NEURAL FLUCTUATION SMOOTHING RULE FOR SCHEDULING JOBS WITH VARIOUS PRIORITIES IN A SEMICONDUCTOR MANUFACTURING FACTORY

2009 ◽  
Vol 17 (03) ◽  
pp. 397-417 ◽  
Author(s):  
TOLY CHEN ◽  
YU-CHENG LIN
Keyword(s):  
Cycle Time ◽  
Semiconductor Manufacturing ◽  
Optimal Solution ◽  
Time Estimation ◽  
Pareto Optimal Solution ◽  
Experimental Results ◽  
Pareto Optimal ◽  
Cycle Times ◽  
Fuzzy Neural ◽  
Fluctuation Smoothing

A fuzzy-neural fluctuation smoothing rule is proposed in this study to improve the performance of scheduling jobs with various priorities in a semiconductor manufacturing factory. The fuzzy-neural fluctuation smoothing rule is modified from the well-known fluctuation smoothing rule by improving the accuracy of estimating the remaining cycle time of a job, which is done by applying Chen's fuzzy-neural approach with multiple buckets. To evaluate the effectiveness of the proposed methodology, production simulation is also applied in this study. According to experimental results, incorporating a more accurate remaining cycle time estimation mechanism did improve the scheduling performance especially in reducing the average cycle times. Besides, the fuzzy-neural fluctuation smoothing rule was also shown to be a Pareto optimal solution for scheduling jobs with various priorities in a semiconductor manufacturing factory.

Job remaining cycle time estimation with a post-classifying fuzzy-neural approach in a wafer fabrication plant: A simulation study

2009 ◽  
Vol 223 (8) ◽  
pp. 1021-1031 ◽  
Author(s):  
T Chen
Keyword(s):  
Test Data ◽  
Simulation Study ◽  
Cycle Time ◽  
Estimation Error ◽  
Back Propagation ◽  
Time Estimation ◽  
Wafer Fabrication ◽  
Fuzzy Neural ◽  
Fuzzy Back Propagation Network ◽  
Propagation Network

A post-classifying fuzzy-neural approach is proposed in this study for estimating the remaining cycle time of each job in a wafer fabrication plant, which has seldom been investigated in past studies but is a critical task for the wafer fabrication plant. In the methodology proposed, the fuzzy back-propagation network (FBPN) approach for job cycle time estimation is modified with the proportional adjustment approach to estimate the remaining cycle time instead. Besides, unlike existing cycle time estimation approaches, in the methodology proposed a job is not preclassified but rather post-classified after the estimation error has been generated. For this purpose, a back-propagation network is used as the post-classification algorithm. To evaluate the effectiveness of the methodology proposed, production simulation is used in this study to generate some test data. According to experimental results, the accuracy of estimating the remaining cycle time could be improved by up to 64 per cent with the proposed methodology.

A PCA-FBPN Approach for Job Cycle Time Estimation in a Wafer Fabrication Factory

2012 ◽  
Vol 2 (2) ◽  
pp. 50-67 ◽  
Author(s):  
Toly Chen
Keyword(s):  
Cycle Time ◽  
Back Propagation ◽  
Principal Component ◽  
Time Estimation ◽  
Cycle Times ◽  
Forecasting Performance ◽  
Semiconductor Manufacturer ◽  
Fuzzy Back Propagation Network ◽  
Propagation Network

Variable replacement is a well-known technique to improve the forecasting performance, but has not been applied to the job cycle time forecasting, which is a critical task to a semiconductor manufacturer. To this end, in this study, principal component analysis (PCA) is applied to enhance the forecasting performance of the fuzzy back propagation network (FBPN) approach. First, to replace the original variables, PCA is applied to form variables that are independent of each other, and become new inputs to the FBPN. Subsequently, a FBPN is constructed to estimate the cycle times of jobs. According to the results of a case study, the hybrid PCA-FBPN approach was more efficient, while achieving a satisfactory estimation performance.

Production Planning for Mass Customization in Additive Manufacturing: Build Orientation Determination, 2D Packing and Scheduling

2018 ◽  
Author(s):  
Yosep Oh ◽  
Chi Zhou ◽  
Sara Behdad
Keyword(s):  
Production Planning ◽  
Cycle Time ◽  
Projection Area ◽  
Efficient Production ◽  
Scheduling Problems ◽  
Build Orientation ◽  
Different Shapes ◽  
Orientation Determination ◽  
Ultimate Purpose

The efficient production planning of Additively Manufactured (AM) parts is a key point for industry-scale adoption of AM. This study develops an AM-based production plan for the case of manufacturing a significant number of parts with different shapes and sizes by multiple machines with the ultimate purpose of reducing the cycle time. The proposed AM-based production planning includes three main steps: (1) determination of build orientation; (2) 2D packing of parts within the limited workspace of AM machines; and (3) scheduling parts on multiple AM machines. For making decision about build orientation, two main policies are considered: (1) laying policy in which the focus is on reducing the height of parts; and (2) standing policy which aims at minimizing the projection area on the tray to reduce the number of jobs. A heuristic algorithm is suggested to solve 2D packing and scheduling problems. A numerical example is conducted to identify which policy is more preferred in terms of cycle time. As a result, the standing policy is more preferred than the laying policy as the number of parts increases. In the case of testing 3,000 parts, the cycle time of standing policy is about 6% shorter than laying policy.

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