Intelligent Dynamic Production Scheduling in High-Mix Low-Volume Manufacturing Systems Under Uncertain Environment

Author(s):  
Sk Ahad Ali ◽  
Hamid Seifoddini ◽  
Hong Sun

Today’s globalization market drives industries toward increased expectations on lean production. These expectations have put industries under pressure to become more agile under highly dynamic market and manufacturing conditions in the high-mix low-volume manufacturing systems. Dynamic production scheduling is a key factor in fulfilling the customer’s expectation. It becomes more critical due to dynamics and uncertainty in the manufacturing systems. This research addresses the uncertainty consideration of machine and labor for dynamic production scheduling. Fuzzy based system is used to capture the labor and machine uncertainty and implemented in simulation environment. Based on the variability from the simulation environment, a genetic algorithm based optimization tool is developed for dynamic production scheduling. The proposed method is validated with real-world applications.

2010 ◽  
Vol 09 (02) ◽  
pp. 101-116
Author(s):  
AHAD ALI ◽  
HAMID SEIFODDINI ◽  
JAY LEE

Today's globalization market drives industries toward increased expectations on lean production and quick response to the customers. These expectations have put industries under pressure to become more agile under highly dynamic market and uncertain manufacturing environment. An efficient material allocation becomes critical due to dynamics and uncertainty in the manufacturing systems. This research addresses the optimization needs for effective material allocations in high-mix low-volume manufacturing. Multi-constraint based genetic algorithm and intelligent simulation are used for efficient material allocation. The proposed effective material allocation is validated through real-world application in electronics manufacturing.


2015 ◽  
Vol 21 (S4) ◽  
pp. 218-223 ◽  
Author(s):  
D. Dowsett

AbstractTwo techniques for use with SIMION [1] are presented, boundary matching and genetic optimization. The first allows systems which were previously difficult or impossible to simulate in SIMION to be simulated with great accuracy. The second allows any system to be rapidly and robustly optimized using a parallelized genetic algorithm. Each method will be described along with examples of real world applications.


2020 ◽  
Vol 53 (2) ◽  
pp. 10006-10010
Author(s):  
Gabriele Ancora ◽  
Gianluca Palli ◽  
Claudio Melchiorri

2011 ◽  
Vol 63-64 ◽  
pp. 399-402
Author(s):  
Ling Hong Lai

To solve the dynamic and complex problem of production scheduling, depending on the introduction between multi-agent and hybrid genetic algorithm in mixed-model flow production scheduling, this paper proposed a mixed-model flow production scheduling method based on multi-agent and hybrid genetic algorithm. On the basis of this model, the mixed-model flow production scheduling procedure and strategy based on multi-agent and hybrid genetic algorithm were established. Finally, mixed-model flow production scheduling simulation system based on multi-agent and hybrid genetic algorithm was demonstrated and validated by QUEST software. It has shown the proposed method can improve the benefit of production scheduling, and provide a support for adapting to complex and dynamic production scheduling in mixed-model flow production.


Author(s):  
PETER BENTLEY

This issue of AIEDAM is the second in a series of three “mini” special issues on Evolutionary Design by computers. The papers continue the theme that began in Vol. 13, No. 3, 1999, of using Evolutionary Computation for design problems. The first paper by Eby, Averill, Punch and Goodman provides an excellent overview of the most recent work at Michigan State University on this subject. They describe their work on the optimization of flywheels by an injection island genetic algorithm, and show the importance of minimizing the computation time devoted to evaluation for such real-world applications.


2015 ◽  
Vol 26 (3) ◽  
pp. 225-234 ◽  
Author(s):  
Danilo Sipoli Sanches ◽  
Josimar da Silva Rocha ◽  
Marcelo Favoretto Castoldi ◽  
Orides Morandin ◽  
Edilson Reis Rodrigues Kato

2021 ◽  
Vol 289 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Carlos E. Andrade ◽  
Rodrigo F. Toso ◽  
José F. Gonçalves ◽  
Mauricio G.C. Resende

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