Advanced SIMION Techniques: Boundary Matching and Genetic Optimization

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.

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Reducing execution time on genetic algorithm in real-world applications using fitness prediction: parameter optimization of SRM control

The 2003 Congress on Evolutionary Computation, 2003. CEC '03. ◽

10.1109/cec.2003.1299624 ◽

2004 ◽

Cited By ~ 2

Author(s):

A. Mutoh ◽

T. Nakamura ◽

S. Kato ◽

H. Itoh

Keyword(s):

Genetic Algorithm ◽

Parameter Optimization ◽

Real World ◽

Execution Time ◽

Real World Applications

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A Hybrid Genetic Algorithm for Pallet Loading in Real-World Applications

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.2719 ◽

2020 ◽

Vol 53 (2) ◽

pp. 10006-10010

Author(s):

Gabriele Ancora ◽

Gianluca Palli ◽

Claudio Melchiorri

Keyword(s):

Genetic Algorithm ◽

Real World ◽

Hybrid Genetic Algorithm ◽

Pallet Loading ◽

Real World Applications

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

Engineering/Technology Management ◽

10.1115/imece2005-82415 ◽

2005 ◽

Author(s):

Sk Ahad Ali ◽

Hamid Seifoddini ◽

Hong Sun

Keyword(s):

Genetic Algorithm ◽

Real World ◽

Production Scheduling ◽

Manufacturing Systems ◽

Lean Production ◽

Simulation Environment ◽

Real World Applications ◽

Key Factor ◽

Low Volume ◽

Dynamic Production

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.

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Special Section: Evolutionary Design

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060499135005 ◽

1999 ◽

Vol 13 (5) ◽

pp. 325-325

Author(s):

PETER BENTLEY

Keyword(s):

Genetic Algorithm ◽

Recent Work ◽

Real World ◽

Special Section ◽

Computation Time ◽

Evolutionary Design ◽

Michigan State University ◽

State University ◽

Design Problems ◽

Real World Applications

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.

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