Machine Learning in Production Systems Design Using Genetic Algorithms
To create a solution for a specific problem in machine
learning, the solution is constructed from the data or by use a search
method. Genetic algorithms are a model of machine learning that can
be used to find nearest optimal solution. While the great advantage of
genetic algorithms is the fact that they find a solution through
evolution, this is also the biggest disadvantage. Evolution is inductive,
in nature life does not evolve towards a good solution but it evolves
away from bad circumstances. This can cause a species to evolve into
an evolutionary dead end. In order to reduce the effect of this
disadvantage we propose a new a learning tool (criteria) which can be
included into the genetic algorithms generations to compare the
previous population and the current population and then decide
whether is effective to continue with the previous population or the
current population, the proposed learning tool is called as Keeping
Efficient Population (KEP). We applied a GA based on KEP to the
production line layout problem, as a result KEP keep the evaluation
direction increases and stops any deviation in the evaluation.
[1] G. Tompkins and F. Azadivar, "Genetic algorithms in optimizing
Simulated Systems", In WSC -95. Proceeding of the 1995 Conference on
Winter Simulation, ACM, 1995, pp. 757-762.
[2] S. Forrest, "Genetic Algorithms", ACM Computing Surveys, Vol. 28 No.
1, 1996, pp. 77-83.
[3] D. Lawrence, Handbook of Genetic Algorithms, Van No strand Reinhold,
New York, 1991.
[4] D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine
Learning, Addison-Wesley, New York, 1989.
[5] D. Levine, A Parallel Genetic Algorithm for the Set Partitioning Problem,
Ph.D. Thesis ANL-94/23 The Argonne National Laboratory, 9700 South
Cass Avenue, Argonne, IL 60439, 1994.
[6] D. Whitley, "The GENITOR algorithm and selection pressure: Why
rank-based allocation of reproductive trials is best", In J. Shaffer, editor:
Proceedings of the Third International Conference on Genetic
Algorithms, San Mateo, 1989, pp. 116-121.
[7] J. A. Tompkins, J. A. White, Y. A. Bozer, E. H. Frazella and J. M.
Tanchoco, Facilities Planning, 2nd edition, Wiley, New York, 1996.
[8] G. Suresh and S. Sahu, "Multi objective Facility Layout Using Simulated
Annealing", International Journal of Production Economics, Vol. 32,
1993, pp. 39-54.
[9] D. G. Conway and M. A. Venkataramanan, "Genetic Search and Dynamic
Facility Layout Problem". Computers and Operations Research, Vol. 21
No. 8, 1994, pp. 955-960.
[10] A. D. Raoot and A. Rakshit, "Fuzzy Heuristic for the Quadratic
Assignment Formulation to the Facility Layout Problem". International
Journal of Production research, Vol. 32, No. 3, 1994, pp. 563-581.
[11] S. S. Heragu and A. S. Alfa, "Experimental Analysis of Simulated
Annealing Based Algorithm for the Layout problem", European Journal
of Operational Research, Vol. 57, No. 2, 1992, pp. 190-202.
[12] M. Solimanpur, P. Vrat and R. Shankar, "An Ant Algorithm for the Single
Row Layout Problem in Flexible Manufacturing System", Computers &
Operations Research, Vol. 32, 2005, pp. 583-598.
[13] K. R. Kumar and G. C. hadjinicola, "A Heuristic Procedure for the Single
Row Facilities Layout Problem", European Journal of Operational
Research, Vol. 87, 1995, pp. 65-73.
[14] M. Bragila, "Optimization of a Simulated Annealing Based Heuristic for
Single Row Machine Layout Problem by Genetic Algorithm",
International Transaction in Operational Research, Vol. 3, No.1, 1996,
pp. 37-49.
[15] G. C. Lee and Y. D. Kim, "Algorithms for Adjusting Shapes of
Departments in Block Layouts on the Grid-Based Plan", OMEGA, Vol. 28,
2000, pp. 111-122.
[16] T. Yang and B. Peters, "Flexible Machine Layout Design for Dynamic
and Uncertain Production Environments", European Journal of
Operational Research, Vol. 108, 1998, pp. 49-64.
[17] T. Yang, B. Petersand M. Tu, "Layout Design for Flexible Manufacturing
Systems Considering Single Loop Directional Flow Patterns", European
Journal of Operational Research, Vol. 146, 2005, pp. 440-455.
[18] S. Bock and K. Hoberg, "Detailed Layout Planning for Irregularly-Shaped
Machines with Transportation Path Design", European Journal of
Operational Research, to be published..
[19] Kim, J. G. and Kim, Y. D. (2000). Layout Planning for Facilities with
Fixed Shapes and Input and Output Points. International Journal of
Production research, 38: 4635-4653.
[1] G. Tompkins and F. Azadivar, "Genetic algorithms in optimizing
Simulated Systems", In WSC -95. Proceeding of the 1995 Conference on
Winter Simulation, ACM, 1995, pp. 757-762.
[2] S. Forrest, "Genetic Algorithms", ACM Computing Surveys, Vol. 28 No.
1, 1996, pp. 77-83.
[3] D. Lawrence, Handbook of Genetic Algorithms, Van No strand Reinhold,
New York, 1991.
[4] D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine
Learning, Addison-Wesley, New York, 1989.
[5] D. Levine, A Parallel Genetic Algorithm for the Set Partitioning Problem,
Ph.D. Thesis ANL-94/23 The Argonne National Laboratory, 9700 South
Cass Avenue, Argonne, IL 60439, 1994.
[6] D. Whitley, "The GENITOR algorithm and selection pressure: Why
rank-based allocation of reproductive trials is best", In J. Shaffer, editor:
Proceedings of the Third International Conference on Genetic
Algorithms, San Mateo, 1989, pp. 116-121.
[7] J. A. Tompkins, J. A. White, Y. A. Bozer, E. H. Frazella and J. M.
Tanchoco, Facilities Planning, 2nd edition, Wiley, New York, 1996.
[8] G. Suresh and S. Sahu, "Multi objective Facility Layout Using Simulated
Annealing", International Journal of Production Economics, Vol. 32,
1993, pp. 39-54.
[9] D. G. Conway and M. A. Venkataramanan, "Genetic Search and Dynamic
Facility Layout Problem". Computers and Operations Research, Vol. 21
No. 8, 1994, pp. 955-960.
[10] A. D. Raoot and A. Rakshit, "Fuzzy Heuristic for the Quadratic
Assignment Formulation to the Facility Layout Problem". International
Journal of Production research, Vol. 32, No. 3, 1994, pp. 563-581.
[11] S. S. Heragu and A. S. Alfa, "Experimental Analysis of Simulated
Annealing Based Algorithm for the Layout problem", European Journal
of Operational Research, Vol. 57, No. 2, 1992, pp. 190-202.
[12] M. Solimanpur, P. Vrat and R. Shankar, "An Ant Algorithm for the Single
Row Layout Problem in Flexible Manufacturing System", Computers &
Operations Research, Vol. 32, 2005, pp. 583-598.
[13] K. R. Kumar and G. C. hadjinicola, "A Heuristic Procedure for the Single
Row Facilities Layout Problem", European Journal of Operational
Research, Vol. 87, 1995, pp. 65-73.
[14] M. Bragila, "Optimization of a Simulated Annealing Based Heuristic for
Single Row Machine Layout Problem by Genetic Algorithm",
International Transaction in Operational Research, Vol. 3, No.1, 1996,
pp. 37-49.
[15] G. C. Lee and Y. D. Kim, "Algorithms for Adjusting Shapes of
Departments in Block Layouts on the Grid-Based Plan", OMEGA, Vol. 28,
2000, pp. 111-122.
[16] T. Yang and B. Peters, "Flexible Machine Layout Design for Dynamic
and Uncertain Production Environments", European Journal of
Operational Research, Vol. 108, 1998, pp. 49-64.
[17] T. Yang, B. Petersand M. Tu, "Layout Design for Flexible Manufacturing
Systems Considering Single Loop Directional Flow Patterns", European
Journal of Operational Research, Vol. 146, 2005, pp. 440-455.
[18] S. Bock and K. Hoberg, "Detailed Layout Planning for Irregularly-Shaped
Machines with Transportation Path Design", European Journal of
Operational Research, to be published..
[19] Kim, J. G. and Kim, Y. D. (2000). Layout Planning for Facilities with
Fixed Shapes and Input and Output Points. International Journal of
Production research, 38: 4635-4653.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:59849", author = "Abu Qudeiri Jaber and Yamamoto Hidehiko Rizauddin Ramli", title = "Machine Learning in Production Systems Design Using Genetic Algorithms", abstract = "To create a solution for a specific problem in machine
learning, the solution is constructed from the data or by use a search
method. Genetic algorithms are a model of machine learning that can
be used to find nearest optimal solution. While the great advantage of
genetic algorithms is the fact that they find a solution through
evolution, this is also the biggest disadvantage. Evolution is inductive,
in nature life does not evolve towards a good solution but it evolves
away from bad circumstances. This can cause a species to evolve into
an evolutionary dead end. In order to reduce the effect of this
disadvantage we propose a new a learning tool (criteria) which can be
included into the genetic algorithms generations to compare the
previous population and the current population and then decide
whether is effective to continue with the previous population or the
current population, the proposed learning tool is called as Keeping
Efficient Population (KEP). We applied a GA based on KEP to the
production line layout problem, as a result KEP keep the evaluation
direction increases and stops any deviation in the evaluation.", keywords = "Genetic algorithms, Layout problem, Machinelearning, Production system.", volume = "2", number = "6", pages = "795-8", }
