The Usage of Artificial Neural Networks For Finite Capacity Planning

In this study finite scheduling and artificial neural networks are applied for finite capacity planning. Utilisation of artificial neural networks on solving finite scheduling problems is examined. Also a model is developed by using multi layer perceptron (MLP) networks and carried out to solve a real world problem in a job shop scheduling system, in an automotive firm.

[1]  Frans C. A. Groen,et al.  The Optimal Number of Learning Samples and Hidden Units in Function Approximation With a Feedforward , 1993 .

[2]  Daniel J. Fonseca,et al.  Artificial neural networks for job shop simulation , 2002, Adv. Eng. Informatics.

[3]  Martin A. Riedmiller,et al.  RPROP - A Fast Adaptive Learning Algorithm , 1992 .

[4]  Choon-Ling Sia,et al.  Predictive capacity planning: a proactive approach , 1997, Inf. Softw. Technol..

[5]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[6]  Ben Kröse,et al.  A method for finding the optimal number of learning samples and hidden units for function approximation with a feedforward network , 1993 .

[7]  Can Akkan,et al.  Finite-capacity scheduling-based planning for revenue-based capacity management , 1997, Eur. J. Oper. Res..

[8]  J. Nazuno Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[9]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[10]  Gautam Mitra,et al.  Computational solution of capacity planning models under uncertainty , 2000, Parallel Comput..

[11]  Patrick K. Simpson,et al.  Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations , 1990 .

[12]  Sanjay Jain,et al.  Expert Simulation For On-line Scheduling , 1989, 1989 Winter Simulation Conference Proceedings.

[13]  MoonChiung,et al.  Integrated process planning and scheduling with minimizing total tardiness in multi-plants supply chain , 2002 .

[14]  Felix T. S. Chan,et al.  A decision support system for production scheduling in an ion plating cell , 2006, Expert Syst. Appl..

[15]  Shengxiang Yang,et al.  A new adaptive neural network and heuristics hybrid approach for job-shop scheduling , 2001, Comput. Oper. Res..

[16]  Pupong Pongcharoen,et al.  The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of product structure , 2004, Eur. J. Oper. Res..

[17]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[18]  Ling Li,et al.  Using MLP networks to design a production scheduling system , 2003, Comput. Oper. Res..

[19]  Mitsuo Gen,et al.  A genetic algorithm with modified crossover operator and search area adaptation for the job-shop scheduling problem , 2005, Comput. Ind. Eng..

[20]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[21]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[22]  Raktim Sen Combining Infinite Capacity Scheduling and Finite Capacity Scheduling: An Experimental Investigation of an Alternative Scheduling Procedure , 2000 .

[23]  Jondarr Gibb Back propagation Family Album , 1996 .

[24]  Sun Hur,et al.  Integrated process planning and scheduling with minimizing total tardiness in multi-plants supply chain , 2002 .

[25]  Valerie Tardif,et al.  Diagnostic scheduling in finite-capacity production environments , 1997 .

[26]  K. D. Barber,et al.  Medium to short term finite capacity scheduling: A planning methodology for capacity constrained workshops , 1990 .

[27]  Amir Azaron,et al.  A hybrid method for solving stochastic job shop scheduling problems , 2005, Appl. Math. Comput..

[28]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[29]  Klaus Jansen,et al.  Parallel Machine Scheduling Problems with Controllable Processing Times , 2000, ICALP Satellite Workshops.

[30]  Shlomo Geva,et al.  A constructive method for multivariate function approximation by multilayer perceptrons , 1992, IEEE Trans. Neural Networks.