Using simulation and neural networks to develop a scheduling advisor

ABSTRACT

The research using artificial intelligence and computer simulation introduces a new approach for solving the job shop-scheduling problem. The new approach is based on the development of a neural network-scheduling advisor, which is trained using optimal scheduling decisions. The data set, which is used to train the neural network, is obtained from simulation experiments with small-scale job shop scheduling problems. The paper formulates the problem and after a review of the current solution methods it describes the steps of a new methodology for developing the neural network-scheduling advisor and collecting the data required for its training. The paper concludes by mentioning the expected findings that can be used to evaluate the degree of success of the new methodology.

REFERENCES

• 1: Carvalho, M. Dougherty, M. Fowkers, A. Wardman, M. R. Forecasting Travel Demand: A comparison of logit and artificial neural network methods. Journal of Operational Research Society. 1998; 49717-722.
• 2: Curram, S. Representing intelligent decision making in discrete event simulation. PhD Thesis University of Warwick; 1997.
• 3: Flitman, A. Towards the application of artificial intelligence techniques for discrete event simulation. PhD Thesis University of Warwick; 1986.
• 4: Flitman, A.M. Hurrion, R. D. Linking discrete-event simulation models with expert systems. Journal of Operational Research Society. 1987; 38(8):723-733.
• 5: Gupta, A. Lam, M.S. Estimating Missing Values Using Neural Networks. Journal of Operational Research Society. 1996 Feb; 47(2):229-238.
• 6: Hurrion, R. The design use and required facilities of an interactive visual simulation language to explore production planning problems. PhD thesis University of London; 1976.
• 7: Hurrion, R. Visual interactive simulation system an aid to decision making. Omega. 1978a; 6.
• 8: Hurrion, R. An investigation of visual interactive simulation methods using the job-shop scheduling problem. Journal of operational Research Society. 1978b; 29.
• 9: Hurrion, R. An interactive visual simulation system for industrial management. European Journal of Operational Research. 1980; 31.
• 10: Hurrion, R. Visual interactive modeling. European Journal of Operational Research. 1986; 23.
• 11: Liang Moskowitz Yih. Integrating neural networks and semi-markov processes for automated knowledge acquisition an application to real time scheduling. Decision Science. 1992; 23.
• 12: F. Maire, Rule-extraction by backpropagation of polyhedra, Neural Networks, v.12 n.4-5, p.717-725, June 1999 [doi>10.1016/S0893-6080(99)00013-1]
• 13: Pierreval Ralambondrainy. A simulation learning technique for generating knowledge about manufacturing systems behavior. Journal Of Operational Research Society. 1990; 41.
• 14: Stewart Robinson , John S. Edwards , W. Yongfa, An expert systems approach to simulating the human decision maker, Proceedings of the 30th conference on Winter simulation, p.1541-1546, December 13-16, 1998, Washington, D.C., United States
• 15: M. L. Vaughn, Derivation of the multilayer perception weight constraints for direct network interpretation and knowledge discovery, Neural Networks, v.12 n.9, p.1259-1271, Nov. 1999 [doi>10.1016/S0893-6080(99)00062-3]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.5 Model Development
          Subjects: Modeling methodologies

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Connectionism and neural nets
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Scheduling

General Terms:
Algorithms, Design, Performance