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Integrating intelligent job-scheduling into a real-world production-scheduling system

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The paper addresses the problem of scheduling production orders (jobs). First, an approach based on simulated annealing and Hopfield nets is described. Since performance was unsatisfactory for real-world applications, we changed the problem representation and tuned the scheduling method, dropping features of the Hopfield net and retaining simulated annealing. Both computing time and solution quality were significantly improved. The scheduling method was then integrated into a software system for short-term production planning and control (‘electronic leitstand’). The paper describes how real-world requirements are met, and how the scheduling method interacts with the leitstand's database and graphical representation of schedules.

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References

  • Aarts, E. H. L. and Korst, J. H. M. (1987) Boltzmann Machines and Their Applications. Lecture Notes in Computer Science 258. Springer-Verlag, Berlin, pp. 34–50.

    Google Scholar 

  • Adelsberger, H. H. and Kanet, J. J. (1991) The leitstand — a new tool for computer-integrated manufacturing. Production and Inventory Management Journal, 32(1), 43–48.

    Google Scholar 

  • Bilbro, G., Mann, R., Miller, T. K., Synder, W. E., Van den Bout, D. E. and White, M. (1989) Optimization by mean field annealing, in Advances in Neural Information Processing Systems I, Touretzky, D. S. (ed.), Morgan Kaufmann, San Mateo, CA, pp. 91–98.

    Google Scholar 

  • Drexl, A. (1991) Scheduling of project networks by job assignment. Management Science, 37 (12), 1590–1602.

    Google Scholar 

  • Hinton, G. E. and Sejnowski, T. J. (1986) Learning and relearning in Boltzmann machines, in Parallel Distributed Processing, Vol. 1, Rumelhart, D. E. and McClelland, J. L. (eds), MIT Press, Cambridge, MA, pp. 282–317.

    Google Scholar 

  • Hopfield, J. J. and Tank, D. W. (1985) ‘Neural’ computation of decisions in optimization problems. Biological Cybernetics, 52, 141–152.

    Google Scholar 

  • Kurbel, K. (1993) Production scheduling in a leitstand system using a neural-net approach, in Artificial Intelligence Technology — Applications and Management, Balagurusamy, E. and Sushila, B. (eds), Tata McGraw-Hill, New Delhi, pp. 297–305.

    Google Scholar 

  • Kurbel, K., Schneider, B. and Singh, K. (1995) Parallelization of hybrid simulated annealing and genetic algorithm for short-term production scheduling, in Proceedings of International Conference on Intelligence, Knowledge and Integration for Manufacturing, Zhong, B. (ed.), Southeast University Press, Nanjing, China, pp. 321–326.

    Google Scholar 

  • Mahfoud, W. S. and Goldberg, D. E. (1992) Parallel recombinative simulating annealing: a genetic algorithm, in Technical Report No. 92002, Department of General Engineering, University of Illinois.

  • Takefuji, Y. (1992) Neural Network Parallel Computing, Kluwer Academic, Boston, MA, pp. 37–50.

    Google Scholar 

  • Talbot, F. (1982) Resource-constrained project scheduling with time-resource: the nonpreemptive tradeoffs case. Management Science, 28 (10), 1197–1210.

    Google Scholar 

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Kurbel, K., Ruppel, A. Integrating intelligent job-scheduling into a real-world production-scheduling system. J Intell Manuf 7, 373–377 (1996). https://doi.org/10.1007/BF00123913

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