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.
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.
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.
Drexl, A. (1991) Scheduling of project networks by job assignment. Management Science, 37 (12), 1590–1602.
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.
Hopfield, J. J. and Tank, D. W. (1985) ‘Neural’ computation of decisions in optimization problems. Biological Cybernetics, 52, 141–152.
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.
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.
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.
Talbot, F. (1982) Resource-constrained project scheduling with time-resource: the nonpreemptive tradeoffs case. Management Science, 28 (10), 1197–1210.
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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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DOI: https://doi.org/10.1007/BF00123913