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This article describes a feasible approach to solve large-scale and real-world scheduling problems. Assuming that a problem can be divided into several subproblems, our approach applies different optimization methods to different classes of subproblems. This fundamental idea is realized in a scheduling problem solver that provides a variety of useful optimization methods, including rule-base systems and genetic algorithms. To show the solver's feasibility, we applied it to a scheduling problem that occurs in the steelmaking process. Finally, we discuss some future directions of the solver.
rule-based expert system, genetic algorithms, operations research, process scheduling, steelmaking process
Ken?ichi Sato, Kazuro Hamada, Masanao Yufu, Toshimitsu Baba, "Hybridizing a Genetic Algorithm with Rule-Based Reasoning for Production Planning", IEEE Intelligent Systems, vol. 10, no. , pp. 60-67, October 1995, doi:10.1109/64.464934
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