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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

K. Sato, K. Hamada, M. Yufu and T. Baba, "Hybridizing a Genetic Algorithm with Rule-Based Reasoning for Production Planning," in IEEE Intelligent Systems, vol. 10, no. , pp. 60-67, 1995.
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