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In recent years, the hybrid control framework has received attention from the research community. Variations of this control framework are available in the literature. In this paper, a hybrid intelligent agent-based scheduling and control system architecture is presented for an actual industrial warehouse order-picking problem, where goods are stored at multiple locations and the pick location of goods can be selected dynamically in near-real time. The presented architecture includes a higher level optimizer, a middle-level guide agent, and lower level agents. The need for a higher level optimizer and communication between higher and lower level controllers is demonstrated. A mathematical model and a genetic algorithm for the resource assignment problem are presented. Simulation results demonstrating efficiency of the new approach are also presented.