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Planning and scheduling are critical operational roles to any manufacturing business. In most companies in the chemical industry, these roles are handled by human planners and schedulers who have to make complex decisions under uncertainty to balance inventory costs, production costs, and customer service levels while respecting the constraints of the manufacturing facility or equipment that they are working with. Balancing these trade-offs is a difficult task, particularly given the uncertainty around customer demand, pricing, and equipment reliability. In this thesis, we approach scheduling problems using reinforcement learning to learn a policy for generating schedules that meet the stated business criteria.