Due date assignment using feedback control with reinforcement learning

Good due date assignment for an order requires the calculation of a time buffer that will account for the uncertainties associated with the arrival of future orders in a dynamic environment. This paper presents a method that controls the size of this time buffer for a discrete manufacturing system. The applicability of the method to an unrestricted class of discrete manufacturing systems is preserved by the use of a feedback control paradigm, and control knowledge is acquired using reinforcement learning. The current trajectory of the state of the shop is considered so that due date performance is improved during transient conditions. Results of simulation experiments demonstrate the effectiveness of the approach.

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