This study attempts to solve a dynamic order promising problem, where customer requests arrive in a random fashion, and the producer processes customer orders on a batch basis. This decision process repeated for every predefined batching interval, and the current decision-making must take into account the previously committed orders. The problem is formulated as a mixed integer programming model with fuzzy constraints, which express the decision-maker’s subjective judgment regarding customer’s price tolerance. The proposed model embeds the advanced available-to-promise (AATP) concept to support accurate computation of profit and customer order promising. A genetic algorithm is developed to solve the problem. Experiments by computer simulations are carried out to demonstrate the proposed approach.
