Abstract: As the diversification of customer demand increases,the make-to-order(MTO) model,i.e.,adapting production scheme according to customers’ orders,has attracted increasingly more attention from industry.How to determine whether to accept incoming orders according to the limited production capacity and order status of the enterprise,which is crucial for the enterprise to improve profits.On the basis of the traditional order acceptance problems,this paper proposes a more complete model.Besides the traditional model elements(including delayed delivery cost,rejection cost,and production cost),we further consider the order inventory cost,customer priority and others.Moreover,we model the optimal order acceptance problem as a Markov decision process(MDP).In addition,because the classic MDP method relies on solving and estimating high-dimensional state value function,its computation complexity is high.Therefore,in order to reduce the complexity,this paper proves that the optimal strategy based on the state value function in the classical MDP problem can be defined and constructed by the value function based on the after-state equivalent,thus transforming the multi-dimensional control problem into a one-dimensional control problem.At the same time,in order to solve the continuous state space,this paper combines neural network to parameterize the after-state value function,and solves the problem of large state space.Finally,simulation experiments verify the applicability and superiority of the proposed order acceptance strategy model and algorithm.

Key words: Order acceptance, Reinforcement learning, Markov decision process, Neural network, After-state