Approximated Dynamic Programming for Production and Inventory Planning Problem in Cold Rolling Process of Steel Production

We study a multi-product production and inventory planning problem with uncertain demand in the cold rolling stage of steel production processes. The problem is to determine the production amount of each product in each planning period so that the sum of production, inventory holding, and backorder costs is minimized. We first formulate it into a Markov decision process (MDP) model, considering dynamic demand. Aiming at the proposed large-scale MDP model, we develop the improved Approximated Dynamic Programming (ADP) algorithms, which are composed of the reformulation and the approximation functions for the value function in MDP. Linear and two quadratic approximate functions are proposed to approximate the value function. Numerical experiments show the optimal gaps of the different approximation methods and illustrate the efficiency of the proposed ADP methods.