In this paper, we study a class of manufacturing systems which consist of multiple plants and each of the plants has capability of producing multiple distinct products. The production lines of a certain plant may switch between producing different kinds of products in a time-sharing mode. We optimize the capacity configuration of such a system s production lines with the objective to maximize the overall profit in the capacity planning horizon. Uncertain demand is incorporated in the model to achieve a robust configuration solution. The optimization problem is formulated as a nonlinear polynomial stochastic programming problem, which is difficult to be efficiently solved due to demand uncertainties and large search space. We show the NP-hardness of the problem first, and then apply ordinal optimization(OO) method to search for good enough designs with high probability. At lower level, an mixed integer programming(MIP) solving tool is employed to evaluate the performance of a design under given demand profile.
Based on the two-dimensional(2D) system theory, an integrated predictive iterative learning control(2D-IPILC)strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control(ILC) and model predictive control(MPC) is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By minimizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type(Ptype) ILC despite the model error and disturbances.
