OPTIMAL HIERARCHICAL STOCHASTIC PRODUCTION PLANNING AND CONTROL
最优递阶随机生产计划与控制

The paper explores the problem of optimal hierarchical stochastic production planning and control in agile manufacturing workshops (AMW). A stochastic nonlinear programming model of production with delay interaction (which is a static optimization model to solve the dynamic optimization problem) is built up and transformed into a deterministic nonlinear programming model and further into a linear programming model by adding constraints. Then, a Karmarkar's algorithm and an interaction/prediction algorithm are used to solve the model, and the corresponding programs have been written. Through hierarchical stochastic production planning examples, the Karmarkar's algorithm, interaction/prediction algorithm and linear programming method in Matlab are compared with, thus showing that the proposed approaches are very suitable for optimally decomposing AMW's random product demand plans into short term stochastic plans to be executed by FMS in AMW, especially for the case where workpieces are transferred between FMS through a shop storage with a delay of a production period.