The "Multi Period Multi Product (MPMP) Production Planning Problem" is well known in the literature. The problem essentially consists of matching production levels of individual products to fluctuations of demand for a number of periods into the future, subject to constraints of capacity. Solution approaches to this problem can be categorised in two classes: Analytic methods (mathematical programming, numerical search etc.) and simulation. The choice of approach is based on issues such as the complexity and linearity of the system functions. In the literature analytic modelling and simulation have often been presented as mutually exclusive alternative methods for solving problems. Thus, the well known relative advantages of the alternative technique not chosen might be sacrificed. Furthermore, the results obtained may either lack accuracy and realism, or be too complex to be easily interpreted. It may therefore be preferable to use a hybrid approach that combines aspects of both of these alternatives. Such an integrated approach is presented here. This paper studies a hybrid algorithm combining mathematical programming and simulation models of a manufacturing system for the MPMP problem, and demonstrates how the analytic model working in co-operation with the simulation model can give better results than either method alone. The resulting production plan can be both mathematically optimal and practically feasible. (C) 1999 Elsevier Science B.V. All rights reserved.