A comparative study of aggregate production planning strategies under conditions of uncertainty and cyclic product demands

Abstract

For circumstances of cyclic demand and a highly stochastic planning environment, six aggregate production planning strategies were computationally compared. The experimental conditions included three demand patterns, three planning horizons, and two capacity levels. Each method's performance was compared to an expected value with perfect information (EVwPI), which was determined with the assistance of an integrated modelling approach. The six methods were also statistically compared with each other. The results showed relatively small differences (3.5%) between the more effective methods and the EVwPI. Simple chase strategies also appeared to be almost as effective as strategies based on linear programming solutions.