An Approach for Production Planning Optimization Under Correlated Uncertain Demand

Production planning optimization is one of the most important issues for manufactures and scholars. Production is planned to meet the future demand. Under the uncertainty of demand, profit is maximized and opportunity loss is minimized. In real case, however, the demands of products are usually correlated. Hence, in this paper, a method is proposed for production planning optimization under the correlated and uncertainty demand. Correlated random numbers are introduced to Monte Carlo simulation to meet the real case. The production planning is multi-objective, thus genetic algorithm is employed. In order to search the optimal solutions effectively and efficiently, GENOCOP system is utilized to initialize population. The algorithm is tested on real data, and a wonderful performance is shown.