Determining production capacity planning in the stochastic demand environment for the food processing manufacturers: A case study

This research studies production scheduling problems with stochastic customer demand for food processing factories to determine a multi-period production schedule that minimizes the total cost and achieves a predetermined customer service level. Based on the practical food processing conditions, this study constructs a mixed integer programming (MIP) mathematical model and applies chance constrained programming (CCP) to transform probabilistic constraints of customer demand into deterministic constraints of customer demand with the associated normal distributions. Using the numerical data, this research verifies the proposed methodology. For sensitivity analysis, the results show that increasing overtime hours decreases the total cost and enhancing customer service level increases the total cost. Furthermore, this study investigates the impacts of different forecasting and production schedules on out-of-stock costs, inventory costs, and both.