A rolling horizon approach for production planning and condition-based maintenance under uncertain demand

Abstract

In reality, the forecast of uncertainties often becomes more accurate with the approaching of the forecasted period. This article proposes a rolling horizon approach to dynamically determine the production plan and the maintenance plan for a degradation system under uncertain environment. In each rolling horizon, demand forecasts are updated with new information from customers, and the degradation level of system is confirmed by inspection. By taking advantage of the updated uncertainties, at each decision point, the maintenance plan is determined by an advance-postpone balancing approach and the production plan is optimized by a heuristic algorithm in a two-stage stochastic model. Numerical results validate that the rolling horizon approach has great superiority over traditional stochastic programming approach in terms of real total cost and service level.