A decomposition algorithm for multi-item production planning with independent random demand

Production planning in a multiproduct setting where the demands for different items are independent random variables that are also featured with a dynamic behavior over the planning horizon is a challenging task. With a particular focus on maintenance facilities, this study proposes a multistage stochastic programming (MSP) model for operations planning under independent random demand of faulty components in the modular-structured devices (e.g., gas turbines) received for repair and overhaul services. A Lagrangian relaxation-based decomposition heuristic is also developed to efficiently solve the problem for real-size instances. This heuristic relies on decomposing the MSP model into submodels corresponding to component STs and coordinating them via a subgradient algorithm to obtain a high-quality feasible solution. Our numerical experiments conducted on a range of problem instances endorse the significant value of incorporating demand uncertainty and the effectiveness of the proposed solution methodology in overcoming computational complexity.

Automated summary

This study proposes a multistage stochastic programming model for operations planning under independent random demand. A decomposition heuristic is developed to efficiently solve the problem by decomposing the model into submodels and coordinating them via a subgradient algorithm to obtain a high-quality feasible solution.