Optimization Models for Production and Procurement Decisions Under Uncertainty
研究了制造商在面临多种产品随机需求时，如何通过随机规划和鲁棒优化模型来协调生产和采购决策，并通过数值实验比较了两种模型的效率与实践收益。
This paper studies an integrated optimization problem on production and procurement for a manufacturer who faces stochastic demands of multiple products made by multiple components. This paper first proposes a deterministic model for the problem when the demands of products are known. Then the model is extended to the case of uncertain demands. A stochastic programming model and a robust optimization model are proposed to handle the production and procurement decisions under uncertain demands. The stochastic programming model can cope with arbitrary probability distributions of products' random demands; while the robust optimization model applies to situations in which limited information about probability distributions is available. Some numerical experiments are performed to investigate the efficiency of the proposed models. We also compare the above two models and evaluate the potential benefits in practice.