A robust optimization approach to model supply and demand uncertainties in inventory systems

Robust optimization for lot-sizing problems under yield uncertainty

“…The authors propose a constraint sampling approximation to mitigate over conservative solutions. In a similar approach, Chu et al (2019) propose a robust model for the procurement perspective of a stationary inventory management problem under the budgeted uncertainty set. They restrict the maximum value of the production yield to its nominal value, and they show that the problem can be formulated as a nominal problem with modified deterministic demand in terms of the accumulated deviation of both the uncertain demand and uncertain yield.…”

Robust optimization approaches for purchase planning with supplier selection under lead time uncertainty

“…On the contrary, Solyalı, Cordeau, & Laporte (2016) consider fixed setup cost, and the authors show that the facility location formulation of the lot-sizing problem yields a more efficient robust formulation. Chu, Huang, & Thiele (2019) provide a reformulation for the case with demand and supply uncertainty. However, Chu et al (2019) study the case of supply quantity uncertainty with a single supplier, whereas we consider delivery date uncertainty with supplier selection.…”

“…Chu, Huang, & Thiele (2019) provide a reformulation for the case with demand and supply uncertainty. However, Chu et al (2019) study the case of supply quantity uncertainty with a single supplier, whereas we consider delivery date uncertainty with supplier selection. We refer the interested reader to ( Lu & Shen, 2020 ) for a recent review on the application of robust optimization to operation management problems.…”

Robust pricing for airlines with partial information

“…Robust optimization under limited information has also been extensively studied (Perakis and Sood 2006;Yang et al 2019;Chu et al 2019). For example, Yang et al (2019) examined the remanufacturing decision problem with partial random yield information.…”

“…Rather than requiring full yield information, this method only requires the support and mean of the proportional yield is required by the method, which reduces sampling cost and is more appropriate for real-world situations. Chu et al (2019) developed a robust optimization framework that considers both supply and demand uncertainty, applying the framework to a multi-period, single-station inventory problem and also extending its application to multi-echelon cases. The proposed robust policies lead to cost benefits by addressing concerns on both supply and demand uncertainty.…”