This study investigates optimal inventory positioning in multi-stage supply chains by utilizing a mixed-integer nonlinear programming model that incorporates a scenario-based disruption delay, and the subsequent expediting to mitigate inventory shortages. The goal is to determine the amount and positioning of inventory in the supply chain to minimize the total inventory and expediting costs, subject to meeting customer service requirements. Discretization techniques are applied to linearize non-convex objective functions and reformulate the problem as an equivalent mixed-integer linear programming, which can be solved by the standard solvers. To demonstrate the model, numerical experiments are conducted to determine the optimal solution under different disruption scenarios. These experiments provide interesting insights regarding inventory decisions that can serve as guidelines for building resilience in the supply chain.

@article{oscm-2023-80,
  title={Multi-Echelon Inventory Optimization under Disruption Risk},
  year={2023},
  author={Shunichi Ohmori and Alex J Ruiz Torres and Farzad Mahmoodi},
  journal={Operations and Supply Chain Management: An International Journal},
  volume={16},
  number={2},
  pages={177--189},
  doi={10.31387/oscm0530382}
}

Shunichi Ohmori, Alex J Ruiz Torres, Farzad Mahmoodi (2023). Multi-Echelon Inventory Optimization under Disruption Risk. Operations and Supply Chain Management: An International Journal, 16(2), 177-189. https://doi.org/10.31387/oscm0530382