A Novel Scenario Generation Technique for a Stochastic Supply Chain Network

Abstract

In this work, we propose a multi-product, single-period supply chain network design model under uncertainty. The main contribution is a hybrid scenario generation approach that combines Latin Hypercube Sampling (LHS) with a scenario reduction method to efficiently generate representative scenarios for uncertain, correlated customer demands. The supply chain network includes plants, distribution centers, and customers, and the resulting mixed-integer linear programming (MILP) formulation seeks to minimize total network cost while incorporating a customer satisfaction constraint.

Keywords

Mixed-integer linear programming (MILP); Uncertainty; Stochastic programming; Latin Hypercube Sampling (LHS)

Publication Details

• Conference: 6th International Conference of Iranian Operations Research Society
  
• Dates: May 8–9, 2013
  
• Location/Organizer: Research Center of Operations Research

Citation

@inproceedings{KabirShafaeiKeyvanshokooh2013ScenarioGeneration,
  title        = {A Novel Scenario Generation Technique for a Stochastic Supply Chain Network},
  author       = {Kabir, Elnaz and Shafaei, Rasool and Keyvanshokooh, Esmaeil},
  booktitle    = {6th International Conference of Iranian Operations Research Society},
  year         = {2013},
  month        = may
}