Simulation-based optimization framework for multi-echelon inventory systems under uncertainty

Yunfei Chu, Fengqi You*, John M. Wassick, Anshul Agarwal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

69 Scopus citations


Inventory optimization is critical in supply chain management. The complexity of real-world multi-echelon inventory systems under uncertainties results in a challenging optimization problem, too complicated to solve by conventional mathematical programing methods. We propose a novel simulation-based optimization framework for optimizing distribution inventory systems where each facility is operated with the ( r, Q) inventory policy. The objective is to minimize the inventory cost while maintaining acceptable service levels quantified by the fill rates. The inventory system is modeled and simulated by an agent-based system, which returns the performance functions. The expectations of these functions are then estimated by the Monte-Carlo method. Then the optimization problem is solved by a cutting plane algorithm. As the black-box functions returned by the Monte-Carlo method contain noises, statistical hypothesis tests are conducted in the iteration. A local optimal solution is obtained if it passes the test on the optimality conditions. The framework is demonstrated by two case studies.

Original languageEnglish (US)
Pages (from-to)1-16
Number of pages16
JournalComputers and Chemical Engineering
StatePublished - Feb 2 2015


  • Agent-based modeling
  • Cutting plane algorithm
  • Simulation-based optimization
  • Statistical hypothesis tests
  • Supply chain management

ASJC Scopus subject areas

  • General Chemical Engineering
  • Computer Science Applications


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