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. 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, 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.
|Title of host publication
|Proceedings of the 2014 Winter Simulation Conference, WSC 2014
|Andreas Tolk, Saikou Y. Diallo, Ilya O. Ryzhov, Levent Yilmaz
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Jan 1 2015
|2014 Winter Simulation Conference, WSC 2014 - Savannah, United States
Duration: Dec 7 2014 → Dec 10 2014
|2014 Winter Simulation Conference, WSC 2014
|12/7/14 → 12/10/14
ASJC Scopus subject areas
- Modeling and Simulation
- Computer Science Applications