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2336. Robust Spare Parts Inventory Management with Emergency Shipment
Invited abstract in session TD-34: Trends and Open Problems in Robust Optimization, stream Stochastic, Robust and Distributionally Robust Optimization.
Tuesday, 14:30-16:00Room: 43 (building: 303A)
Authors (first author is the speaker)
| 1. | Zhao Kang
|
| DEPARTMENT OF INDUSTRIAL ENGINEERING & INNOVATION SCIENCES, Eindhoven University of Technology | |
| 2. | Ahmadreza Marandi
|
| Industrial Engineering, Eindhoven University of Technology | |
| 3. | Rob Basten
|
| School of Industrial Engineering, Eindhoven University of Technology | |
| 4. | Ton de Kok
|
| School of IE, TUE | |
| 5. | Joan Stip
|
| ASML |
Abstract
We develop an Adaptive Robust Optimization (ARO) model for managing spare parts inventory in the early stages of a product's life cycle. This model takes into account demand uncertainty and emergency shipments. To obtain the exact solutions of the ARO model, we establish its equivalence to a deterministic counterpart. We prove that the deterministic counterpart can be decomposed into two subproblems. Based on this decomposition, we develop an efficient algorithm to obtain near-optimal solutions for thousands of stock-keeping units.
We conduct a case study at ASML, highlighting the practical implications of our ARO model for spare parts inventory in the semiconductor industry. Our results show that the ARO solution is more cost-effective than that of the conventional stochastic optimization model. Additionally, the adaptability of the ARO model to parameter variations, such as the emergency shipment time and cost, demonstrates its superiority in providing flexible and economically viable solutions.
Keywords
- Robust Optimization
- Inventory
Status: accepted
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