| Autor: |
Liu, Yishun, Liu, Weiping, Lin, Shaochong, Yang, Chunhua, Huang, Keke, Shen, Zuo-Jun Max |
| Zdroj: |
IEEE Transactions on Automation Science and Engineering: A Publication of the IEEE Robotics and Automation Society; October 2024, Vol. 21 Issue: 4 p5852-5865, 14p |
| Abstrakt: |
Non-ferrous metals, as important basic raw materials, are the strategic supports for national economic development. For non-ferrous metal smelting enterprises, raw material procurement is the focal and most important session. Due to the fluctuation of production volumes and the future changes in raw-material prices, the procurement cost of raw materials is high and with a high risk of shortage. In this paper, we propose a multi-period rolling robust procurement model considering price and demand uncertainties. In particular, we design a data-driven method to construct the budget-based uncertainty sets and derive the robust counterpart of the robust procurement model. Comparative experiments on the real data with classic and advanced procurement policies show that our proposed solution approach achieves the lowest cost under the premise of continuous supply of raw materials. Interestingly, we observe that limited capital and warehouse capacity can effectively restrain unreasonable behavior and thus not to cause big losses in uncertain environments. In addition, a relatively long planning horizon can be counterproductive. These valuable and actionable insights can well guide practical decision-making. Note to Practitioners—For the raw material procurement of non-ferrous metal smelter, this article proposes a multi-period rolling robust procurement model considering price and demand uncertainties. Taking account of the dynamic characteristics of raw-material prices and the seasonal characteristics of raw-material demands, a data-driven method to construct budget-based uncertainty sets is designed. In particular, we derive the solvable robust counterpart of the robust procurement model. The proposed approach can reduce costs ensuring the continuous supply of raw materials. Some interesting and actionable managerial insights are obtained that can well guide practical decision-making, and the proposed data-driven approach is realizable. |
| Databáze: |
Supplemental Index |
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