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2270. Data-driven robust optimization of closed-loop supply chain design for household plastic waste
Invited abstract in session TB-23: Supply chain design in the circular economy, stream Circular Economy, Remanufacturing and Recycling .
Tuesday, 10:30-12:00Room: 82 (building: 116)
Authors (first author is the speaker)
| 1. | JunSeop Lee
|
| yonsei.uni | |
| 2. | Byung Do Chung
|
| Industrial Engineering, Yonsei University |
Abstract
As household plastic products have become essential parts of our lives, increasing the plastic recycling rate is imperative. However, the global average recycling rate for plastic is remarkably low at 9%, and the low profitability of uncertain waste management is one of the major reasons. In this study, we developed a data-driven robust optimization model of closed-loop supply chain design to address the uncertain volume of plastic waste. Subsequently, we proposed a theorem capable of reformulating the worst-case of the uncertain public sectors model as a tractable model. Based on this proposed theorem, we have constructed a data driven robust bilevel optimization program to express decentralized decision-making between a manufacturer and public sectors. Conducting sensitivity analysis using real annual operational data, we confirmed that if public sectors adopt a conservative solution regarding uncertain plastic waste volume, it can lead to a decrease in manufacturer profit and a reduction in the plastic recycling rate. Furthermore, we found that if the manufacturer is concerned about fluctuations in the purchasing price for plastic waste resulting from the conservative solution of public sectors, the manufacturer may attempt to purchase plastic waste from various regions.
Keywords
- Supply Chain Management
- Robust Optimization
- Game Theory
Status: accepted
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