1299. Reactive-proactive Rescheduling in Blood Supply Chain Management
Invited abstract in session MD-11: Scheduling in healthcare, stream OR in Healthcare (ORAHS).
Monday, 14:30-16:00Room: Clarendon SR 1.03
Authors (first author is the speaker)
| 1. | Daniel Santos
|
| CEGIST, Instituto Superior Técnico, Universidade de Lisboa | |
| 2. | Maria Meneses
|
| Engenharia e Gestão, Instituto Superior Técnico | |
| 3. | Ana Barbosa-Povoa
|
| Departamento de Engenharia e Gestão, Instituto Superior Técnico, Universidade de Lisboa |
Abstract
The timely and efficient availability of blood products is crucial in the Blood Supply Chain. However, the inherent uncertainty of blood donations and demand, coupled with various disturbances to planned activities, poses a significant challenge for effective planning and management of this network. To address these challenges, this research introduces a reactive-proactive rescheduling model for Blood Supply Chain management, called Blood-OPE. This model leverages real-time data and known disturbances to adjust the existing master plan by optimizing reactive and proactive measures. The main goal is to maintain high operational performance, that is, the service level provided to demand nodes, while minimizing deviations from the master plan, logistical challenges, in particular, nervousness caused by changing commitments made with third-party organizations, and waste. To demonstrate the model applicability, Blood-OPE is applied to the Portuguese Blood Supply Chain network and quantifies the trade-offs between different rescheduling flexibilities and cost efficiency. The main findings show that rescheduling enhances operational performance, reduces waste, and improves service reliability by meeting safety stock targets. Overall, Blood-OPE can effectively support the operational planning of Blood Supply Chain management networks while maintaining feasibility within the network's requirements, since it is general enough to be applied to most contexts.
Keywords
- Health Care
- Supply Chain Management
- Scheduling
Status: accepted
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