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• W2891675231 abstract "Emergency distribution is an important aspect of disaster response. However, when planning such activities, decision makers should consider not only uncertain and dynamic input data such as supply and demand, but also the real-time adjustment requirements of the existing distribution plans that account for the deviation between the predicted and actual (or observed) values of the input data. Consequently, we present a multi-commodity, multi-period distribution model that considers both relief commodities and injured people to minimize the total weighted unmet demand throughout the planning horizon. Furthermore, we propose a rolling horizon-based framework, based on the robust model predictive control (RMPC) approach, to obtain robust relief distribution plans and adjust them in accordance with updated real-time information. We then use a numerical example based on the Great Wenchuan Earthquake that occurred on May 12, 2008, in Sichuan Province, China, to investigate the application of our proposed model and framework, and we perform a detailed analysis of the influence of the settings of robust optimization parameters." @default.
• W2891675231 created "2018-09-27" @default.
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• W2891675231 date "2019-09-01" @default.
• W2891675231 modified "2023-10-17" @default.
• W2891675231 title "A robust model predictive control approach for post-disaster relief distribution" @default.
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• W2891675231 doi "https://doi.org/10.1016/j.cie.2018.09.005" @default.
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