A robust model predictive control approach for post-disaster relief distribution. (September 2019)

• Record Type: Journal Article
• Authors: Liu, Yajie; Lei, Hongtao; Wu, Zhiyong; Zhang, Dezhi
• Is Part Of: Computers & industrial engineering. Volume 135(2019)
• Page Start: 1253
• Page End: 1270
• Publication Date: 2019-09
• Subjects: Relief distribution -- Robust optimization -- Model predictive control -- Plan adjustment -- Disaster response; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.09.005
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A post-disaster relief distribution model for commodities and injured people is proposed. A robust optimization approach is adopted to cope with uncertainties in demand and supply. A model predictive control approach is utilized to adjust existing plan accordance with updated information. A numerical example is utilized to investigate the application of the proposed model and approach. 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.