Supplier selection decision making in disaster response. (May 2020)

• Record Type: Journal Article
• Authors: Olanrewaju, Oluwasegun Gabriel; Dong, Zhijie Sasha; Hu, Shaolong
• Is Part Of: Computers & industrial engineering. Volume 143(2020)
• Publication Date: 2020-05
• Subjects: Disaster response -- Humanitarian logistics -- Supplier selection -- Procurement -- Multi-stage stochastic programming; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106412
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A supplier selection problem is explored in disaster response. Suppliers' characteristics and uncertain demand are considered. A multi-stage stochastic programming model is developed to capture the dynamic nature. Numerical experiments with sensitivity analysis are carried out. A case study based on real-world disasters is designed for managerial insights. Abstract: In the United States, natural disasters have seriously affected the country's economy as well as people. They have also been challenging the country's emergency response capacity. In order to reduce the damage caused by disasters, there is a need for the proper planning and efficient management of relief supplies in place before the onset of a disaster. This paper proposes to integrate the decision making of supplier selection into the preparedness stage in responding to disasters for timely distribution of relief supplies. Agreement terms including the commitment quantity of the relief agency, the reserve capacity of the suppliers, and the quantity discount rate are considered. Compared to the traditional two-stage stochastic programming approach, which is commonly used in the field of humanitarian logistics, a multi-stage stochastic programming model is presented because of the stochastic nature of the proposed problem, and the need to make sequential decisions over time. In this research, a real-world setting which considers disasters such as earthquakes, floods and hurricanes in the mainland United States is used as a case study. The sensitivities of the model for variation of parameters are also studied. This paper provides the relief agencies insights on how the agreement terms affect the supplier selection decision, and how the total expected costs of having an agreement in place and procuring relief items from the suppliers can be minimized.