A multi-stage stochastic programming model for relief distribution considering the state of road network. (May 2019)

• Record Type: Journal Article
• Authors: Hu, Shaolong; Han, Chuanfeng; Dong, Zhijie Sasha; Meng, Lingpeng
• Is Part Of: Transportation research. Volume 123(2019)
• Page Start: 64
• Page End: 87
• Publication Date: 2019-05
• Subjects: Emergency logistics -- Transportation -- Uncertain and dynamic road capacity -- Multi-stage stochastic programming -- Progressive hedging algorithm; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2019.03.014
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: A multi-stage stochastic programming model is proposed for relief distribution. The state of road network and multiple types of vehicles are considered. The uncertain and dynamic network capacity is characterized by the scenario tree. A solution methodology based on progressive hedging algorithm is developed. Abstract: As an important aspect in disaster operations management, relief distribution has been challenged by lots of factors, such as unpredictable occurrence time, intensity and location of secondary disasters (e.g. aftershocks and landslides, which usually occur after an earthquake), and availability of vehicles. A multi-stage stochastic programming model is developed for disaster relief distribution with consideration of multiple types of vehicles, fluctuation of rental, and the state of road network. The state of road network is characterized using uncertain and dynamic road capacity. The scenario tree is employed to represent the uncertain and dynamic road capacity, and demonstrate the decision process of relief distribution. A progressive hedging algorithm (PHA) is proposed for solving the proposed model in large-scale size. Based on a real-world case of Yaan earthquake in China, numerical experiments are presented to study the applicability of the proposed model and demonstrate the effectiveness of the proposed PHA. Useful managerial insights are provided by conducting numerical analysis.