Distributional robustness and lateral transshipment for disaster relief logistics planning under demand ambiguity

Abstract

This paper considers facility location, inventory pre‐positioning and vehicle routing as strategic and operational decisions corresponding to preparedness and response phases in disaster relief logistics planning. For balancing surpluses and shortages, an effective lateral transshipment strategy is proposed to evenly distribute the relief resources between warehouses after the disaster occurs. To handle ambiguity in the probability distribution of demand, we develop a risk‐averse two‐stage distributionally robust optimization (DRO) model for the disaster relief logistics planning problem, which specifies the worst‐case mean‐conditional value‐at‐risk (CVaR) as a risk measure. For computationally tractability, we transform the robust counterpart into its equivalent linear mixed‐integer programming model under the discrepancy‐based ambiguity set centered at the nominal (empirical) distributions on the observed demand from the historical data. We verify the effectiveness of the proposed DRO model and the value of lateral transshipment strategy by an illustrative small‐scale example. The numerical results show that the proposed DRO model has advantage on avoiding over‐conservative solutions compared to the classic robust optimization model. We also illustrate the applicability of the proposed DRO model by a real‐world case study of hurricanes in the southeastern United States. The computational results demonstrate that the proposed DRO model has superior out‐of‐sample performance and can mitigate the adverse effects of Optimizers' Curse compared with the traditional stochastic programming model.

Subsidy strategy for reserving flexible capacity of emergency supply production

“…Yao et al (2018) examine the optimal pre-disaster inventory of certain relief commodities and compare the social efficiency between insourcing and outsourcing transportation services. Some studies focus on the problem of pre-positioning design and logistical planning (Duran et al, 2011;Elci and Noyan, 2018;Paul and Zhang, 2019;Wang et al, 2022;Liu et al, 2023). Duran et al (2011) develop a model to help managers determine a desired configuration for a stockpiled inventory network and provide a roadmap of how to achieve it.…”

“…Paul and Zhang (2019) develop a two-stage stochastic programming model to derive optimal locations of distribution points, medical supply levels, and transportation decisions. Wang et al (2022) use a risk-averse two-stage distributionally robust optimization model to address the strategic and operational decisions on facility location, inventory pre-positioning, and vehicle routing. Liu et al (2023) develop a two-phase optimization model to locate testing facilities and adjust capacity to satisfy the varying demand caused by pandemics.…”

“…Wang et al. (2022) use a risk‐averse two‐stage distributionally robust optimization model to address the strategic and operational decisions on facility location, inventory pre‐positioning, and vehicle routing. Liu et al.…”

A prepositioning model for prioritized demand points considering lateral transshipment

“…In prepositioning research, the primary goals often involve reducing the expenses associated with establishing relief centers and transportation (Galindo and Batta, 2013; Khayal et al , 2015; Lin et al , 2012), as well as decreasing unsatisfied demand and overall costs (Rawls and Turnquist, 2010, 2011, 2012; Moreno et al , 2018; Haghgoo et al , 2022; Wang et al , 2022a, 2022b). Other objectives include reducing commodity procurement costs and average response time while increasing responsiveness (Duran et al , 2011; Klibi et al , 2018), minimizing the total cost, time and distance traveled (Abazari et al , 2021), reducing maximum response time and unmet demand (Afshar and Haghani, 2012) and decreasing unsatisfied demand and total cost while increasing responsiveness (Rezaei-Malek and Tavakkoli-Moghaddam, 2014: Rezaei-Malek et al , 2016a, 2016b; Bozorgi-Amiri and Khorsi, 2016).…”

“…Wang et al (2021) designed a model that considers LT opportunities and facility location and allocation decisions under uncertainty and applied it to the Gulf Coast region of the USA, showing that LT is more cost-effective and flexible than DS. Wang et al (2022aWang et al ( , 2022b) formulated a distribution robust optimization model that integrates facility location, inventory prepositioning, vehicle routing and LT decisions for disaster relief logistics planning. They used a case study of storms in the southeastern US to demonstrate the practicality of their model and showed that it outperforms the traditional stochastic programming model.…”