Prepositioning disaster relief supplies using robust optimization

You

et al. 2021

Preprint

The key of the post-disaster humanitarian logistics (PD-HL) is to build a good facility location and capacity planning (FLCP) model for delivering relief supplies to affected areas in time. To fully exploit the historical PD data, this paper adopts the data-driven distributionally robust (DR) approach and proposes a novel multi-period FLCP model under the ∞-Wasserstein joint chance constraints (MFLCP-W). Specifically, we sequentially decide locations from a candidate set to build facilities with supply capacities, which are expanded if more economical, and use a finite number of historical demand samples in chance constraints to ensure a high probability of on-time delivery. To solve the MFLCP-W model, we equivalently reformulate it as a mixed integer second-order cone program and then solve it by designing an effective outer approximation algorithm with two tailored valid cuts. Finally, a case study under hurricane threats shows that MFLCP-W outperforms its counterparts in the terms of the cost and service quality, and that our algorithm converges significantly faster than the commercial solver CPLEX 12.8 with a better optimality gap.

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-period facility location and capacity planning under $\infty$-Wasserstein joint chance constraints in humanitarian logistics

You

et al. 2021

Preprint

“…• To the best of our knowledge and according to the recent survey of Sabbaghtorkan et al (2020), our paper is the first to addresses the distributional ambiguity of uncertain parameters (1)-( 6). In contrast to Velasquez et al (2020)'s recent robust optimization (RO) approach for disaster inventory prepositioning, we incorporate the uncertainty and distributional ambiguity of maximum order quantity available to procure post-disaster and arc capacities. In addition, we consider the uncertainty of demand and the usable fraction of prepositioned relief items post-disaster.…”

Section: Contributions Of the Papermentioning

confidence: 99%

“…In practice, it is unlikely that decision-makers can infer the post-disaster conditions or estimate the actual probability distributions of random parameters accurately, especially with limited data before the disaster and in the immediate aftermath (Velasquez et al, 2020;Zokaee et al, 2016). If the SP uses the true distribution, it will provide an excellent basis for the plan.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Optimization Models for Location and Inventory Prepositioning of Disaster Relief Supplies

Shehadeh¹,

Tucker²

2020

Preprint

In this paper, we study the problem of disaster relief inventory prepositioning under uncertainty. Specifically, we aim to determine where to open warehouses and how much relief item inventory to preposition in each, pre-disaster. During the post-disaster phase, prepositioned items are distributed to demand nodes, and additional items are procured and distributed as needed. There is uncertainty in the (1) disaster level, (2) locations of affected areas, (3) demand of relief items, (4) usable fraction of prepositioned items post-disaster, (5) procurement quantity, and (6) arc capacity.We propose and analyze two-stage distributionally robust optimization (DRO) and stochastic programming (SP) models, assuming unknown and known distributions of uncertainty, respectively.The first and second stages correspond to pre-and post-disaster phases, respectively. We propose a decomposition algorithm to solve the DRO model and a Monte Carlo Optimization procedure to solve the SP. To illustrate potential applications of our approach, we conduct extensive experiments using a hurricane season and an earthquake as case studies. Our results demonstrate the (1) superior post-disaster operational performance of the DRO decisions under various distributions compared to SP decisions, (2) trade-off between DRO pessimism and SP optimism, and (3) computational efficiency of our approaches.

“…In order to respond to the emergency response after a disaster, many emergency disaster relief systems have been designed. Serhan et al [12] and German A et al [13] achieve an emergency response by prepositioning disaster relief supplies. A certain amount of emergency supplies were stored in specific locations to respond to disasters.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicles Rescue System Design and Traffic Model Planning

2021

Unmanned Aerial Vehicles (UAV) are widely used in disaster relief and road exploration in recent years. This paper mainly studied the emergency response of UAVs after disasters. The UAV response system is mainly suitable for the distribution of necessities and road exploration after geological disasters and tsunamis in coastal areas. By analyzing the problem and making reasonable assumptions, the optimization model was established with the traffic planning theory, and MATLAB software was used to program and solve the problem. An optimal scheduling scheme was presented to solve these problems. The normalization method was used to select a highly capable UAV. Taking the minimum volume of idle space buffer material as the objective function and taking into account the constraints, such as payload of unmanned aerial vehicle, a single objective programming model was established. The results are as follows: Each International Standards Organization (ISO) cargo container has five UAVs B, one UAV C, one UAV F and one UAV H. It provides 188 days of relief requirements with ISO cargo containers’ space utilization of 71.4%. The research shows that the UAV response system has the functions of necessities distribution and road exploration after disasters, and can be used to deal with the emergency response after disasters in coastal areas, and has a wide range of applicability.