Relief Allocation Research Articles

Integrating Risk-Averse and Constrained Reinforcement Learning for Robust Decision-Making in High-Stakes Scenarios

This paper considers a risk-averse Markov decision process (MDP) with non-risk constraints as a dynamic optimization framework to ensure robustness against unfavorable outcomes in high-stakes sequential decision-making situations such as disaster response. In this regard, strong duality is proved while making no assumptions on the problem’s convexity. This is necessary for some real-world issues, e.g., in the case of deprivation costs in the context of disaster relief, where convexity cannot be ensured. Our theoretical results imply that the problem can be exactly solved in a dual domain where it becomes convex. Based on our duality results, an augmented Lagrangian-based constraint handling mechanism is also developed for risk-averse reinforcement learning algorithms. The mechanism is proved to be theoretically convergent. Finally, we have also empirically established the convergence of the mechanism using a multi-stage disaster response relief allocation problem while using a fixed negative reward scheme as a benchmark.

Modeling Multi-Objective Optimization with Updating Information on Humanitarian Response to Flood Disasters

Unpredictable natural disasters brought by extreme climate change compound difficulties and cause a variety of systemic risks. It is thus critical to provide possibilistic scheduling schemes that simultaneously involve emergency evacuation and relief allocation. But the existing literature seldom takes emergency evacuation and relief supplies as a joint consideration, nor do they explore the impact of an unpredictable flood disaster on the scheduling scheme. A multi-stage stochastic programming model with updating information is constructed in this study, which considers the uncertainty of supply and demand, road network, and multiple types of emergency reliefs and vehicles. In addition, a fuzzy algorithm based on the objective weighting of two-dimensional Euclidean distance is introduced, through moderating an effect analysis of the fuzzy number. Computational results show that humanitarian equity for allocating medical supplies in the fourth period under the medium and heavy flood is about 100%, which has the same as the value of daily and medical supplies within the first and third period in the heavy scenarios. Based on verifying the applicability and rationality of the model and method, the result also presents that the severity of the flood and the fairness of resources is not a simple cause-and-effect relationship, and the consideration of survivor is not the only factor for humanitarian rescue with multi-period. Specifically, paying more attention to a trade-off analysis between the survival probability, the timeliness, and the fairness of humanitarian service is essential. The work provides a reasonable scheme for updating information and responding to sudden natural disasters flexibly and efficiently.

Disastrous Discretion: Political Bias in Relief Allocation Varies Substantially with Disaster Severity

Abstract Allocation decisions are vulnerable to political influence, but it is unclear in which situations politicians use their discretionary power in a partisan manner. We analyze the allocation of presidential disaster declarations in the United States, exploiting the spatiotemporal randomness of all hurricane strikes from 1965-2018 along with changes in political alignment. We show that decisions are unbiased when disasters are either very strong or weak. Only after medium-intensity hurricanes do areas governed by presidents' co-partisans receive up to twice as many declarations. This hump-shaped political bias explains 8.3 percent of overall relief spending, totaling about USD 400 million per year.

ReliefChain: A blockchain leveraged post disaster relief allocation system over smartphone-based DTN.

One of the major concerns in any emergency relief operation is appropriate allocation of scarce emergency relief materials to the affected community. Due to several reasons ranging from lack of mechanism to accurately assess demand and utility of relief materials to malicious participation of some of the stakeholders, such allocation may become ad-hoc. Thus, it becomes imperative to have an unchallengeable and globally accessible record of relief requirement vis-à-vis allocation for efficient relief management. Emergency response organizations (e.g. UNICEF) have recommended the adoption of blockchain technology to create such immutable records. However, the usage of blockchain is restricted by the availability of end-to-end internet connection which may not be available in a post-disaster scenario. This paper proposes ReliefChain, a blockchain leveraged post disaster relief allocation system over delay tolerant network that works in such environments. We validate relief requirements to mitigate resource diversion, forecasting the exact demand and enumerating precise utilities of relief items. We design smart contracts for creating new transactions to upload relief requirements and allocations in the blockchain network. The proposed system executes these smart contracts to create an immutable and globally accessible record of relief requirement and allocation. Effectiveness of the proposed system is evaluated through extensive simulation in Ethereum platform. Results substantiate the efficiency of the system over a system using baseline methodologies, in terms of design parameters like shelter specific deficit and average resource deficit while not compromising the blockchain performance in terms of processing time and gas consumption even in presence of malicious forwarders.

A New Cooperative Recourse Strategy for Emergency Material Allocation in Uncertain Environments

Emergency material allocation is an important issue in the urgent handling of public health emergencies. This article models the relief allocation and transportation route planning as an uncertain capacitated arc routing problem, which is a classic combinatorial optimization problem that considers stochastic factors such as uncertain demand and travel time in the service. The stochastic of demand leads to the route failure that the vehicle cannot serve the tasks successfully unexpected. Most existing research uses the independent recourse strategy. That is, each vehicle takes a back-and-forth trip separately when its remaining capacity cannot meet the actual demand of the task. This leads to a considerable recourse cost. However, a few studies have considered vehicular cooperation to deal with route failure, which is beneficial for pooling the capacity of multiple vehicles. In this paper, we propose a new recourse strategy called OneFAll that lets one vehicle take charge of all the failed tasks. In this case, other vehicles can finish the service once they are full. We develop the genetic programming hyper-heuristic with the OneFAll recourse strategy for solving the uncertain capacitated arc routing problem. The experimental studies show that our proposed method outperforms the existing genetic programming hyper-heuristic with the independent recourse strategy to the uncertain capacitated arc routing problem for the ugdb and uval benchmark instances. Moreover, our strategy outperforms the recourse strategy that failed tasks are returned to the unassigned task set for any vehicle to complete. This reflects that there exists resource waste if all vehicles are involved to repair the failed routes.

Fuzzy-Control-Based Chance-Constrained Programming for Humanitarian Relief Allocation Problem

In this article, the issue of multicenter and single-area humanitarian relief allocation with uncertain travel time and imprecise transportation information is investigated. Expert human knowledge using fuzzy control is employed to select rescue paths, and the considered problem is formulated as a fuzzy chance-constrained model to ensure that the allocated goods can be delivered to the disaster area on time within a desired probability. A new method is presented to transform chance-constrained programming into a mixed-integer model utilizing triangle fuzzy numbers and the robust optimization problem. A practical example is used to test the validity of the theoretic results obtained.

Designing a Distribution Network and Developing a Relief Allocation Model for Refugee Camps

In this research, the cost of food and services are reduced in the refugee camps. Distribution-network and relief allocation model are used to minimize the cost. Three distribution-networks were developed from there cross-docking was the most priced efficient so that cross-docking is selected. In the relief allocation model, three steps were followed to develop a cost-friendly model. This allocation model was developed through TOPSIS. Vector normalization, AHP for criteria weights, calculation of the Euclidean distance, and identifying the relief percentages were measured to develop the Relief allocation model

Disaster relief logistics under demand-supply incongruence environment: A sequential approach

Several logistics problems involving disaster relief have attracted growing research interest in the recent decade. A commonality among these problems is that demand–supply incongruence is always observed in post-disaster logistics operations due to the limited relief supplies and the randomly increasing relief demands. In such situations, the decision maker fails to meet the total relief demands simultaneously. This study employs a novel decision-making framework, where the traditional disaster relief logistics actions (e.g., vehicles routing and relief allocation) are replaced by periodic, sequential actions involving demand point location and assignment. A sequential approach allows the decision maker, in the face of every demand-point, to decide whether to locate and assign it to relief suppliers immediately or later, which sequentially influences the decision in the next period. A dynamic optimization model is built and solved by using particle swarm optimization algorithm. The results of a case study indicate the advantages of the sequential approach.

Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand

Disasters such as fires, earthquakes, and floods cause severe casualties and enormous economic losses. One effective method to reduce these losses is to construct a disaster relief network to deliver disaster supplies as quickly as possible. This method requires solutions to the following problems. 1) Given the established distribution centers, which center(s) should be open after a disaster? 2) Given a set of vehicles, how should these vehicles be assigned to each open distribution center? 3) How can the vehicles be routed from the open distribution center(s) to demand points as efficiently as possible? 4) How many supplies can be delivered to each demand point on the condition that the relief allocation plan is made a priority before the actual demands are realized? This study proposes a model for risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand that solves the four problems simultaneously.The novel contribution of this study is its presentation of a new model that includes conditional value at risk with regret (CVaR-R)—defined as the expected regret of worst-case scenarios—as a risk measure that considers both the reliability and unreliability aspects of demand variability in the disaster relief facility location and vehicle routing problem. Two objectives are proposed: the CVaR-R of the waiting time and the CVaR-R of the system cost. Due to the nonlinear capacity constraints for vehicles and distribution centers, the proposed problem is formulated as a bi-objective mixed-integer nonlinear programming model and is solved with a hybrid genetic algorithm that integrates a genetic algorithm to determine the satisfactory solution for each demand scenario and a non-dominated sorting genetic algorithm II (NSGA-II) to obtain the non-dominated Pareto solution that considers all demand scenarios. Moreover, the Nash bargaining solution is introduced to capture the decision-maker’s interests of the two objectives. Numerical examples demonstrate the trade-off between the waiting time and system cost and the effects of various parameters, including the confidence level and distance parameter, on the solution. It is found that the Pareto solutions are distributed unevenly on the Pareto frontier due to the difference in the number of the distribution centers opened. The Pareto frontier and Nash bargaining solution change along with the confidence level and distance parameter, respectively.

Integration of super-pixel segmentation and deep-learning methods for evaluating earthquake-damaged buildings using single-phase remote sensing imagery

ABSTRACTRapid identification of post-earthquake collapsed buildings can be used to conduct immediate damage assessments (scope and extent), which could potentially be conducive to the formulation of emergency response strategies. Up to the present, the assessments of earthquake damage are mainly achieved through artificial field investigations, which are time-consuming and cannot meet the urgent requirements of quick-response emergency relief allocation. In this research study, an intelligent assessment method based on deep-learning, super-pixel segmentation, and mathematical morphology was proposed to evaluate the damage degrees of earthquake-damaged buildings. This method firstly utilized the Deeplab v2 neural network to obtain the initial damaged building areas. Then, the simple linear iterative cluster (SLIC) method was employed to segment the test images so as to accurately extract the area boundaries of the earthquake-damaged buildings. Next, the images subdivided by SLIC can be merged according to the initial damaged building areas identified by Deeplab v2 neural network. Finally, a mathematical morphological method was introduced to eliminate the background noise. Experimental results demonstrated that the proposed algorithm was superior to others in both convergent speed and accuracy. Besides, its parameter selection was flexible and easily realized which was of great significance to earthquake damage assessments and provided valuable guidance for the formulation of future emergency response plans after earthquake events.

Post-disaster multi-period road network repair: work scheduling and relief logistics optimization

We develop a multi-period bi-level programming model for the post-disaster road network repair work scheduling and relief logistics problem. A maximum relative satisfaction degree-based steady-state parallel genetic algorithm is designed to solve this model. In order to validate and test the effectiveness of the presented mathematical model and method, we use a network generator to create numerical examples with different scales and characteristics of road network. Our numerical analysis of the solutions shows that the proposed mathematical model and method can effectively assist the decision-makers to deal with the road network repair work scheduling and relief logistics optimization problem during the emergency response phase. This mathematical model and the approach being developed are applied to deal with the case of Wenchuan earthquake in China. The results show that the required CPU time is short enough such that it meets the time limitation in the emergency response phase, and the strategy of road network repair scheduling will allow repair of the damaged roads to be completed before the end of the planning time horizon by 14.93%. Furthermore, the strategy of relief logistics can provide an efficient relief allocation and transportation path.

Inventory slack routing application in emergency logistics and relief distributions.

Various natural and manmade disasters during last decades have highlighted the need of further improving on governmental preparedness to emergency events, and a relief supplies distribution problem named Inventory Slack Routing Problem (ISRP) has received increasing attentions. In an ISRP, inventory slack is defined as the duration between reliefs arriving time and estimated inventory stock-out time. Hence, a larger inventory slack could grant more responsive time in facing of various factors (e.g., traffic congestion) that may lead to delivery lateness. In this study, the relief distribution problem is formulated as an optimization model that maximize the minimum slack among all dispensing sites. To efficiently solve this problem, we propose a two-stage approach to tackle the vehicle routing and relief allocation sub-problems. By analyzing the inter-relations between these two sub-problems, a new objective function considering both delivery durations and dispensing rates of demand sites is applied in the first stage to design the vehicle routes. A hierarchical routing approach and a sweep approach are also proposed in this stage. Given the vehicle routing plan, the relief allocation could be easily solved in the second stage. Numerical experiment with a comparison of multi-vehicle Traveling Salesman Problem (TSP) has demonstrated the need of ISRP and the capability of the proposed solution approaches.

Multi-objective open location-routing model for relief distribution networks with split delivery and multi-mode transportation under uncertainty

In this study, the response phase of the management of natural disasters is investigated. One of the important issues in this phase is determining the distribution areas and timely distribution of relief to affected areas in which transportation routing is of a critical matter. In the event of disasters, especially flood and earthquake, terrestrial transportation is not that much easy due to the damage to many infrastructures. For this reason, we propose that delivering relief from the distribution areas to disaster stricken places should be done simultaneously by terrestrial as well as aerial transportation modes to increase route reliability and reduce travel time. In this study, for relief allocation after earthquake, we offer a mixed-integer nonlinear open location-routing model in uncertainty condition. This model includes several contradictory objectives and variety of factors such as travel time, total costs, and reliability. In order to solve this model, a hybrid solution by combining robust optimization and fuzzy multi-objective programming has been used. The performance and effectiveness of the offered model and solution approach has been investigated through a case study on the earthquake in East Azerbaijan, Iran. Our computational results show the solution we have offered for real problems has been effective.

A robust approach to the two-level humanitarian relief operation

During the initial disaster response phase, an important issue in humanitarian relief logistics is to tackle the complexity of decision-making that covers the appropriate relief allocation and the associated transportation plan. We propose a two-level modelling methodology to alleviate that challenge, where the upper-level model focuses on the strategic relief distribution policy among the recipients whereas the lower-level model deals with the transportation routes, vehicle deployment and loading problems at the operational level based on the upper-level solution. Furthermore, in view of the demand uncertainty in impacted areas, a robust model is developed to guarantee the feasibility of relief distribution solutions. Also, a case study is conducted to illustrate the benefit of the presented robust framework, which is not only favourable in terms of the reliability and equity level relative to the deterministic one but also capable of offering advice on the vehicle arrangement.

Determining the optimal decision time of relief allocation in response to disaster via relief demand updates

Relief allocation has recently attracted increased interest in research. Most studies in the literature have not focused on the trade-off between demand forecast accuracy and relief allocation efficiency. Thus, we employ the Bayesian updating framework, which allows the decision-maker to postpone his decision until sufficient accurate demand information is available. Different from the previous studies, we propose a novel method of relief demand updating, and devise a lost function as a function of time delay. Then, we develop a relief allocation model as an optimal stopping problem, to determine the optimal delay in response to typhoon disaster. Finally, the proposed model is applied to a case of post-disaster rescue. The computational results indicate the potential advantages of the model.

Coordinating efficiency and equity in disaster relief logistics via information updates

This paper addresses a multi-supplier, multi-affected area, multi-relief, and multi-vehicle relief allocation problem in disaster relief logistics. A multi-objective optimisation model based on disaster scenario information updates is proposed in an attempt to coordinate efficiency and equity through timely and appropriate decisions regarding issues such as vehicle routing and relief allocation. An optimal stopping rule is also proposed to determine the optimum period of delay before responding to disaster, because decision making requires accurate disaster information. The main contribution of this paper is solving relief allocation problem in a novel way by correlating operational research with statistical decision making and Bayesian sequential analysis. Finally, a case is presented based on the post-disaster rescue in Eastern China after supertyphoon Saomai to test the applicability and show the potential advantages of the proposed model.

The Political Economy of Relief Aid Allocation: Evidence from Madagascar

This paper studies the political economy of relief allocation using evidence from aid programs after cyclone Gafilo hit Madagascar in 2004. Relief was provided by the government as well as local and international aid agencies. Aid was more likely in areas with a higher need for relief, in more easily accessible communes and in cyclone-affected communes with higher radio coverage and stronger political support for the government. Compared to relief provided by the government, aid by agencies was less affected by media or political factors, but more likely to go to poorer and more easily accessible communes, unconditional on impact.

Targeting Cyclone Relief within the Village: Kinship, Sharing, and Capture

This article investigates the targeting of cyclone relief within villages in Fiji. It focuses on how relief allocation is linked with informal risk sharing and elite capture, both of which are directly related to kinship. The results are as follows. First, food aid is initially targeted toward kin groups according to their aggregate shocks and then shared among group members. Right after the cyclone, when aid is scarce, households with damage to their housing and with greater crop damage are allocated less aid within the group. Instead, they receive greater net private transfers in other forms, especially in labor sharing. Consistent patterns are found in village, cropping, and housing rehabilitations. Second, there is no elite capture of food aid in the kin group, and instead, traditional kin leaders share food with others; however, non-kin-based community leaders capture aid when it is allocated across kin groups. Third, distinct from food aid demanded by all, tarpaulins demanded by victims only strongly target individual housing damage at the village level—not the kin group—independent of social status. As with food aid, victims with greater crop damage are given a lower priority. Implications for relief policies are discussed.

A congestion-management allocation mechanism for multiple transaction networks

In this paper, we present a physical-flow-based congestion management allocation mechanism for multiple transaction networks. We use the multiple transaction network framework constructed to characterize transmission congestion and then determine the contribution to congestion attributable to each transaction on a physical-flow basis. This allocation scheme explicitly addresses the issue of counter flows. The allocation results are used in the congestion relief of the independent grid operator (IGO) so as to allow it to acquire relief services to remove the overload congestion attributed to each transaction from the network in the most economic manner. The congestion charges attributable to each transaction for its usage of the network are also determined. We tested the proposed scheme on several systems and we illustrate its capabilities on a network based on the IEEE 57-bus system. We discuss the policy implications of the congestion relief allocation solution. The proposed scheme provides physically reasonable results and is applicable to large-scale networks.