Evolutionary dynamics of supervision-compliance game on optimal pre-positioning strategies in relief supply chain management

Emergency supply reserves are indispensable material bases in relief supply chain management. However, limited in-kind relief resources stockpiled in government-managed depositories may fail to meet the surging demand following the disaster. This paper develops an optimal pre-positioning strategy for emergency supplies with pre-purchasing contracts between local governments (LGs) and emergency supply manufacturers (ESMs) to properly address demand uncertainty in different disaster scenarios. Physical materials and production capacity are integrated into a holistic and hybrid reserve model to mitigate overstock or stock-out risks. Applying an evolutionary game-theoretic framework, contract enforcement has been extensively analyzed to avoid LGs dereliction of duty and ESMs’ breach of contract. A novel dynamic penalty mechanism is proposed to control the fluctuations in strategy choices and effectively improve ESMs’ compliance without LGs’ excessive inputs on supervision. The numerical simulation results, along with sensitivity analyses on major cost-accounting, demand characteristics, and environmental parameters, show that safety stock is the primary guarantee in most cases, while reactive stock acts as an important supplement for disasters with long-term consequences. The joint reserve policy (except for no action strategy) outperforms the price-only contract on the total reserved quantity of emergency supplies at a lower long-term average cost. The initial state and cost-benefit structures dominate the complex interplay and periodical fluctuations in the supervision-compliance game. The doomed cycle of order, disorder, and reorder in contract performance management can be well managed under the proposed dynamic penalty mechanism, which appears much more efficient and incentive-compatible in promoting both parties to fulfill their obligations.     

A multi-stage stochastic programming approach for pre-positioning of relief supplies considering returns

A dynamic pre-positioning problem is proposed to efficiently respond to victims’ need for relief supplies under uncertain and dynamic demand in humanitarian relief. The problem is formulated as a multi-stage stochastic programming model that considers pre-positioning with the dynamic procurement and return decisions about relief supplies over a time horizon. To validate the advantages of dynamic pre-positioning, three additional pre-positioning strategies are presented: pre-positioning with one-time procurement and without returns, pre-positioning with one-time procurement and returns, and pre-positioning with dynamic procurement and without returns. Using data from real-world disasters in the United States in the Emergency Events Database, we present a numerical analysis to study the applicability of the proposed models. We develop a sample average approximation approach to solving the proposed model in large-scale cases. Our main contribution is that we integrate dynamic procurement and return strategies into pre-positioning to decrease both costs and shortage risks in uncertain and dynamic contexts. The results illustrate that dynamic pre-positioning outperforms the other three strategies in cost savings. It also indicates that a higher return price is particularly helpful for decreasing unmet demand. The proposed models can help relief agencies evaluate and choose the solutions that will have the greatest overall effectiveness in the context of different relief practices.     

Pre-positioning and dynamic delivery planning for short-term response following a natural disaster

Natural disasters often result in large numbers of evacuees being temporarily housed in schools, churches, and other shelters. The sudden influx of people seeking shelter creates demands for emergency supplies, which must be delivered quickly. A dynamic allocation model is constructed to optimize pre-event planning for meeting short-term demands (over approximately the first 72 h) for emergency supplies under uncertainty about what demands will have to be met and where those demands will occur. The model also includes requirements for reliability in the solutions – i.e., the solution must ensure that all demands are met in scenarios comprising at least 100α% of all outcomes. A case study application using shelter locations in North Carolina and a set of hurricane threat scenarios is used to illustrate the model and how it supports an emergency relief strategy.     

A two-period newsvendor model for prepositioning with a post-disaster replenishment using Bayesian demand update

Humanitarian aid agencies usually resort to inventory prepositioning to mitigate the impact of disasters by sending emergency supplies to the affected area as quickly as possible. However, a lack of replenishment opportunity after a disaster can greatly hamper the effectiveness of the relief operation due to uncertainty in demand. In this paper, a prepositioning problem is formulated as a two-period newsvendor model where the response phase is divided into two periods. The model acknowledges the demand to be uncertain even after the disaster and utilises the Bayesian approach to revise the demand of the second period. Based on the revised demand, an order is placed at the beginning of the second period to be replenished instantaneously. A two-stage solution methodology is proposed to find the prepositioning quantity and post-disaster replenishment quantity, which minimise the total expected costs of relief operations. A numerical example is presented to demonstrate the solution methodology, and sensitivity analysis is performed to examine the effect of model parameters. The results highlight the indifferent characteristics shown by the replenishment quantity with the variation in model parameters.     

Optimization models in emergency logistics: A literature review

Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s. Using techniques of content analysis, this paper reviews optimization models utilized in emergency logistics. Disaster operations can be performed before or after disaster occurrence. Short-notice evacuation, facility location, and stock pre-positioning are drafted as the main pre-disaster operations, while relief distribution and casualty transportation are categorized as post-disaster operations. According to these operations, works in the literature are broken down into three parts: facility location, relief distribution and casualty transportation, and other operations. For the first two parts, the literature is structured and analyzed based on the model types, decisions, objectives, and constraints. Finally, through the content analysis framework, several research gaps are identified and future research directions are proposed.     

Analysis and forecast of donations at domestic hunger relief organizations impacted by natural disasters

Food banks are non-profit, charitable organizations that distribute food and products to people in need. Food bank facilities become disaster relief centers after natural disasters. The uncertainty associated with the arrival of donations and demand make the planning and operations of food banks challenging during the disaster relief period. The goal of this research is to analyse and forecast the amount of donations received by food bank facilities in the U.S. when operating as disaster relief centers. This paper analyses the donations received by two food bank facilities affected by Hurricane Harvey in 2017. An extensive numerical study is performed that compares the donation behavior at each facility before and after the hurricane event. Multiple forecasting models are evaluated to determine their accuracy in predicting the observed behavior. The results show that under disaster operations, the best performing techniques for both food banks were smoothing techniques (i.e., CMA and Holt) and econometric models.     

Planning the delivery of relief supplies upon the occurrence of a natural disaster while considering the assembly process of the relief kits

Models for the distribution of relief supplies often assume immediate availability of relief items upon the occurrence of a natural disaster. However, such an assumption does not always apply in realistic settings. In some cases, at least it is necessary to assemble relief items into kits before distributing them among the affected population. This paper presents a rolling horizon methodology that considers dynamic parameters such as demand, capacities and demand priorities for the problem of distributing relief supplies after the occurrence of a natural disaster by including such assembling activities before the delivery.     

Debris removal during disaster response: A case for Turkey

Debris occurs from the ruin and wreckage of structures during a disaster. Proper removal of debris is of great importance because it blocks roads and prohibits emergency aid teams from accessing disaster-affected regions. Poor disaster management, lack of efficiency and delays in debris removal cause disruptions in providing shelter, nutrition, healthcare and communication services to disaster victims, and more importantly, result in loss of lives. Due to the importance of systematic and efficient debris removal from the perspectives of improving disaster victims quality of life and allowing the transportation of emergency relief materials, the focus of this study is on providing emergency relief supplies to disaster-affected regions as soon as possible by unblocking roads through removing the accumulated debris. We develop a mathematical model for the problem that requires long CPU times for large instances. Since it is crucial to act quickly in an emergency case, we also propose a heuristic methodology that solves instances with an average gap of 1% and optimum ratio of 80.83%.     

Prioritizing disaster-prone areas for large-scale earthquakes' preparedness: Methodology and application

This paper presents a new hybrid fuzzy multi-attribute decision-making approach to prioritize disaster-prone areas which are known as potential demand points (PDPs) regarding their vulnerability under large-scale earthquakes. Significant criteria for prioritizing PDPs are first determined. Then, the fuzzy DEMATEL is applied to specify interrelationship between the criteria, and the weights of criteria are achieved by the fuzzy ANP. Finally, the fuzzy PROMETHEE II is used to rank the PDPs. The proposed methodology is validated using a conventional relief pre-positioning network design model. Numerical results demonstrate the applicability and usefulness of the presented approach in configuring a more responsive relief network in practice.     

Analyzing the volume and nature of emergency medical calls during severe weather events using regression methodologies

An effective emergency medical service (EMS) response to emergency medical calls during extreme weather events is a critical public service. Nearly all models for allocating EMS resources focus on normal operating conditions. However, public health risks become even more critical during extreme weather events, and hence, EMS systems must consider additional needs that arise during weather events to effectively respond to and treat patients. This paper seeks to characterize how the volume and nature of EMS calls are affected during extreme weather events with a particular focus on emergency preparedness. In contrast to other studies on disaster relief, where the focus is on delivery of temporary commodities, we focus on the delivery of routine emergency services during blizzards and hurricane evacuations. The dependence of emergency service quality on weather conditions is explored through a case study using real-world data from Hanover County, Virginia. The results suggest that whether it is snowing is significant in nearly all of the regression models. Variables associated with increased highway congestion, which become important during hurricane evacuations, are positively correlated with an increased call volume and the likelihood of high-risk calls. The analysis can aid public safety leaders in preparing for extreme weather events.     

基于救灾物资满意度的最大覆盖选址问题

与传统选址问题不同的是,文中根据受灾区域的受灾程度进行了分层,考虑了不同受灾区域的物品需求满意度、救灾预算成本等因素,并以救灾物资需求满意度为目标建立了最大问题覆盖选址模型,最后,通过数值实验,讨论了不同预算成本以及各受灾区域物资的不同满足比例对物资需求满意度、分配中心的数量和地址的影响。     

Reliable design of a congested disaster relief network: A two-stage stochastic-robust optimization approach

The preparedness of humanitarian relief networks can be enhanced by pre-positioning resources in strategic locations and using them when disasters strike, a strategy that gives rise to a two-stage planning problem. This paper presents a novel two-stage stochastic-robust optimization approach for integrated planning of pre- and post-disaster positioning and allocation of relief resources, while taking into consideration the uncertainty about demand for relief services and disruptions in the relief facilities and the transportation network. The proposed approach enables planners to effectively use limited historical data and imperfect experts’ opinions to obtain robust solutions while avoiding the over-conservatism of classical robust optimization methods. The objective sought is to minimize the expected total time victims need to receive assistance, including both access time to facilities and waiting/service time in them. Congestion in relief facilities is accounted for by modeling them as queuing systems and penalizing waiting time. A decomposition method based on column-and-constraint generation is implemented to solve the problem, whereas the nonlinear terms corresponding to queuing in the second-stage problem are handled using a direct search procedure. Applicability of the proposed approach is demonstrated through a real case study and the numerical results are analyzed to draw managerial insights.     

Redesign strategies of a comprehensive robust relief network for disaster management

We address the problem of designing as well as redesigning a relief network over multiple periods as a strategic decision which plays a critical role in the post-disaster management. Design of the relief network has a significant impact on the effective performance of disaster response operations. For considering both the uncertainty and dynamism of the decision-making environment, a comprehensive scenario-based robust approach embedded in the rolling horizon framework is proposed. The proposed mixed-integer linear programming model is inspired by a real case study of a disaster management in Iran, which aims to minimize the total cost of network management. Furthermore, restorative strategies are considered to increase the efficiency and robustness of the proposed relief network under disaster. To tackle the proposed optimization model, a heuristic solution algorithm is adopted. The results indicate that the proposed robust relief network provides an affordable access to its demand points in a sustainable manner under disaster. In addition, extensive computational results illustrate the efficiency of the proposed model in dealing with the considered disaster management issues.     

论自然灾害应急物流管理体系的构建

中国是世界上受自然灾害影响非常严重的国家之一,应急物流是救灾应急管理中必不可少的内容和环节.自然灾害应急物流管理体系,是为了实现在突发自然灾害环境下对应急物资、人员、信息和财产等进行有效组织和保障而建立的综合管理体系.文章根据我国目前突发自然灾害应急物流的实际情况,以自然灾害应急物流管理体系为研究对象,对自然灾害背景下应急物流管理体系的构建进行了研究,同时还提出了促进应急物流管理体系建设的一些建议.     

Government intervention on cooperation,competition, and coopetition of humanitarian supply chains

We considered a humanitarian environment composed of donors and non-governmental organizations (NGOs) that the non-profits may adopt competitive or coopetitive inter-organizational interaction for managing the disasters. We also assumed that the government intervenes in the relief operations by applying one of its two policies; social welfare maximization (SWM) or budget consumption minimization (BCM). Using game theory (GT) approach, we develop 4 scenarios and, as a result, 4 mathematical programming models for examining the effect of the NGOs interactions and the government policies on the performance of donors, NGOs and government. We find that coopetition of NGOs facilitates the achievement of the government's objectives, and it also helps the non-profits to become more successful in providing relief. The government prefers to provide the indirect relief to the nonprofits to manage emergency operations successfully, and the financial aids are given only in the condition of reducing the level of NGOs cooperation. We also conclude that the cooperation of NGOs increases the donors' utility. A numerical example is conducted to test the findings of the models.     

A social activity and physical contact-based routing algorithm in mobile opportunistic networks for emergency response to sudden disasters

Sudden disasters such as earthquake, flood and hurricane necessitate the employment of communication networks to carry out emergency response activities. Routing has a significant impact on the functionality, performance and flexibility of communication networks. In this article, the routing problem is studied considering the delivery ratio of messages, the overhead ratio of messages and the average delay of messages in mobile opportunistic networks (MONs) for enterprise-level emergency response communications in sudden disaster scenarios. Unlike the traditional routing methods for MONS, this article presents a new two-stage spreading and forwarding dynamic routing algorithm based on the proposed social activity degree and physical contact factor for mobile customers. A new modelling method for describing a dynamic evolving process of the topology structure of a MON is first proposed. Then a multi-copy spreading strategy based on the social activity degree of nodes and a single-copy forwarding strategy based on the physical contact factor between nodes are designed. Compared with the most relevant routing algorithms such as Epidemic, Prophet, Labelled-sim, Dlife-comm and Distribute-sim, the proposed routing algorithm can significantly increase the delivery ratio of messages, and decrease the overhead ratio and average delay of messages.     

Sustainable humanitarian supply chain management – exploring new theory

Disaster response operations aim at helping as many victims as possible in the shortest time, with limited consideration of the socio-economic context. During the disaster rehabilitation phase, the perspective needs to broaden and comprehensively take into account the local environment. We propose a framework of sustainable humanitarian supply chain management (SCM) that facilitates such comprehensive performance. We conceptualise the framework by combining literature from the fields of sustainable and humanitarian SCM. We test the framework through an analytic induction process by means of multiple case studies of four relief organisations. Our framework suggests that supply chain design needs to be aligned not only to relief organisations’ enablers, but also to the population's long-term requirements as well as any socio-economic and governmental contingency factors. A good fit between these dimensions leads to sustainable performance. The framework provides an instrument for relief organisations to achieve sustainable performance in the disaster rehabilitation phase.     

A dynamic model for disaster response considering prioritized demand points

This paper addresses the problem of distributing relief supplies after the occurrence of a disaster. We develop a dynamic model to serve demand, while prioritizing the response, according to the level of urgency of demand points. Our model is thought to be applied during a planning horizon and it considers dynamic demand, capacity constraints and priorities. To evaluate the applicability of our model, we use a real case study of a flood occurred in Colombia. We also test the computational solvability of our model and we propose and test different solution methodologies for solving larger instances of our problem.     

Location of temporary depots to facilitate relief operations after an earthquake

The type of humanitarian logistics problem of interest is an earthquake with significant damage, prioritized items for delivery, and an extensive time period over which supplies need to be delivered. The problem of interest is an outgrowth of a recent paper by [10], where they focused on supplying relief items from a central depot for a prolonged period of time. The drawback of their approach is that long travel distances of vehicles are required between demand points and the central depot. In this paper, we propose the location of temporary depots around the disaster-affected area, along with the required vehicles and resources, to improve logistical efficiency. A two-phase heuristic approach is proposed; it locates temporary depots and allocates covered demand points to an open depot in Phase I, and explores the best logistics performance under the given solution from Phase I in Phase II. Results from computational experiments and an earthquake case study are used to illustrate the benefits of this approach.     

Minimising emergency response time of ambulances through pre-positioning in Dhaka city,Bangladesh

This study conducted a large-scale survey in Dhaka, Bangladesh; the survey involved 95 major hospitals, more than 3000 emergency room patients, and 2 of the largest ambulance operators. Currently, most ambulances are parked within the vicinity of hospitals and are either dispatched or fetched by the acquaintances of the patient on demand, resulting in lengthy round trips. Reducing the response time of ambulances would certainly improve the emergency service, and pre-positioning of the ambulances could be a solution to reducing the response time. This study used two approaches to address the problem. First, the location-allocation problem was solved to find the optimal number of ambulance locations by maximising the demand coverage. Second, separate location-allocation for the peak and off-peaks, using K-means clustering, was applied to systematically optimise the ambulance positioning in small clusters near demand points. These approaches could substantially improve the existing emergency response time. Distributing ambulances near demand points yielded greater improvements in response time than when the ambulances are stationed near hospitals.