Post-disaster Relief Operations Research Articles

Designing a sustainable-resilient humanitarian supply chain for post-disaster relief process, an earthquake case study in Haiti

Purpose Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief. Design/methodology/approach A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations. Findings A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained. Originality/value Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

An AI-based framework for earthquake relief demand forecasting: A case study in Türkiye

Accurately forecasting the demand for relief assistance is crucial for efficient post-disaster relief operations. This paper presents an AI-based framework for earthquake relief demand forecasting, aiming to optimize the distribution of emergency resources. To identify higher-risk areas, the framework integrates historical earthquake data with socioeconomic information. This information aids in identifying regions that require immediate relief assistance. The initial dataset for relief demand quantification includes essential parameters such as Latitude, Longitude, Population, Demand, Supply, Cost, Deaths, Injuries, Infrastructural Damage, and Cascading Disaster. By analyzing these inputs and factors, AI algorithms can quantify the relief demand scenario accurately, resulting in accurate and reliable forecasts of relief supply requirements. The iterative ML techniques in generating comprehensive data for Türkiye greatly enhance the quality and accuracy of relief demand prediction. The prediction models used in the framework, namely Least Angle Regression and Linear Regression, demonstrate exceptional performance in forecasting relief demand. They achieve a remarkable R2 score of 1, indicating a perfect fit to the data, and a Loss Error function value of zero, signifying highly accurate predictions. Additionally, the Decision Tree algorithm showcases strong capabilities in iterative learning, achieving an accuracy rate of 88.99 %. In addition to providing valuable insights for tailored relief strategies, the framework extends beyond relief demand by predicting outcomes such as casualties, injuries, and infrastructure damage. With a comprehensive understanding of the earthquake’s impact, relief organizations can strategize their response and allocate resources accordingly. The thorough methodology and innovative application of ML algorithms within our pioneering AI-based framework significantly contribute to the field. This signifies a fundamental shift in the approach to envisioning and executing relief operations following seismic events.

Humanitarian transportation network design via two-stage distributionally robust optimization

Natural disasters are highly unpredictable, with varying degrees of magnitude, and thus require a reliable and robust humanitarian relief network. Faced with the adverse effects of disasters, we advocate taking pre-disaster preventive actions, e.g., road link strengthening, to mitigate post-disaster disruptions to road networks. In this paper, we study a highly integrated humanitarian relief network design problem, in which the network strengthening plan and inventory pre-positioning scheme are optimized cooperatively. The proposed problem is formulated as a two-stage distributionally robust optimization (DRO) model, in which the first and second stages correspond to pre- and post-disaster relief operations, respectively. By leveraging prior data and the Wasserstein metric, a tailored ambiguity set is constructed to capture both node- and link-wise uncertainties. We demonstrate that the two-stage DRO model over the Wasserstein ambiguity set has an equivalent reformulation that can be solved via the L-shaped method with bilinear subproblems. Based on observations of the problem structure and the spirit of separation optimization, both exact and heuristic approaches are proposed to address the bilinear subproblems. Via the case study of the Yushu earthquake, we highlight the value of jointly optimizing network strengthening and inventory pre-positioning decisions. Specifically, with the same investment budget, integrating network strengthening into inventory pre-positioning can easily achieve an approximately 60% reduction in shortage penalties. Furthermore, our method can produce reliable solutions, based on which we explore several managerial implications.

Period Poverty: An Added Poverty In Humanitarian Crisis.

The term 'period poverty' is broadly used, indicating an individual's need for menstrual products without sufficient funds to purchase them (1). Period poverty is defined as “a lack of access to menstrual products”. More than half of the world's population are menstruating people; therefore in a situation specifically during a humanitarian crisis, high-period poverty becomes challenging (2). Period poverty, like other types of poverty, is a worldwide issue and it is frequently used when people are unable to access menstrual products due to their low socioeconomic level. These items could include sanitary napkins, inner garments, and other hygiene items. It can significantly impact an individual's life, but the stigma surrounding periods poses a greater threat to women's health because it discourages people from discussing it. Several factors, including hygiene facilities, waste management, and education, pose physical, mental, and emotional challenges for numerous women. More than half of the world's population menstruates; therefore, high-period poverty becomes a challenge, especially during humanitarian crises (2). It is usually observed that adolescents and young women are frequently underrepresented populations in humanitarian settings, particularly in times of conflict like forced displacement, armed conflict, or natural catastrophes. Wherever a disaster strikes, search and rescue teams prioritize helping those evacuated to shelters and supporting efforts to provide basic necessities like food and water. As a result, other urgent needs of displaced populations, particularly displaced girls and women, may be overlooked (4). It has been observed from the global humanitarian crises, that menstruation and hygiene products such as wipes, soaps, and other menstrual products were the most important things they needed at that time (5). During the recent floods (June- Aug 2022) in Pakistan, over 33 million people were affected, and over 6.4 million required humanitarian assistance. Approximately 634 thousand people were compelled to reside in camps. Access to healthcare facilities, personnel, and essential medications and medical supplies was restricted (6)More than eight million women of reproductive age affected by floods took extreme measures to manage their periods, such as one woman who described how she had to use tree leaves during her period. (7). Post-disaster relief and rehabilitation operations rarely prioritize the menstrual hygiene needs of women. Nonetheless, there is a growing awareness of the importance of integrating comprehensive, participatory, and gender-sensitive approaches into response programs with a focus on meeting the needs of women (8). ---Continue

A simulation approach for collaborative humanitarian aid distribution management: the case of Bogotá city

Collaboration mechanisms in humanitarian supply chains increase efficiency levels in post-disaster relief operations. The scientific literature on the topic is mostly based on qualitative approaches to identify the barriers and benefits of collaborative relationships between stakeholders. Thus, this paper proposes the use of system dynamics simulation to evaluate the impact on the average response time of aid delivery resulting from implementing different collaborative strategies. In this sense, collaborative strategies are designed for humanitarian operations in the immediate response phase considering elements such as information exchange, shared resources, and infrastructure. To design and assess the impact of these collaborative strategies in a disaster scenario, we analyze the case of the city of Bogotá-Colombia and the surrounding cities, we have used public information for building and tuning the parameters of the simulation model. The simulation model results allow us to identify the main factors that reduce the average response times to serve the affected population. It can be concluded that the strategy which includes sharing resources and infrastructure, exchanging information, and the creation of joint knowledge has a greater improvement in response times becoming the best policy for disaster management given the results obtained in a collaborative planning environment.

A collaborative humanitarian relief chain design for disaster response

Pre- and post-disaster relief operations often involve several humanitarian organizations (HOs) working simultaneously. Balanced and systemic resource-sharing schemes may enable HOs to collaborate effectively and conduct efficient relief operations. In this study, a novel two-stage stochastic model is developed which depends on information- and resource-sharing as coordination mechanisms to ensure proper relief operations while multiple HOs exist in the relief chain. A collaborative humanitarian relief chain (CHRC) consisting of several HOs is designed to make the required decisions on the ways in which relief items are procured, pre-positioned, and distributed pre- and post-disaster. The model is also tweaked to handle potential risks of disruption by an efficient approach. Furthermore, a Lagrangian relaxation algorithm is developed to solve large-scale problems. The performance of the proposed model is tested by being applied to a real-world disaster, namely the 2017 Kermanshah earthquake, in Iran. Several sensitivity analyses are performed to evaluate the applicability of the model and compare the performance of the collaborative decision-making approach proposed in this study to decentralized approaches. Lastly, a number of managerial insights are drawn from the findings.

Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment

In smart cities, relief items distribution is a complex task due to the factors such as incomplete information, unpredictable exact demand, lack of resources, and causality levels, to name a few. With the development of Internet of Things (IoT) technologies, dynamic data update provides the scope of distribution schedule to adopt changes with updates. Therefore, the dynamic relief items distribution schedule becomes a need to generate humanitarian supply chain schedules as a smart city application. To address the disaster data updates in different time periods, a dynamic optimised model with a sliding time window is proposed that defines the distribution schedule of relief items from multiple supply points to different disaster regions. The proposed model not only considers the details of available resources dynamically but also introduces disaster region priority along with transportation routes information updates for each scheduling time slot. Such an integrated optimised model delivers an effective distribution schedule to start with and updates it for each time slot. A set of numerical case studies is formulated to evaluate the performance of the optimised scheduling. The dynamic updates on the relief item demands’ travel path, causality level and available resources parameters have been included as performance measures for optimising the distributing schedule. The models have been evaluated based on performance measures to reflect disaster scenarios. Evaluation of the proposed models in comparison to the other perspective static and dynamic relief items distribution models shows that adopting dynamic updates in the distribution model cover most of the major aspects of the relief items distribution task in a more realistic way for post-disaster relief management. The analysis has also shown that the proposed model has the adaptability to address the changing demand and resources availability along with disaster conditions. In addition, this model will also help the decision-makers to plan the post-disaster relief operations in more effective ways by covering the updates on disaster data in each time period.

Sustainability of Vertical Farming in Comparison with Conventional Farming: A Case Study in Miyagi Prefecture, Japan, on Nitrogen and Phosphorus Footprint

The reduced requirement for nutrients in vertical farming (VF) implies that the potential for lower environmental impact is greater in VF than in conventional farming. In this study, the environmental impacts of VF were evaluated based on a case study of VF for vegetables in Miyagi Prefecture in Japan, where VF has been utilized in post-disaster relief operations in the wake of the 2011 Great East Japan Earthquake. The nitrogen (N) and phosphorus (P) footprints of these VFs were determined and analyzed to quantify the potential reduction in N and P emissions. First, the N and P footprints in conventional farming were calculated. Then, those footprints were compared with three different scenarios with different ratios for food imports, which equate to different levels of food self-sufficiency. The results show a decrease in the N and P footprints with increased prefectural self-sufficiency due to the introduction of VF. In addition to reducing the risks to food supply by reducing the dependence on imports and the environmental impacts of agriculture, further analysis reveals that VF is suitable for use in many scenarios around the world to reliably provide food to local communities. Its low vulnerability to natural disasters makes VF well suited to places most at risk from climate change anomalies.

Humanitarian supply chain management: modeling the pre and post-disaster relief operations

PurposeThis study proposed a mathematical model for decision-making in the pre- and post-disaster phases. This research aims to develop a mathematical model for three important fields in the context of humanitarian logistics; stock prepositioning, facility location and evacuation planning in the humanitarian supply chain (HSC) network design.Design/methodology/approachThis study applied three optimization techniques; classical approach (CA), pattern search algorithm (PSA) and Genetic Algorithm (GA) to solve the proposed mathematical model. The proposed mathematical model attempts to minimize the total relief items supply chain cost and evacuation chain cost of the HSC. A real case study of cyclone Fani, 2019 in Orissa, India is applied to validate the proposed mathematical model and to show the performance of the model.FindingsThe results demonstrate that heuristic approach; PSA performs better and optimal solutions are obtained in almost all the cases as compared to the GA and CA.Research limitations/implicationsThis study is limited to deterministic demands in the affected regions, and different scenarios of the disaster events are not considered.Social implicationsThe finding reveals that the proposed model can help the humanitarian stakeholders in making decisions on facility location, relief distribution and evacuation planning in disaster relief operations.Originality/valueThe results of this study may offer managerial insights to practitioners and humanitarian logisticians who are engaged in HSC implementation.

Information Retrieval from Microblogs During Disasters: In the Light of IRMiDis Task

In last few years ,microblogging sites like Twitter have been evolved as a repository of critical situational information during various mass emergencies. However, messages posted on microblogging sites often contain non-actionable information such as sympathy and prayer for victims. Moreover, messages sometimes contain rumors and overstated facts. In such situations, identification of tweets that report some relevant and actionable information is extremely important for effective coordination of post-disaster relief operations. Thus, efficient IR methodologies are required to identify such critical information. Additionally, cross-verification of such critical information is a practical necessity to ensure the trustworthiness. To this end, we organized the ‘Information Retrieval from Microblogs during Disasters (IRMiDis)’ shared task with the FIRE conference for consecutive 3 years (2016, 2017 and 2018). In each year’s track, we have given task associated with extraction of a specific situational information to aid post-disaster relief operation and asked the participants to propose methodologies. Our present study provides a brief description of the tasks (research problem) given in these tracks, a summary of methodologies of all submitted runs and finally a brief description of our proposed methodologies to address the research problems of IRMidis track.

Mount Merapi eruption

PurposeUneven distribution and mistarget beneficiaries are among problems encountered during post-disaster relief operations in 2010 Mount Merapi eruption. The purpose of this paper is to develop an empirically founded agent-based simulation model addressing the evacuation dynamics and to explore coordination mechanism and other promising strategies during last-mile relief delivery.Design/methodology/approachAn agent-based model which was specified and parameterized by empirical research (interviews and survey) was developed to understand the mechanism of individual decision making underlying the evacuation dynamics. A set of model testing was conducted to evaluate confidence level of the model in representing the evacuation dynamics during post-disaster of 2010 Mount Merapi eruption. Three scenarios of last-mile relief delivery at both strategic and operational levels were examined to evaluate quantitatively the effectiveness of the coordination mechanism and to explore other promising strategies.FindingsResults indicate that the empirically founded agent-based modeling was able to reproduce the general pattern of observable Internal Displaced Persons based on government records, both at micro and macro levels, with a statistically non-significant difference. Low hazard perception and leader-following behavior which refuses to evacuate are the two factors responsible for late evacuation. Unsurprisingly, coordination through information sharing results in better performance than without coordination. To deal with both uneven distribution and long-term demand fulfillment, coordination among volunteers during aid distribution (at downstream operation) is not sufficient. The downstream coordination should also be accompanied with coordination between aid centers at the upstream operation. Furthermore, the coordination which is combined with other operational strategies, such as clustering strategy, using small-sized trucks and pre-positioning strategy, seems to be promising. It appears that the combined strategy of coordination and clustering strategy performs best among other combined strategies.Practical implicationsThe significant role of early evacuation and self-evacuation behavior toward efficient evacuation indicates that human factor (i.e. hazard perception and cultural factor) should be considered in designing evacuation plan. Early warning system through both technology and community empowerment is necessary to support early evacuation. The early warning system should also be accompanied with at least 69 percent of the population performing self-evacuation behavior for the effective evacuation. As information sharing through coordination is necessary to avoid redundant efforts, uneven distribution and eventually to reduce unmet demand, the government can act as a coordinating actor to authorize the operation and mobilize the resources. The combination of coordination and another strategy reducing lead time such as clustering analysis, thus increasing responsiveness, is seemly strategy for efficient and effective last-mile relief distribution.Originality/valueLiterature on coordination is dominated by qualitative approach, which is difficult to evaluate its effectiveness quantitatively. Providing realistic setting of the evacuation dynamics in the course of the 2010 Mount Merapi eruption, the empirically founded agent-based model can be used to understand the factors influencing the evacuation dynamics and subsequently to quantitatively examine coordination mechanisms and other potential strategies toward efficient and effective last-mile relief distribution.

Extracting Resource Needs and Availabilities From Microblogs for Aiding Post-Disaster Relief Operations

Microblogging sites like Twitter are the important sources of real-time information during disaster/emergency events. During such events, the critical situational information posted is immersed in a lot of conversational content; hence, reliable methodologies are needed for extracting the meaningful information. In this paper, we focus on a particular application that is critical for efficient management of post-disaster relief operations—identifying tweets that inform about resource needs and resource availabilities. Two broad types of methodologies can be practically applied to identify such tweets during an ongoing disaster event: 1) supervised classification approaches, where the classifier models are trained on microblogs posted during prior events and applied on those posted during the ongoing event and 2) unsupervised pattern matching and information retrieval approaches that can be directly applied on the microblogs posted during the ongoing event. In this paper, we experiment with several supervised and unsupervised approaches to address the problem, including several neural network-based classification and retrieval models. We also propose two novel neural retrieval models (unsupervised) for the said application, which effectively combine word-level embeddings and character-level embeddings. We conduct experiments on tweets posted during two disaster events and observe that the two approaches perform well in different scenarios. Specifically, if good quality training data are available from prior events, then classification approaches perform better; however, if such training data are not available, then unsupervised retrieval methods outperform supervised classification approaches.

Utilizing microblogs for assisting post-disaster relief operations via matching resource needs and availabilities

During a disaster event, two types of information that are especially useful for coordinating relief operations are needs and availabilities of resources (e.g., food, water, medicines) in the affected region. Information posted on microblogging sites is increasingly being used for assisting post-disaster relief operations. In this context, two practical challenges are (i) to identify tweets that inform about resource needs and availabilities (termed as need-tweets and availability-tweets, respectively), and (ii) to automatically match needs with appropriate availabilities. While several works have addressed the first problem, there has been little work on automatically matching needs with availabilities. The few prior works that attempted matching only considered the resources, and no attempt has been made to understand other aspects of needs/availabilities that are essential for matching in practice. In this work, we develop a methodology for understanding five important aspects of need-tweets and availability-tweets, including what resource and what quantity is needed/available, the geographical location of the need/availability, and who needs / is providing the resource. Understanding these aspects helps us to address the need-availability matching problem considering not only the resources, but also other factors such as the geographical proximity between the need and the availability. To the best of our knowledge, this study is the first attempt to develop methods for understanding the semantics of need-tweets and availability-tweets. We also develop a novel methodology for matching need-tweets with availability-tweets, considering both resource similarity and geographical proximity. Experiments on two datasets corresponding to two disaster events, demonstrate that our proposed methods perform substantially better matching than those in prior works. Additionally, our proposed methodologies are reusable across different types of disaster events.

Post-disaster map builder: Crowdsensed digital pedestrian map construction of the disaster affected areas through smartphone based DTN

Constructing digital pedestrian maps of the disaster affected areas is a vital requirement for post-disaster relief operation. Digital maps help disaster management agencies in decision making and mobilizing the manpower and resources in the disaster affected areas. However, due to the intermittent connectivity after large scale natural disasters, the existing web based digital mapping systems remain inaccessible in the disaster affected areas. Moreover, the road networks in such areas change drastically because of disaster (for instance waterlogging due to flood, structural collapse or incidental destructions like a landslide). Hence, existing analog (paper based) route maps of such areas become obsolete. In this work, we propose Post-Disaster Map Builder, a crowdsenced digital pedestrian map construction system over smartphone based DTN. The proposed system generates digital pedestrian maps of the disaster affected areas using battery powered mobile handheld devices. Here, trajectory traces collected by the volunteers are periodically shared among the other volunteers of the disaster affected area through smartphone based DTN. Pedestrian maps of the disaster affected areas are gradually constructed in the mobile handheld devices of the volunteers by combining the collected traces over time. The proposed system is thoroughly evaluated through simulation and testbed implementation. The results reveal that our proposed system can automatically construct digital pedestrian maps of disaster affected areas with high accuracy at the cost of marginal delay.

Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends

Online Social Media, such as Twitter, Facebook and WhatsApp, are important sources of real-time information related to emergency events, including both natural calamities, man-made disasters, epidemics, and so on. There has been lot of recent work on designing information systems that would be useful for aiding post-disaster relief operations, as well as for pre-disaster preparedness. A special issue on “Exploitation of Social Media for Emergency Relief and Preparedness” was conducted for the journal Information Systems Frontiers. The objective of this special issue was to present a platform for dissemination of the empirical results of various technologies for extracting vital and actionable information from social media content in disaster situations. The papers included in this issue are expected to be the stepping stones for future explorations and technical innovations towards technologies meant for utilizing various online and offline information sources for enhancing pre-disaster preparedness and post-disaster relief operations.

DPDRM: A decentralized post-disaster resource management scheme using energy efficient smart phone based DTN

Large-scale natural disasters affect traditional communication infrastructure due to incidental destructions and damages. Delay-tolerant networks (DTN) proves to be a viable option for exchanging information during such scenarios. Different disaster management agencies setup camps and mobilize their resources and manpower in the affected areas after the disaster. Resource management is an integral part of post-disaster relief operation. Each camp manages its own resource inventory. Periodic sharing of resource inventories from different camps is essential for maintaining a near-accurate unified global inventory at each node that enables successful search and retrieval of resource requests (queries). But, due to the intermittent network connectivity, maintaining such a consistent resource inventory and successful search/retrieval of queries becomes challenging. We map this post-disaster resource management as a content sharing problem with strict time constraint and propose an automated, energy efficient and decentralized resource management scheme over DTN. The proposed scheme allows periodic group based sharing of resource inventories and supports search and retrieval of queries within a restricted time period. Here, resource inventories are shared among the group members by employing publish-subscribe mechanism. Moreover the queries are propagated to the group members based on the probability of encounters. This probabilistic propagation controls the unintended replication of query. Finally, the proposed scheme is evaluated through ONE simulator based on a realistic post-disaster scenario of the 2015 Nepal earthquake. The results reveal that our proposed scheme outperforms one of the state-of-the-art content sharing schemes, discover-predict-deliver in terms of response delivery, average energy consumption and average delivery latency.

Post-disaster relief operations considering psychological costs of waiting for evacuation and relief resources

After a disaster, a huge number of homeless victims should be evacuated from disaster sites to temporary shelters. However, because the evacuation capacity is insufficient, victims must be evacuated in batches and some of them must wait for evacuation at disaster sites; even the victims who have been evacuated to shelters must wait for resources, because the relief resources (e.g. tents) are insufficient. The psychological penalty cost perceived by victims may increase when they are waiting for evacuation at disaster sites or for relief resources at shelters, whereas psychological interventions can decrease the magnitude of the perceived psychological cost. Novel mathematical programs were developed for multi-step evacuation and sheltering under minimization of monetary and psychological penalty costs. By developing typical scenarios, the resource scarcity and psychological severity are investigated. With Wenchuan County (the epicenter of the 2008 Sichuan earthquake) as the test case, the influence and the sensitivity of parameters, tradeoff between monetary and psychological costs, and the effects of various scenarios are assessed using six experiments. Analytical results reveal the complexity and managerial insights by applying the proposed models to post-disaster evacuation and sheltering.

Menstrual hygiene: a ‘silent’ need during disaster recovery

Post-disaster relief and recovery operations seldom focus on women’s priorities regarding menstrual hygiene. There is an increasing awareness to incorporate inclusive, participatory, and gender-sensitive strategies for implementation of response programmes. This article presents empirical findings related to menstrual hygiene management (MHM), demonstrating it is integral to women’s privacy and safety during recovery. Using case studies from India, the 2012 Assam floods and 2013 Cyclone Phailin in Odisha, this article explores menstrual hygiene practices in a post-disaster context. The data were collected through participatory learning and action tools such as focus group discussions, household interviews, priority ranking, and observations. It emerged that menstrual hygiene was overlooked at the household level during recovery; women and adolescent girls faced seclusion and isolation, exacerbating privacy and security concerns post-disasters. Some humanitarian agencies have an ad hoc approach towards MHM, which is limited to distribution of sanitary pads and does not address the socio-cultural practices around MHM. There is a need for strategic planning to address MHM with a gender-sensitive and inclusive approach. This article draws practical and policy inferences from the research for stronger approaches towards initiating behaviour change in MHM, and addressing attitudes and knowledge regarding menstrual hygiene.

Critical Systems Thinking on the Inefficiency in Post-Earthquake Relief: A Practice in Longmen Shan Fault Area

The Longmen Shan Fault area is one of the most active seismic zones in the world, has a relatively dense population and a low economic development level. Therefore, the provision of effective post-earthquake relief is very important for saving lives and restricting financial losses. However, providing urgent relief is a complex problem that requires efficient coordination between the organizations, and departments involved, and the specific environments, for the supply of urgent materials, energy and information. In practice, although there are sufficient relief supplies, because of the lack of systemic thinking, relief activities are often chaotic leading to inefficiencies and even increases in the damage and loss of life. In this study, a critical systems thinking approach is used to reflect on the system contexts, boundaries, structures and functions of the emergency relief systems. Using systems modeling we constructed simulations for different relief plans and their consequences, and a work shop of metasynthetic engineering was constructed to integrate the different views, knowledge and contexts. At the practical stage, after the model’s validity was verified from the 2008 Wenchuan earthquake data, based on the 2013 Lushan earthquake simulation results, some recommendations are made to improve future post-disaster relief operations. Finally, the significance of critical systems thinking is demonstrated to ensure effective and efficient of post-earthquake relief. Some limitations and future research also have been presented in conclusion.

Connecting a population dynamic model with a multi-period location-allocation problem for post-disaster relief operations

In this study, we propose a mathematical model and heuristics for solving a multi-period location-allocation problem in post-disaster operations, which takes into account the impact of distribution over the population. Logistics restrictions such as human and financial resources are considered. In addition, a brief review on resilience system models is provided, as well as their connection with quantitative models for post-disaster relief operations. In particular, we highlight how one can improve resilience by means of OR/MS strategies. Then, a simpler resilience schema is proposed, which better reflects an active system for providing humanitarian aid in post-disaster operations, similar to the model focused in this work. The proposed model is non-linear and solved by a decomposition approach: the master level problem is addressed by a non-linear solver, while the slave subproblem is treated as a black-box coupling heuristics and a Variable Neighborhood Descent local search. Computational experiments have been done using several scenarios, and real data from Belo Horizonte city in Brazil.