Disaster Relief Logistics Research Articles

Multistage stochastic programming with a random number of stages: Applications in hurricane disaster relief logistics planning

We consider a logistics planning problem of prepositioning relief commodities in preparation for an impending hurricane landfall. We model the problem as a multi-period network flow problem where the objective is to minimize the total expected logistics cost of operating the network to meet the demand for relief commodities. We assume that the hurricane’s attributes evolve over time according to a Markov chain model, and the demand quantity at each demand point is calculated based on the hurricane’s attributes (intensity and location) at the terminal stage, which corresponds to the hurricane’s landfall. We introduce a fully adaptive multi-stage stochastic programming (MSP) model that allows the decision-maker to adapt their logistics decisions over time according to the evolution of the hurricane’s attributes. In addition, we develop a novel extension of the standard MSP model to address the challenge of having a random number of stages in the planning horizon due to the uncertain landfall time of the hurricane. We benchmark the performance of the adaptive decision policy given by the MSP models with alternative decision policies, including a static policy, a rolling-horizon policy, a wait-and-see policy, and a decision-tree-based policy, all based on two-stage stochastic programming models. Our numerical results and sensitivity analyses provide key insights into the value of MSP in the hurricane disaster relief logistics planning problem.

Building a Sustainable and Resilient U.S. Disaster Response and Relief Logistics System: Lessons from Domestic and International Events

The United States (U.S.) has experienced frequent and severe natural disasters in recent time. This has in turn uncovered significant challenges in disaster relief and emergency response systems, particularly in the area of logistics. These challenges often lead to delays, inefficiencies and environmental consequences. Hence, creating a two-sided challenge, that requires both sustainability initiatives as well as a resilient Logistics system to fix. The lessons learned from prior domestic and international occurrences such as Hurricane Katrina, the 2011 Japan tsunami, and the Haiti earthquake presents the basis for the paper to examine the weaknesses and develop means of improving the current system. Therefore, this paper focuses on how the U.S. can improve its disaster response and relief system while building a resilient logistics system through unique strategies and recommendations inspired by the lessons learned from previous happenings. The recommendations for improvement include the implementation of green technologies, the decentralization of the system, improved public-private partnerships, and the optimization of the supply chain through the uses of predictive analytics and blockchain technology (Ahmed et al., 2024; Bealt et al., 2016; Hoshiba & Ozaki, 2014).

Risk Analysis of Post-Disaster Logistics Distribution Failure

In conducting logistical activities of disaster relief in natural disaster management. it is often not possible to implement as it should. and there are always potential risks that arise. One of the problems faced is the delayed distribution of post-disaster logistics. Therefore. risk management is necessary so that disaster relief logistics activities can run well. In addition. there is a need for a risk mitigation strategy to mitigate potential risks in the post-disaster logistics distribution process. The study aims to identify potential risks to post-disaster logistics distribution activities and find out which risk management strategies are a priority to address immediately. Failure Mode and Effect Analysis (FMEA) is a systematic approach to identifying failures and giving a value or score to each risk. From the results of the study. 32 potential risks occurred. As a result. 13 risks were identified with RPN values above critical point values. Thus. 13 were obtained as priority management risks. where the risks with the top 3 RNA values are Disruption of the post-earthquake telephone network Difficulty coordination of the logistics team with a rating of 25.98. Long logistic delivery travel time with a value of 21.86. and Condition of the vehicle used is not good with a score of 21.66. Thus. for 13 of these risks. mitigation is carried out for treatment priorities. Risk management strategies are applied to risks that have RPN values above critical values to minimize the impact.

The Lighthouse Disaster Centre concept for citizens based on a comparison of logistics processes in Brazil and Poland

Natural disasters have become more recurrent in the world. Thus, solutions must be addressed for fast and effective response for citizens. This paper proposed the concept of a lighthouse disaster centre for citizens by comparing Brazil and Poland's disaster relief logistics processes. Moreover, a systematic literature review showed that scholars do not address citizens' perspectives. This research gap was confirmed in the documental analysis of legislation in both countries. The lighthouse disaster centre is an effective way to include citizens in disaster relief strategies and procedures in the post-disaster humanitarian logistics context.

A location-inventory-distribution model under gradual injection of pre-disaster budgets with application in disaster relief logistics: a case study

A location-inventory-distribution model under gradual injection of pre-disaster budgets with application in disaster relief logistics: a case study

Two-stage distributionally robust optimization for disaster relief logistics under option contract and demand ambiguity

Disaster relief logistics (DRL) provides adequate relief supplies to victims of natural disasters (e.g., earthquakes and volcanic eruptions). This study explicitly considers supplier selection and inventory pre-positioning corresponding to static preparedness decisions, and post-disaster procurement and delivery associated with dynamic response decisions in actual DRL operations. To tackle issues triggered by shortage and surplus of multi-class relief resources, a flexible option contract is adopted to purchase relief items from suppliers. To measure the risk of demand ambiguity, a worst-case mean-quantile-deviation criterion is introduced to reflect the decision-maker’s risk-averse attitude. To handle the ambiguity in the probability distribution of demand, a novel two-stage distributionally robust optimization (DRO) model is developed for the addressed DRL problem. The proposed DRO model can be transformed into equivalent mixed-integer linear programs when the ambiguity sets incorporate all distributions within L1-norm and joint L1- and L∞-norms from a nominal (reference) distribution. A computational study of earthquakes in Iran is conducted to illustrate the applicability of the proposed DRO model to real-world problems. The experimental results demonstrate that our proposed DRO model has superior out-of-sample performance and can mitigate the effect of Optimization Bias compared to the traditional stochastic programming model. Some managerial insights regarding the proposed approach are provided based on numerical results.

Application of cloud computing and big data in three-stage dynamic modeling of disaster relief logistics and wounded transportation: a case study.

Collecting and sharing information about affected areas is an important activity for optimal decision-making in relief processes. Defects such as over-sending some items to affected areas and mistakes in transferring injured people to medical centers in accidents are due to improper management of this information. Because cloud computing as a processing and storage platform for big data is independent of the device and location and can also perform high-speed processing, its use in disasters has been highly regarded by researchers. In this environment, a three-stage dynamic procedure for evacuation operations and logistics issues is presented. The first stage of the proposed model is image processing and tweet mining in a cloud center in order to determine the disaster parameters. In stage II, a mixed-integer multi-commodity model is presented for the relief commodity delivery, wounded people transportation with capacity constraints, and locating of the possible on-site clinics and local distribution centers near disaster areas. In stage III, by using a system of equations, detailed vehicle load/unload instructions are obtained. Finally, the effectiveness of the proposed model on the data of an earthquake disaster in Iran is investigated. The results of comparing the proposed approach with a two-stage algorithm show that the total number of unsatisfied demand for all types of commodities in the proposed approach was better than the other. Also, the number of survivors in the three-stage model is significantly higher than in the two-stage one. The better performance of the proposed algorithm is due to the fact that online data is continuously available and that decisions such as sending relief items and dispatching are made more effectively.

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

AbstractThis 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.

Design of Disaster Relief Logistics Network System by Combining Three Data Envelopment Analysis-based Methods

Design of Disaster Relief Logistics Network System by Combining Three Data Envelopment Analysis-based Methods

Multi-Objective Modeling of Relief Items Distribution Network Design Problem in Disaster Relief Logistics Considering Transportation System and CO2 Emission

The present study aims to propose a multi-objective mixed integer mathematical programming model for designing a relief items distribution network in sustainable disaster relief logistics. The first objective function minimizes the total network costs. Which are divided into two parts: 1- relief costs including (transportation costs, inventory costs and fixed costs of facilities) 2- social costs (deprivation cost). The second objective function minimizes the amount of pollution generated by the network. Considering the related literature review, this is the first study that to propose a robust fuzzy optimization approach for relief items distribution network design problem considering environmental (CO2 emission), social (deprivation cost) and economic impacts under reliability and uncertainty. Then, the multi-objective model was solved using the multi-choice goal programming. To indicate the validity of the proposed model, a case study was evaluated based on real data (2019 flood in Sari city, Mazandaran Province). Using the proposed model, decision-makers and managers are able to make strategic and tactical decisions with the least cost and time, and in relief planning can enhance the structure of distribution networks and inventory and reduce victims’ dissatisfaction.

Risk-averse two-stage distributionally robust optimisation for logistics planning in disaster relief management

Relief logistics is vital to disaster relief management. Herein, a risk-averse two-stage distributionally robust programming model is proposed to provide decision support for planning disaster relief logistics. It is distinct from the conventional disaster relief logistics planning problem in that (i) the facility location-inventory model and the multi-commodity network flow formulation are integrated; (ii) the probability distribution information of the supply, demand, and road link capacity is partially known, and (iii) the two-stage distributionally robust optimisation (DRO) method based on the worst-case mean-conditional value-at-risk criterion is developed. For tractability, we reformulate the proposed DRO model as equivalent mixed-integer linear programs for box and polyhedral ambiguity sets, which can be directly solved to optimality using the CPLEX software. To evaluate the validity of the proposed DRO model, we conduct numerical experiments based on a real-world case study addressing hurricane threats in the Gulf of Mexico region of the United States. Furthermore, we compare the performance of the proposed DRO model with that of the conventional two-stage stochastic programming model. Finally, we report the managerial implications and insights of using the risk-averse two-stage DRO approach for disaster relief management.

Ant Colony Optimization Algorithm Implementation for Distribution of Natural Disaster Relief Logistics in Jombang Regency Web Base

Ten sub-districts in Jombang, Indonesia, namely jombang downtown, Bareng, Wonosalam, Mojoagung, Mojowarno, Ploso,Kudu, Bandarkedungmulyo, Sumobito dan kesamben experience the impact of natural disasters of floods and landslides each year. All of these sub-districts consists of 32 villages. Disasters that become an annual routine, namely floods and landslides, make residents suffer moral and material losses. The Regional Disaster Management Agency (BPBD) is the main actor in helping this disaster such as donors who want to help the victims of natural disasters is accommodated by BPBD with the aim of equitable distribution of aid and on target. However, the practice in the field encountered obstacles namely not finding the shortest track from the location of the natural disaster to be visited in accordance with the track and logistics needs at the disaster site. The Ant Colony Optimization (ACO) algorithm is used by researchers to solve the problems encountered by Jombang BPBD. There are 6 iterations in implementing this algorithm. ACO algorithm test outcomes to find the track in the distribution of logistics to the disaster location that obtained the value of the number of ants 10, the initial pheromone of 0.117 from C greedy obtained 85,5 and the distance obtained from the google map. With the ACO algorithm found 3 alternatives for logistics distribution tracks in natural disaster areas in Jombang.

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.

AFET VE İNSANİ YARDIM LOJİSTİĞİ ALANINDA YAPILAN ÇALIŞMALARIN BİBLİYOMETRİK ANALİZİ

Dünyada yaşanan doğal afetler ve insan kaynaklı felaketler pek çok bölgeye zarar vermekte, can ve mal kaybına neden olarak büyük zararlara yol açmaktadır. Bu afetlerin ve felaketlerin verdiği zararların azaltılabilmesi afet öncesi ve afet sonrasındaki sürecin iyi bir şekilde yönetilebilmesiyle mümkündür. Bu nedenle; özellikle son yıllarda afet ve insani yardım lojistiğine olan ilgi artmıştır. Bu çalışmada amaç; 2004-2019 yılları arasında Web of Science (WOS) ve Scopus veri tabanından alınan afet ve insani yardım lojistiği alanında yapılan 854 makaleyi bibliyometrik analiz yöntemiyle incelemek ve afet ve insani yardım lojistiği alanındaki araştırmaların hangi yönde ilerlediğini ortaya koymaktır. Çalışmanın verileri 2019 yılı Mayıs ayında toplanmıştır. Yapılan çalışma ile son yıllarda daha fazla ilgi duyulan bir alan olan afet ve insani yardım lojistiğiyle ilgili çalışmaların hangi yıllarda artış gösterdiğini belirlenmiş, bu alanda önde gelen ülke, kurum, dergi ve yazarların genel profilleri ortaya çıkarılmıştır. Analizler sonucunda elde edilen veriler; tablolar ve şekiller ile desteklenerek açıklanmıştır. Analiz sonuçlarına göre; özellikle 2015’den sonra afet lojistiği konusuna daha fazla ilgi duyulduğu, yapılan çalışmaların en çok atıfta bulunduğu derginin European Journal of Operational Research dergisi olduğu, bu alanda en çok çalışan bölümün işletme bölümü, en fazla yayın yapan ve atıf patlamasının yaşandığı ülkenin ABD, son yıllarda en çok çalışmanın yapıldığı ülkenin ise Hollanda olduğu sonucuna ulaşılmıştır.

A Multi-Objective Multi-Period Model for Humanitarian Relief Logistics with Split Delivery and Multiple Uses of Vehicles

Disaster relief logistics is a significant element in the management of disaster relief operations. In this paper, the operational decisions of relief logistics are considered in the distribution of resources to the affected areas to include scheduling, routing, and allocation decisions. The proposed mathematical model simultaneously captures many aspects relevant to real life to face the challenging situation of disasters. Characteristics such as multiple uses of vehicles and split delivery allow for better use of vehicles as one of the primary resources of disaster response. A multi-period multi-criteria mixed-integer programming model is introduced to evaluate and address these features. The model utilizes a rolling horizon method that provides possibilities to adjust plans as more information becomes available. Three objectives of efficiency, effectiveness, and equity are jointly considered. The augmented epsilon constraint method is applied to solve the model, and a case study is presented to illustrate the potential applicability of our model. Computational results show that the model is capable of generating efficient solutions.

Problems of Realization of Humanitarian Aid in Borana Zone, Ethiopia

Disaster is a serious of the functioning community or a society. In response to disaster, Humanitarian Aid Agencies operates under challenging environment. Historically, Borana zone is among the area from the southern part of Ethiopia which is highly vulnerable to both natural and manmade disaster. The researcher aim to identify problem Humanitarian aid agencies faced during their operation. The researcher employed descriptive research design and purposive sampling technique. And data was collected from 139 respondents through questionnaire, FGD [1] and interview. The finding revealed that; there are serious communication barriers, fund shortage and allocation problems, lack of physical infrastructure and facilities, lack of control and follow up among Humanitarian Aid Agencies and government bodies, lack of collaboration among humanitarian agencies, lack of coordination among government and None Governmental Organizations officials. And drought, disease and economic crises are the top three disasters that are being major cause for humanitarian aid. In general, based the findings the researcher recommended that HAAs [2] , Government bodies and Pastoral communities should set solution for the issue at hand. Keywords : Disaster, Humanitarian Aid Agency, Disaster Relief logistics, Humanitarian Aid and Relief Supply. DOI: 10.7176/IAGS/81-01 Publication date: March 31 st 2020 [1] FGD: Focus Group Discussion [2] HAA: Humanitarian Aid Agency

Integrated and periodic relief logistics planning for reaction phase in uncertainty condition and model solving by PSO algorithm

Disaster relief logistics is considered to be one of the major activities in disaster management. This research studies response phase of the disaster management cycle. To do so, a multi-purpose integrated model for a three-level relief cycle logistics is provided under an uncertainty condition and on a periodic basis. In this model, inventory transfer, vehicle routing, distribution and sending relief goods are modeled on a periodic basis. In addition, in order to solve the proposed mathematical model, ultra-initiative particles swarm algorithm in combination with variable neighborhood search based on Pareto archive is proposed. To prove the efficiency of the proposed particles swarm algorithm, several sample problems are randomly selected considering the solved problems in the literature and are solved by particles swarm algorithm. These problems are also solved by genetic algorithm and the results obtained from these two algorithms are compared in terms of quality, dispersion and integrity indices. The results show that compared to genetic algorithm, particles swarm algorithm is more capable of producing more integrated, qualified and dispersed responses. Moreover, the results show that the solution time of genetic algorithm is less than that of the proposed algorithm.

Integrated and periodic relief logistics planning for reaction phase in uncertainty condition and Model Solving by particles swarm optimization algorithm

Disaster relief logistics is considered to be one of the major activities in disaster management. This research studies response phase of the disaster management cycle. To do so, a multi-purpose integrated model for a three-level relief cycle logistics is provided under an uncertainty condition and on a periodic basis. In this model, inventory transfer, vehicle routing, distribution and sending relief goods are modeled on a periodic basis. In addition, in order to solve the proposed mathematical model, ultra-initiative particles swarm algorithm in combination with variable neighborhood search based on Pareto archive is proposed. To prove the efficiency of the proposed particles swarm algorithm, several sample problems are randomly selected considering the solved problems in the literature and are solved by particles swarm algorithm. These problems are also solved by genetic algorithm and the results obtained from these two algorithms are compared in terms of quality, dispersion and integrity indices. The results show that compared to genetic algorithm, particles swarm algorithm is more capable of producing more integrated, qualified and dispersed responses. Moreover, the results show that the solution time of genetic algorithm is less than that of the proposed algorithm.

Synchronized Humanitarian, Military and Commercial Logistics: An Evolving Synergistic Partnership

The increasing complexity and often transboundary scope of complex emergencies are exceeding the capacity of humanitarian logistics systems. The military plays a growing role in supplementing and even leading humanitarian assistance and disaster relief logistics. However, issues relating to military involvement such as sovereignty and cost have refocused the conversation onto capabilities and capacities of commercial logistics providers, who have not been fully engaged and integrated into disaster preparedness, response, and recovery activities. The commercial sector is part of the larger supply chain management system that includes contracting, procurement, storage, and transportation of food, water, medicine, and other supplies, as well as human resources, and necessary machinery and equipment. Military and commercial logistics share many of these elements and tasks daily. The most effective and efficient response combines key elements from humanitarian, military, and commercial logistics systems. Such trilateral cooperation represents the next step in an evolving partnership paradigm that is truly synergistic. We present a Synchronized Disaster Relief Model, as well as multiple examples of how military, commercial, and humanitarian supply chains each bring unique capabilities to disaster relief operations, and how these three supply chains can complement each other in a synergistic manner, through synchronized action.

AHP분석을 통한 재해구호지원센터 적정 설치방안

AHP분석을 통한 재해구호지원센터 적정 설치방안