Humanitarian Relief Logistics Research Articles

Identification of Key Drivers for Performance Measurement in Sustainable Humanitarian Relief Logistics: An Integrated Fuzzy Delphi-DEMATEL Approach

Sustainable humanitarian relief logistics (SHRL) is gaining attention due to increased disasters, unpredictable demand, large volumes, high delivery stakes, and limited resources, evaluated through adaptable performance drivers. This study presents a novel hybrid framework for SHRL, combining the Fuzzy Delphi Method (FDM) and Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL). Initially, FDM is utilized to reach a consensus among experts concerning key performance indicators (KPIs) for humanitarian logistics and supply chains. By incorporating the inherent uncertainty and vagueness in expert judgments, FDM refines the list of key performance indicators that reflect the real-life conditions and constraints in disaster operations. Finally, the fuzzy DEMATEL approach was used to analyze the interrelationships among factors, identifying cause-and-effect behavior and ranking them, forming a robust theoretical framework. Based on the acquired results, the KPIs attached to the Quality (P1) aspect of the proposed framework have gained significant importance and are the main cause in a cause-and-effect relationship which impacts and is helpful to improve the performance of humanitarian organizations in all phases of disaster management. The KPIs prompt delivery (D1), and delivery accuracy (D2) are more significant, while capacity building and training (D19) and delivery compliance (D15) are least significant in SHRL scenarios. This research is expected to support humanitarian organizations in enhancing their capabilities, thereby improving the effectiveness and efficiency of aid delivery in disaster-stricken areas.

Location and replenishment problems of disaster stations for humanitarian relief logistics along with an application

Location and replenishment problems of disaster stations for humanitarian relief logistics along with an application

Modelling a bi-level multi-objective post-disaster humanitarian relief logistics network design problem under uncertainty

Post-disaster humanitarian relief logistics focuses on managing the supply and shipment of relief items. Owing to the complexity arising from the hierarchical decision relationship and the uncertainties of demand and transportation, it is challenging to develop an effective humanitarian relief logistics network (HRLN). A globalized robust bi-level multi-objective HRLN model involving uncertainties in transportation time, transportation cost and demand is developed to address this problem, in which an upper-level decision-maker focuses on the allocation problem and a lower-level decision-maker considers the supply problem. For tractability, the goal programming approach is employed to trade off conflicting timeliness, satisfaction and operational cost objectives. The proposed globalized robust bi-level HRLN model is reformulated as its globalized robust counterpart formulations under two different perturbation structures. The primal–dual approach is adopted to transform the bi-level models into equivalent mixed-integer conic programs. A case study concerning an Iranian earthquake is conducted to prove the performance of the proposed model. Globalized robust models are more flexible and able to immunize against uncertainty by comparison with traditional robust optimization and deterministic solutions. The managerial insights of using the globalized robust bi-level optimization approach are reported for disaster response management. The design of an emergency aviation network to circumvent the effects of COVID-19 could be considered in future research.

A systematic literature review on humanitarian logistics using network analysis and topic modeling

The aim of this paper is to conduct a systematic literature review to understand the research trends, identify area of improvements and recommend the future research direction in humanitarian relief logistics (HRL). Using Netminer 4.0, keyword network analysis, keyword frequency analysis, and topic modeling analysis were undertaken. As a result, it has been identified that the number of studies on HRL continuously increased since it mounted in the mid-2010s, with the main issues changing by period. Keyword network analysis and keyword frequency analysis discovered that establishment of the HRL identity and disaster response, preparation and recovery were the main issues from 2005 to 2009. Whereas followed by education and practical logistics flow (2010 – 2014) and provision and coordination (2015 – 2019) to be the main focused area of research. Through topic modeling analysis, nine topics (preparation, coordination, stakeholders, sustainable development, facilities location, recovery, supplying and sourcing, emergent response, transportation, supplying and sourcing) were extracted. Facilities location was the most frequent topic that studied in the number of papers. Meanwhile, recovery, preparation, stakeholders, and emergent response showed downward trends but facilities location, coordination, and supplying and sourcing showing upward. This study is significant in the sense that it suggests research trends in different approach using keyword network analysis, keyword frequency analysis, network analysis and topic modeling which are one of the first attempts in research trend analysis in HRL field. This study will support HRL studies by understanding macroscopic and comprehensive way and set as a steppingstone for future research direction.

Performance indicators for humanitarian relief logistics in Taiwan

The purpose of this work is to explore the international humanitarian relief organizations in Taiwan, and find the most crucial performance indicators of humanitarian logistics, according to the experience and opinion of those large NGOs in Taiwan. The research gathered detailed information on situations from literature and collected the data by questionnaire. Based on the information gathered from questionnaires, quantitative weight was calculated through the participation of experts by using AHP in this research. According to the humanitarian relief organizations' experiences and opinion in this research, the most critical performance indicators in humanitarian relief logistics is responsiveness, and the following is agility, reliability, cost, and dialog with authorities. Although the AHP method has been applied in many research of performance indicators chosen, there is no research using AHP to prioritize the performance indicators in the humanitarian logistics field, especially in Taiwan. Future research could include the smaller or reputable organizations in Taiwan to get more comprehensive opinions. Besides, the chosen indicators could be utilized in the actual case of humanitarian rescue action.

A new humanitarian relief logistic network for multi-objective optimization under stochastic programming.

Millions of affected people and thousands of victims are consequences of earthquakes, every year. Therefore, it is necessary to prepare a proper preparedness and response planning. The objectives of this paper are i) minimizing the expected value of the total costs of relief supply chain, ii) minimizing the maximum number of unsatisfied demands for relief staff and iii) minimizing the total probability of unsuccessful evacuation in routes. In this paper, a scenario based stochastic multi-objective location-allocation-routing model is proposed for a real humanitarian relief logistics problem which focused on both pre- and post-disaster situations in presence of uncertainty. To cope with demand uncertainty, a simulation approach is used. The proposed model integrates these two phases simultaneously. Then, both strategic and operational decisions (pre-disaster and post-disaster), fairness in the evacuation, and relief item distribution including commodities and relief workers, victim evacuation including injured people, corpses and homeless people are also considered simultaneously in this paper. The presented model is solved utilizing the Epsilon-constraint method for small- and medium-scale problems and using three metaheuristic algorithms for the large-scale problem (case study). Empirical results illustrate that the model can be used to locate the shelters and relief distribution centers, determine appropriate routes and allocate resources in uncertain and real-life disaster situations.

Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand

The 2004 Indian Ocean tsunami led to improvements in Thailand’s early warning systems and evacuation procedures. However, there was no consideration of better aid delivery, which critically depends on estimates of the affected population. With the widespread use of geographical information systems (GIS), there has been renewed interest in spatial population estimation. This study has developed an application to determine the number of disaster-impacted people in a given district, by integrating GIS and population estimation algorithms, to facilitate humanitarian relief logistics. A multi-stage spatial interpolation is used for estimating the affected populations using ArcGIS software. We present a dasymetric mapping approach using a population-weighted technique coupled with remote sensing data. The results in each target area show the coordinates of each shelter location for evacuees, with the minimum and maximum numbers of people affected by the tsunami inundation. This innovative tool produces not only numerical solutions for decision makers, but also a variety of maps that improve visualisation of disaster severity across neighbourhoods. A case study in Patong, a town of Phuket, illustrates the application of this GIS-based approach. The outcomes can be used as key decision-making factors in planning and managing humanitarian relief logistics in the preparedness and response phases to improve performance with future tsunami occurrences, or with other types of flood disaster.

Structural analysis of the challenges in operating pre-positioned warehouses for humanitarian relief logistics

인도적 구호물류에서 사전배치창고를 운영하는 것은 재앙적 사건의 발생에 빠르게 대응할 수 있다는 장점에도 불구하고 위험이 높은 전략으로 종종 인식되고 있다. 본 연구에서는 사전배치창고 운영상의 중대한 난맥에 대해 분석함으로써 그러한 난맥이 어떻게 발생하고 상호작용하며 확대되는지 이해하고자 한다. 이를 위해 실증적 자료 분석이 가미된 해석적 구조분석(ISM)을 이용하여 17개의 대표적 운영상 난맥을 분석한다. 17개의 요인은 사전배치창고의 운영을 독립적으로 저해하는 것으로 알려져 있지만, 본 연구는 이들의 연결성을 도식화된 모형을 통해 나타낸다. 분석 결과 운영상 난맥은 3개의 그룹, 즉 인도적 구호의 방해, 물류 결정 관련 문제, 외부적 요인에 의한 차질로 나누어진다. 여기서 물류 결정 관련 문제 그룹은 높은 운송 비용을 중심으로한 두 개의 자기확장적 순환에 의해 악화된다는 것을 보여준다. 본 연구는 사전배치창고 운영의 장점과 단점에 관한 통찰력을 제공하여 구호물자의 사전배치 시 효과성을 증대시키는데 일조할 수 있을 것이다.

Pareto-based grouping meta-heuristic algorithm for humanitarian relief logistics with multistate network reliability

This article considers a biobjective location-routing problem to deliver relief resources to the victims affected by a disaster under uncertainty in demand, transportation infrastructure, and travel time. Since transportation networks are exposed to a considerable level of uncertainty, choosing the reliable path for relief goods to be transmitted to the affected areas ensures the arrival of these supplies. For the first time, route reliability is calculated based on the multistate theory, and the universal generating function technique is used for network reliability assessment. The problem is formulated as a multiperiod robust biobjective mixed-integer programming model. Two objective functions are considered: (a) decreasing the sum of arrival times of relief vehicles at the demand nodes for delivering aids to the affected areas, and (b) increasing the minimum route reliability for all the serving vehicles. A novel multiobjective grouping algorithm is proposed to obtain the Pareto-optimal solutions of the problem. Then, its performance is compared with two other multiobjective grouping algorithms. To evaluate the solution method, the algorithms are implemented on various test problems and compared statistically. A case study is presented to illustrate the potential applicability of our model. Additionally, to determine the effect of the changes in the main parameters of the problem on the value of objective functions, the sensitivity analyses are performed and the managerial insights are given.

Pre-Positioning Facility Location and Resource Allocation in Humanitarian Relief Operations Considering Deprivation Costs

Alleviating human sufferings during and in the aftermath of disasters is one of the most important goals in humanitarian relief logistics. The lack of relief commodities, especially life-saving items, is a life-threatening loss to victims and must be considered when making emergency supply allocation and transportation decisions, even in the pre-disaster prepositioning phase. This paper proposes a scenario-based stochastic program that integrates the decisions of prepositioning facility locations, quantities of stocked emergency supplies, and service allocations in each scenario in the same modeling framework. The estimation of victims’ losses for waiting for emergency supplies is measured in the typical deprivation cost function and treated as one of the main bases of decision making, besides traditional transportation costs, in determining the service allocation strategies in each scenario. Specifically, a case study with data from the hurricane threat in the Gulf Coast area of the US was conducted to demonstrate the application of this model and the significance of considering victims’ welfare loss in humanitarian relief logistics. Some interesting managerial insights were also drawn from a series of numerical experiments and sensitivity analyses.

A Novel Approach for Determining Shelter Location-Allocation in Humanitarian Relief Logistics

This study proposes a methodology that integrates the epsilon constraint method (EC) and artificial neural network (ANN) to determine shelter location-allocation. Since shelter location-allocation is a critical part of disaster response stage, fast decision-making is very important. A multi-objective optimization model is formulated to simultaneously minimize total cost and minimize total evacuation time. The proposed model is solved by EC because it generates the optimal solutions without intervention of decision-makers during the solution process. However, EC requires intensive computational time, especially when dealing with large-scale data. Thus, ANN is combined with EC to facilitate prompt decision-making and address the complexity. Herein, ANN is supervised by the optimal solutions generated by EC. The applicability of the proposed methodology is demonstrated through a case study of shelter allocation in response to flooding in Surat Thani, Thailand. It is plausible to use this proposed methodology to improve disaster response for the benefit of victims and decision-makers.

Modeling equitable and effective distribution problem in humanitarian relief logistics by robust goal programming

Humanitarian relief food distribution problem is the pickup of the food supply from different donors and the distribution to individuals in need. The problem now becomes a meaningful work to alleviate people’s bad situations. Motivated by this prevalent steam of significant topic, we focus on the humanitarian relief problem. In this paper, we examine the equitable and effective food distribution to poor people who need food in the general poverty scenario. In the modeling process, we make a classification of the demand nodes. Then, we introduce an innovative robust goal programming model with priority factors and optimize the distribution decisions under uncertain supply and capacity characterized by an uncertainty set. Under robust optimization techniques, the solving process of our proposed robust model is discussed and depends on a parametric cone program which can be efficiently solved by the commercial software. Finally, we conduct a number of numerical experiments about the Food Bank of Central and Eastern North Carolina in America to illustrate the application of our new model. The computational results show that our model can realize the trade-off between equity and effectiveness under uncertainty for humanitarian relief problem.

A multi-objective optimization model for shelter location-allocation in response to humanitarian relief logistics

Decision-making for shelter location-allocation influences the success of disaster response and affects the security of victims. This paper proposes a multi-objective optimization model for determining shelter location-allocation in response to humanitarian relief logistics. Three objective functions are formulated to improve both efficiency and effectiveness. The first objective is to minimize total costs, including fixed costs for opening the shelters, transportation costs, and service costs. The second objective is to minimize the total time for evacuating victims from all affected areas to allocated shelters. The third objective is to minimize the number of shelters required to provide thorough service to victims. The Epsilon Constraint method (EC) and Goal Programming (GP) are employed for solving the proposed model. The applicability of the proposed model is validated through a case study of flooding in Surat Thani, Thailand. The Pareto efficiency obtained from solving the proposed model is compared with current shelter location-allocation plans determined by the government sector. The comparisons reveal that the results obtained from solving the proposed model outperform current shelter location-allocation plans. Furthermore, the results of this study could provide an advantage to decision-makers considering appropriate strategies for disaster response.

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.

Designing a sustainable humanitarian relief logistics model in pre- and postdisaster management

In this study, a three-level relief chain problem is proposed in pre- and postdisaster phases in order to support sustainability in the context of humanitarian relief operations. We develop a comprehensive relief logistics network considering the strategic and tactical planning issues of facility location, procurement, and resource allocation, and including multi-objective mixed-integer stochastic programing model which minimizes total costs of the humanitarian relief supply chain (the costs of the preparedness and response phases), maximizes the social welfare (through maximizing the minimum satisfaction rates among the disaster areas), and minimizes the environmental impacts (ecological impacts of the packaging of relief goods, and CO2 emissions in transportation routes of the proposed network in pre- and postdisaster phases), simultaneously. The proposed multi-objective sustainable humanitarian relief logistics problem (SHRLP) is solved using the Compromise Programing (CP) technique and Lexicographic Optimization Method (LOM). Two numerical examples with different scales are provided based on surveying data of possible Tehran earthquakes. The results of solving numerical examples via CP and LOM methods are presented and compared. The efficient Pareto optimal solutions are also provided to examine different preferences of the objectives. LOM produces better solutions than the CP technique in terms of the satisfaction rates and fair distribution of relief items. Also, the results indicate that the novel model is able to survey and model both the sustainability approach and the uncertainty of a disaster relief condition in support of more efficient and effective plans for sustainable facility locations, and procurement and delivery of reliefs.

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.

Real-time location systems selection by using a fuzzy MCDM approach: An application in humanitarian relief logistics

The real-time location systems (RTLSs) with different positioning technologies allow real-time and high-precision localization of assets. Since the usage of RTLSs technologies and their population increase, RTLSs technology selection problem that addresses many factors should be considered. Therefore, it is important to determine the performance criteria and evaluation of these technologies should be investigated before RTLSs technology is applied to the system. This paper aims to select the most appropriate RTLSs technology by using a combined fuzzy based decision-making approach. Thus, the first paper for selection of RTLSs systems in a holistic approach by combining benefit and risk factors has been revealed. The developed approach is applied to humanitarian relief logistics warehouse with four alternatives which are given as Ultra-Wide Band, Wi-Fi, UHF RFID and Active RFID for the selection. The proposed approach has been integrated with interval-valued intuitionistic fuzzy (IVIF) sets that allow to deal with fuzziness inherent in decision making processes. For this aim, firstly IVIF DEMATEL is used to determine the inner and outer dependencies of the sub and main criteria; secondly, weights of the sub-criteria are obtained by using IVIF ANP. Finally, the best RTLSs technology to be used in humanitarian logistics warehouse is selected by using IVIF TOPSIS. As a result of the calculations, the best system is determined as the “Wi-Fi RTLS” system based on the given context. By the way, a sensitivity analysis has been also implemented to test and validate the developed methodology.

Developing a bi-objective location-allocation-inventory problem for humanitarian relief logistics considering maximum allowed distances limitations

The crises are the inevitable realities of human life, which occur naturally or by mankind suddenly and require urgent and fundamental measures to resolve it. In this research, a linear bi-objective mathematical model for the preparation phase of crisis management is formulated. The proposed model studies location-allocation problem with multiple commodities and attempts to minimise total costs along with maximising the minimum weight of constructed locations. Moreover, the maximum allowed distances for damaged areas from main roads and equipped hospitals as novel limitations are considered. Also, the model is verified by some applied examples and to solve it, some of the well-known multi-objective decision making (MODM) approaches includes weighted sum method, LP-metric method, and goal programming technique are used. Moreover, in order to measure efficiency of the proposed model, a sensitivity analysis is performed, which finally, the best constructed locations with minimum weights and costs are selected. The obtained results can be useful for crisis organisation.

Developing a bi-objective location-allocation-inventory problem for humanitarian relief logistics considering maximum allowed distances limitations

The crises are the inevitable realities of human life, which occur naturally or by mankind suddenly and require urgent and fundamental measures to resolve it. In this research, a linear bi-objective mathematical model for the preparation phase of crisis management is formulated. The proposed model studies location-allocation problem with multiple commodities and attempts to minimise total costs along with maximising the minimum weight of constructed locations. Moreover, the maximum allowed distances for damaged areas from main roads and equipped hospitals as novel limitations are considered. Also, the model is verified by some applied examples and to solve it, some of the well-known multi-objective decision making (MODM) approaches includes weighted sum method, LP-metric method, and goal programming technique are used. Moreover, in order to measure efficiency of the proposed model, a sensitivity analysis is performed, which finally, the best constructed locations with minimum weights and costs are selected. The obtained results can be useful for crisis organisation.

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.