Relief Chain Research Articles

Humanitarian Logistics and Emergency Response in Humanitarian Organizations in Kenya.

Disasters were unpredictable and unavoidable, necessitating adequate disaster response plans. Humanitarian logistics, which gained prominence after the 2004 Asian tsunami, played an important part in crisis prevention. Despite weak links in relief chain management, issues remained. Humanitarian logistics had a huge impact on disaster relief efforts, as studies revealed a variety of challenges, including a lack of sufficient SCM software and poor facilities. Despite its importance, few studies have explored the impact of disaster assistance on humanitarian organizations, particularly in Kenya. The study sought to address this gap by evaluating the impact of humanitarian logistics on emergency response in Nairobi, Kenya. Humanitarian groups, policymakers, and NGOs profited from the research, which informed strategic planning and policy creation to strengthen disaster response operations. The Resource Dependency Theory (RDT) proposed that organizations rely on external resources for survival and growth. It underlined the importance of forming strategic alliances to secure resources. In humanitarian settings, logistics were critical in procuring and allocating resources for successful emergency response. The research methodology used a cross-sectional descriptive design to investigate the association between humanitarian logistics management techniques and emergency response in Kenyan humanitarian organizations. Semi-structured questionnaires were used to obtain data from 64 Nairobi-based firms. Data analysis required the use of both descriptive and inferential statistical techniques, such as regression and correlation analysis. The study found a significant positive association (β = 0.732, p < 0.001) between logistics management practices and emergency response, indicating that improving logistics leads to better response outcomes. The study recommended prioritizing logistical development and resource allocation to improve emergency response capabilities.

Operations research approaches for improving coordination, cooperation, and collaboration in humanitarian relief chains: A framework and literature review

Given the considerable number of actors in the humanitarian space, coordination is essential for successful disaster response. Furthermore, the sheer size of challenges and limited resources increasingly highlight the need for improved cooperation and collaboration in humanitarian supply chains. A significant number of studies in the literature explore the 3Cs (coordination, cooperation and collaboration), using conceptual, empirical and analytical methods. This paper aims to provide an overview and analysis of the Operations Research (OR) approaches that support decision making for improved 3Cs in humanitarian relief chains and to identify future research directions. To achieve this aim, we first present a holistic view of the discussions in the literature and derive a conceptual framework for 3C mechanisms in humanitarian operations. Based on our framework, we analyse studies that develop OR methods to address the design and management of 3C mechanisms in humanitarian relief chains. We also identify current gaps and future research directions.

Resilient relief supply planning using an integrated procurement-warehousing model under supply disruption

In order to mitigate the damage caused by natural disasters, appropriate relief supply planning is essential for humanitarian relief chains. This paper develops a resilient relief supply plan by formulating a novel integrated procurement-warehousing model under supply disruption via utilizing a portfolio of long-term contracts between a relief organization, multiple main and backup suppliers, and a third party who provides warehousing service. By using a portfolio of supply sources, a tailored two-stage scenario-based stochastic programming model is presented for determining an integrated relief procurement-warehousing plan. The plan includes (1) a number of multi-period quantity flexibility contracts with the main suppliers at pre-disaster, for prepositioning of relief supplies while ensuring to receive extra required amounts at post-disaster, (2) a number of dynamic option contracts with backup suppliers for decreasing the impacts of risks pertaining to the demand uncertainty and supply disruption at the main suppliers, and (3) a multi-period warehousing contract with a third party which allows the relief organization to adjust the required storage space in different periods to avoid extra cost pertaining to the storage space surplus/shortage. An effective differential evolution (DE) solution algorithm is also devised to solve the real scale instances efficiently. Applicability of the proposed model is illustrated by a real case study and useful managerial insights are extracted through conducting several sensitivity analyses.

A Stochastic Optimization Model for Designing a Humanitarian Relief Chain Considering Operational and Disruption Risk

A Stochastic Optimization Model for Designing a Humanitarian Relief Chain Considering Operational and Disruption Risk

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.

Emergency Relief Chain for Natural Disaster Response Based on Government-Enterprise Coordination.

Public health and effective risk response cannot be promoted without a coordinated emergency process during a natural disaster. One primary problem with the emergency relief chain is the homogeneous layout of rescue organizations and reserves. There is a need for government-enterprise coordination to enhance the systemic resilience and demand orientation. Therefore, a bi-level multi-phase emergency plan model involving procurement, prepositioning and allocation is proposed. The tradeoff of efficiency, economy and fairness is offered through the multi-objective cellular genetic algorithm (MOCGA). The flood emergency in Hunan Province, China is used as a case study. The impact of multi-objective and coordination mechanisms on the relief chain is discussed. The results show that there is a significant boundary condition for the coordinated location strategy of emergency facilities and that further government coordination over the transition phase can generate optimal relief benefits. Demand orientation is addressed by the proposed model and MOCGA, with the realization of the process coordination in multiple reserves, optimal layout, and transition allocation. The emergency relief chain based on government-enterprise coordination that adapts to the evolution of disasters can provide positive actions for integrated precaution and health security.

Develop a situation-based prioritization program as a road map to enhance the pre-resilience in flood management using machine learning methods

PurposeThis study aims to review the features and challenges of the flood relief chain, identifies administrative measures during and after the flood occurrence and prioritizes them using two machine learning (ML) and analytic hierarchy process (AHP) methods. This paper aims to provide a prioritization program based on flood conditions that optimize flood management and improves society’s resilience against flood occurrence.Design/methodology/approachThe collected database in this paper has been trained by using ML algorithms, including support vector machine (SVM), Naive Bayes (NB) and k-nearest neighbors (kNN), to create a prioritization program. Furthermore, the administrative measures in two phases of during and after the flood are prioritized by using the AHP method and questionnaires completed by experts and relief workers in flood management.FindingsAmong the ML algorithms, the SVM method was selected with 91.37% accuracy. The prioritization program provided by the model, which distinguishes it from other existing models, considers five conditions of the flood occurrence to prioritize actions (season, population affected, area affected, damage to houses and human lives lost). Therefore, the model presents a specific plan for each flood with different occurrence conditions.Research limitations/implicationsThe main limitation is the lack of a comprehensive data set to determine the effect of all flood conditions on the prioritization program and the relief activities that have been done in previous flood disasters.Originality/valueThe originality of this paper is the use of ML methods to prioritize administrative measures during and after the flood and presents a prioritization program based on each flood’s conditions. Therefore, through this program, the authority and society can control the adverse impacts of flood more effectively and help to reduce human and financial losses as much as possible.

Developing a bi-objective resilience relief logistic considering operational and disruption risks: a post-earthquake case study in Iran

Today, according to the occurrence of numerous disasters in allover over the world, designing the proper and comprehensive plan for relief logistics has received a lot of attention from crisis managers and people. Besides, considering resilience capability along with operational and disruption risks leads to the robustness of the humanitarian relief chain (HRC), and this comprehensive framework ensures the essential supplies delivery to the beneficiaries and is close to real-world problems. The resilience parameters used for the second objective are obtained by a strong Best Worst Method (BWM). Another supposition of the model is the consideration of uncertainty in all stages of the proposed problem. Moreover, the multiple disasters (sub-sequent minor post disasters) which can increase the initial demand are considered. Furthermore, the proposed model is solved using three well-known metaheuristic algorithms includes non-dominated sorting genetic algorithm (NSGA-II), network reconfiguration genetic algorithm (NRGA), and multi-objective particle swarm optimization (MOPSO), and their performance is compared by several standard multi-objective measure metrics. Finally, the obtained results show the robustness of the proposed approaches, and some directions for future researches are provided.

Analysis of optimized response time in a new disaster management model by applying metaheuristic and exact methods

Purpose The purpose of this paper is to maximize the total demand covered by the established additive manufacturing and distribution centers and maximize the total literal weight assigned to the drones. Design/methodology/approach Disaster management or humanitarian supply chains (HSCs) differ from commercial supply chains in the fact that the aim of HSCs is to minimize the response time to a disaster as compared to the profit maximization goal of commercial supply chains. In this paper, the authors develop a relief chain structure that accommodates emerging technologies in humanitarian logistics into the two phases of disaster management – the preparedness stage and the response stage. Findings Solving the model by the genetic and the cuckoo optimization algorithm (COA) and comparing the results with the ones obtained by The General Algebraic Modeling System (GAMS) clear that genetic algorithm overcomes other options as it has led to objective functions that are 1.6% and 24.1% better comparing to GAMS and COA, respectively. Originality/value Finally, the presented model has been solved with three methods including one exact method and two metaheuristic methods. Results of implementation show that Non-dominated sorting genetic algorithm II (NSGA-II) has better performance in finding the optimal solutions.

A cooperative game theory approach for location-routing-inventory decisions in humanitarian relief chain incorporating stochastic planning

This paper describes a new simulation based mathematical model for locating distribution centres, vehicle routing, and inventory problems under earthquake conditions. In this paper, the proposed network includes affected areas, suppliers, distribution centres, and hospitals. Additionally, the basic infrastructures of the city, which are very fragile at the time of the earthquake, are identified. Then, the demand of each relief commodity is calculated depending on the different earthquake scenarios using simulation. The estimated demand is incorporated into the mathematical model as an uncertain parameter. The proposed methodology is designed as a two-stage model so that in the first stage the location and inventory of distribution centres are addressed. Thereafter, in the second stage the routing decisions are taken for the distribution of relief commodities from the distribution centres and suppliers to the affected areas. Due to the NP-hardness of the second stage model, this model is solved using multi-objective stochastic fractal search. This algorithm is one of the population-based and stochastic optimization techniques and inspired by the natural phenomenon of fractal growth. It should be noted that in the second stage, a cooperative game theory of coalition type is considered, which resulted in synergies that minimize the relief golden time. The kind of cooperation is the use of potential of co-operators’ vehicle. In this stage, the possibility for the players to cooperate by sharing people and commodities transportation requests is considered. Also, to validate the model, a real case study is provided for a possible earthquake in Tehran. Finally, the comparison between the simulation results and the values obtained from the real system evaluates the performance of the implemented model. Considering normality and a 95% confidence interval, it can be concluded that the proposed model provides a precise representation of the real system's performance.

A novel humanitarian and private sector relief chain network design model for disaster response

The private sector is widely regarded as a useful element in the emergency response to disasters. Humanitarian organizations tend to cooperate with the private sector to deliver aids to affected areas and mitigate the complexity of disasters. In this paper, we propose an efficient relief chain consisting of a humanitarian organization (HO) and third-party logistics providers (3PLPs) to tackle the issue of decentralized planning and decision-making in humanitarian logistics. The relief chain is expected to benefit both the HO and the 3PLPs. To this end, we developed a novel bi-level two-stage multi-objective stochastic programming model. The robust equivalent of the proposed model is also developed to cope with uncertainty. A Benders decomposition algorithm is tailored to solve large-scale problems. An actual earthquake is investigated as case-study to test the model's real-world applicability. Finally, a number of managerial insights are inferred by analyzing the findings of this research.

Humanitarian relief chain agility: identification and evaluation of enabling factors

PurposeThis research paper aims to identify and evaluate the enabling factors of agility capability in humanitarian relief chain network.Design/methodology/approachThe research phases were implemented based on an integrated framework. First, a reference framework of the enablers has been constructed based on a literature review. Then, a hybrid evaluation approach is applied that combines fuzzy decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) to achieve reliable results. It provides a road map to identify and evaluate the interactions between the enabling factors and determines the weights correspond to their relative importance. This approach takes advantage of fuzzy set theory to deal with ambiguities, uncertainties and vagueness inherent in the evaluation process.FindingsRelief chain agility is a vital determinant of the effectiveness to succeed humanitarian missions during and after natural and unnatural disasters such as earthquakes, epidemics and terrorist attacks. Results shed light on the essential enabling factors, relationships among them, and their importance for developing humanitarian relief chain agility enhancing the overall performance quality.Originality/valueThe integrated framework is implemented for the Red Crescent, a nongovernmental organization in Iran, which is trying to optimize the agility of their humanitarian relief chain network. In short, the findings are beneficial for identification and utilization of the essential prerequisites of agility in order to develop an agile humanitarian relief chain.

A multi-objective robust possibilistic programming approach to coordinating procurement operations in the disaster supply chain using a multi-attribute reverse auction mechanism

This paper presents a multi-attribute reverse auction framework to facilitate the purchase operations between suppliers and a relief organization. The proposed auction framework consists of announcement, construction, and evaluation phases. In the announcement phase, the relief organization provides suppliers with information about demand and required relief items. In the construction phase, a fuzzy bi-objective mathematical model is developed from a supplier’s perspective; by solving the model, the suppliers offer their bids, each of which stipulating price, quantity, and delivery time for each item. The evaluation phase is formulated from the relief organization’s perspective; suppliers are first ranked based on their proposed bids and other qualitative criteria by the fuzzy PROMETHEE method. Then, a fuzzy tri-objective mathematical model is solved to determine winner(s) and allocate orders. For the first time, this paper develops a multi-objective robust possibilistic programming (MORPP) approach to cope with the uncertainty of parameters in the relief chain. Furthermore, multi-objective models are solved by an augmented ∊-constraint method. Finally, applicability and validity of the proposed auctioning framework are tested through a real case study in Iran and the managerial insights are provided.

PRE-POSITIONING OF A RELIEF CHAIN IN HUMANITARIAN LOGISTICS UNDER UNCERTAINTY IN ROAD ACCIDENTS: A REAL-CASE STUDY

Road accidents, as one of the causes of mortality in human societies, have many adverse social and economic consequences. Various measures can improve the safety of travel. One of them is to find appropriate location of relief infrastructures. The proper pre-positioning of relief chains locations can decrease road users’ fatalities. Thus this paper presents a multi-objective mathematical model to the pre-positioning of a relief chain for road accidents. Then the model is solved by a non-dominated sorting genetic algorithm. Factors that affect the location of relief chains are identified using a multi-attribute decision-making method. Finally, potential locations are proposed for the relief chain locations.

Application of Industry 4.0 technologies for effective coordination in humanitarian supply chains: a strategic approach

The coordination in post-disaster functions is a major challenge for the humanitarian supply chains. Due to uncertainty and temporary nature of the disasters, it becomes very difficult to ensure coordination among the aid organizations engaged in the humanitarian supply chain. The increasing incidents of the natural, technological and man-made disasters have attracted the attention of the researchers, governmental and non-governmental actors. Most of the disasters are not under the control of the human being. Some of the man-made disasters also create a huge loss of the human lives with damages to the public and private properties due to some social and political causes. From literature, it is observed that the technologies of Industry 4.0 may help in improving the coordination among stakeholders of the commercial supply chains but the application of industry 4.0 technologies in humanitarian relief chains is not well explored. Therefore, the present study has bridged the existing research gaps by exploring the application of Industry 4.0 technologies for improving the coordination in humanitarian supply chains. A hybrid approach comprising of Analytic Hierarchy Process and ELECTRE has been used to propose a strategic approach for mapping the measures of coordination with critical success factors. Findings of the study will help stakeholders of humanitarian supply chains in implementing Industry 4.0 technologies across different functions for prompt service and accurate decision making.

A distributionally robust optimisation model for last mile relief network under mixed transport

The last mile relief network is the final stage of the relief chain but the most critical stage for ensuring the timely delivery of relief supplies after a disaster. Due to the suddenness of the disaster, balancing the shortages of relief supplies and the high demands of victims is a serious problem. We introduce a mixed transport way of relief supply transportation between points of distributions and demand nodes in our problem to face the manpower and resource limitations. We establish a bi-objective distributionally robust optimisation model to balance transportation time and transportation safety, where the demand, transportation time, freight and safety coefficient are assumed to be uncertain variables with partial distribution information. We also deduce the refinement robust counterparts under the ambiguous sets to prove the safe tractable approximations of chance constraints. Finally, we conduct a case study of Tonghai county earthquake to illustrate the efficiency of our proposed distributionally robust model.

Optimization model for temporary depot problem in flood disaster response

The objective of this research is to establish a model that characterizes integrated humanitarian operations management in response to flood disasters, during which an optimal framework that describes the interactions between different elements in the relief supply chain is crucial. In principle, it is necessary to develop a model that controls the total flow of supply distribution and evacuation planning, as well as the location routing of facilities that handle relief supplies from the sources all the way down the relief chain to the hands of the recipients. The location-routing model was proposed in response to the temporary depot problem that is particular to such disasters. To formulate this problem, the use of a multi-period approach to describe the problem was presented, and multimodal transportation was considered in order to more closely mimic the realistic behavior of a flooding disaster.

A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns

Relief distribution and victim evacuation are the crucial emergency relief operations after sudden-onset disasters to alleviate the repercussions of catastrophes in the concerned areas, although aftershocks and unfair distribution of relief items can affect these planning and beget plenty of undesirable reflections. In this paper, a new multi-objective reliable optimization model to organize a humanitarian relief chain is rendered to make a broad range of decisions, including reliable facility location-allocation, fair distribution of relief items, assignment of victims, and routing of trucks. For this purpose, the first objective function is to minimize the total logistics costs, the second one is to minimize the total time of relief operations, and the third one minimizes the variation between upper and lower bounds of transportation cost of distribution centers to regulate the workload of them. What is more, due to the fact that the uncertain essence of catastrophes, such as demand, the capacity of facilities, miscellaneous costs and transportation times, a novel uncertainty approach, including robust optimization and the neutrosophic set, is proposed to surmount these obstacles. Ultimately, a real case study is examined to illustrate the validity of the proposed model and solution method. The obtained results reveal that via increasing the capacity of the emergency centers by 30%, the total cost of the humanitarian relief network is reduced by 18%, and the operating time is reduced by 9%. What is more, if the probability of disruption in one of the distribution centers reaches zero, the logistics costs will be reduced to approximately 20%, and also the distance between the maximum and minimum distance traveled will be reduced by 30%, and the workload between distribution centers will be more balanced.

Integrated decision support framework for enhancing disaster preparedness: A pilot application in Indonesia

The combination of climate change, rampant urbanization, political and social instabilities, is expected to increase the frequency and ferocity of humanitarian crises in the near future. Unpredictability of demand, narrow lead times, highly chaotic and rapidly changing circumstances demand for a different approach to disaster management. The traditional event-based approach to manage natural hazards is, in fact, no longer economically viable, and it should be transformed into a risk-based management slant with a strong emphasis on planning, in preparedness. This paper is an attempt in that direction and proposes an integration of multi-criteria decision-making, network optimization, and discrete event simulation, to address inventory prepositioning for improving efficiency, effectiveness, and agility of relief chains. While humanitarian logistics has been traditionally relying on the sole practical experience of practitioners, this paper makes a case for a paradigm shift as it proposed an interdisciplinary approach integrating that practical experience with analytical and dynamic simulation methods that have been widely applied in the commercial supply chain arena. A real-life case study about Indonesia, one of the countries with the highest disaster risk exposure on a global scale, has been investigated with the use of anyLogistix software. The findings of this work depict the ideal network configuration along with transportation and inventory policies for the case at hand.

A Location-Routing Model for Assessment of the Injured People and Relief Distribution under Uncertainty

Throughout history, nature has exposed humans to destructive phenomena such as earthquakes, floods, droughts, tornadoes, volcanic eruptions, and tropical and marine storms. The large scale of damages and casualties caused by natural disasters around the world has led to extensive applied research in the field of preparation and development of a comprehensive system for disaster management to minimize the resulting casualties and financial damages. Based on this motivation and challenges to the field, this research designs an integrated relief chain to optimize simultaneously the preparedness and response phases of disaster management. Decisions to improve the supply chain include locating distribution centers of relief supplies; the amount of inventory stored in facilities in pre-disaster phase, locating temporary care centers and transportation points of the injured, how to allocate relief services to the affected areas, and routing of the vehicles used to distribute relief supplies and evacuate the injured. The results show that decreasing the capacity of distribution centers increases the amount of shortage of supplies and increasing the capacity of these centers reduces the amount of shortage of supplies.