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This paper aims at analyzing the determinants of access to relief under social assistance programs among rural households during COVID-19 outbreaks in India.

The study is based on the data of COVID-19-Related Shocks Survey, which covered 5,200 rural households across 6 states of India namely Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh Rajasthan, and Uttar Pradesh. The access to relief has been assessed as relief-in kind (RIK) as a free special package of wheat, rice, and pulses, supplied through the public distribution system; and direct benefit transfer (DBT) in cash under the Pradhan Mantri Kisan Samman Nidhi (PM-KISAN) Yojana and the Pradhan Mantri Jan-Dhan Yojana (PMJDY). The association between demographic profiles of rural households and access to relief has been analyzed using the chi-square test. Further, marginal effects have been estimated to assess the determinants of rural households' access to relief.

The results show a significant association between types of relief vis-à-vis demographic profiles of the rural households. A significant difference in access to relief among rural households is also evident across the states. Further, the analysis of the marginal effects indicates that female-headed households belonging to lower social class, depending on non-agricultural occupation with lower income, belonging to below poverty line families and seeking wage employment, are more likely to access relief as food grains; whereas male respondents with lower age, belonging to lower-income quartile with memberships in Self Help Groups are more likely to access the cash benefit transfers.

The COVID-19 pandemic has affected the food security and livelihood of many across the globe, which necessitated provisioning a package of support to everyone, particularly rural poor households. The World Bank undertook the COVID-19-Related Shocks Survey to provide a quick policy response for managing the risk of COVID-19 outbreak effectively. The results of this study provide timely insights for developing an effective relief strategy for rural households during a crisis.

There is limited investigation on access to relief by rural households during the COVID-19 outbreaks and factors affecting the access to relief in terms of cash and kind. This study has utilized a reliable data source to analyze the access of relief packages by the rural communities during the coronavirus outbreak.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2021-0632.

Organisations involved in relief delivery tend to have cross-boundary mandates, which cause ambiguity of roles during delivery of relief services to the targeted victims. Having no clear role, specialisation affects service timeliness and increases resource duplication among the relief organisations. The objective of this study is to understand how organisational networks and organisational learning as complex adaptive system metaphors improve both organisational adaptability and role clarity in humanitarian logistics.

Using ordinary partial least squares regression through SmartPLS version 3.3.3, the authors tested the study hypotheses basing on survey data collected from 315 respondents who were selected randomly to complete a self-administered questionnaire from 101 humanitarian organisations. Common method bias (CMB) associated with surveys was minimised by implementing both procedural and post statistics methods.

The results indicate that organisational networks and organisational learning have a significant influence on organisational adaptability and role clarity. The results also show that organisational adaptability partially mediates in the relationship between organisational networks, organisational learning and role clarity.

The major limitation of the study is that the authors have used cross-sectional data to test this research hypotheses. However, this was minimised following Guide and Ketokivi's (2015) recommendation on how to address the limitations of cross-sectional data or the use of longitudinal data that can address CMB and endogeneity problems.

Managers in humanitarian organisations can use the authors’ framework to understand, first, how complex adaptive system competence can be used to create organisational adaptability and, second, how organisational adaptability can help organisational networks and organisational learning in improving role clarity among humanitarian organisations by collaboratively working together.

This research contributes to the existing body of knowledge in humanitarian logistics and supply chain management by empirically testing the anecdotal and conceptual evidence. The findings may be useful to managers who are contemplating the use of organisational networks, organisational learning and organisational adaptability to improve role clarity in disaster relief-related activities.

The Covid-19 pandemic has rekindled interest in sovereign debt crises amidst calls for debt relief for developing and emerging countries. But has debt relief lessened the debt burdens of emerging and developing economies? The purpose of this paper is to empirically address this question. In particular, the focus is on the implications of debt relief and institutional qualities for sovereign debt in emerging and developing economies.

The model extends the framework on the probability of default by incorporating the receipt of debt relief by a debtor country. Doing so allows to better explain movements of sovereign defaults relating to debt relief. The model is estimated via the regular probit regression.

The analysis shows that the debt relief provided, thus, far, failed to ease the debt overhang problems of developing and emerging countries and reduced investment. The current debt relief schemes may underscore the prospects of self-enforcing and self-fulfilling sovereign debt crises rather than eliminating the dilemma completely. Regarding the forms of debt relief, the analysis shows that debt forgiveness offers favourable prospects in terms of debt sustainability and economic outcomes than debt rescheduling. Perhaps, the sovereign debt crises, particularly in low-income countries, hinge on insolvency problems rather than transitory illiquidity issues.

Any debt relief mechanism should consider seriously the potential incentive effect that reinforces expectations of future debt-relief initiatives. Importantly, solving the sovereign debt problem requires a programme for sustained investment and economic growth, while not discounting the critical role of prudent debt management policies and institutions.

This study contributes a different angle to the debate on sovereign debt distress. Aside from the structural and economic factors, this study investigates the role of debt management policy in the debtor nation and the implications of debt relief benefits for sovereign risk. The framework also focuses on whether the different forms of debt relief exert distinctive impacts.

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

The number of natural and man-made disasters is remarkable and threatened human lives at the time of occurrence and also after that. Therefore, an efficient response following a disaster can eliminate or mitigate the adverse effects. This paper aims to help address those challenges related to humanitarian logistics by considering disaster network design under uncertainty and the management of emergency relief volunteers simultaneously.

In this paper, a robust fuzzy stochastic programming model is proposed for designing a relief commodity supply chain network in a disaster by considering emergency relief volunteers. To demonstrate the practicality of the proposed model, a case study is presented for the 22 districts of Tehran and solved by an exact method.

The results indicate that there are many parameters affecting the design of a relief commodity supply chain network in a disaster, and also many parameters should be controlled so that, the catastrophe is largely prevented and the lives of many people can be saved by sending the relief commodity on time.

This model helps decision-makers and authorities to explore optimal location and allocation decisions without using complex optimization algorithms.

To the best of the authors’ knowledge, employee workforce management models have not received adequate attention despite their role in relief and recovery efforts. Hence, the proposed model focuses on the problem of managing employees and designing a disaster logistics network simultaneously. The robust fuzzy stochastic programming method is applied for the first time for controlling the uncertainties in the design of humanitarian relief supply chains.

The purpose of this paper is to investigate the use of freight containers to store relief items instead of operating a permanent warehouse building.

A mathematical model is developed to determine the location and quantity of containers as well as the type and amount of relief supplies to store in order to investigate the practicality of using freight containers for storage. The model is tested using earthquake risk data, estimates of population under risk, and the distances between cities. An experimental study is performed using Turkish Prime Ministry Disaster and Emergency Management Presidency (abbreviated as AFAD in Turkish) data for total number of relief supplies.

Considering the earthquake risk of possible locations, the results of the study indicate the target locations for containers. The idea of using containers as storage facilities helped beneficiaries to be reached within a short distance and in an efficient way.

The presented model is not implemented in real life disaster relief operations even if it is tested with real earthquake risk, demand and distance data.

To apply this model in practice, the container locations within cities should be determined and managerial operations such as maintenance, environmental, and security planning have to be considered.

This study presents the first analysis of three sub-topics’ intersection: warehousing, pre-positioning in disaster relief, and containerization. To the best of authors’ knowledge, containers have not been considered for storage of relief items in humanitarian logistics before.

Lagos is flooded perennially due to its low-lying coastal location. Empirical studies on the impacts of the disaster and humanitarian relief for the victims are scanty. The purpose of this paper is to assess the causes and severity of the floods, the aids to victims and the supply chains in six flood-prone local government areas (LGAs) of the metropolis comparatively.

Data were collected using a questionnaire instrument that was administered to 292 victims and/or residents of 42 flooded neighborhoods in six LGAs of Lagos in the years 2010-2012. The administration was some few weeks after major incidences were reported for the respective years.

The results show that floods are recurrent in the areas during every wet season (between May and October), emanate from both natural and human-induced causes and lead to large-scale human displacement and loss of assets. Humanitarian reliefs are from neighbors, spiritual homes and relatives mostly and are supplied directly to victims. These suggest gloomy implications for sustainable disaster management, humanitarian services and supply chains. Areas for further studies are highlighted.

Studies in humanitarian relief and supply chains are scanty in the part of the world. The paper is original and not been considered for publication elsewhere.

Unfortunately, the occurrence of natural disasters is inevitable all over the world. Hence, this paper aims to analyze a scenario-based collaborative problem in a relief supply chain (RSC), where nongovernmental organizations can participate in relief activities with governmental organizations. This study focuses on location-allocation, inventory management and distribution planning under uncertain demand, budget, transportation and holding costs where government and private distribution centers receive relief items from suppliers then send them to affected areas. The performance of the proposed model is surveyed in a real case study in Dorud.

This paper develops a nonlinear mixed-integer programming model that seeks to maximize the coverage of demand points and minimize operating costs and traveled distance. The linear programming-metric technique and grasshopper optimization algorithm are applied to survey the model's applicability and efficiency.

This study compares noncollaborative and collaborative cases in terms of the number of applied distribution centers and RSC's goals, then demonstrates that the collaborative model not only improves the coverage of demand points but also minimizes cost and traveled distance. In fact, the presented approach helps governments efficiently surmount problems created after a disaster, notwithstanding existing uncertainties, by determining a strategic plan for collaboration with nongovernmental organizations for relief activities.

Relief strategies considered in previous research have not been sufficiently examined from the perspective of collaboration of governmental and nongovernmental organizations and provided an approach to develop the coverage of affected areas and reducing costs and traveled distance despite various uncertainties. Hence, the authors aim to manage RSCs better by offering a mathematical model whose performance has been proved in a real case study.

Emergency relief supply chain (ERSC) design is an important strategic decision that significantly affects the overall performance of emergency management activities. The performance of an ERSC can be measured by several performance measures some of which may conflict with each other. The purpose of this paper is to propose an ERSC design framework by simultaneously taking total logistics cost (TLC), risk level, and amount of demands covered in an ERSC into consideration.

The study considers TLC of an ERSC as the sum of logistics cost from distribution warehouses (DWHs) to Break of Bulbs (BOBs) and from BOBs to affected neighborhoods. The risk level of an ERSC is measured by estimating the expected number of disrupted relief items (EDI) distributed from DWHs through BOBs to neighborhoods. The covered demand (CDM) is defined as total populations that are supported in case of an emergency, the populations within the maximal coverage distance (MCD) from relief facilities. Based on these performance measures, the authors formulate a Goal Programming (GP) model to distribute emergency relief items to affected locations. Ideal values of these performance measures are decided, and the GP model seeks to minimize the weighted sum of the percentage deviations of those performance measures from the ideal values. The relationships among performance measures have been thoroughly analyzed through detailed trade-off studies under two realistic case studies by changing weights of each performance measure.

Three performance measures are interdependent over specific values of weights. TLC and EDI have a trade-off relationship when the weight on each measure increases. TLC and CDM also have a trade-off relationship when the weight on EDI increases. However, this relationship becomes less apparent when the MCD increases. EDI and CDM also have the same trade-off relationship when the weight on TLC changes. Therefore, decision makers should thoroughly analyze these trade-off relationships when they design ERSCs. Overall, the study identified that an ERSC with higher MCD outperforms one with lower MCD in terms of TLC, EDI, and CDM.

The study presents a design framework to generate more balanced ERSCs by simultaneously taking three conflicting performance measures into consideration, and demonstrated the feasibility of the framework through realistic case studies. The trade-off analysis provides useful insights and theoretical knowledge to researchers and practitioners in the discipline of emergency logistics management. The results from this study are expected to contribute to the development of more balanced ERSCs.

We examine how design and implementation practices for supply chain performance management that have proven successful in commercial organisations apply to humanitarian organisations (HOs) to guide the process of designing and implementing performance management in humanitarian organisations.

We identify from the literature ten successful practices regarding the design and implementation of supply chain performance management in commercial businesses. We apply these, using action research over a four-year period, at Médecins sans Frontières (MSF) Belgium and draw conclusions from this.

We find that tools and techniques, such as workshops and technical sheets, are essential in designing and implementing supply chain performance measurement projects at HOs. Furthermore, making a link to an IT project is crucial when implementing performance measurement systems at HOs. Overall, our case study shows that performance management practices used in business can be applied and are relevant for humanitarian supply chains.

Previous research has argued that there are few empirical studies in the domain of performance management at humanitarian organisations. To the best of our knowledge, this paper is the first to provide a longitudinal understanding of the design and implementation of supply chain performance measurement at HOs.