Assessing the factors affecting implementation of unmanned aerial vehicles in Indian humanitarian logistics: a g-DANP approach

Purpose The purpose of this paper is to analyze the factors affecting the implementation of unmanned aerial vehicles (UAVs) in Indian humanitarian logistics. The factors listed are significant as they are hindering the incorporation of this new technology into the humanitarian supply chain, thus creating inefficiencies in the humanitarian logistics sector. Design/methodology/approach This research is approached using a two-step process. In the first step, the particular barriers for UAV implementation are determined by a literature review and consultation with experts. Next, the proposed framework, a combination of grey-decision-making trial and evaluation laboratory (grey-DEMATEL) and analytic network process (ANP), i.e. g-DANP, is used to determine a hierarchical structure for the factors and sub-factors. The grey hypothesis provides sufficient analytical data to an otherwise lacking DEMATEL technique. Also, the use of ANP gives weightage to each factor, allowing us to categorize their importance further. Findings This study reveals that factors like expensive commercial solutions and high transport energy costs are significant factors of the “cause” group, whereas the uncertain cost for maintenance and repair and deficiency of high-level computing are crucial factors of the “effect” category. The mentioned factors, along with many others, are the main reasons for the delayed incorporation of UAVs in humanitarian logistics. Practical implications The results of this study present insights for humanitarian supply chain managers, UAV producers and policymakers. Those in the humanitarian logistics sector can use the findings of this study to plan for various challenges faced as they try and implement UAVs in their supply chain. Originality/value This research is unique as it analyses the general factors hindering the implementation of UAVs in Indian humanitarian logistics. The study enriches existing literature by providing an analytic approach to determine the weightage of various interrelations between the identified factors affecting UAV incorporation in the humanitarian supply chain.

Distributionally Robust Optimization Under a Decision-Dependent Ambiguity Set with Applications to Machine Scheduling and Humanitarian Logistics

We introduce a new class of distributionally robust optimization problems under decision-dependent ambiguity sets. In particular, as our ambiguity sets, we consider balls centered on a decision-dependent probability distribution. The balls are based on a class of earth mover’s distances that includes both the total variation distance and the Wasserstein metrics. We discuss the main computational challenges in solving the problems of interest and provide an overview of various settings leading to tractable formulations. Some of the arising side results, such as the mathematical programming expressions for robustified risk measures in a discrete space, are also of independent interest. Finally, we rely on state-of-the-art modeling techniques from machine scheduling and humanitarian logistics to arrive at potentially practical applications, and present a numerical study for a novel risk-averse scheduling problem with controllable processing times. Summary of Contribution: In this study, we introduce a new class of optimization problems that simultaneously address distributional and decision-dependent uncertainty. We present a unified modeling framework along with a discussion on possible ways to specify the key model components, and discuss the main computational challenges in solving the complex problems of interest. Special care has been devoted to identifying the settings and problem classes where these challenges can be mitigated. In particular, we provide model reformulation results, including mathematical programming expressions for robustified risk measures, and describe how these results can be utilized to obtain tractable formulations for specific applied problems from the fields of humanitarian logistics and machine scheduling. Toward demonstrating the value of the modeling approach and investigating the performance of the proposed mixed-integer linear programming formulations, we conduct a computational study on a novel risk-averse machine scheduling problem with controllable processing times. We derive insights regarding the decision-making impact of our modeling approach and key parameter choices.

Mitigating interorganizational conflicts in humanitarian logistics collaboration: the roles of contractual agreements, trust and post-disaster environmental uncertainty phases

PurposeGovernance is the key to establishing effective collaboration among humanitarian logistics partners addressing an ongoing relief work. With a focus on humanitarian interorganizational collaboration, this research draws on governance theories to investigate how conflicts can be mitigated in this challenging setting.Design/methodology/approachThe focus on governance extends attention to the frontiers of contractual agreement, trust and environmental uncertainty to be applied in the humanitarian setting. To develop perspectives, an online survey of 289 field executives working in humanitarian organizations across the globe is conducted. The findings are based on hierarchical regressions.FindingsEnvironmental uncertainty, in humanitarian logistics, is not straightforward, but wields distinctive challenges in the response phase (immediate to the disaster) as well as the recovery phase (beginning of build back) – to loom prospects of conflict between partners. Findings outline that contractual agreement can increase conflict during the response phase (high environmental uncertainty), but mitigate it during the recovery phase (low environmental uncertainty). Furthermore, contractual agreement interactively strengthens the ability of trust to reduce conflict. Yet, trust acting alone shows best outcome to mitigate conflict.Research limitations/implicationsContrary to the established understanding in traditional logistics suggesting the vitality of contracts to easily mitigate challenges posed by environmental uncertainty, the humanitarian setting extends a unique outset for interorganizational governance based on the temporality of response and recovery phases.Originality/valueThis research pioneers to quantitatively examine the setting of humanitarian logistics based on survey. Given the difficulty of data acquisition, the extant research has largely relied on qualitative investigations when considering the agenda of governance.

Organisational networks, organisational learning, organisational adaptability and role clarity among humanitarian organisations during relief delivery

PurposeOrganisations 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.Design/methodology/approachUsing 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.FindingsThe 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.Research limitations/implicationsThe 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.Practical implicationsManagers 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.Originality/valueThis 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.

Bibliometric Analysis of the Potential of Technologies in the Humanitarian Supply Chain

This study presents a bibliometric analysis of research on technology in the humanitarian supply chain. The methodology includes performance analysis and science mapping to explore the application of technologies in humanitarian supply chains. This paper contributes to the literature by examining the most influential authors, trends, journals, countries, institutions, and the recent humanitarian supply chain collaboration. The information presented in this research was obtained with the Scopus database. The study identified 342 documents after applying filters to screen for duplicates and manuscripts unrelated to the topic. The articles were analyzed using MS Excel and VOSviewer. The research provides an overview of state of the art showing a high collaboration between the authors Ramesh A. and Kabra C, and the most relevant institutions were the Griffith Business School and the Delft University of Technology. Journal of Humanitarian Logistics and Supply Chain Management and Journal of Disaster Research were the most productive journals. The terms analysis shows that “disasters”, “disaster prevention”, “humanitarian logistics”, and “human” are the most used keywords. The study identifies future research lines related to the interaction between critical technologies to deliver real benefits to the humanitarian supply chain. As a result, it proposes integrating the significant contributions of new technologies, such as blockchain, big data, artificial intelligence, 3D printing, virtual and augmented reality, and the social media relief phase following the disaster. It also indicates gaps in knowledge in terms of research related to human-made disasters and health emergencies.

Addressing a Humanitarian Relief Chain Considering Uncertain Demand and Deprivation Costs by a Hybrid LP-GA Method: An Earthquake in Kermanshah

Today, a great deal of attention to numerous disasters such as earthquakes, floods and terrorist attacks is motivated by humanitarian logistics. A comprehensive plan for relief logistic items under uncertainty is a challengeable concern for both academic and logistics practitioners. This study contributes another robust plan for the humanitarian logistics for the earthquake disaster in Kermanshah, Iran. The proposed framework evaluates both operational and disruption risks simultaneously to study the Humanitarian Relief Chain (HRC) network after an earthquake. The main novelty is the simultaneous consideration of the deprivation costs and demand under uncertainty. The deprivation cost leads to a reduction in high social costs for the decision-makers of the HRC. The proposed HRC also guarantees the delivery of the essential supplies to beneficiaries under both operational and disruption risks. As an optimization model, it seeks to minimize total costs consisting of inventory holding cost, shortage cost, deprivation costs and transportation cost and maximizes each facility's weighted resilience level as the second objective. A robust optimization model is established to deal with uncertain levels of the transport network paths, supply condition, amount of demand and deprivation costs which are assumed uncertain. The resilience parameters used for the second objective are obtained by a Best Worst Method (BWM). Another significant contribution was a hybrid approach combining the LP-metric method and Genetic Algorithm (GA) as the LP–GA approach for optimizing large-scale instances. Regarding the analyses, including tuning, validation and comparison of the proposed approach, its performance is showed by several standard multi-objective assessment metrics. As a final point, the achieved outcomes demonstrate that the suggested model is highly sensitive to uncertain parameters. This encourages further development and application of the proposed HRC with the use of a hybrid LP-GA approach as a strong technique for solving optimization problems.