The growing need for humanitarian assistance has inspired an increasing amount of academic publications in the field of humanitarian logistics. Over the past two decades, the humanitarian logistics literature has developed a powerful toolbox of standardized problem formulations to address problems ranging from distribution to scheduling or locations planning. At the same time, the humanitarian field is quickly evolving, and problem formulations heavily rely on the context, leading to calls for more evidence-based research. While mixed methods research designs provide a promising avenue to embed research in the reality of the field, there is a lack of rigorous mixed methods research designs tailored to translating field findings into relevant HL optimization models. In this paper, we set out to address this gap by providing a systematic mixed methods research design for HL problem in disasters response. The methodology includes eight steps taking into account specifics of humanitarian disasters. We illustrate our methodology by applying it to the 2015 Nepal earthquake response, resulting in two evidence-based HL optimization models.

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Literature about humanitarian logistics (HL) has developed a lot of innovative decision support systems during the last decades to support decisions such as location, routing, supply, or inventory management. Most of those contributions are based on quantitative models but, generally, are not used by practitioners who are not confident with. This can be explained by the fact that scenarios and datasets used to design and validate those HL models are often too simple compared to the real situations. In this chapter, a scenario-based approach based on a five-step methodology has been developed to bridge this gap by designing a set of valid scenarios able to assess disaster needs in regions subject to recurrent disasters. The contribution, usable by both scholars and practitioners, demonstrates that defining such valid scenario sets is possible for recurrent disasters. Finally, the proposal is validated on a concrete application case based on Peruvian recurrent flood and earthquake disasters.@en

In the humanitarian response, multiple decision-makers (DMs) need to collaborate in various problems, such as locating temporary relief distribution centres (RDCs). Several studies have argued that maximising demand coverage, reducing logistics costs and minimising response time are among the critical objectives when locating RDCs after a sudden-onset disaster. However, these objectives are often conflicting and the trade-offs can considerably complicate the situation for finding a consensus. To address the challenge and support the DMs, we suggest investigating the stability of non-dominated alternatives derived from a multi-objective model based on Monte Carlo Simulations. Our approach supports determining what trade-offs actually matter to facilitate discussions in the presence of multiple stakeholders. To validate our proposal, we extend a location-allocation model and apply our approach to an actual data-set from the 2015 Nepal earthquake response. Our analyses show that with the relative importance of covering demands ≤0.4, the trade-offs between logistics costs and response time affects the numbers and locations of RDCs considerably. We show through a small experiment that the outputs of our approach can effectively support group decision-making to develop relief plans in disasters response.

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Locating distribution centers is critical for humanitarians in the immediate aftermath of a sudden-onset disaster. A major challenge lies in balancing the complexity and uncertainty of the problem with time and resource constraints. To address this problem, we propose a location–allocation model that divides the topography of affected areas into multiple layers; considers constrained number and capacity of facilities and fleets; and allows decision-makers to explore trade-offs between response time and logistics costs. To illustrate our theoretical work, we apply the model to a real dataset from the 2015 Nepal earthquake response. For this case, our method results in a considerable reduction of logistics costs.

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Humanitarian supply chains (HSCs) contribute significantly to achieving effective and rapid responses to natural and man-made disasters. Though humanitarian organizations have during the last decades made considerable efforts to improve the response to crises in terms of effectiveness and efficiency, HSCs are still faced with so many challenges, one of which is the incorporation of sustainability dimensions (economic, social and environmental) in the management of their supply chains. In the literature, some authors have highlighted that the planning and achievement of sustainability performance objectives in humanitarian operations is hindered by the lack of decision support systems (DSS). Therefore, this paper proposes a multi-objective Master Planning DSS for managing sustainable HSCs. This Master Planning DSS includes: (1) the definition of a set of metrics for measuring the performance of a sustainable HSC; (2) an algorithm to solve the multi-objective problem; and (3) a Master Planning mathematical model to support the tactical planning of the sustainable HSC. Using the information gathered from field research and the literature, an illustrative numerical example is presented to demonstrate the implementation and utility of the proposed DSS. The results show that the order in which the three sustainability dimensions (economic, social and environmental) are prioritized has some impact on the performance measures. Therefore, it is important to fix a tolerance that would enable to obtain an acceptable balance (trade-off) between the three sustainability objectives, in line with the prioritization choice of the decision maker. The paper can be accessed via the following link: https://rdcu.be/NUkB as a part of Springer’s SharedIt Initiative.

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The Humanitarian Supply Chain (HSC) is key to successful relief operations. Today, HSC managers faces many challenges: (1) the increasing gap between funding and appeals; (2) donors ask for more transparency and accountability; (3) the growing pressure to switch to sustainable development. To maintain a competitive position (order winner) in the near future, considering sustainability becomes a fundamental addition to the established effectiveness and efficiency measures. In literature, the lack of Decision Support Systems for planning and achieving of sustainability objectives is described as barrier to sustainable humanitarian operations. We propose a sustainability maturity assessment method to improve the sustainability of HSC operations. Using the information gathered from field research with the IFRC as well as from the literature a proof of concept is presented to demonstrate the relevance of the proposal. @en

To develop decision support systems (DSS) that improve humanitarian supply chain (HSC) performance, there is a need for methods that support on field data gathering and knowledge structure. We propose a meta-model to structure the knowledge of HSC and to obtain a shared vision of the HSC. It has been developed to provide a framework to class gathered data by connecting it to HSC concepts. The meta-model includes four packages, defining the collaborative ecosystem (context, actors, objectives, and behavior). The concepts gathered during our field research experiences, added to the HSC core literature, have permitted to build the HSC meta-model layer. Models built from this meta-model can be used as knowledge bases for future DSS, dedicated to support vertical and horizontal coordination and collaboration of actors.@en

Purpose This paper proposes a research design for effective use of qualitative data from field research in a quantitative analysis of humanitarian logistics. Design/methodology/approach Our research design is composed of qualitative and quantitative components. The qualitative part has four steps: preparation, data collection, data combination and analysis, and interpretation. We triangulate qualitative data through semi-structured interviews, observations, document review, and online surveys. We use frequency analysis to map out qualitative data to themes that were identified through literature review and best practices. After combining data from various sources, we apply content analysis and then interpret the findings. Findings The results of implementing our research design for Nepal case show its effectiveness for capturing the complexities of and the relation between variables in disasters. Its workflow and research methods exclusively enable using field insights to test theories, inform models, and find field driven research directions. We demonstrate these findings through examples from our field research after 2015 Nepal earthquake. Research limitations/implications (if applicable) Limitations: our disaster field research was constrained by the typical short time to prepare/conduct the research, lack of access to key informant and, limited sample size. Furthermore, the results of our research are based on one case. Implications: our research design resulted in an effective and a traceable use of field data for addressing challenges that practitioners were experiencing. Therefore, proposed research design can be adopted in future for conducting and empirically grounded research on HL. Practical implications (if applicable) Proposed research design enables developing field informed models that can be used as a decision support system in the immediate response. Original/value In this paper, we explicitly explain how we translated Nepal field work into a model and what the problems were in such settings. @en

Humanitarian supply chains (HSCs) play a central role in effective and efficient disaster relief operations. Transportation has a critical share in HSCs and managing its risks helps to avoid further disruptions in relief operations. However, there is no common approach to or culture of risk management that its applicability has been studied through recent cases. This paper incorporates an empirical research design and makes a threefold contribution: first, it identifies in-country transportation risks during Nepal response. Second, we evaluate afore identified risks through an expert driven risk assessment grid. Third, we use our field data to study how some humanitarian organizations in Nepal response used logistics service providers for managing moderate- and high-level transportation risks. In this paper, we use both qualitative and quantitative methods. Our qualitative analysis reveals that some of the most important in-country transportation risks were delivery delays; market fluctuations; insufficient capacity; loss of cargo; cargo decay; unreliable information; and ethical concerns. Our quantitative work shows that while participants categorized the first three risks as high-level, the rest were ranked as moderate-level. More investigation in our field data indicates that using logistics service providers (LSPs) helped humanitarians significantly to manage afore in-country transportation risks during Nepal response. It also improved overall HSC performance with respect to flexibility, effectiveness, efficiency, and responsiveness. While this finding empirically confirms the “tools” role of LSPs for managing in-country transportation risks in response, it implies another role for LSPs; “contributors” to performance improvements.

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The efficient and effective response to disasters critically depends on humanitarian supply chains (HSCs). HSCs need to be flexible to adapt to uncertainties in needs, infrastructure conditions, and behavior of other organizations. The concept of ‘network flexibility’ is, however, not clearly defined. The lack of an unanimous definition has led to a lack of consistent understanding and comparisons. This paper makes a threefold contribution: first, it defines the concept of network flexibility for HSC in the context of sudden onset disasters. Second, it proposes a framework to measure network flexibility in HSCs. Third, we apply our framework to the 2015 Nepal earthquake case and provide evidence-based insights regarding how humanitarian organizations can improve network flexibility in HSCs. Our analyses for Nepal case show that delivery, IT support, and fleet criteria have the most influence on flexibility. Also, the application of our framework on the downstream network of nine humanitarian organizations shows low levels of network flexibility in all but one. This finding explains why several disruptions happened in relief distributions during the Nepal response.

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