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• This paper highlights the limited extent to which dominant development and humanitarian framings engage with considerations of religious inequalities as well as the language of 'religion' and belief. • Failing to attend to these inequalities can have negative, if not catastrophic, consequences for the hoped-for outcomes of development and emergency response initiatives. • Primary research on humanitarian responses in Iraq from 2014 to 2019 portrays the reality of how religion-blind development concepts and framings interplay at the frontline of a humanitarian response to conflict. • Suggestions are provided about how to incorporate considerations of religion, religious inequalities and belief into development frameworks and programming. Within the international development and humanitarian arenas no one disagrees with the language and aspirations of inclusion, protection, rights and equality. Yet, most development framings – policy and operational – have still to develop meaningful ways to attend to the specific advantages or disadvantages associated with religious views or beliefs that shape the way people and communities function and behave within society. This paper explores influential conceptual and programmatic framings of international development and humanitarian response, specifically the human development approach, capital and asset-based framings, horizontal inequalities, the SDGs and humanitarian principles. We analyse the extent to which they engage with considerations of religious inequalities (religious diversity, identity, inclusion) and language of 'religion' and belief. Drawing on an evidenced-based case study of humanitarian/development responses in Iraq in the period from 2014 to 2019, the paper illustrates the reality of how a smorgasbord of religion-blind development concepts and framings interplay at the frontline of a humanitarian response to conflict, and the ways in which multiple actors wrestle and flex to design, target and implement programmes that were conceived in a religion-blind frame. The research shows how the explicit incorporation of the context and nature of religious diversity dynamics into frameworks has critical real-world impacts on the design, delivery, monitoring and impacts of interventions. Failing to incorporate an understanding of inequalities experienced by people marginalized because of their religious affiliation or belief (whether intentional or through mere oversight) has very likely muted the hoped-for outcomes of many development and emergency response initiatives. Our hope is that international development and humanitarian framings can better deliver on their objective to "leave no one behind" by ensuring inequalities on account of religious identity or practice are appropriately considered. [ABSTRACT FROM AUTHOR]

Disaster relief presents many unique logistics challenges, including damaged transportation infrastructure, multiple conflicting goals, and secondary disasters caused by spontaneous disaster relief. The delivery and distribution of humanitarian relief materials faced by nonprofit organizations (NPOs) poses a critical challenge due to the highly unpredictable nature of disasters, conflicting missions, and the need to navigate government regulations in disaster management. In this paper, we propose a novel multi-objective disaster relief modeling system for NPOs, considering (1) prioritization (providing medical services to seriously injured victims), (2) effectiveness (degree of satisfaction), (3) efficiency (operational costs), and (4) equity (cost of maintaining order). To incorporate the practical constraints of disaster relief operations, we include factors such as road damage, transportation capacity limitations, and route flow saturation as part of the model formulation. We then utilize fuzzy multi-objective programming to optimize the proposed disaster relief model, considering two relief distribution schemes: a common NPOs' spontaneous relief scheme and a newly designed traffic control scheme with government regulation. Based on the real-world scenario of the 2008 Wenchuan earthquake, we demonstrate that the designed traffic control scheme exhibited remarkable improvement among the NPOs' four objectives compared to the spontaneous relief scheme. Moreover, in the process of solving fuzzy multi-objective programming problems, artificial intelligence technology is employed to handle the fuzziness in multi-objective and automatically adjust parameters, making the solution results better and in line with practical situations. And we also validate the proposed model can be solved in polynomial time, meeting the engineering practice requirements, and can be easily extend to most disaster relief distribution scenarios. [Display omitted] • Fuzzy multi-objective programming model for humanitarian relief materials' delivery and distribution was built. • Prioritization, effectiveness, efficiency and equity were considered as the NPOs objectives. • Roadway capacity is considered as a constraint condition. • The NPOs' spontaneous relief scheme and traffic control scheme were discussed specially. • Delivery and distribution of humanitarian relief materials was analyzed based on 2008 Wenchuan earthquake. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) with human intelligence (HI) has been asserted to provide transformational power across the humanitarian supply chain (HSC). However, there is little rigorous work that analyses the enablers that promote AI–HI integration and application in the HSC. Thus, this paper reports a hybrid decision support framework for analysing enablers of AI–HI integration in the HSC with complicated, uncertain, and periodic information. First, to collect interdependent preference data from experts, the complex spherical fuzzy weighted Heronian mean operator with a weighted distance measures-based optimization model is established to generate a group decision matrix. Next, to measure the influence strength of enablers, a complex spherical fuzzy decision-making trial and evaluation method is established to determine enabler weights, taking into account their interactive relationships. After that, to explore the enabler level of AI–HI integration in different participants of the HSC, the complex spherical fuzzy measurement of alternatives and ranking according to the compromise solution method is developed by combining the former two procedures. Finally, a case study of enablers analysis for AI–HI integration in HSC is presented to assess the feasibility of the current method, which includes sensitivity and comparison studies. The results reveal that the factor "enhancing the efficiency of relief operations" (0.084) is the most important driving factor for AI–HI integration. The outcomes of this study can provide a new decision support method for understanding the enablers of AI–HI integration in key parts of the HSC. • An integrated CSF-decision framework is proposed to assess enablers of AI-HI integration. • The inter-dependencies between estimation information distributions are considered. • The expansion of the CSF-DEMATEL is proposed to assign enablers weights. • An improved CSF-MARCOS model is developed to estimate enablers of AI-HI integration in HSC. [ABSTRACT FROM AUTHOR]

• An option contract with all-unit and incremental quantity discounts is presented. • Suppliers' profit is considered to motivate them to take a active part in disaster response. • A multi-objective stochastic programming model is formulated for supplier selection. • A machine learning based sample average approximation is proposed for large-scale cases. The humanitarian relief plays an important role in reducing the impact of disasters and avoiding humanitarian crises. As one of the essential activities, selecting a series of proper suppliers is particularly helpful for a successful and efficient disaster response to provide victims with necessary supplies. To optimize the benefits of the relief agency, victims, and suppliers, this paper proposes a supplier selection problem with consideration of the option contract, in which all-unit quantity and incremental quantity discounts are integrated. The problem is formulated as a multi-objective stochastic programming model with the objectives of minimizing the cost of the relief agency and maximizing the profit of suppliers, which are two conflict objectives, and also reducing the shortage risk for victims. Moreover, a machine learning based sample average approximation (SAA) is designed to solve the proposed model in large-scale cases. Specifically, stratified random sampling is integrated into K-means++ to improve quality of samples. The numerical analysis demonstrates that the proposed strategy can achieve a win–win situation for the relief agency, victims, and suppliers. It also justifies the efficiency of applying the machine learning method to enhance SAA for solving large-scale stochastic programming models. [ABSTRACT FROM AUTHOR]