Enhancing humanitarian logistics under uncertainty: A data-driven distributionally robust optimization approach with worst-case mean-CVaR

Purpose For the first time, this study aims to define a supply chain of goods and services, including vital medical services and goods based on mobile hospitals, with a focus on prioritizing regions. Mobile hospitals are tasked with providing services to patients in disaster-stricken areas. This service is provided based on the prioritization of the areas. Design/methodology/approach Severe injuries and the destructive effects of unexpected accidents and natural events have made managing unexpected events and treating the injured one of the most important issues. In crisis conditions, the time needed to serve and send relief goods is limited. Therefore, the need to define special conditions for relief to reduce casualties is evident. Findings The problem is modeled in mixed integer mathematical programming with two objectives: minimizing the cost of assistance and the time required to provide services to the injured in crisis conditions. The model seeks to determine the distribution centers, the type and amount of loading of mobile hospitals, as well as the schedule and route of relief in the best service conditions. NSGA-II and MOPSO algorithms have been used to solve the model, defining a new chromosomal structure corresponding to the problem. Originality/value The results show that the NSGA-II algorithm performs better in the problem. A sensitivity analysis has been done on the parameters of the problem, which can help managers and decision-makers make relevant decisions.

Application of Social Media Data to Enhance the Performance of Humanitarian Logistics: A Case Study of Vietnam in the Aftermath of Typhoon Yagi

Big data technologies have greatly enhanced the effectiveness of humanitarian logistics. However, most research in this area has focused on developed countries, with limited application to emerging economies. This study aims to address that gap by systematically reviewing global literature to broaden the understanding of big data-driven humanitarian supply chain management in developing countries. We analysed a collection of 64 scholarly articles using bibliometric techniques. The findings indicate that research in this field is experiencing exponential growth. The conceptual structure of the literature identifies six major themes: (1) big data and humanitarian logistics (motor theme), (2) digital technologies (a transitional theme evolving from foundational to central), (3) humanitarian supply chains (base theme), (4) emergency logistics (emerging theme), (5) blockchain technology, and (6) sustainability in humanitarian supply chains. This paper discusses both theoretical and practical implications relevant to emerging economies. By contextualising global knowledge for developing countries, we can enhance the legitimacy and applicability of considerable data-based humanitarian supply chain management research.

Problem. The full-scale military aggression against Ukraine has fundamentally altered the architecture of logistics systems, rendering traditional «Just-in-Time» supply chain models ineffective. The blockade of Black Sea ports and the constant threat of shelling have forced a strategic shift of cargo flows to western land borders. This environment is characterized not by risk (stochastic uncertainty with known probabilities), but by epistemic (deep) uncertainty, where historical data becomes irrelevant. Consequently, classic deterministic and stochastic models, which focus on minimizing financial costs, are inadequate for humanitarian logistics, where the priority lies in minimizing social consequences and ensuring the physical survival of the population. Goal. The primary objective of this study is to enhance the efficiency and reliability of transport-technological schemes (TTS) for international cargo delivery in conditions of military conflict. This is achieved by developing a robust optimization methodology that, unlike classical approaches, minimizes total social costs and effectively accounts for the epistemic uncertainty of logistics network parameters. Methodology. The research methodology is based on robust optimization (RO) principles, which utilize «uncertainty sets» rather than probability distributions. The study develops a mathematical model centered on a composite objective function that integrates three key components: direct logistics costs, the cost of risk (threats to cargo and personnel), and the cost of deprivation (a non-linear economic valuation of the humanitarian impact caused by delivery delays). To handle time uncertainty, the authors formulate robust constraints using the concept of an «uncertainty budget» and propose a mechanism for dynamic parameter adaptation using Digital Twin technology. Results. A new mathematical model for selecting rational TTS has been developed. Unlike commercial models, the proposed function prioritizes social needs over financial economy in critical situations. The study successfully formulated a system of robust constraints that guarantees the adherence to delivery deadlines (Hard Time Windows) even under the realization of worst-case delay scenarios on a specified number of route segments. Scenario analysis of four strategies («Peace», «Humanitarian Crisis», «High Threat», «Maximum Uncertainty») demonstrated that adjusting the model's weight coefficients allows for the adaptation of the logistics system to current priorities–ranging from cost minimization to the maximization of safety or speed, regardless of economic factors. Originality. The scientific novelty lies in the integration of the deprivation cost function into a robust routing model specifically designed for conditions of epistemic uncertainty characteristic of wartime. The authors substantiate the shift from the paradigm of minimizing expected costs (stochastic approach) to minimizing costs in the worst-case scenario (robust approach) for critical humanitarian cargo. Practical value. The practical significance of the work involves the proposed concept of dynamic TTS management through the integration of the robust model with Digital Twin technology. This architecture enables operational route re-calculation in real-time based on heterogeneous data regarding traffic, infrastructure destruction, or military threats. This transforms static plans into flexible response mechanisms, ensuring the reliability of humanitarian corridors and the security of supply chains in highly volatile environments.

Multi-objective optimization of a truck-drone delivery system for fair and efficient humanitarian logistics under disruption and disinformation

Effective humanitarian logistics planning is crucial given the increasing frequency and intensity of disasters. This study explores two primary relief supply strategies: inventory pre-positioning and post-disaster third-party supply. Pre-positioning can enhance relief efficiency but is challenged by inaccuracies in demand forecasting, while post-disaster third-party supply offers greater flexibility but faces uncertainties in supply capacity during chaotic disaster scenarios. To address these challenges, we develop a two-stage optimisation framework under uncertain demand and third-party supply capacity. In the first stage, decisions are made concerning relief facility location, contract establishments with third-party suppliers, and inventory pre-positioning before disasters. The second stage focuses on the distribution of relief items from both relief facilities and third-party suppliers in the post-disaster phase. To capture the uncertainties, we use a distributionally robust optimisation (DRO) approach. In this approach, we construct a scenario-wise ambiguity set based on limited historical data and reformulate the DRO model into a tractable mixed-integer linear programming model. We validate the effectiveness of our proposed model through comprehensive numerical experiments and a real-world case study of the Pu'er Earthquake. Our out-of-sample experiments demonstrate that the scenario-wise DRO framework achieves superior performance compared to the stochastic programming approach and the classical moment-based DRO model. The results provide evidence that the decisions obtained by the DRO approach can hedge against the uncertainties. Additionally, we conduct sensitive analyses on various key parameters to provide managerial insights and practical guidance for humanitarian logistics decision making.

Humanitarian logistics is the process of managing resources, supplies, people, and informa-tion so that help can get to people who need it quickly and effectively after a disaster. To effectively coordinate post-disaster aid and make sure that the right goods are in the right places at the right times, the supply chain needs to be flexible and able to change. This study seeks to examine the influence of digital transformation on agility, trust, and collabora-tion within the humanitarian logistics supply chain. Digital transformation enhances supply chain efficiency through the application of information technologies and digitalization proces-ses, while simultaneously fostering trust among stakeholders. This paper examines the inf-luence of digital transformation on agility in humanitarian logistics through a literature review. Digitalization improves the sharing of information during disaster response and makes supply chain processes more adaptable. During disasters, agile supply chains provide faster and better services, while digital solutions make it easier to track and understand the pro-cess. Also, digital transformation makes it easier for people in the supply chain to work to-gether and coordinate their efforts, which makes sure that help is delivered fairly and on ti-me. This study examines the impact of digital transformation on humanitarian logistics by evaluating how digitalization affects supply chain agility, trust, and collaboration. The study's findings indicate that digital transformation is essential for enhancing the efficacy of humanitarian logistics supply chains, and that digital technologies facilitate more efficient res-ponses to disasters in aid activities. The paper offers a model to clarify the connection between digital transformation and supply chain agility. This study emphasizes the advan-tages of digital transformation in humanitarian logistics, especially in improving essential elements like agility, trust, and collaboration. The results give humanitarian logistics players strategic advice on how to make digital transformation work better.

In recent years, studies in humanitarian logistics have increased due to the frequency, dimensions and losses caused by disasters. It is possible to be prepared for the negative situations that may occur before disasters occur and to take necessary precautions with an effective disaster management strategy. Making plans by evaluating the dynamic structure of humanitarian logistics processes in disasters plays an important role before and after the disaster. In addition, the failure to analyze and consider the risks in these processes makes it challenging to manage them effectively and efficiently. Although various approaches have been proposed to improve efficiency, there is still a need for a comprehensive literature review that classifies studies according to their methods, decisions, and risk considerations. In this study, a literature review was carried out to provide a risk-oriented decision-making framework for studies on humanitarian logistics. The studies are categorized according to their methods, the decisions, and the risks they address.

Optimization of resilient humanitarian logistics using a robust combinatorial multi-attribute reverse auction

In humanitarian logistics emergency material transportation and distribution, trucks offer large load capacity and long driving range, whereas drone transportation is independent of ground road conditions but constrained by battery life and payload capacity. The coordination of the two can therefore provide complementary advantages. In this paper, the traveling salesman problem is formulated for a two-echelon emergency material distribution process, spanning transportation from the central warehouse to the distribution center and then to the demand points. In the first stage, transportation from the central warehouse to the distribution center is performed by trucks. In the second stage, trucks and drones collaboratively carry out material distribution from the distribution center to the demand points. Based on the above scenario, this paper aims to minimize the total cost of completing all distribution tasks. The model considers capacity constraints at distribution centers, time window constraints at demand points, and stochastic demand, and establishes a two-echelon traveling salesman problem for humanitarian logistics with truck–drone collaboration. Based on the particle swarm optimization (PSO) framework, a heuristic algorithm named PSO-VD is proposed, which transforms the discrete traveling salesman problem into a continuous encoding and integrates drone routes into truck routes using the 2-opt method. In small-scale instances, the solutions obtained by PSO-VD are compared with those of commercial solvers, demonstrating that the proposed algorithm achieves high accuracy with low computational time. For instances with up to 12 demand points, the algorithm obtains solutions within 150 seconds, with an accuracy deviation of less than 10 % compared to exact solution methods. The applicability of the algorithm proposed in this paper has been demonstrated through large-scale numerical examples. Sensitivity analyses are conducted on key parameters, including the time window penalty coefficient, drone speed, and drone battery capacity, yielding practical managerial insights.

As a collective, Organization has a strong commitment to exposing and challenging domination and oppression, to amplifying scholarly activism, and to standing in solidarity with the oppressed, the dispossessed, and the Indigenous peoples of the world. Therefore, we cannot remain silent observers of the genocide in Gaza, of the oppression of the Palestinian people, and of the political, academic and organizational systems that are complicit in it. In this editorial, we call on MOS scholars to approach and study the genocide in Gaza and the oppression of the Palestinian people as a form of organized violence rooted in settler colonialism and processes of racialization that render Palestinian life disposable, and sustained by corporate, state, and academic infrastructures. Careful analysis exposes how the systematic targeting of health, education, and knowledge infrastructures is entangled with organizational processes involved in arms production, digital platforms, finance, universities, and humanitarian logistics. Building on the critical tradition of the journal, this editorial calls on Organization’s epistemic community to act in solidarity with the people of Palestine through constructing alternative knowledge infrastructures and instituting an academic boycott of Israeli institutions. We urge scholars, editors, and students to reconfigure professional practices and mobilize their work against genocidal, colonial and oppressive organizing.

Background: Urban disasters expose persistent gaps in the operational picture and timely decision-making for response teams, which require user-centred systems that connect analysis to action. This study proposes and formatively validates an integrated framework that couples digital twins and augmented reality for humanitarian logistics. Methods: A mixed methods design combined a structured literature synthesis with a practitioner survey across architecture, engineering, planning, BIM, and construction to assess perceived value and adoption conditions. Results: Findings indicate that practitioners prioritised digital twins for enhancing situational awareness (71.4%) and augmented reality for providing real-time information overlays (64.3%). A majority judged that integrating these technologies would yield substantial improvements in disaster response (67.9%), despite implementation challenges. Conclusions: The framework links live state estimation and short-horizon simulation to role-specific, in-scene AR cues, with the aim of reducing decision latency and improving coordination. Adoption depends primarily on human and organisational factors, including user accessibility, preparation needs, and clear governance. These results suggest a viable pathway to operationalise the bridge between analysis and field action and outline priorities for pilot evaluation.

Sustainable post-disaster humanitarian logistics network design: A real-case study with consecutive disaster risks

In times of high uncertainty, global recession, and lack of adequate funding for humanitarian needs, it is crucial to optimize humanitarian operations to achieve high levels of productivity and efficiency and to cut operational costs without compromising delivery of humanitarian aid to the most vulnerable population. One of the major contributors to cost of operations is transportation costs. In the humanitarian field, fleets are operated to deliver aid and assistance to the needy and most vulnerable population around the globe. These fleets are run in the form of field trips, which constitute a vehicle, a professional driver, humanitarian aid officers, and aid cargo. This study aims at optimizing resources and reducing costs of humanitarian field trips using an automated VBA program. The VBA program utilizes optimization algorithms such as compatibility and overlapping matrices to optimize field trip requisitions. The program also uses the Hungarian method to optimally solve the assignment matrices of vehicles-to-field trips and drivers-to-field trips. The outcome of the study is a user-friendly tool which comprises as inputs: vehicles data, drivers data, field trips history, and field trips requisition plan; and provides as outputs: the optimum list of combined field trips, optimum vehicles-to-field trips assignment based on minimum cost of transportation, and optimum drivers-to-field trips assignment based on the maximum productivity of drivers. The outcome of such a practical and utilizable tool goes beyond the humanitarian context and can be further tested and applied in fleet units of various industries. This study demonstrates the successful application of classical operations research techniques, specifically the assignment problem and the Hungarian method, to address resource allocation challenges in a real-world humanitarian logistics setting, providing a valuable bridge between theory and practice. In addition to mathematical modeling, the research leverages real operational data from a humanitarian fleet management system, validating the tool’s effectiveness on a large dataset of over 3,000 trips. The implementation also accounts for driver fatigue constraints and security conditions in trip combinability, offering a highly realistic and ethical optimization framework. Future extensions may include stochastic modeling, full route optimization, and integration with GIS systems for enhanced operational planning.

Determinant Factors of Humanitarian Logistics Effectiveness: Empirical Evidence from Borno State of Nigeria

Type of the article: Research Article AbstractCorruption in humanitarian logistics and supply chain management remains a serious issue, especially during disaster relief operations, because it disrupts the fair distribution of aid and increases the hardships of affected communities. This study examines how social media can help reduce corruption in humanitarian logistics and supply chain management during disaster relief operations following the devastating 2022 flood in Pakistan. Using a quantitative research design, data were collected through a structured survey conducted between March and June 2023 targeting 380 individuals across three major flood-affected provinces in Pakistan. The participants included staff and volunteers from local NGOs, government agencies, and international relief organizations directly involved in the flood relief operation. This group was chosen for its firsthand experience in managing and delivering aid during the 2022 floods, making the sample both relevant and informed. The results were analyzed using SmartPLS and SPSS software. The findings show that corruption in HLSCM has a significantly negative impact on the effectiveness of flood relief operations (β = –0.561; t-value = 9.765; p-value = 0.000). However, social media played a moderating role in this relationship (β = –0.060; t-value = 1.217; p-value = 0.022), acting as a helpful tool for spreading information, increasing public involvement, and promoting transparency. These insights are useful for donors, disaster management authorities, and aid organizations seeking to improve relief outcomes. This study adds to the current research by showing how social media can help reduce corruption and improve the delivery of aid in the humanitarian logistics of disaster relief operations.

Vulnerability-deprivation-resilience: A sociotechnical approach to humanitarian logistics management

Humanitarian logistics involves the coordinated movement of relief goods, personnel, information, and funds between beneficiaries, donors, and suppliers, which is crucial for successful emergency response operations. In Ethiopia, logistical challenges often hinder access to healthcare services. This study focuses on humanitarian logistics management (HLM) in Ethiopia, aiming to assess the perceptions of HLM practices, identify key challenges, and provide solutions to improve healthcare access for beneficiaries. An explanatory and descriptive case study design was used to explore the logistical challenges. A mixed-methods approach, combining both quantitative and qualitative data, was employed concurrently. The study surveyed 92 professionals in technical and managerial positions from nine directorates involved in core humanitarian logistics activities, using a census sampling method. The study identified 20 key challenges, grouped into five categories. Organizational and economic challenges were the most prominent. The findings revealed notable variations in HLM practices across organizations. Inventory management was the most commonly implemented logistics function, followed by distribution management. In contrast, procurement and transportation management were less effectively practiced. Among the organizations studied, the Ethiopian Public Health Institute demonstrated more effective HLM practices compared to the Ministry of Health and the Ethiopian Pharmaceuticals Supply Service. The study highlights significant inefficiencies in the humanitarian logistics system, particularly in procurement and transportation management. These inefficiencies disrupt humanitarian operations, leading to delays in the delivery of pharmaceuticals to beneficiaries. Inadequate logistics can result in loss of life, increased suffering, and unnecessary costs. This research contributes to understanding the gaps in HLM practices in Ethiopia and provides recommendations for improving logistics efficiency in public health emergencies.

Modeling a humanitarian‐aid covering tour problem with location selection and vehicle assignment decisions