Search Results (120)

Pre-disaster and post-disaster allocation strategies are widely investigated as the single optimization problem in humanitarian supply chain management, while integrated decisions including the above two problems are seldom discussed in the existing literature. Here, this paper proposes a mixed-integer programming model to determine these decisions, including the location of central warehouses and emergency storage points and the quantities of relief items pre-deployed and distributed. Specially, two preferences regarding costs and cost-resilience are considered, and a comparison of two models concerning the above preferences is performed. The results are as follows: (i) When the impact of disasters is at a relatively low or moderate level, the cost-oriented model can reduce the government’s financial burden and increase the coverage of relief items. However, when the severity of the disaster is high, the cost resilience-oriented model can respond to the needs of victims within the shortest time, although these needs cannot be completely met. (ii) Increasing the initial inventory level of emergency storage points and enhancing the victims’ tolerance time through social support can effectively reduce the total costs, while increasing the transportation speed can effectively reduce the response delay time. (iii) Adjusting the unit penalty cost can make the total penalty costs and transportation costs decline within a certain range, but such an adjustment has no influence on the response delay time. This paper not only proposes an integrated framework for pre- and post-disaster allocation decisions but also highlights the importance of incorporating resilience into relief item allocation in disaster contexts. Full article

Background: The adoption of artificial intelligence (AI) in humanitarian logistics is essential for improving coordination and decision making, especially in the challenging landscape of disaster-relief settings. However, the current literature offers limited empirical evidence with respect to the specific impact of AI on coordination and decision making for real-life humanitarian problems. Based on evidence from the humanitarian sector, this paper focuses on how AI could help humanitarian organizations collaborate better, streamline relief supply-chain operations and use resources more effectively. Methods: Twelve key themes influencing AI integration are identified by the study using a Grounded Theory (GT) approach based on interviews with experts from the humanitarian sector. These themes include data reliability, operational limitations, ethical considerations and cultural sensitivities, among others. Results: The findings suggest that AI improves forecasting, planning and inter-organizational coordination and is especially useful during the preparedness and mitigation stages of relief operations. Successful adoption, however, depends on adjusting tools to actual field conditions, building trust and training and striking a balance between algorithmic support and human expertise. Conclusions: The paper offers useful and practical advice for humanitarian organizations looking to use AI technologies in an ethical way while taking into account workforce capabilities, cross-agency cooperation and field-level realities. Full article

Massive relief supplies inter-regional lateral transshipment (MRSIRLT) can significantly enhance the efficiency of disaster response, meet the needs of affected areas (AAs), and reduce deprivation costs. This paper develops an integrated allocation and intermodality optimization model (AIOM) to address the MRSIRLT challenge. A phased interactive framework incorporating adaptive differential evolution (JADE) and improved adaptive large neighborhood search (IALNS) is designed. Specifically, JADE is employed in the first stage to allocate the volume of massive relief supplies, aiming to minimize deprivation costs, while IALNS optimizes intermodal routing in the second stage to minimize the weighted sum of transportation time and cost. A case study based on a typhoon disaster in the Chinese region of Bohai Rim demonstrates and verifies the effectiveness and applicability of the proposed model and algorithm. The results and sensitivity analysis indicate that reducing loading and unloading times and improving transshipment efficiency can effectively decrease transfer time. Additionally, the weights assigned to total transfer time and costs can be balanced depending on demand satisfaction levels. Full article

The frequent occurrence of natural disasters creates a symmetry-breaking scenario between pre-disaster planning and post-disaster rescue operations, such as post-disaster supply–demand mismatches for materials and the risk of potential facility failures. Thus, we propose a dual-uncertainty multi-scenario multi-period facility location allocation model for humanitarian rescue. The model employs two polyhedral uncertainty sets to represent facility failure risks and demand uncertainty at disaster points. Moreover, by constructing diverse disaster scenarios, it simulates material distribution schemes across different relief periods, enhancing its realism. Given that the model integrates three subproblems—facility location, supply–demand matching analysis, and emergency material allocation—we design a hybrid algorithm (DCSA-MA) that combines the discrete crow search algorithm (DCSA) and the material allocation (MA) method for its solution. Experimental results demonstrate that the model maintains a relatively high material satisfaction rate even under significant demand fluctuations. The number of facility failures has a direct bearing on emergency rescue effectiveness. The DCSA-MA method achieves a superior material satisfaction rate compared to other algorithms across various disaster scenarios and multiple rescue periods. Furthermore, DCSA-MA outperforms other algorithms in terms of solution quality, convergence, computational time, and stability. These findings indicate that DCSA-MA is an effective and highly stable approach. Full article

The heritage of public works is composed of networks that are strongly linked to the territory where they are built. With the aim of deepening our knowledge of the appearance and subsequent development of the transport and supply systems in Madrid, we present a study of the main hydraulic works, bridges and railway stations. Based on historical and technological documentation, works and networks are analysed and georeferenced in order to relate their traces and evolution to the city. These built elements define recognisable physical and cultural traces in the form and identity of the city. The documentation and technological and social analysis work was completed with a dissemination and heritage education process. The results show that the physiographic and lithological reality of Madrid, characterised by its intense link with water, has determined the configuration of the urban network and the expansion of the city. Bridges span obstacles and set milestones. Stations are spaces for exchange and connection. The water supply network feeds the urban grid. The city changes, but the traces remain. Urban growth has smoothed, absorbed or hidden the original relief and watercourses, but they are still present in public works and even in the collective memory of the citizens through the force of their cultural and social values. Full article

This paper proposes a two-stage stochastic non-cooperative game model to solve relief supplies procurement and distribution optimization of multiple rescue organizations in crisis rescue. Rescue organizations with limited budgets minimize rescue costs through relief supply procurement, storage, and transportation in an uncertain environment. Under a mild assumption, we establish the existence and uniqueness of the equilibrium point and derive the optimality conditions by using the duality theory, characterizing the saddle point in the Lagrange framework. The problem is further reformulated as a constraint system governed by Lagrange multipliers, and its optimality is characterized by the Karush–Kuhn–Tucker condition. The economic interpretation of the multipliers as shadow prices is elucidated. Numerical experiments verify the effectiveness of the model in cost optimization in crisis rescue scenarios. Full article

Background/Objectives: Philadelphia has experienced a surge in illicit fentanyl adulterated with alpha-2 agonist sedatives. Initially, xylazine (“tranq”) was the predominant adulterant, and a novel multimodal withdrawal protocol was effective at mitigating symptoms. However, since mid-2024, medetomidine—a more potent sedative—has largely supplanted xylazine. Clinicians have reported more severe, treatment-resistant opioid withdrawal during this transition. To assess whether a previously effective withdrawal management protocol retained efficacy after the emergence of medetomidine as the primary fentanyl adulterant in a community. Methods: We conducted a retrospective cohort study of patients receiving protocol-based opioid withdrawal treatment at two emergency departments in Philadelphia between September 2022 and April 2025. Patients were divided into the xylazine era (September 2022–July 2024) and medetomidine era (August 2024–April 2025). The primary outcome was a change in Clinical Opioid Withdrawal Scale (COWS) score from pre- to post-treatment. Secondary outcomes included rates of discharge against medical advice (AMA) and ICU admission, as well as the impact of a revised treatment protocol. Results: Among 1269 encounters with full data, 616 occurred during the xylazine era and 770 during the medetomidine era. Median COWS reduction was greater in the xylazine group (−9.0 vs. −4.0 points, p < 0.001), with more patients achieving symptom relief (COWS ≤ 4: 65.6% vs. 14.2%, p < 0.001). ICU admission occurred in 8.5% of xylazine era patients and 16.8% of medetomidine era patients (p < 0.001). Rates of AMA were higher during the medetomidine era as well (6.5% vs. 3.6%) (p = 0.038). Revision of treatment protocols showed promise. Conclusions: The protocol was significantly less effective during the medetomidine era, though a protocol change may be helping. Findings highlight the need to adapt withdrawal treatment protocols in response to changes in the illicit drug supply. Full article

As supply chains become increasingly digitized and decentralized, ensuring security, traceability, and data integrity has emerged as a critical concern. Blockchain technology has shown significant potential to address these challenges by providing immutable records, transparent data flows, and tamper-resistant transaction logs. However, the effective application of blockchain in real-world supply chains requires the careful evaluation of both architectural design and technical limitations, including scalability, interoperability, and privacy. This review systematically examines existing blockchain-based supply chain solutions, classifying them based on their structural models, cryptographic foundations, and storage strategies. Special attention is also given to underexplored humanitarian logistics scenarios. It introduces a three-dimensional evaluation framework to assess security, traceability, and integrity across different architectural approaches. In doing so, it explores key technological enablers, including advanced mechanisms such as zero-knowledge proofs (ZKPs) and cross-chain architectures, to meet evolving privacy and interoperability demands. Furthermore, this study outlines a conceptual cross-chain interaction scenario involving permissioned and permissionless blockchain networks, connected through a bridge mechanism and supported by representative smart contract logic. The model illustrates how decentralized stakeholders can interact securely across heterogeneous blockchain platforms. By integrating quantitative metrics, architectural simulations, and qualitative analyses, this paper contributes to a deeper understanding of blockchain’s role in next-generation supply chains, offering guidance for researchers and practitioners aiming to design resilient and trustworthy supply chain management (SCM) systems. Full article

This study focuses on Vehicle-Integrated Photovoltaic (VIPV) strategy adopted as an energy supply vector in disaster scenarios. As a matter of fact, energy supply may be a very critical issue in a disaster context, when grid networks may be damaged. Emergency vehicles, including ambulances and trucks, as well as mobile units such as containers and operating rooms, can be equipped with photovoltaic modules and can serve as mobile emergency energy sources, supporting both vehicle operations and disaster relief efforts. A methodology was developed to estimate energy production under unpredictable disaster conditions, by adapting existing VIPV simulation approaches. Obtained results show that VIPV strategy, even under minimal daily energy generation, can be a useful aid for disaster resilience and emergency prompt response. Ambulance performance, analyzed for worst-case scenarios (e.g., December), shows that they can power medical devices for 1 to 15 h daily. Additionally, the ambulance can generate up to 2 $\mathrm{M}\mathrm{W}\mathrm{h}$ annually, reducing CO₂ emissions by up to 0.5 tons. In optimal configurations, mobile operating rooms can generate up to 120 times the daily energy demand for medical devices. Full article

With the increasing emphasis on emission reduction targets, the low-carbon sustainable transformation of industrial energy supply systems is crucial. Addressing the urgent issue of reducing industrial carbon emissions, this study presents an integrated industrial energy supply system (IRE-CCUS-BESS-SPS) that incorporates renewable energy; calcium-based carbon capture, utilization, and storage (CCUS); and battery energy storage systems (BESSs) to improve energy efficiency and sustainability. The system model is designed to achieve a cost-effective and environmentally low-impact energy supply, validated through Aspen Plus V11.0 and Matlab R2019b simulations. The system’s performance is evaluated using a 4E index system encompassing economy, environment, energy, and exergy. The findings indicate that the system’s lifetime net present value (NPV) is positive, with a payback period of 6.09 years. Despite a 12.9% increase in the overall economic cost, carbon emissions are significantly reduced by 59.78%. The energy supply composition includes 48.60% from fuel oil and 22.10% from biomass, with an additional 270.04 kW of heat provided by waste heat boilers. The equalization costs for CO₂ removal (LCCR) and methanation (LCOM) are 122.95 CNY/t and 10908.35 CNY/t, respectively, both exceeding current carbon emission trading costs and methane prices. This research offers a robust framework for designing sustainable industrial energy systems that integrate renewable energy, CCUS, and energy storage technologies for low-carbon operations. The analysis also suggests that government policies, such as direct financial subsidies or tax relief, are effective in accelerating the adoption of CCUS technology. Full article

Natural disasters (e.g., floods, earthquakes) significantly impact citizens, economies, and the environment worldwide. Due to their sudden onset, devastating effects, and high uncertainty, it is crucial for emergency departments to take swift action to minimize losses. Among these actions, planning the locations of relief supply distribution centers and dynamically allocating supplies is paramount, as governments must prioritize citizens’ safety and basic living needs following disasters. To address this challenge, this paper develops a three-layer emergency logistics network to manage the flow of emergency materials, from warehouses to transfer stations to disaster sites. A bi-objective, multi-period stochastic integer programming model is proposed to solve the emergency location, distribution, and allocation problem under uncertainty, focusing on three key decisions: transfer station selection, upstream emergency material distribution, and downstream emergency material allocation. We introduce a multi-armed bandit algorithm, named the Geometric Greedy algorithm, to optimize transfer station planning while accounting for subsequent dynamic relief supply distribution and allocation in a stochastic environment. The new algorithm is compared with two widely used multi-armed bandit algorithms: the $ϵ$-Greedy algorithm and the Upper Confidence Bound (UCB) algorithm. A case study in the Futian District of Shenzhen, China, demonstrates the practicality of our model and algorithms. The results show that the Geometric Greedy algorithm excels in both computational efficiency and convergence stability. This research offers valuable guidelines for emergency departments in optimizing the layout and flow of emergency logistics networks. Full article

The priority during an emergency, regardless of the type, is to rescue as many lives as possible. Field hospitals are usually installed to provide the primary relief to the affected population when hospitals are compromised or absent. There are several sanitary units worldwide ready to be transported to disaster areas. An average field hospital is equipped with an operating room, laboratory, and radiological equipment, but it does not include a unit for the infectious hospital solid waste treatment, which results in improper management with high infection risks and emissions due to incorrect operations (e.g., open incineration). Therefore, the present study identified two market-available solutions (an incinerator and a sterilizer) designed to be transported even under the challenging conditions typical of disasters and are suitable for treating infectious waste. The systems were assessed by a life cycle assessment (LCA), proving an emission savings >90% (considering all impact categories) using the sterilization system. The avoided combustion allows to halve the effect on climate change due to a portable incinerator. This study supplies interesting food for thought for the emergency managers, proving the possibility of integrating the sustainability also in the planning of the response to catastrophic events. Full article

Background: On 1 January 2024, a 7.6 magnitude earthquake struck the Noto Peninsula. We entered the disaster area to provide relief and set up a makeshift clinic in an evacuation center to evaluate the quality and quantity of food provided there. Methods: This cross-sectional study, of mainly older adults, was conducted to analyze the amino acid and fatty acid composition of evacuation shelter meals in comparison with the results of the Japan National Survey, mainly focused on older adults. (1) We analyzed 11 evacuation foods using the “Duplicated Combination” Model and the digestible amino acid score (DIAAS) in relation to the half-life determined by the N-terminal amino acid proteins. (2) Linoleic acid (LA) and alpha-linolenic acid (ALA) levels were compared with European Food Safety Authority (EFSA) recommendations (3). The national survey of emergency food stocks in 198 hospitals and 189 social care institutions conducted in Jan 2024 was analyzed. Results: (1) DIAAS was less than 1.00 for all 11 foods provided and was considered inadequate, (2) the half-life of the protein, whose N-terminal valine has a half-life of 100 h, must be considered a possible deficiency when living in a shelter for more than a week, (3) LA and ALA levels were less than 40% of EFSA recommended, (4) the nationwide survey found that 80% of people have a three-day supply and data on amino acids and fatty acids were not available due to a lack of questionnaires. Conclusion: Analysis of food in evacuation shelters after the Noto Peninsula earthquake revealed the quality of amino acids involved in shelter meals using DIAAS and the lack of LA and ALA for older adults. The “Duplicated Combination” model used in this analysis could be beneficial for developing improved nutrition plans in similar future scenarios, mainly for older adults. Full article

Investigating the experience and improvement measures for China’s distinctive land supply participation in macro-control processes holds significance for full utilization of land policy. However, the spatial heterogeneity and its theoretical and comprehensive analysis of drivers are still poorly revealed. This paper uses spatial analysis methods and micro-scale big data on land transactions to depict the spatiotemporal heterogeneity of land supply, and analyses its driving mechanisms via an endogenous–exogenous factor framework and regression models. Land supply experienced fluctuating “growth–decline–growth” trends in 2001–2021, spatially showed a large cluster in the east, a small cluster in the center and scattering in the west, with the gravity center relocating southwest, and formed a multi-core, hierarchical, circular structure of high density in core cities, density in peripheral cities and sparseness in districts. Endogenously, total land resources and road accessibility facilitated land supply, while topographic relief and urban proximity showed inhibitory effects; land supply positively correlated with land finance dependence, officials’ appraisal pressure, local government competition and officials’ corruption but negatively related with fiscal tax revenues and fiscal transparency; construction land indicators directly determined land supply, while the intensity of use control restricted the conversion of arable land and weakened land supply. Exogenously, urbanization, industrialization, capital investment, technological innovation and marketization level promoted land supply, while the substitution of human capital reduced the demand for land; economic fluctuations showed non-significant relationships with land supply. Differentiated impacts of multiple factors on land supply pattern are emphasized and should be integrated into formulating land policy and optimizing land allocation. Full article

After a typhoon disaster, selecting effective paths is crucial for ensuring the efficiency of emergency rescue operations and the distribution of essential supplies, which are vital for sustainable disaster response and community resilience. Current research into disaster scenarios is less aligned with actual scenarios as road conditions are hard to predict. This paper, set against the backdrop of typhoon disasters, employs netizens’ sentiment data to indirectly assess post-disaster road conditions and refine the calculation formula for road failure probabilities. This approach aims to identify failure-resistant paths to guide disaster relief decisions, thereby supporting sustainable disaster relief operations and minimizing resource expenditure. First, an expression form for road segment failure probability is established, considering factors such as tree falls, landslides, and waterlogging. Second, negative sentiment coefficients, derived from social media data analysis, are used to adjust road failure probabilities, reflecting the sentiments of affected communities. Then, a failure-resistant path selection model based on these adjusted road failure probabilities is proposed to enhance the resilience and sustainability of emergency transport paths. Finally, the model’s effectiveness is validated using Typhoon “In-Fa” in Ningbo as a case study. Full article