Post-disaster water distribution represents a critical and underexplored application of humanitarian supply chain optimization, where delays and inequities in resource allocation can have life-threatening consequences. This paper presents a heuristic optimization framework for emergency bottled water distribution across Florida counties following hurricane-induced disruptions. The proposed framework integrates geospatial facility data, census-derived demand estimates, and transportation distance matrices to model post-disaster allocation behavior under constrained supply conditions. The resulting statewide allocation network consists of 26 bottled water facilities and 67 Florida counties, creating 1,742 potential transportation allocation links between supply and demand nodes. Two allocation variants are developed and compared: a blockchain-enabled collaborative model in which suppliers share demand fulfillment information in real time, and a competitive non-blockchain model representing conventional decentralized logistics behavior. The study evaluates the operational and equity implications of information transparency in humanitarian supply chains using transportation cost, service level, and variance-based equity metrics. Computational experiments conducted under a disaster scenario Supply/Demand ratio of 0.425 demonstrate that the blockchain-enabled model achieves a 49.49% reduction in equity variance relative to the non-blockchain baseline, albeit with a 29.76% increase in transportation cost. Sensitivity analysis across multiple Supply/Demand conditions further confirms the robustness of the observed cost-equity tradeoff. The findings provide quantitative evidence that decentralized information transparency mechanisms inspired by blockchain coordination can substantially improve fairness in humanitarian resource allocation during disaster response operations. The proposed framework contributes to both humanitarian logistics and Industry 4.0 research by demonstrating how collaborative coordination mechanisms influence supply chain behavior under severe disruption conditions.

West Java Province recorded the highest number of disaster occurrences in Indonesia in 2024, with 457 hydrometeorological cases. A gap exists between logistics demand and allocated quantities, with fulfilment shortfalls of 24 to 27% across five aid categories exceeding the 10% threshold, due to a static weighted scoring system without capturing regional demand. This study proposes a three-stage framework: vulnerability variables are forecasted using Simple Linear Regression or Moving Average based on MAPE; Support Vector Regression predicts demand for 27 districts; allocation is proportionally determined subject to stock constraints. Validation NSE ranges from 0.6481 to 0.8038 and the fulfilment shortfall reduces to at most 10% for the 2025 validation period, outperforming the existing weighting approach. SVR outperforms Random Forest in four of the five categories, consistently achieving lower MAPE across all categories. The originality lies in a region-specific, pre-disaster demand prediction framework replacing static provincial weighting.

The Cianjur earthquake damaged 47,000 buildings and displaced 114,683 residents, creating urgent demand for rapid relief distribution. Existing temporary warehouses are concentrated within a single subdistrict, two existing temporary warehouses have 60% of service areas exceeding the 60-minute response threshold. This indicates that the existing temporary warehouse configuration requires optimization to ensure full coverage within the 60-minute response-time threshold. In response to this problem, this study proposes a CMCLP-PSO model with a two-loop structure to determine optimal temporary warehouse locations and allocate relief supplies to affected demand points. The outer loop selects candidate warehouse locations, while the inner loop assigns demand through split allocation. The results show that the selected temporary warehouses can cover 100% of the affected area points within the 60-minute response-time threshold, reducing the proportion of service areas exceeding the threshold from 60% to 0%.

When disaster occurs, the distribution of relief goods to the disaster-affected communities is necessary to minimize the loss and casualties. However, the occurrence of disaster often damages the essential logistics infrastructures, resulting in the isolation of some disaster-affected areas. This situation leads to the relevance of airborne transportation modes, such as drones, in assisting the distribution of relief goods. Therefore, this article considers a drone-assisted relief distribution system, where drones are utilized to deliver relief goods to the isolated areas. Considering the urgent nature of this problem, an evolutionary algorithm with a novel solution representation is proposed as an effective decision-support tool. The algorithm's effectiveness is tested through a series of computational experiments, resulting in 2.98-3.04% performance gaps in best solutions found and 11.37-30.08% gaps in the computational efficiency.