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.