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Health care facility systems are hierarchical as they consist of facilities at different levels such as clinics, health centers, and hospitals. Therefore, finding a proper location for the health care system can be categorized as a hierarchical location problem. Besides, partitioning a given region in a geographical area into different zones is very crucial to make sure the health services are available at their highest possible level for everyone in that region.  In this study, an optimization model for the integrated problem of hierarchical location and partitioning under uncertainty in the Iranian healthcare system is proposed. The objective function of this model maximizes the total social utility of districts while workload balance and distance limitation between the zones are considered as the main constraints. Since this study involves NP-hard problems, three metaheuristic algorithms, including Genetic, Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO) were developed. The numerical results suggest that the Grey Wolf Optimizer (GWO) algorithm indicates a more appropriate level of performance in almost all responses compared to the other algorithms. Therefore, the case study was solved by the Grey Wolf Optimizer (GWO). Based on the results, 10 distrcis with their zones are identified to maximize the overall utility. A sensitivity analysis also performed to show the behavior of the model. It can be stated that the findings of this study can be utilized as a useful management tool in other organizations.

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This research proposes a robust multi-objective optimization model for blockchain-enabled smart supply chains under uncertainty. The model integrates forward and reverse logistics while incorporating blockchain transaction efficiency to enhance transparency, traceability, and trust among stakeholders. The objectives include minimizing total costs, reducing carbon emissions, maximizing service levels, and optimizing blockchain-related operations. To address uncertainties in demand and transportation costs, the model employs fuzzy robust optimization techniques, ensuring reliable decision-making. To solve the proposed model, several metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the newly developed Greedy Man Optimization Algorithm (GMOA) are utilized. Comparative analysis demonstrates the superiority of GMOA in achieving high-quality solutions with lower computational time. The results highlight the model’s practical applicability in designing sustainable, transparent, and efficient supply chains. Sensitivity analyses provide managerial insights, emphasizing the impact of key parameters on total costs and operational performance.