In this paper, a biobjective mixed-integer nonlinear programming model is developed for a hierarchical three-level health service network design problem, which is then transformed to its linear counterpart. The model aims to minimize the total establishment cost and total weighted distance between patient zones and health facilities simultaneously. In order to cope with inherent epistemic uncertainty in input parameters, four variants of a novel hybrid robust possibilistic programming (HRPP) approach are introduced. Finally, a real case study is provided to illustrate the performance and applicability of the proposed HRPP models in practice.
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