A biobjective hierarchical location-allocation approach for the regionalization of maternal-neonatal care. (February 2022)

• Record Type: Journal Article
• Authors: Karakaya, Şakir; Meral, Sedef
• Is Part Of: Socio-economic planning sciences. Number 79(2022)
• Publication Date: 2022-02
• Subjects: Maternal-neonatal -- Health care -- Hierarchical location-allocation -- p-median -- Top-down heuristic -- Lagrangean relaxation; Planning -- Periodicals; Economic policy -- Periodicals; Social policy -- Periodicals; Planification -- Périodiques; Politique économique -- Périodiques; Politique sociale -- Périodiques; ECONOMIC PLANNING; SOCIAL PLANNING; DECISION-MAKING; 361
• DOI: 10.1016/j.seps.2021.101093
• Languages: English
• ISSNs: 0038-0121

Abstract

Abstract: This study proposes a biobjective location-allocation model for the regionalization of maternal-neonatal care to increase both accessibility and cost-efficiency by minimizing total transportation costs to public in accessing services and total service costs to government simultaneously. The model is characterized by a three-level successively inclusive hierarchy with an integrated flow and bidirectional referrals. Since it is difficult to solve for the optimum in a reasonable time, three heuristics are developed: top-down heuristic, novel top-down heuristic, and Lagrangean relaxation. A significant result obtained from the computational study is that at least one of the heuristics provides high-quality solutions in reasonable computational time for any level of the policy variable in weighting the two objectives. To demonstrate its practicality, the proposed hierarchical location-allocation model is applied to a case study based on the southeastern Anatolian region of Turkey. Highlights: We develop a model for the regionalization of maternal-neonatal care system. The model is a biobjective 3-level hierarchical location-allocation type. We propose top-down heuristics as a sequential solution strategy. We propose a Lagrangean relaxation approach as an integrated solution strategy. We use a case study to demonstrate the usefulness of the model.