{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:09:21Z","timestamp":1765357761448,"version":"3.40.5"},"reference-count":23,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["SIMULATION"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:p>Complex sociotechnical systems with multiple competing objectives and nonlinear dynamics pose significant challenges for policy optimization. Traditional simulation-based methods often struggle with high-dimensional policy spaces. This study addresses these challenges by combining genetic algorithms (GAs) with system dynamics (SD) modeling to optimize policy configurations in complex environments. Our approach merges SD\u2019s capacity to simulate intricate system behaviors with GA\u2019s prowess in multi-objective optimization. The SD model uses predefined decision variables to simulate system behavior, while GA iteratively adjusts these variables to find optimal policy configurations. We apply this hybrid approach to a media industry case study, focusing on balancing profit, competitiveness, and audience satisfaction. The research methodology integrates SD\u2019s ability to capture complex system behaviors with GA\u2019s strength in optimizing multiple objectives. An SD model simulates system behavior based on predefined decision variables representing key policy levers. GA then iteratively adjusts these variables based on fitness objectives derived from the SD model. This process evaluates performance and identifies the globally optimal policy configurations. The results show that the hybrid SD\u2013GA framework significantly improves policy solutions compared to conventional methods. Sensitivity analysis confirms the optimized policies\u2019 robustness and comparative assessments highlight our approach\u2019s advantages in navigating complex policy spaces. This study introduces a new SD\u2013GA framework that improves data-driven policy formulation in complex systems. It combines policy informatics and evolutionary algorithms to offer a comprehensive approach to multi-criteria decision-making. Future research could include long-term validation of this approach in the broadcasting industry to further refine and apply this methodology across various domains.<\/jats:p>","DOI":"10.1177\/00375497241301077","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T07:15:27Z","timestamp":1735629327000},"page":"453-476","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Leveraging genetic algorithms and system dynamics for effective multi-objective policy optimization: a case study on the broadcasting industry"],"prefix":"10.1177","volume":"101","author":[{"given":"Azadeh","family":"Sohrabinejad","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7064-3461","authenticated-orcid":false,"given":"Kiamars Fathi","family":"Hafshejani","sequence":"additional","affiliation":[{"name":"Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran"}]},{"given":"Farzad Movahedi","family":"Sobhani","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran"}]}],"member":"179","published-online":{"date-parts":[[2024,12,30]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcosc.2021.696615"},{"key":"e_1_3_4_3_2","first-page":"213","volume-title":"Hybrid modelling and simulation: conceptualizations, methods, and applications","author":"Howick S","year":"2023","unstructured":"Howick S, Megiddo I, Nguyen LKN, et al. Combining SD & ABM: frameworks, benefits, challenges, and future re-search directions. In: Fakhimi M, Mustafee N (eds) Hybrid modelling and simulation: conceptualizations, methods, and applications. Cham: Springer, 2023, pp. 213\u2013244."},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-12047-3"},{"key":"e_1_3_4_5_2","volume-title":"Media economics: concepts and issues","author":"Picard RG.","year":"2010","unstructured":"Picard RG. Media economics: concepts and issues. 2nd ed. Thousand Oaks, CA: Sage, 2010.","edition":"2"},{"key":"e_1_3_4_6_2","first-page":"551","article-title":"The impact of television viewing on young adults\u2019 social interactions","author":"Weaver JB","year":"2013","unstructured":"Weaver JB, Wuensch KL. The impact of television viewing on young adults\u2019 social interactions. 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