{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T15:14:28Z","timestamp":1781363668761,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The scheduling of carrier-based aircraft departure operations is subject to stringent temporal, spatial, and resource constraints. Conventional approaches struggle to yield exact solutions or provide a comprehensive mathematical description of this complex, dynamic process. This study proposes a simulation-based optimization method, establishing a high-fidelity simulation model for aircraft departure scheduling. To address the coupled challenges of path planning under spatial constraints and station matching\/sequencing under operational constraints, we developed (1) a deep reinforcement learning (DRL)-based path planning algorithm (AAE-SAC), and (2) an enhanced particle swarm optimization (PSO) algorithm (LTA-HPSO). This integrated two-stage framework, termed LTA-HPSO + AAE-SAC, facilitates efficient, collision-free departure scheduling optimization. Simulation experiments across varying sortie scales were conducted to validate the framework\u2019s effectiveness and robustness. Notably, for a complex scenario involving 24 aircraft with diverse priorities and stringent spatial constraints, LTA-HPSO + AAE-SAC achieved an average solution time of 185.19 s, reducing scheduling time by 26.18% and 49.54% compared to benchmark algorithms (PSO + Heuristic and PSO + SAC, respectively). The proposed LTA-HPSO + AAE-SAC framework significantly enhances the quality and robustness of carrier-based aircraft departure scheduling.<\/jats:p>","DOI":"10.3390\/e27070662","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T11:23:24Z","timestamp":1750418604000},"page":"662","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Simulation-Based Two-Stage Scheduling Optimization Method for Carrier-Based Aircraft Launch and Departure Operations"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9259-2524","authenticated-orcid":false,"given":"Jue","family":"Liu","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nengjian","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.3846\/transport.2024.20536","article-title":"A multi-objective fuzzy optimization model for multi-type aircraft flight scheduling problem","volume":"39","author":"Wei","year":"2024","journal-title":"Transport"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102793","DOI":"10.1016\/j.jairtraman.2025.102793","article-title":"A study on the strategic behavior of players participating in air-rail intermodal transportation based on evolutionary games","volume":"126","author":"Sun","year":"2025","journal-title":"J. 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