{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/53299"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/53299","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Operational planning for multiple heterogeneous unmanned aerial vehicles in three dimensions","abstract":"Unmanned aerial vehicles are being incorporated in an increasing variety of operations. To take full advantage of the vehicles, the plans for the operations should integrate each vehicle's capabilities when planning the operations. This thesis focuses on planning operations for multiple, heterogeneous UAVs for the purpose of monitoring Earth's phenomena through data collection. The planning is done for flight in three dimensions. The problem also includes time window constraints for data collection and incorporates human input in the planning process. Two solution methods are presented: (1) a mixed-integer program, and (2) an algorithm that utilizes a meta-heuristic to generate composite variables for a linear program, called the Composite Operations Planning Algorithm. The suitability of the two methods to solve the operations planning problem is compared based on the ability of each of the methods to find high-value, feasible solutions for large-scale, operationally sized problems in a reasonable amount of time. The analysis shows that the Composite Operations Planning Algorithm can develop operations plans for problems including 15 UAVs and 5000 nodes in less than 25 minutes using a desktop computer.","abstract_html":"Unmanned aerial vehicles are being incorporated in an increasing variety of operations. To take full advantage of the vehicles, the plans for the operations should integrate each vehicle's capabilities when planning the operations. This thesis focuses on planning operations for multiple, heterogeneous UAVs for the purpose of monitoring Earth's phenomena through data collection. The planning is done for flight in three dimensions. The problem also includes time window constraints for data collection and incorporates human input in the planning process. Two solution methods are presented: (1) a mixed-integer program, and (2) an algorithm that utilizes a meta-heuristic to generate composite variables for a linear program, called the Composite Operations Planning Algorithm. The suitability of the two methods to solve the operations planning problem is compared based on the ability of each of the methods to find high-value, feasible solutions for large-scale, operationally sized problems in a reasonable amount of time. The analysis shows that the Composite Operations Planning Algorithm can develop operations plans for problems including 15 UAVs and 5000 nodes in less than 25 minutes using a desktop computer.","abstract_has_math":false,"creators":["Negron, Blair Ellen Leake"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Operations Research Center.","school":null,"contributors":[],"advisors":["Stephan E. Kolitz and Hamsa Balakrishnan."],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009","date_published":"2009","updated_at":"2026-07-22T22:22:19Z","subjects":["Operations Research Center."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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