{"id":{"repo_id":"catalunya","oai_identifier":"oai:openaccess.uoc.edu:10609/153966"},"canonical_url":"https://search.dev.ndltd.org/etd/catalunya/oai:openaccess.uoc.edu:10609/153966","repository":{"repo_id":"catalunya","name":"Universitat Oberta de Catalunya","base_url":"https://openaccess.uoc.edu/server/oai/request"},"display":{"title":"Metaheuristic algorithms for air-route optimization under climate change","abstract":"This thesis studies air-route optimisation under climate-driven turbulence using both exact algorithms and metaheuristics. We first build a transparent baseline on synthetic grids and small airport networks where edge costs are still-air travel times. This allows reproducible comparisons between Dijkstra’s algorithm (exact, nonnegative additive costs) and a basic Ant Colony Optimisation (ACO) solver in terms of optimality gap, convergence, and runtime. In a second stage we introduce turbulence via a normalised exposure field and integrate it into the objective as a soft penalty that preserves additivity. This design keeps Dijkstra applicable while making ACO turbulence-aware with no architectural changes. We examine sensitivity to the turbulence weight, report time–exposure trade-offs, and identify when avoidance merely re-weights a dominant corridor versus when it induces genuine rerouting (e.g., hub switches). On a 20×20 grid, ACO matches the Dijkstra optimum on the additive baseline with higher compute cost; when turbulence is active, a modest colony may under-explore, which we quantify and remedy by tuning. On a toy airport network (e.g., BCN–BSL–BER) the optimal itinerary remains stable across a wide range of turbulence weights, yielding a near-vertical time–exposure curve. Contributions are: (i) a clean evaluation scaffold for exact vs. metaheuristic routing; (ii) a turbulence cost model compatible with both solvers; and (iii) a sensitivity protocol that clarifies when turbulence avoidance changes routes. We close with limitations, lessons learned, and lines for future work on multi-objective fuel/CO2 trade-offs and 3D, time-varying fields.","abstract_html":"This thesis studies air-route optimisation under climate-driven turbulence using both exact algorithms and metaheuristics. We first build a transparent baseline on synthetic grids and small airport networks where edge costs are still-air travel times. This allows reproducible comparisons between Dijkstra’s algorithm (exact, nonnegative additive costs) and a basic Ant Colony Optimisation (ACO) solver in terms of optimality gap, convergence, and runtime. In a second stage we introduce turbulence via a normalised exposure field and integrate it into the objective as a soft penalty that preserves additivity. This design keeps Dijkstra applicable while making ACO turbulence-aware with no architectural changes. We examine sensitivity to the turbulence weight, report time–exposure trade-offs, and identify when avoidance merely re-weights a dominant corridor versus when it induces genuine rerouting (e.g., hub switches). On a 20×20 grid, ACO matches the Dijkstra optimum on the additive baseline with higher compute cost; when turbulence is active, a modest colony may under-explore, which we quantify and remedy by tuning. On a toy airport network (e.g., BCN–BSL–BER) the optimal itinerary remains stable across a wide range of turbulence weights, yielding a near-vertical time–exposure curve. Contributions are: (i) a clean evaluation scaffold for exact vs. metaheuristic routing; (ii) a turbulence cost model compatible with both solvers; and (iii) a sensitivity protocol that clarifies when turbulence avoidance changes routes. We close with limitations, lessons learned, and lines for future work on multi-objective fuel/CO2 trade-offs and 3D, time-varying fields.","abstract_has_math":false,"creators":["Icochea López, Brian Martin"],"institution":"Universitat Oberta de Catalunya (UOC)","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-09-02","date_published":"2025-09-02","updated_at":"2026-07-27T19:06:49Z","subjects":[],"languages":["eng"],"rights":["CC BY-NC-ND"],"rights_urls":["http://creativecommons.org/licenses/by-nc-nd/4.0/es/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10609/153966","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Icochea López, Brian Martin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-19T10:20:09Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-19T10:20:09Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-09-02"]},{"key":"dc:publisher","label":"Institution","values":["Universitat Oberta de Catalunya (UOC)"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/masterThesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["CC BY-NC-ND"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by-nc-nd/4.0/es/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10609/153966"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis studies air-route optimisation under climate-driven turbulence using both exact algorithms and metaheuristics. We first build a transparent baseline on synthetic grids and small airport networks where edge costs are still-air travel times. This allows reproducible comparisons between Dijkstra’s algorithm (exact, nonnegative additive costs) and a basic Ant Colony Optimisation (ACO) solver in terms of optimality gap, convergence, and runtime. In a second stage we introduce turbulence via a normalised exposure field and integrate it into the objective as a soft penalty that preserves additivity. This design keeps Dijkstra applicable while making ACO turbulence-aware with no architectural changes. We examine sensitivity to the turbulence weight, report time–exposure trade-offs, and identify when avoidance merely re-weights a dominant corridor versus when it induces genuine rerouting (e.g., hub switches). On a 20×20 grid, ACO matches the Dijkstra optimum on the additive baseline with higher compute cost; when turbulence is active, a modest colony may under-explore, which we quantify and remedy by tuning. On a toy airport network (e.g., BCN–BSL–BER) the optimal itinerary remains stable across a wide range of turbulence weights, yielding a near-vertical time–exposure curve. Contributions are: (i) a clean evaluation scaffold for exact vs. metaheuristic routing; (ii) a turbulence cost model compatible with both solvers; and (iii) a sensitivity protocol that clarifies when turbulence avoidance changes routes. We close with limitations, lessons learned, and lines for future work on multi-objective fuel/CO2 trade-offs and 3D, time-varying fields."]},{"key":"dc:title","label":"Title","values":["Metaheuristic algorithms for air-route optimization under climate change"]}]}],"canonical_facts":{"dc:creator":["Icochea López, Brian Martin"],"dc:date.accessioned":["2026-01-19T10:20:09Z"],"dc:date.available":["2026-01-19T10:20:09Z"],"dc:date.issued":["2025-09-02"],"dc:description.abstract":["This thesis studies air-route optimisation under climate-driven turbulence using both exact algorithms and metaheuristics. We first build a transparent baseline on synthetic grids and small airport networks where edge costs are still-air travel times. This allows reproducible comparisons between Dijkstra’s algorithm (exact, nonnegative additive costs) and a basic Ant Colony Optimisation (ACO) solver in terms of optimality gap, convergence, and runtime. In a second stage we introduce turbulence via a normalised exposure field and integrate it into the objective as a soft penalty that preserves additivity. This design keeps Dijkstra applicable while making ACO turbulence-aware with no architectural changes. We examine sensitivity to the turbulence weight, report time–exposure trade-offs, and identify when avoidance merely re-weights a dominant corridor versus when it induces genuine rerouting (e.g., hub switches). On a 20×20 grid, ACO matches the Dijkstra optimum on the additive baseline with higher compute cost; when turbulence is active, a modest colony may under-explore, which we quantify and remedy by tuning. On a toy airport network (e.g., BCN–BSL–BER) the optimal itinerary remains stable across a wide range of turbulence weights, yielding a near-vertical time–exposure curve. Contributions are: (i) a clean evaluation scaffold for exact vs. metaheuristic routing; (ii) a turbulence cost model compatible with both solvers; and (iii) a sensitivity protocol that clarifies when turbulence avoidance changes routes. We close with limitations, lessons learned, and lines for future work on multi-objective fuel/CO2 trade-offs and 3D, time-varying fields."],"dc:identifier.uri":["https://hdl.handle.net/10609/153966"],"dc:language.iso":["eng"],"dc:publisher":["Universitat Oberta de Catalunya (UOC)"],"dc:rights":["CC BY-NC-ND"],"dc:rights.uri":["http://creativecommons.org/licenses/by-nc-nd/4.0/es/"],"dc:title":["Metaheuristic algorithms for air-route optimization under climate change"],"dc:type":["info:eu-repo/semantics/masterThesis"]},"updated_at":"2026-07-27T19:06:49Z"}