Integrating Flight Scheduling, Fleet Assignment, and Aircraft Routing Problems with Codesharing Agreements under Stochastic Environment
Airlines face the imperative of resource management to curtail costs, necessitating the solution of several optimization problems such as flight planning, fleet assignment, aircraft routing, and crew scheduling. These problems present some challenges. The first pertains to the common practice of add...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/12/1031 |
| _version_ | 1797382461524541440 |
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| author | Kübra Kızıloğlu Ümit Sami Sakallı |
| author_facet | Kübra Kızıloğlu Ümit Sami Sakallı |
| author_sort | Kübra Kızıloğlu |
| collection | DOAJ |
| description | Airlines face the imperative of resource management to curtail costs, necessitating the solution of several optimization problems such as flight planning, fleet assignment, aircraft routing, and crew scheduling. These problems present some challenges. The first pertains to the common practice of addressing these problems independently, potentially leading to locally optimal outcomes due to their interconnected nature. The second challenge lies in the inherent uncertainty associated with parameters like demand and non-cruise time. On the other hand, airlines can employ a strategy known as codesharing, wherein they operate shared flights, in order to minimize these challenges. In this study, we introduce a novel mathematical model designed to optimize flight planning, fleet assignment, and aircraft routing decisions concurrently, while accommodating for codesharing. This model is formulated as a three-stage non-linear mixed-integer problem, with stochastic parameters representing the demand and non-cruise time. For smaller-scale problems, optimization software can effectively solve the model. However, as the number of flights increases, conventional software becomes inadequate. Moreover, considering a wide array of scenarios for stochastic parameters leads to more robust results; however, it is not enabled because of the limitations of optimization software. In this work, we introduce two new simulation-based metaheuristic algorithms for solving large-dimensional problems, collectively called “simheuristic.” These algorithms integrate the Monte Carlo simulation technique into Simulated Annealing and Cuckoo Search. We have applied these simheuristic algorithms to various problem samples of different flight sizes and scenarios. The results demonstrate the efficacy of our proposed modeling and solution approaches in efficiently addressing flight scheduling, fleet assignment, and aircraft routing problems within acceptable timeframes. |
| first_indexed | 2024-03-08T21:05:43Z |
| format | Article |
| id | doaj.art-a43a5aaa801e402fabb9ab91d1272985 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-08T21:05:43Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-a43a5aaa801e402fabb9ab91d12729852023-12-22T13:45:17ZengMDPI AGAerospace2226-43102023-12-011012103110.3390/aerospace10121031Integrating Flight Scheduling, Fleet Assignment, and Aircraft Routing Problems with Codesharing Agreements under Stochastic EnvironmentKübra Kızıloğlu0Ümit Sami Sakallı1Department of Industrial Engineering, Kırıkkale University, 71451 Kırıkkale, TurkeyDepartment of Industrial Engineering, Kırıkkale University, 71451 Kırıkkale, TurkeyAirlines face the imperative of resource management to curtail costs, necessitating the solution of several optimization problems such as flight planning, fleet assignment, aircraft routing, and crew scheduling. These problems present some challenges. The first pertains to the common practice of addressing these problems independently, potentially leading to locally optimal outcomes due to their interconnected nature. The second challenge lies in the inherent uncertainty associated with parameters like demand and non-cruise time. On the other hand, airlines can employ a strategy known as codesharing, wherein they operate shared flights, in order to minimize these challenges. In this study, we introduce a novel mathematical model designed to optimize flight planning, fleet assignment, and aircraft routing decisions concurrently, while accommodating for codesharing. This model is formulated as a three-stage non-linear mixed-integer problem, with stochastic parameters representing the demand and non-cruise time. For smaller-scale problems, optimization software can effectively solve the model. However, as the number of flights increases, conventional software becomes inadequate. Moreover, considering a wide array of scenarios for stochastic parameters leads to more robust results; however, it is not enabled because of the limitations of optimization software. In this work, we introduce two new simulation-based metaheuristic algorithms for solving large-dimensional problems, collectively called “simheuristic.” These algorithms integrate the Monte Carlo simulation technique into Simulated Annealing and Cuckoo Search. We have applied these simheuristic algorithms to various problem samples of different flight sizes and scenarios. The results demonstrate the efficacy of our proposed modeling and solution approaches in efficiently addressing flight scheduling, fleet assignment, and aircraft routing problems within acceptable timeframes.https://www.mdpi.com/2226-4310/10/12/1031flight schedulingfleet assignmentaircraft routingsimheuristicsimulated annealingcuckoo search |
| spellingShingle | Kübra Kızıloğlu Ümit Sami Sakallı Integrating Flight Scheduling, Fleet Assignment, and Aircraft Routing Problems with Codesharing Agreements under Stochastic Environment Aerospace flight scheduling fleet assignment aircraft routing simheuristic simulated annealing cuckoo search |
| title | Integrating Flight Scheduling, Fleet Assignment, and Aircraft Routing Problems with Codesharing Agreements under Stochastic Environment |
| title_full | Integrating Flight Scheduling, Fleet Assignment, and Aircraft Routing Problems with Codesharing Agreements under Stochastic Environment |
| title_fullStr | Integrating Flight Scheduling, Fleet Assignment, and Aircraft Routing Problems with Codesharing Agreements under Stochastic Environment |
| title_full_unstemmed | Integrating Flight Scheduling, Fleet Assignment, and Aircraft Routing Problems with Codesharing Agreements under Stochastic Environment |
| title_short | Integrating Flight Scheduling, Fleet Assignment, and Aircraft Routing Problems with Codesharing Agreements under Stochastic Environment |
| title_sort | integrating flight scheduling fleet assignment and aircraft routing problems with codesharing agreements under stochastic environment |
| topic | flight scheduling fleet assignment aircraft routing simheuristic simulated annealing cuckoo search |
| url | https://www.mdpi.com/2226-4310/10/12/1031 |
| work_keys_str_mv | AT kubrakızıloglu integratingflightschedulingfleetassignmentandaircraftroutingproblemswithcodesharingagreementsunderstochasticenvironment AT umitsamisakallı integratingflightschedulingfleetassignmentandaircraftroutingproblemswithcodesharingagreementsunderstochasticenvironment |