{"title":"考虑多机场延误传播的航空公司地下延误计划的航班重新调度","authors":"Jiyeon Lee, Ilkyeong Moon","doi":"10.1080/21680566.2023.2254939","DOIUrl":null,"url":null,"abstract":"The purpose of this study is to reschedule flights for an airline's profit to correspond to the airport’s changed capacity. In the event of a ground delay program (GDP), the number of flights the airport can accommodate is reduced. We formulated a mixed-integer linear programming (MILP) model to reschedule flights. The MILP models were divided into two versions to handle the uncertainty of the future. In scenarios in which the GDP is changed again, an optimal model obtains solutions for each scenario. The stochastic model solution obtains a minimizing expectation cost of all scenarios. All flights are connected to both the origin and destination airports, and one aircraft may be used for more than one flight. Therefore, we considered delay propagation not only within the same airport but from other airports by extending the setup to include several airports at once. Because the objective of this study is to minimize the operation cost of airline, we also considered costs associated with airline resources such as aircrafts and crews. Related experiments were conducted including comparison between two suggested versions.","PeriodicalId":48872,"journal":{"name":"Transportmetrica B-Transport Dynamics","volume":"15 1","pages":"0"},"PeriodicalIF":3.3000,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Flight rescheduling of an airline underground delay program considering delay propagation in multiple airports\",\"authors\":\"Jiyeon Lee, Ilkyeong Moon\",\"doi\":\"10.1080/21680566.2023.2254939\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of this study is to reschedule flights for an airline's profit to correspond to the airport’s changed capacity. In the event of a ground delay program (GDP), the number of flights the airport can accommodate is reduced. We formulated a mixed-integer linear programming (MILP) model to reschedule flights. The MILP models were divided into two versions to handle the uncertainty of the future. In scenarios in which the GDP is changed again, an optimal model obtains solutions for each scenario. The stochastic model solution obtains a minimizing expectation cost of all scenarios. All flights are connected to both the origin and destination airports, and one aircraft may be used for more than one flight. Therefore, we considered delay propagation not only within the same airport but from other airports by extending the setup to include several airports at once. Because the objective of this study is to minimize the operation cost of airline, we also considered costs associated with airline resources such as aircrafts and crews. Related experiments were conducted including comparison between two suggested versions.\",\"PeriodicalId\":48872,\"journal\":{\"name\":\"Transportmetrica B-Transport Dynamics\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2023-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportmetrica B-Transport Dynamics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/21680566.2023.2254939\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportmetrica B-Transport Dynamics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21680566.2023.2254939","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
Flight rescheduling of an airline underground delay program considering delay propagation in multiple airports
The purpose of this study is to reschedule flights for an airline's profit to correspond to the airport’s changed capacity. In the event of a ground delay program (GDP), the number of flights the airport can accommodate is reduced. We formulated a mixed-integer linear programming (MILP) model to reschedule flights. The MILP models were divided into two versions to handle the uncertainty of the future. In scenarios in which the GDP is changed again, an optimal model obtains solutions for each scenario. The stochastic model solution obtains a minimizing expectation cost of all scenarios. All flights are connected to both the origin and destination airports, and one aircraft may be used for more than one flight. Therefore, we considered delay propagation not only within the same airport but from other airports by extending the setup to include several airports at once. Because the objective of this study is to minimize the operation cost of airline, we also considered costs associated with airline resources such as aircrafts and crews. Related experiments were conducted including comparison between two suggested versions.
期刊介绍:
Transportmetrica B is an international journal that aims to bring together contributions of advanced research in understanding and practical experience in handling the dynamic aspects of transport systems and behavior, and hence the sub-title is set as “Transport Dynamics”.
Transport dynamics can be considered from various scales and scopes ranging from dynamics in traffic flow, travel behavior (e.g. learning process), logistics, transport policy, to traffic control. Thus, the journal welcomes research papers that address transport dynamics from a broad perspective, ranging from theoretical studies to empirical analysis of transport systems or behavior based on actual data.
The scope of Transportmetrica B includes, but is not limited to, the following: dynamic traffic assignment, dynamic transit assignment, dynamic activity-based modeling, applications of system dynamics in transport planning, logistics planning and optimization, traffic flow analysis, dynamic programming in transport modeling and optimization, traffic control, land-use and transport dynamics, day-to-day learning process (model and behavioral studies), time-series analysis of transport data and demand, traffic emission modeling, time-dependent transport policy analysis, transportation network reliability and vulnerability, simulation of traffic system and travel behavior, longitudinal analysis of traveler behavior, etc.