Model-based solution approach for a short-term flight rescheduling problem in aerial passenger transportation to maritime units

被引:9
|
作者
De la Vega, Jonathan [1 ]
Santana, Mateus [1 ]
Pureza, Vitoria [1 ]
Morabito, Reinaldo [1 ]
Bastos, Yan [2 ]
Ribas, Paulo Cesar [2 ]
机构
[1] Univ Fed Sao Carlos, Dept Prod Engn, BR-13565905 Sao Carlos, SP, Brazil
[2] Petrobras SA, Res & Dev Ctr, Av Horacio de Macedo 950, Rio De Janeiro, RJ, Brazil
基金
巴西圣保罗研究基金会;
关键词
aircraft recovery problem; short-term flight rescheduling; aerial passenger transportation; mixed-integer programming; discrete-time approximations; oil and gas industry; AIRCRAFT SCHEDULE RECOVERY; TIME DECISION-SUPPORT; INTEGRATED AIRCRAFT; HEURISTIC ALGORITHM; SYSTEM OPERATIONS; OIL INDUSTRY; LANDING RISK; AIRLINE; CANCELLATIONS; MANAGEMENT;
D O I
10.1111/itor.13079
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
This paper addresses the problem faced by a Brazilian oil and gas company of recovering flights for passenger transportation (mainly teams of employees) to maritime units. Due to unexpected events such as bad weather or aircraft mechanical failures, the original timetable very often cannot be fully met, resulting in flight delays on the same day or even postponements to the following days. As a result, the operation of the maritime units and the scheduling of employee shifts are affected to some extent. Based on a case study conducted at the company, we present a detailed continuous-time mixed-integer programming model that aims to include pending flights in the daily scheduling of an aerodrome with a minimum overall delay and usage of aircraft (helicopters), subject to flights with different rescheduling priorities, aerodrome and aircraft time windows, single runways at the aerodrome and single landing spots at each maritime unit, postponement and shift regulations, heterogeneous fleet of helicopters, mandatory stops for the crew to rest and have lunch, among others. We also present a discrete-time simplification of the former model and some simple solution approaches based on these models in order to cope with larger problem instances. The approach performance is assessed using real-life problem instances whose data were collected in the case study, using a general-purpose optimization software. The results show the potential of these approaches in dealing with this short-term flight rescheduling problem.
引用
收藏
页码:3400 / 3434
页数:35
相关论文
共 50 条
  • [41] Short-Term Holiday Travel Demand Prediction for Urban Tour Transportation: A Combined Model Based on STC-LSTM Deep Learning Approach
    Wanying Li
    Hongzhi Guan
    Yan Han
    Haiyan Zhu
    Ange Wang
    KSCE Journal of Civil Engineering, 2022, 26 : 4086 - 4102
  • [42] Short-Term Holiday Travel Demand Prediction for Urban Tour Transportation: A Combined Model Based on STC-LSTM Deep Learning Approach
    Li, Wanying
    Guan, Hongzhi
    Han, Yan
    Zhu, Haiyan
    Wang, Ange
    KSCE JOURNAL OF CIVIL ENGINEERING, 2022, 26 (09) : 4086 - 4102
  • [43] Short-Term Subway Inbound Passenger Flow Prediction Based on AFC Data and PSO-LSTM Optimized Model
    Jiaxin Liu
    Rui Jiang
    Dan Zhu
    Jiandong Zhao
    Urban Rail Transit, 2022, 8 : 56 - 66
  • [44] Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow (vol 14, e0222365, 2019)
    Chen, Quanchao
    Wen, Di
    Li, Xuqiang
    Chen, Dingjun
    Lv, Hongxia
    Zhang, Jie
    Gao, Peng
    PLOS ONE, 2020, 15 (03):
  • [45] Short-term passenger flow prediction of rail transit based on VMD-LSTM neural network combination model
    Liang, Dong
    Xu, Jie
    Li, Siyao
    Sun, Chuankai
    PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020), 2020, : 5131 - 5136
  • [46] Naive Bayes-Based Transition Model for Short-Term Metro Passenger Flow Prediction under Planned Events
    Zhao, Yangyang
    Ma, Zhenliang
    TRANSPORTATION RESEARCH RECORD, 2022, 2676 (09) : 309 - 324
  • [47] Short-Term Subway Inbound Passenger Flow Prediction Based on AFC Data and PSO-LSTM Optimized Model
    Liu, Jiaxin
    Jiang, Rui
    Zhu, Dan
    Zhao, Jiandong
    URBAN RAIL TRANSIT, 2022, 8 (01) : 56 - 66
  • [48] Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model
    Duan, Huiming
    Xiao, Xinping
    Pei, Lingling
    COMPLEXITY, 2017, : 1 - 16
  • [49] MODEL-BASED META-ANALYSIS IN RHEUMATOID ARTHRITIS: CORRELATIONS BETWEEN SHORT-TERM AND LONG-TERM TREATMENT EFFECTS
    Wang, Y.
    Zhu, R.
    Sun, J.
    Su, Z.
    Davis, J. C.
    Mandema, J. W.
    Tang, M.
    Davis, J. D.
    Jin, J.
    Xiao, J.
    CLINICAL PHARMACOLOGY & THERAPEUTICS, 2013, 93 : S101 - S101
  • [50] A multi-index prediction method for flight delay based on long short-term memory network model
    Jiang, Yunpeng
    Miao, Jiahe
    Zhang, Xinyue
    Le, Ningning
    PROCEEDINGS OF 2020 IEEE 2ND INTERNATIONAL CONFERENCE ON CIVIL AVIATION SAFETY AND INFORMATION TECHNOLOGY (ICCASIT), 2020, : 159 - 163