Figures (6)  Tables (6)

    • Figure 1. 

      Research contents and relationship between each part of the paper.

    • Figure 2. 

      Number of published articles in various research fields.

    • Figure 3. 

      Literature rates of different recovery resources before and after 2010.

    • Figure 4. 

      Trends in the number of studies over time and the type of recovery resources.

    • Figure 5. 

      Literature rates of disruption types before and after 2010.

    • Figure 6. 

      Literature rates of recovery options before and after 2010.

    • Network Cancel Delay Aircraft swap Cruise speed Fleet Objective Model Objectives
      Chen et al.[29] NA NO YES YES NO Single Multi nonlinear Flight non-connection, duty swap, delay time, delay number, delay number over 30 min
      Liu et al.[31] NA NO YES YES NO Multi Single Nonlinear Operation, delay, passenger cost
      Babić et al.[32] NA YES YES YES NO Multi Single Nonlinear Max revenue minus operational and disturbance costs
      Liu et al.[30] NA NO YES YES NO Single Multi Nonlinear Delay time, duty swap, variance of flight delay time, number of delayed flight, number of long-delayed flight
      Gao et al.[36] NA NO YES NO NO Single Multi Nonlinear Weighted flight delay time
      Mou et al.[37] NA YES YES YES NO Multi Multi Nonlinear Delay minutes, delay, and cancellation cost
      Aktürk et al.[38] CN NO YES YES YES Multi Single Conic IP Delay, deadhead, additional fuel and carbon emission, passengers spilled cost
      Vos et al.[17] TN YES YES YES NO Single Single LP Operation, delay, cancellation, aircraft ground cost
      Guimarans et al.[33] NA NO YES YES NO Single Single CP Delay time
      Xu et al.[22] TBN YES YES YES NO Single Single IP Delay, cancellation cost
      Hu et al.[24] CN YES YES YES NO Multi Multi IP Min delay and cancellation cost, maximal flight delay time, min the number of swapped aircraft
      Wu et al.[25] CN YES YES YES NO Single Single IP Delay time
      Wu et al.[45] CN YES YES YES NO Multi Single IP Delay, cancellation cost
      Wu et al.[46] CN YES YES YES NO Multi Single IP Delay, cancellation cost
      Zhang[27] CN YES YES YES NO Single Single IP Cancellation, aircraft assignment, terminal balance violation cost
      Bouarfa et al.[40] NA NA NA NA NO NA NA NA NA
      Khaled et al.[41] NA YES YES YES NO Single Multi IP Operation recovery cost, number of flight changed, number of impacted airports
      Liang et al.[28] CN YES YES YES NO Multi Single IP Flight cancellation, route cost
      Lin et al.[34] NA NO YES YES NO Single Single Nonlinear Delay time
      Wang et al.[42] NA YES YES YES NO Multi NA NA NA
      Şafak et al.[39] NA YES YES YES YES Multi Single MICQ Max revenue minus fuel burn, passenger spilled, flight arrival tardiness, crew service, aircraft swap cost
      Vink et al.[18] TN YES YES YES NO Multi Single Mixed IP Operation and disruption cost
      Pei et al.[43] NA NO YES YES NO Multi NA NA NA
      Lee et al.[19] TN YES YES YES YES Multi Single Nonlinear Expected recovery cost
      Ji et al.[47] NA YES YES NO NO Multi Single Nonlinear Delay time
      Huang et al.[20] TN YES YES YES NO Multi Single IP Retimed, cancelled or assigned cost
      Şi̇mşek et al.[48] NA YES YES YES YES Multi Single Nonlinear Fuel consumption and CO2 emission cost
      Zang et al.[21] TN YES YES YES NO Multi Single Nonlinear Delay, cancellation cost

      Table 1. 

      The first part of the proposed method overview for aircraft recovery.

    • Delay cost Aircraft maintenance Airport congestion Solution method Data Data dimension Solution time (s)
      AC Flight
      Chen et al.[29] NA NO NO Hybrid multi-objective genetic algorithm RL 7 70 600
      Liu et al.[31] Linear NO NO Hybrid particle swarm optimization heuristic RL NA 34 236
      Babić et al.[32] Linear YES NO Heuristic JAT Airways 9 47 NA
      Liu et al.[30] NA NO NO Hybrid multi-objective genetic algorithm RL 7 84 450
      Gao et al.[36] NA NO YES Polynomial algorithm Generated 4 8 NA
      Mou et al.[37] Linear NO NO Polynomial algorithm Generated 5 10 NA
      Aktürk et al.[38] Nonlinear YES YES CPLEX An airline in the US 60 207 248.4
      Vos et al.[17] Nonlinear YES NO Selection algorithm Kenya Airways 43 NA 600
      Guimarans et al.[33] NA NO NO Large neighbourhood search RL 48 294 205.514
      Xu et al.[22] Linear NO NO CPLEX RL 60 254 949.7
      Hu et al.[24] Linear NO NO Heuristic based on ε-constraints and neighbourhood search A major Chinese airline 104 401 1200
      Wu et al.[45] NA NO NO CPLEX RL 12 140 7.02
      Wu et al.[25] Linear NO NO CPLEX RL 30 215 NA
      Wu et al.[46] Linear NO NO CPLEX RL 27 162 286.6
      Zhang[27] Linear YES NO Heuristic + CPLEX RL 44 638 150
      Bouarfa et al.[40] NA NA NA Multi-agent system approach NA NA NA NA
      Khaled et al.[41] NA YES NO ε-Constraints + CPLEX RL 11 111 30
      Liang et al.[28] Linear YES YES Column generation + CPLEX RL 44 638 356.13
      Lin et al.[34] NA NO NO Fast variable neighbourhood search RL 12 70 0.3
      Wang et al.[42] NA YES NO Simulation RL 628 5071 NA
      Şafak et al.[39] nonlinear NO NO CPLEX United Airlines 81 300 8074
      Vink et al.[18] Nonlinear YES NO Selection algorithm An airline in the US 100 600 44
      Pei et al.[43] NA YES NO AHP + algorithm A Chinese airline 29 92 NA
      Lee et al.[19] Nonlinear NO YES Look-ahead approximation and sample average approximation RL NA 852 300
      Ji et al.[47] NA NO NO Build-in flight feasibility verification algorithm RL NA 300 14.69
      Huang et al.[20] NA NO NO Iterative copy generation algorithm Nine RL scenarios 4-162 789 0.5-855
      Şi̇mşek et al.[48] NA NO Yes Aircraft Swapping and Search Algorithm Bureau of Transportation Statistics (2021) NA NA NA
      Zang et al.[21] Linear YES YES Decision-decomposition-based algorithm Four Chinese airlines 733 2,877 23.82

      Table 2. 

      The second part of the proposed method overview for aircraft recovery.

    • Network Fixed f Cancel f Delay f Crew swap Objective Objectives
      AhmadBeygi et al.[49] SP YES NO NO YES Single Pairing cost minus flight dual contribution
      Chang et al.[50] NA NO YES NO YES Multi Number of deadhead trip, unconnected flight, schedule changes and affected crews
      Fang et al.[51] NA YES NO NO YES Multi Deviation cost of flight time and duty time
      Liu et al.[52] SC NO YES NO YES Single Number of uncovered flights
      Luo et al.[59] SP YES NO NO YES Single Pairing cost
      Bayliss et al.[55] NA NO NO YES NO Single Expected crew delay
      Chen et al.[53] NA YES NO NO YES Multi Number of crew changes, number of duty changes, maximal duty changes, largest changed flight time, derivation of the changed duties, derivation of changed flight time
      Bayliss et al.[56] NA NO YES NO YES Single Cancellation
      Bayliss et al.[57] NA NO YES YES YES Single Delay and cancellation
      Wen et al.[60] DN YES NO NO NO Single Robustness-related cost
      Herekoğlu et al.[61] NA NO YES YES YES Multi Assignment cost, swapping cost, deadheading costs, cancellation costs, delaying costs, penalties
      Zhong et al.[62] NA NO YES NO YES Multi Deviation of duty time, total recovery cost

      Table 3. 

      The first part of the overview of proposed methods for crew recovery.

    • Solution method Data Data dimension Solution time (s)
      Aircraft Crew Flight Recovery period (d)
      AhmadBeygi et al.[49] CPELX A major US hub-and-spoke carrier NA NA 329 1 2065
      Chang et al.[50] Genetic algorithm An international Taiwanese airline NA 70 628 18 600
      Fang et al.[51] Hybrid simulated annealing Domestic airlines NA 87 342 NA 195.04
      Liu et al.[52] Simulated annealing A major airline in the US NA 482 1069 236
      Luo et al.[59] Primal-dual sub-problem simplex method in a branch-and-price framework Three airlines NA NA NA 30 NA
      Bayliss et al.[55] Greedy heuristic Generated 37 120 300 1 1400
      Chen et al.[53] Evolutionary algorithm A short-haul airline in Taiwan 270 1048 14 1080
      Bayliss et al.[56] Heuristic + CPLEX Generated 37 148 243 3 3600
      Bayliss et al.[57] heuristic + CPLEX Generated 74 209 566 2 600
      Wen et al.[60] Customized column generation based solution algorithm An airline in Hong Kong NA NA 98 3 NA
      Herekoğlu et al.[61] Column generation-based
      solution approach      		 A major European airline company 400 13500 1873 3 5438
      Zhong et al.[62] Ad-hoc particle swarm optimization -based optimizer Vari Flight Company NA NA 166 4 NA

      Table 4. 

      The second part of the overview of proposed methods for crew recovery.

    • Aircraft Crew Passenger Net-work Cancel Delay Aircraft swap Fleet Cruise speed Objective Model Objectives
      Eggenberg et al.[75] YES NO YES CN YES YES YES Multi NO Single IP Passengers delay, cancellation cost
      Jafari et al.[68] YES NO YES NA YES YES YES Multi NO Single Nonlinear Aircraft assignment, flight delay, flight cancellation, passenger disruption cost
      Bisaillon et al.[11] YES NO YES NA YES YES YES Multi NO Single NA Aircraft and flight operation, passenger disruption cost, constraints violation
      Artigues et al.[9] YES NO YES NA NA NA NA NA NA NA NA NA
      Mansi et al.[78] YES NO YES TBN YES YES YES Multi NO Single NA Aircraft and flight operation, passenger disruption cost, constraints violation
      Petersen et al.[86] YES YES YES CN YES YES YES Multi NO Single IP Flight delay and cancellation, aircraft assignment, crew pairing and deadheading, passenger delay and unassignment cost
      Jozefowiez et al.[76] YES NO YES NA YES YES YES Multi NO Single NA Aircraft and flight operation, passenger disruption cost, constraints violation
      Brunner [85] YES YES YES NA YES YES YES NA NO Single Mixed IP Flight arrival and departure delay, flight cancellation, crews' and passengers' misconnection cost
      Sinclair et al.[80] YES NO YES TN YES YES YES Multi NO Single NA Aircraft and flight operation, passenger disruption cost, constraints violation
      Hu et al.[35] YES NO YES TBN YES YES YES Multi NO Single IP Flight delay, passenger transiting, passenger refunding cost
      Maher[87] YES YES YES CN YES YES YES Multi NO Single IP Flight delay and cancellation, crew deadheading, and passenger reassignment cost
      Zhang et al.[65] YES YES NO TN YES YES YES Multi NO Single IP Flight delay and cancellation, crew misconnection, aircraft and crew swap cost
      Arıkan et al.[88] YES NO YES CN NO YES YES Multi YES Single Conic IP Aircraft delay, passengers delay, spill cost, swap cost, and increased fuel cost
      Hu et al.[81] YES NO YES CN YES YES YES Multi NO Single IP Passengers' delay, reassignment, refund cost
      Maher[66] YES YES NO CN YES YES YES Single NO Single IP Flight delay and cancellation, reserve crew, crew duty and deadhead cost
      Sinclair et al.[79] YES NO YES TN YES YES YES Multi NO Single NA Aircraft and flight operation, passenger disruption cost, constraints violation
      Zhang et al.[77] YES NO YES TN YES YES YES Multi NO Single NA Aircraft and flight operation, passenger disruption cost, constraints violation
      Arıkan et al.[88] YES YES YES CN YES YES YES Multi YES Single conic IP Flight cancellation, aircraft ferrying, crew deadheading, passenger delay, reallocation and refund, additional fuel cost, constraints violation
      Marla et al.[72] YES NO YES TN YES YES YES Multi YES Single IP Flight delay and cancellation, aircraft swap, passenger disruption, additional fuel cost
      Santos et al.[73] YES NO YES CN NO YES YES Multi NO Single Mixed IP Additional operation, passenger disruption cost
      McCarty et al.[64] NO NO YES CN NO YES YES NA NO Single Mixed IP Passengers' expected delay cost
      Yang et al.[82] YES NO YES CN YES YES YES Multi NO Multi IP Airline recovery, passenger uitility cost
      Yeti̇moğlu et al.[74] YES NO YES CN YES YES NO NA YES Single Nonlinear Revenue - fuel and CO2 emission cost - overnight passenger cost - spilled passenger cost.
      Evler et al.[89] YES YES YES NA YES YES YES Multi NO Single Mixed IP Operating and delay cost, cancel cost, connection cost, cost of assigning turnaround recovery options
      Xu et al.[90] YES YES YES CN YES YES YES Multi NO Single Mixed IP Cost of flight cancellation, delay, crew deadhead and unassigned passengers
      Zhao et al.[83] YES NO YES TN YES YES YES Single NO Single Mixed IP Disruption cost, passenger delay cost, curfew violation cost, cancel cost.
      Cadarso et al.[92] YES NO YES TN YES YES YES Multi YES Single Mixed IP Flight operating cost, extra fuel consumption cost, flight delay cost, passenger reaccommodation cost, passenger delay cost, crew cost, penalizes aircraft changes
      Ding et al.[91] YES YES YES TN YES YES YES NA YES Single Mixed IP Flight cancellation cost, passenger delay cost, external link cost, additional fuel cost and following schedule cost
      Chen et al.[84] YES NO YES NA YES YES YES NA NO Multi IP The total delay cost of each flight, the sum of the passenger delay time of each flight

      Table 5. 

      The first part of the overview of proposed methods for integrated recovery.

    • Delay cost Aircraft maintenance Airport congestion Solution method Data Data dimension Solution time (s)
      AC Flight Crew Passenger Passenger
      itinerary
      Eggenberg et al.[75] Linear YES NO Column generation Thomas Cook Airlines 100 760 NA 30000 NA 3603
      Jafari et al.[28] Linear NO NO Lingo Swedish domestic airline 13 100 NA 2236 8 NA
      Bisaillon et al.[11] Linear YES YES Large neighbourhood search 2009 ROADEF Challenge 618 2178 NA NA 29151 600
      Artigues et al.[28] Linear NA NA NA NA NA NA NA NA NA NA
      Mansi et al.[78] Linear YES YES Two stage heuristic 2009 ROADEF Challenge 618 2178 NA NA 29151 600
      Petersen et al.[86] Linear YES NO Bender decomposition, column generation Hub-and-spoke airline in the US NA 800 NA NA NA 2407
      Jozefowiez et al.[76] Linear YES YES Three stage 2009 ROADEF Challenge 618 2178 NA NA 29151 600
      Brunner[85] Nonlinear NO NO CPLEX American Airlines NA 71 26 651 651 NA
      Sinclair et al.[80] Linear YES YES Large neighbourhood search 2009 ROADEF Challenge 618 2178 NA NA 29151 600
      Hu et al.[35] Nonlinear NO NO CPLEX A major airline in China 188 628 NA NA NA 106
      Maher[87] Linear NO NO Column and row generation RL 48 262 79 28492 NA 1800
      Zhang et al.[65] Linear YES NO Iteration heuristic + CPLEX Regional airline in the US 70 351 134 NA NA 72.418
      Arıkan et al.[88] Linear NO NO CPLEX Airline in US NA 1429 NA NA NA 142
      Hu et al.[81] Linear NO NO GRASP A major airline in China 87 340 NA NA NA 600
      Maher[66] Linear NO NO Column and row generation RL 123 441 182 NA NA 1200
      Sinclair et al.[79] Linear YES YES Column generation 2009 ROADEF Challenge 618 2178 NA NA 29151 1385
      Zhang et al.[77] Linear YES YES Three stage 2009 ROADEF Challenge 618 2178 NA NA 29151 600
      Arıkan et al.[88] Nonlinear NO NO CPLEX A major U.S. airline 402 1254 NA NA NA 1212.4
      Marla et al.[72] Linear NO NO Xpress A major European airline NA 250 NA NA NA 120
      Santos et al.[73] Nonlinear NO YES CPLEX Kenya airways 45 140 NA 10000 NA 3600
      McCarty et al.[64] Linear NO NO Benders Decomposition + CPELX Delta Airlines NA NA NA 200 15 93.9
      Yang et al.[82] Linear NO NO Genetic algorithm A major airline in China 59 209 NA 24860 NA 11
      Yeti̇moğlu et al.[74] NA NO NO Novel math-heuristic algorithm A major airline in America 53 208 NA NA 2033 NA
      Evler et al.[89] Linear YES NO Rolling horizon algorithm Frankfurt airport 17 85 NA NA NA 45
      Xu et al.[90] Linear NO NO Branch-and-cut solution method, large neighborhood search heuristic One main legacy carrier in the US NA 230 172 NA NA 937.71
      Zhao et al.[83] Linear YES NO Two-stage algorithm, rolling horizon approach GE Aviation 73 207 NA 594 NA NA
      Cadarso et al.[92] Linear YES YES An original solution approach A IBERIA airline 19 1074 NA 1204 32 600
      Ding et al.[91] Linear YES YES Variable neighborhood search algorithm Generated 50 NA NA NA NA 0.359
      Chen et al.[84] Nonlinear NO NO Genetic algorithm-II A Chinese airline in Fuzhou airport NA NA NA 1818 NA 14.57

      Table 6. 

      The second part of the overview of proposed methods for integrated recovery.