CN111291888A - Scheduling optimization method for airport special vehicles - Google Patents
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Abstract
The invention relates to the field of airport vehicle scheduling, in particular to an airport special vehicle scheduling optimization method, which is characterized by comprising the following steps: and constructing a combined scheduling model of the flight refueling service and the customer service based on the time constraint relation of the refueling service and the customer service, and solving the model by using an NSGA-II algorithm. Compared with manual scheduling, the invention has the following advantages: the oil consumption of the special vehicle and the vehicle circulation time are greatly saved; the use cost of the vehicle is reduced; compared with the single scheduling, the joint scheduling of the refueling truck and the ferry vehicle is as follows: the using quantity of the vehicles is greatly reduced, the running distance is relatively reduced, and the difference of the arrival time difference is not large; the vehicle scheduling optimization method is not only limited to vehicle scheduling in airports, but also can be popularized to vehicle scheduling in other fields. Therefore, the dispatching scheme designed by the combined dispatching of the refueling truck and the ferry vehicle is superior to manual dispatching and single dispatching in comprehensive consideration.
Description
Technical Field
The invention relates to the field of airport vehicle scheduling, in particular to an airport special vehicle scheduling optimization method.
Background
With the increasing expansion of the size and the traffic volume of airports, the bottleneck problems of low operation efficiency and insufficient collaborative decision capability of busy airports are increasingly highlighted. How to quickly respond to basic business and provide decision support for related departments becomes a key problem for realizing efficient operation of airports. The airplane punctuality rate is one of important index indexes for evaluating the aviation service quality. The air transportation punctuality rate can be influenced by a plurality of factors, wherein airport scheduling and other factors account for nearly 30%, and the punctuality rate of the flight is greatly influenced.
At present, the dispatching mode of special vehicles at airports by most airports in China is mainly manual dispatching and is a dispatching mode of single-vehicle single-flight service. The scheduling method has extremely low efficiency, and the possibility of flight delay caused by the fact that path optimization is not considered is very high. Because special vehicles are generally high in cost, an airport only provides limited vehicles to complete service, and the method of one-time service of one vehicle also causes waste of resource cost. For large hub airports, or for medium and small airports at peak transportation, the take-off and landing of flights can reach higher densities within a short period of time. Therefore, an effective airport special vehicle dispatching mode needs to be found in colleges and universities, and the method has great economic and social significance for improving the utilization rate of airport resources and improving the service quality of airports. The existing research is developed aiming at single type guarantee vehicle scheduling, Anna Norin, Mao X, Xing Z and the like sequentially research the deicing vehicle scheduling, and Anna Norin and Di Yuan adopt a greedy random self-adaptive search algorithm to search the optimal running path for the deicing vehicle to work, so that flight delay caused by overlong deicing time is reduced; mao X and the like propose a traditional centralized deicing scheduling method which cannot solve the problem of autonomy of different interest-related parties, and a new scheduling method based on Agent is proposed for the problem, so that the method can better solve the problem of airport deicing scheduling; and Xing Z and the like research the scheduling problem of the deicing vehicle by using related knowledge of the game theory, and verify that the research of the deicing scheduling problem based on the game theory method is feasible through experiments. JiaYan Du et al studied the problem of trailer dispatch in flight ground support services. Establishing a MIP model related to trailer dispatching; angus Cheung et al studied the individual dispatch of three different airport special vehicles, namely, clean water vehicles, tractors and cleaning vehicles; the problem of dynamic refueling truck scheduling and the problem of individual baggage truck scheduling were studied in succession by the paddle army, yangxedong et al; the yellowbird poems are subjected to simulation experiments by means of SIMIO simulation software, and the dispatching of the refueling truck and the ferry vehicle is respectively researched.
Most of the existing research aims at single type of guarantee vehicles, and service constraint relations among different types of guarantee vehicles are not considered. The rigor of the airport special vehicle service process cannot be better reflected.
Disclosure of Invention
Aiming at the defects, the invention provides a method for jointly scheduling a refueling truck and a ferry vehicle.
The technical scheme adopted by the invention for solving the technical problems is as follows: a scheduling optimization method for airport special vehicles is characterized in that a combined scheduling model of flight refueling service and passenger service is constructed based on a time constraint relation of refueling service and passenger service, and the model is solved by using an NSGA-II algorithm.
The combined scheduling model of flight refueling service and customer service is specifically described as follows: has m1Refuelling vehicle and m2Ferry vehicles having n flights, p, parked at different positions to be servediIndicating the fueling service time, q, for flight iiRepresents the service time of flight i, [ a ]i,bi]Time window representing flight i fueling service start time, [ c ]i,di]A time window representing the start time of the customer service on flight i, based on the time constraint relationship between the fueling service and the customer service, [ a ]i,bi]Should be earlier than [ c ]i,di]。
The time constraint relation between the refueling service and the customer service is specifically as follows:
when it is satisfied with
And
when the flight number is larger than the preset value, each flight number is represented to have service only by one refueller and one ferry;
when s is satisfiedi∈[ai,bi]Namely ai≤si≤biAnd si+pi≤ti≤diRepresenting the time relation between each flight refuelling vehicle and the ferry vehicle; when simultaneously satisfying
si+pi+hij+M(xik+xjk-2)≤sj+M(1-μij)
ti+qi+h′ij+M(yil+yjl-2)≤tj+M(1-νij)
When, represents the constraint relationship between fueling service and service time of flight i and flight j (i < j), where M is a sufficiently large constant, if and only if xik＝xjk＝μijWhen 1, there is a constraint si+pi+hij≤sjSimilarly, if and only if yil＝yjl＝νijWhen 1, there is a constraint ti+qi+h′ij≤tjOtherwise this is not true;
when x is satisfiedik+xjk-1≤μij+μji≤1、yil+yjl-1≤νij+νjiAnd when the value is less than or equal to 1, representing the relation between decision variables.
Solving the model by using an NSGA-II algorithm, comprising the following specific steps:
step one, performing chromosome coding by using a real number code and a vehicle serial number, namely, a service vehicle corresponding to all flights is arranged on a chromosome, the length of the chromosome is K ═ M + D, M is the number of objective functions, D is the number of total flights, and the coding form is as follows:
s11s22s33…snk…sflightnumNj+l11l22…lnk…llightnumNb+D1…D3wherein s isnkThe tank service van for the nth flight is denoted as k cars, lnkThe ferry vehicle serving the nth flight is represented as a k vehicle, positions D +1 to k are objective function values, the objective function reads one chromosome each time, only a decision variable part is calculated, and the objective function values are returned, and the objective function values are stored at the tail ends of the chromosomes, so that the calculation and the data processing are convenient;
calculating the fitness value of each individual in the population according to the airport special vehicle scheduling model, and then performing non-inferior layering according to the fitness value of the individual to calculate the crowdedness of the individual at the same level;
step three, performing the following operations in each generation: selecting parents suitable for reproduction; performing cross mutation operations between selected parents; performing a selection operation between a parent and a descendant; replacing the non-adapted individuals with the adapted individuals to ensure that the population number is a constant;
step four, generating a parent population: according to the principle that the parent population and the offspring population are combined in the third step, the parent population and the offspring population are combined to obtain a new parent population;
judging whether a termination condition is met, wherein the termination condition of the algorithm is the maximum iteration algebra, and if the termination condition is met, terminating the iteration; otherwise, returning to the step two.
The chromosome coding in the step one comprises the following specific steps: the total number of the refuelers and the ferry vehicles is initially set, the vehicles are respectively numbered, the vehicle numbers from 0 to the total number of the special vehicles are randomly generated for each flight match until all N flights are matched, chromosomes of the refuelers and the ferry vehicles are spliced into a one-dimensional vector, and the objective function value is stored at the tail end of the chromosome to finally form a chromosome.
The fitness value of each individual is respectively as follows:
the total number of the refueling truck and the ferry vehicle is minimum
Shortest vehicle form path
The time difference between the vehicle arrival time and the earliest permitted service starting time is minimum
min(∑f1+∑f2)
The specific process of the cross mutation operation in the third step is as follows: adopting a single-point cross variation mode, matching every two individuals in a group into pairs randomly, wherein the exchange mode of each pair of individuals is as follows: randomly selecting two positions, exchanging elements between k1 and k2 in the two parents, calculating the fitness of the two new filial generations after exchange, and finishing the exchange to generate two filial generations if the fitness of the two filial generations is greater than that of the parents; if the fitness of the child is larger than that of the parent and the fitness of the child is smaller than that of the parent, the large child is reserved, and the small child is restored to be the parent; if the fitness of the offspring is smaller than that of the parent, the exchange is cancelled, namely the fitness of the two new offspring after the exchange is calculated, and the exchange mode is determined according to the value of the fitness smaller than that of the parent.
The specific process of selecting operation in the third step is as follows: and (3) adopting a binary tournament selection method, randomly selecting two individuals and comparing the fitness of the two individuals during selection, wherein the individual with the best fitness is selected as a parent until the number of the pairing pools is reached.
The invention has the beneficial effects that: according to the invention, through the combined scheduling of the refueling truck and the ferry vehicle, based on the time constraint relation of the refueling service and the boarding service, a combined scheduling model of flight refueling service and boarding service is provided, and the NSGA-II algorithm is used for solving the model, and experimental results show that the provided model can better solve the problem of cooperative scheduling of the refueling truck and the ferry vehicle.
(1) Compared with manual scheduling, the invention has the following advantages: the oil consumption of the special vehicle and the vehicle circulation time are greatly saved; the use cost of the vehicle is reduced.
(2) Compared with single scheduling, the combined scheduling of the refueling vehicle and the ferry vehicle has the advantages that the number of vehicles used is greatly reduced, the running distance is relatively reduced, and the difference of arrival time is small.
(3) The vehicle scheduling optimization method is not only limited to vehicle scheduling in airports, but also can be popularized to vehicle scheduling in other fields.
And comprehensively considering, the scheduling scheme designed by the joint scheduling of the refueling truck and the ferry vehicle is superior to manual scheduling and independent scheduling.
Drawings
FIG. 1 is a flow chart of the present invention for solving a model using the NSGA-II algorithm;
FIG. 2 is a diagrammatic illustration of a selected Tianjin airport stand layout of the present invention;
FIG. 3 is a value diagram of two objective functions corresponding to the Pareto optimal solution of the present invention;
fig. 4 is a schematic diagram of objective function values corresponding to the Pareto optimal solution of the present invention, and the abscissa and ordinate axes respectively correspond to three objective function values.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the predetermined object, the following detailed description of the embodiments, structures, features and effects according to the present invention will be given with reference to the accompanying drawings and preferred embodiments.
The technical scheme adopted by the invention for solving the technical problems is as follows: a scheduling optimization method for airport special vehicles is characterized in that a combined scheduling model of flight refueling service and passenger service is constructed based on a time constraint relation of refueling service and passenger service, and the model is solved by using an NSGA-II algorithm.
The combined scheduling model of flight refueling service and customer service is specifically described as follows: has m1Refuelling vehicle and m2Ferry vehicles having n flights, p, parked at different positions to be servediIndicating the fueling service time, q, for flight iiRepresents the service time of flight i, [ a ]i,bi]Time window representing flight i fueling service start time, [ c ]i,di]Indicating the start time of a customer service on flight iTime window, based on the time constraint relationship between the fueling service and the customer service, [ a ]i,bi]Should be earlier than [ c ]i,di]。
The time constraint relation between the refueling service and the customer service is specifically as follows:
when it is satisfied with
And
when the flight number is larger than the preset value, each flight number is represented to have service only by one refueller and one ferry;
when s is satisfiedi∈[ai,bi]Namely ai≤si≤biAnd si+pi≤ti≤diRepresenting the time relation between each flight refuelling vehicle and the ferry vehicle; when simultaneously satisfying
si+pi+hij+M(xik+xjk-2)≤sj+M(1-μij)
ti+qi+h′ij+M(yil+yjl-2)≤tj+M(1-νij)
When, represents the constraint relationship between fueling service and service time of flight i and flight j (i < j), where M is a sufficiently large constant, if and only if xik＝xjk＝μijWhen 1, there is a constraint si+pi+hij≤sjSimilarly, if and only if yil＝yjl＝νijWhen 1, there is a constraint ti+qi+h′ij≤tjOtherwise this is not true;
when x is satisfiedik+xjk-1≤μij+μji≤1、yil+yjl-1≤νij+νjiAnd when the value is less than or equal to 1, representing the relation between decision variables.
Wherein m is1: total number of refuelers; m is2: the total number of ferry vehicles;n: the total number of flights to be serviced at different positions; a isi: flight i receives the earliest service starting time allowed by the service of the refuelling truck; bi: flight i receives the latest service starting time allowed by the service of the refuelling car; [ a ] Ai,bi]: a tank service time window; c. Ci: flight i receives the earliest permitted service starting time of the last customer service; di: flight i receives the latest starting service time allowed by the last customer service; [ c ] isi,di]: a ferry service time window; p is a radical ofi: the fuel oil filling time is 30 min; q. q.si: the service time of the ferry vehicle is 15 min; h isij: the time required for the refueller to reach the station of the flight j from the station of the flight i; dij: the distance from the refueller to the station of the flight j from the station of the flight i; h'ij: the time required for the ferry vehicle to reach the location of the flight j from the location of the flight i; d'ij: the distance from the place where the flight i is located to the place where the flight j is located by the ferry vehicle; d0i: the distance from the tank truck to the flight i stand; d'0i: the distance of the ferry car from the yard to flight i; x is the number ofik: whether a refueller k is assigned to flight i; y isil: whether ferry i is assigned to flight i; z is a radical ofk: whether the fuelling vehicle k is in use; z'l: whether the ferry vehicle is used or not; si: flight i's fueling service start time; t is ti: the service start time of the last passenger of flight i; s'i: the time when the refueller arrives at the stop of the flight i; t'i: and the time when the ferry vehicle arrives at the stand where the flight i is located.
As shown in fig. 1, the solution of the model by using the NSGA-II algorithm includes the following specific steps:
step one, performing chromosome coding by using a real number code and a vehicle serial number, namely, a service vehicle corresponding to all flights is arranged on a chromosome, the length of the chromosome is K ═ M + D, M is the number of objective functions, D is the number of total flights, and the coding form is as follows:
s11s22s33…snk...sflightnumNj+l11l22...lnk...llightnumNb+D1...D3wherein s isnkThe tank service van for the nth flight is denoted as k cars, lnkThe ferry vehicle serving the nth flight is represented as a k vehicle, positions D +1 to k are objective function values, the objective function reads one chromosome each time, only a decision variable part is calculated, and the objective function values are returned, and the objective function values are stored at the tail ends of the chromosomes, so that the calculation and the data processing are convenient;
calculating the fitness value of each individual in the population according to the airport special vehicle scheduling model, and then performing non-inferior layering according to the fitness value of the individual to calculate the crowdedness of the individual at the same level;
step three, performing the following operations in each generation: selecting parents suitable for reproduction; performing cross mutation operations between selected parents; performing a selection operation between a parent and a descendant; replacing the non-adapted individuals with the adapted individuals to ensure that the population number is a constant;
step four, generating a parent population: according to the principle that the parent population and the offspring population are combined in the third step, the parent population and the offspring population are combined to obtain a new parent population;
judging whether a termination condition is met, wherein the termination condition of the algorithm is the maximum iteration algebra, and if the termination condition is met, terminating the iteration; otherwise, returning to the step two.
The chromosome coding in the step one comprises the following specific steps: the total number of the refuelers and the ferry vehicles is initially set, the vehicles are respectively numbered, the vehicle numbers from 0 to the total number of the special vehicles are randomly generated for each flight match until all N flights are matched, chromosomes of the refuelers and the ferry vehicles are spliced into a one-dimensional vector, and the objective function value is stored at the tail end of the chromosome to finally form a chromosome.
Flight acceptance special car service number as represented by chromosome
The chromosome coded by the method can be regarded as spliced by two child gene strings, and the code has the advantages that all flights have the same probability of receiving the services of each refueller and ferry vehicle, the generated initial population is rich, and the value of the objective function is stored at the tail end of the chromosome, so that the calculation and the data processing are convenient.
The fitness value of each individual is respectively as follows:
the total number of the refueling truck and the ferry vehicle is minimum
Shortest vehicle form path
The time difference between the vehicle arrival time and the earliest permitted service starting time is minimum
min(∑f1+∑f2)
The specific process of the cross mutation operation in the third step is as follows: adopting a single-point cross variation mode, matching every two individuals in a group into pairs randomly, wherein the exchange mode of each pair of individuals is as follows: randomly selecting two positions, exchanging elements between k1 and k2 in the two parents, calculating the fitness of the two new filial generations after exchange, and finishing the exchange to generate two filial generations if the fitness of the two filial generations is greater than that of the parents; if the fitness of the child is larger than that of the parent and the fitness of the child is smaller than that of the parent, the large child is reserved, and the small child is restored to be the parent; if the fitness of the offspring is smaller than that of the parent, the exchange is cancelled, namely the fitness of the two new offspring after the exchange is calculated, and the exchange mode is determined according to the value of the fitness smaller than that of the parent.
The specific process of selecting operation in the third step is as follows: and (3) adopting a binary tournament selection method, randomly selecting two individuals and comparing the fitness of the two individuals during selection, wherein the individual with the best fitness is selected as a parent until the number of the pairing pools is reached.
Further, the model is constructed based on the following assumptions: the number of airport special vehicles is limited; each flight receives the refueling service and the boarding service once respectively, and only needs one refueling vehicle and one ferry vehicle; the total service time of the refueling truck and the ferry vehicle is not limited; service is started once and is not interrupted until completion.
Experimental results and analysis:
1. experimental data and pretreatment thereof
The invention selects 126 outbound flights and departure flight data of the Tianjin airport in 1 day of 9 months to carry out an experiment on the algorithm. This section is tested refueling truck and ferry vehicle coordinated scheduling.
(1) Airport stand layout used in the experiments herein, which now has 63 passenger aircraft stands, as shown in fig. 2, is generally distributed in three areas: t1 terminal, T2 terminal, and remote gate. At civil aviation airports, special vehicles at airports must travel on a prescribed route, i.e. the connecting lines between the stations in fig. 1 cannot freely enter other areas to travel. All 63 stand positions are numbered in sequence according to the adjacency relation: 409. 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 101, 102, 103, 104, 105, 106, 107, 108, 109, 501, 502, 503, 504, 110, 111, 112, 113, 114, 115, 116, 117, 118, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, the distance between adjacent stands being about 40 meters. The parking lot of the special vehicle is positioned between the No. 501 parking stand and the No. 109 parking stand, the number is D, and the distance matrix (unit: meter) between the parking lot and partial parking stands is shown in the table 1:
TABLE 1
(2) Time window for station-passing flight and departure flight
According to the regulations of civil aviation bureaus: for fuel oil filling service, an airport refueling truck needs to finish the fuel oil filling service of an airplane five minutes before the start of boarding. The passenger refueling, or special case refueling exceptions, should also be completed at least five minutes before the expected departure time. Table 2 shows the allowable starting service time windows for some of the departure flights requiring fueling and receiving passenger service from the ferry.
TABLE 2
2. Theory of experimental results
The invention mainly utilizes MATLAB to carry out experiments. Through multiple experimental debugging, the main parameters of the genetic algorithm are set as follows: the population size is 100, the maximum iteration algebra is 800, the cross probability is 0.8, and the variation probability is 0.3.
Many leading edge solutions can be generated in the solving process, and the Pareto optimal solution can be finally obtained on the premise of meeting the algorithm termination condition. Fig. 2 and fig. 3 show objective function values corresponding to the Pareto optimal solution, where fig. 3 shows values of two objective functions corresponding to the Pareto optimal solution, fig. 4 shows objective function values corresponding to the Pareto optimal solution, and the horizontal and vertical axes respectively correspond to three objective function values.
The above experimental results for the airport refueller and ferry vehicle dispatch show that: compared with manual scheduling: the total distance of the special vehicle in the airport is reduced from 112.96km to 60.4km, the amplitude reduction reaches 46.5 percent, the special vehicle runs for less 52.56km, and the oil consumption of the special vehicle and the vehicle circulation time are greatly saved; the total number of the special vehicles is only 24 at least, so that the use cost of the vehicles is reduced. Compared with single scheduling, the combined scheduling of the refueling truck and the ferry vehicle has the advantages that the number of vehicles is greatly reduced, the number of the used combined scheduling vehicles is only 20 at least, the running distance is relatively reduced, the difference of the arrival time difference is small, comprehensive consideration is given, and the scheduling scheme designed by the combined scheduling of the refueling truck and the ferry vehicle is superior to manual scheduling and single scheduling.
The vehicle scheduling optimization method is not only limited to vehicle scheduling in airports, but also can be popularized to vehicle scheduling in other fields, and experiments prove that the scheduling scheme of the invention is superior to manual scheduling and independent scheduling, so that the vehicle use cost can be greatly reduced, and the time can be saved.
Claims (8)
1. The scheduling optimization method for the special type vehicles in the airport is characterized by comprising the following steps: and constructing a combined scheduling model of the flight refueling service and the customer service based on the time constraint relation of the refueling service and the customer service, and solving the model by using an NSGA-II algorithm.
2. The airport special vehicle scheduling optimization method according to claim 1, wherein: the combined scheduling model of flight refueling service and customer service is specifically described as follows: has m1Refuelling vehicle and m2Ferry vehicles having n flights, p, parked at different positions to be servediIndicating the fueling service time, q, for flight iiRepresents the service time of flight i, [ a ]i,bi]Time window representing flight i fueling service start time, [ c ]i,di]Time window indicating the start time of the customer service on flight i, [ a ]i,bi]Before [ c ]i,di]。
3. The airport special vehicle scheduling optimization method according to claim 1, wherein: the time constraint relation between the refueling service and the customer service is specifically as follows:
when it is satisfied with
And
in time, it means that each flight has and only consists of one refueller and one ferryVehicle service;
when s is satisfiedi∈[ai,bi]Namely ai≤si≤biAnd si+pi≤ti≤diRepresenting the time constraint relation between each flight refuelling vehicle and the ferry vehicle; when simultaneously satisfying
si+pi+hij+M(xik+xjk-2)≤sj+M(1-μij)
ti+qi+h′ij+M(yil+yjl-2)≤tj+M(1-νij)
When, represents the constraint relationship between fueling service and service time of flight i and flight j (i < j), where M is a sufficiently large constant, if and only if xik＝xjk＝μijWhen 1, there is a constraint si+pi+hij≤sjSimilarly, if and only if yil＝yjl＝νijWhen 1, there is a constraint ti+qi+h′ij≤tjOtherwise this is not true;
when x is satisfiedik+xjk-1≤μij+μji≤1、yil+yjl-1≤νij+νjiWhen the decision variables are less than or equal to 1, representing the relation between the decision variables;
wherein m is1: total number of refuelers; m is2: the total number of ferry vehicles; n: the total number of flights to be serviced at different positions; a isi: flight i receives the earliest service starting time allowed by the service of the refuelling truck; bi: flight i receives the latest service starting time allowed by the service of the refuelling car; [ a ] Ai,bi]: a tank service time window; c. Ci: flight i receives the earliest permitted service starting time of the last customer service; di: flight i receives the latest starting service time allowed by the last customer service; [ c ] isi,di]: a ferry service time window; p is a radical ofi: the fuel oil filling time is 30 min; q. q.si: the service time of the ferry vehicle is 15 min; h isij: the refueller is from the position of the flight iThe time required to reach the flight j at the flight level; dij: the distance from the refueller to the station of the flight j from the station of the flight i; h'ij: the time required for the ferry vehicle to reach the location of the flight j from the location of the flight i; d'ij: the distance from the place where the flight i is located to the place where the flight j is located by the ferry vehicle; d0i: the distance from the tank truck to the flight i stand; d'0i: the distance of the ferry car from the yard to flight i; x is the number ofik: whether a refueller k is assigned to flight i; y isil: whether ferry i is assigned to flight i; z is a radical ofk: whether the fuelling vehicle k is in use; z'l: whether the ferry vehicle is used or not; si: flight i's fueling service start time; t is ti: the service start time of the last passenger of flight i; s'i: the time when the refueller arrives at the stop of the flight i; t'i: and the time when the ferry vehicle arrives at the stand where the flight i is located.
4. The airport special vehicle scheduling optimization method according to claim 1, wherein: solving the model by using an NSGA-II algorithm, comprising the following specific steps:
step one, performing chromosome coding by using a real number code and a vehicle serial number, namely, a service vehicle corresponding to all flights is arranged on a chromosome, the length of the chromosome is K ═ M + D, M is the number of objective functions, D is the number of total flights, and the coding form is as follows: s11s22s33...snk...sflightnumNj+l11l22...lnk...llightnumNb+D1...D3Wherein s isnkThe tank service van for the nth flight is denoted as k cars, lnkThe ferry vehicle serving the nth flight is represented as a k vehicle, positions D +1 to k are objective function values, the objective function reads one chromosome each time, only a decision variable part is calculated, and the objective function values are returned, and the objective function values are stored at the tail ends of the chromosomes, so that the calculation and the data processing are convenient;
calculating the fitness value of each individual in the population according to the airport special vehicle scheduling model, and then performing non-inferior layering according to the fitness value of the individual to calculate the crowdedness of the individual at the same level;
step three, performing the following operations in each generation: selecting parents suitable for reproduction; performing cross mutation operations between selected parents; performing a selection operation between a parent and a descendant; replacing the non-adapted individuals with the adapted individuals to ensure that the population number is a constant;
step four, generating a parent population: according to the principle that the parent population and the offspring population are combined in the third step, the parent population and the offspring population are combined to obtain a new parent population;
judging whether a termination condition is met, wherein the termination condition of the algorithm is the maximum iteration algebra, and if the termination condition is met, terminating the iteration; otherwise, returning to the step two.
5. The airport special vehicle scheduling optimization method according to claim 4, wherein: the chromosome coding in the step one comprises the following specific steps: the total number of the refuelers and the ferry vehicles is initially set, the vehicles are respectively numbered, the vehicle numbers from 0 to the total number of the special vehicles are randomly generated for each flight match until all N flights are matched, chromosomes of the refuelers and the ferry vehicles are spliced into a one-dimensional vector, and the objective function value is stored at the tail end of the chromosome to finally form a chromosome.
6. The airport special vehicle scheduling optimization method according to claim 4, wherein: the fitness value of each individual is respectively as follows:
the total number of the refueling truck and the ferry vehicle is minimum
Shortest vehicle form path
The time difference between the vehicle arrival time and the earliest permitted service starting time is minimum
min(∑f1+∑f2)。
7. The airport special vehicle scheduling optimization method according to claim 4, wherein: the specific process of the cross mutation operation in the third step is as follows: adopting a single-point cross variation mode, matching every two individuals in a group into pairs randomly, wherein the exchange mode of each pair of individuals is as follows: randomly selecting two positions, exchanging elements between k1 and k2 in the two parents, calculating the fitness of the two new filial generations after exchange, and finishing the exchange to generate two filial generations if the fitness of the two filial generations is greater than that of the parents; if the fitness of the child is larger than that of the parent and the fitness of the child is smaller than that of the parent, the large child is reserved, and the small child is restored to be the parent; if the fitness of the offspring is smaller than that of the parent, the exchange is cancelled, namely the fitness of the two new offspring after the exchange is calculated, and the exchange mode is determined according to the value of the fitness smaller than that of the parent.
8. The airport special vehicle scheduling optimization method according to claim 4, wherein: the specific process of selecting operation in the third step is as follows: and (3) adopting a binary tournament selection method, randomly selecting two individuals and comparing the fitness of the two individuals during selection, wherein the individual with the best fitness is selected as a parent until the number of the pairing pools is reached.
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