CN117391401B - Dispatching method of airport electric ground service vehicle - Google Patents
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Abstract
The invention discloses a dispatching method of electric ground service vehicles in an airport, which belongs to the technical field of industrial system optimization design and comprises the following steps of establishing a mixed integer planning model of dispatching problems of the electric ground service vehicles in the airport by taking the total distance travelled by the service flights of the electric ground service vehicles and the standard deviation of the total occupied time of the service flights of each vehicle as targets; solving the model, and establishing a pareto optimal solution set screening strategy to obtain an electric ground service vehicle dispatching scheme. The invention quantifies the service mode of the electric ground service vehicle for providing service for different flights, provides an optimized scheduling method based on the service mode, provides guidance and basis for scheduling the electric ground service vehicle for different flights, and simultaneously provides an improved second-generation non-dominant sorting genetic algorithm for the mathematical model, which has high calculation speed, is convenient for obtaining a better scheduling scheme quickly, and provides an effective way for a decision maker to easily make a schedule of the electric ground service vehicle service flight.
Description
Technical Field
The invention belongs to the technical field of industrial system optimization design, in particular relates to the technical field of path planning and optimization in the dispatching process of various electric ground service vehicles, and particularly relates to a dispatching method of airport electric ground service vehicles.
Background
At present, many airports still rely on conventional fuel-powered ground service vehicles, and the vehicles have high fuel consumption and cause non-negligible pollution to the environment. In contrast, the electric ground service vehicle effectively slows down the air quality problem by reducing the exhaust emission and relying on renewable energy sources, and makes a significant contribution to the environmental protection effort of airports. In addition, the wide application of the electric ground service vehicle is expected to improve the sustainability level of an airport, so that the operation cost is reduced, the safety of the vehicle and the comfort of a driver are ensured, and positive assistance is provided for the modernization and sustainability development of the ground service of the airport. These advantages indicate that electric ground service vehicles are gradually replacing traditional fuel oil ground service vehicles, and become the main choice in the future.
In view of the fact that the energy consumed in traveling constitutes one of the main operating costs in the ground service provided by the electric ground service vehicle, the minimization of the travel distance of the vehicle in the ground service can be achieved by optimizing the route planning and task allocation of the electric ground service vehicle. This not only helps to reduce energy costs, but also reduces wear and maintenance costs for the vehicle. In addition, in order to avoid fatigue driving and ensure the balance of task loads, the safety of the vehicle is improved, and the tasks of the vehicle must be reasonably distributed.
While the scheduling of airport electric ground service vehicles involves the cooperation of different types of vehicles and departments, while being limited by inbound and outbound flights. To ensure smooth progress of airport ground services, different scheduling plans must be formulated to accommodate different types of flights. Current airport vehicle scheduling methods typically schedule different vehicles independently and rely on manual scheduling, which may lead to unreasonable schedules when resources are scarce, and thus cause flight delays.
In view of the foregoing, it is necessary to design an innovative airport electric ground service vehicle dispatching method to effectively solve the above technical problems.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a dispatching method of an airport electric ground service vehicle.
The invention is realized by the following technical scheme:
a dispatching method of airport electric ground service vehicles comprises the following steps:
s1, establishing a mixed integer programming model of an airport electric ground service vehicle scheduling problem by taking the total distance travelled by the electric ground service vehicle service flights and the standard deviation of the occupied time of each vehicle as targets, wherein the mixed integer programming model comprises an objective function and constraint conditions;
s2, solving the mixed integer programming model by using real flight data and electric ground service vehicle operation parameters to obtain a pareto optimal solution set containing various scheduling schemes;
s3, establishing a pareto optimal solution set screening strategy, and obtaining an optimal scheduling scheme for processing the airport electric ground service vehicles.
The invention provides an improved second-generation non-dominant ordered genetic algorithm (NSGA 2 algorithm) based on the characteristics of the mixed integer programming model. First, a dynamic priority scheduling algorithm is designed to generate chromosomal genomic codes of the initial population for improving the convergence rate of the results and generating a better scheduling scheme. In addition, the crossover and mutation operations aiming at parent chromosomes in the population lack directionality, and invalid solutions are likely to be introduced, a correlation-based destruction operator and a priority-based optimal greedy insertion repair operator are introduced, and a feasible and more reasonable new solution conforming to constraints is generated to be close to a global optimal solution by performing correlation-based removal destruction and priority-based optimal greedy insertion operator repair on individuals subjected to crossover and mutation. When multi-objective comparison is carried out, the pareto dominance method is used for screening non-inferior solutions in the population, and the average value of the optimal results is selected as an evaluation index to evaluate the advantages and disadvantages of the obtained non-inferior solution set.
Specifically, the modified NSGA2 algorithm includes the steps of:
s2.1, presetting parameters, wherein the set parameters comprise the scale of the external populationMNumber of iterationsGen；
S2.2, initializing a population: initializing a population by adopting a real number coding mode, coding chromosomes in the population by adopting a double-layer coding mode, wherein the first layer of coding is used for arranging flights waiting to be allocated with electric ground service vehicles according to the arrival time or departure time sequence, and the second layer of coding is used for allocating the numbers of the electric ground service vehicles to corresponding flights, wherein the electric ground service vehicles can be allocated to the flights according to a dynamic priority scheduling algorithm;
s2.3, calculating the objective function value of each chromosome in the populationDAndST；
s2.4, carrying out genetic operation comprising binary crossover and polynomial variation by adopting individuals in the population to generate a offspring population;
s2.5, sequentially adopting a destruction operator and a repair operator to process chromosomes in the offspring population; a correlation-based destruction operator, a priority-based optimal greedy inserted repair operator may be employed.
S2.6, merging child parent population to generate 2NThe population of the individual chromosomes, and carrying out rapid non-dominant sorting and crowding distance calculation;
s2.7, selecting chromosomes with low pareto grade and large crowding distance from the population combined by the child and the parent by using elite retention strategy until selectingNThe individual chromosomes form a new generation population;
and S2.8, repeating the steps S2.3-S2.7, and jumping out of the current loop and returning to the pareto optimal solution set if the current genetic algebra reaches the target genetic algebra.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention takes the limitation of vehicle charging demands and service flight time windows into consideration, establishes a brand new double-target electric ground service vehicle dispatching model, and can generate an electric ground service vehicle dispatching scheme meeting the demands for users;
(2) The invention uses dynamic priority dispatch algorithm to generate chromosome genome code of initial population, which is not only helpful to balance the demand of multiple target values, so that the dispatch scheme generated finally is more reasonable, but also can improve the convergence rate of the result;
(3) The invention also improves the NSGA2 algorithm, introduces a correlation-based destruction operator and a priority-based optimal greedy inserted repair operator, and provides an improved self-adaptive non-dominant sorting genetic algorithm, compared with the existing other algorithms, the improved self-adaptive non-dominant sorting genetic algorithm can quickly obtain a better scheduling scheme, provides an efficient and general effective way for a decision maker to easily distribute ground service vehicles to serve flights, and is superior to the most advanced traditional meta-heuristic method in performance at present.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described.
FIG. 1 is a flow chart of a scheduling method provided by the present invention;
FIG. 2 is a schematic diagram of the steps of the modified NSGA2 algorithm;
FIG. 3 is a schematic diagram of generating an initial population based on a dynamic priority scheduling algorithm;
FIG. 4 is a schematic diagram of a repair operator for a correlation-based destruction operator and a priority-based greedy insertion;
FIG. 5 is a graph relating to an electric passenger car obtained by four algorithms;
FIG. 6 is a graph relating to an electric food cart obtained by four algorithms;
fig. 7 is a graph of four algorithms for an electric sewage vehicle.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention.
Example 1: as shown in fig. 1, the dispatching method for processing airport electric ground service vehicles of the embodiment includes the following steps:
s1, establishing a mixed integer programming model of an airport electric ground service vehicle scheduling problem with the aim of minimizing the total distance travelled by the electric ground service vehicle service flights and the standard deviation of the occupation time of each vehicle service flight, wherein the mixed integer programming model comprises an objective function and constraint conditions:
the objective function includes:
in the middle of，DThe total distance travelled by the flights is served for the ground service vehicles;STthe standard deviation of the flight occupation time is served for all electric ground service vehicles;D ij is an electric ground service vehicleiService flightsjTotal distance travelled;OT i is an electric ground service vehicleiTotal time taken up by the service flight;x ij is an electric ground service vehicleiService flightsjStatus judging function, wherein, electric ground service vehicleiService flightsjThenx ij =1, otherwisex ij ＝0；iNumbering the electric ground service vehicles;n v the total number of the electric ground service vehicles;jnumbering flights;n f is the total number of flights;
the constraint conditions include: and (4) occupying time constraint:
；
in the method, in the process of the invention,VST ij is an electric ground service vehicleiService flightsjTime to start occupied;VET ij is an electric ground service vehicleiService flightsjEnding the occupied time;landmelectric ground service vehicle for being driven successivelyiA flight for the service;EVthe method is an electric ground service vehicle set;AFis a collection of inbound flights;DFa set of flights for departure;AT j for flightsjTime of entering port;DT j for flightsjTime of departure;ST ij is an electric ground service vehicleiFor flightsjTime of providing the service;PT ij is an electric ground service vehicleiService flightsjThe time taken to perform the preliminary work;CT ij is an electric ground service vehicleiService flightsjThe time it takes to perform the tail sweeping work;TTP ij is an electric ground service vehicleiService flightsjTravel time for performing the preliminary work;TTC ij is an electric ground service vehicleiService flightsjRunning time for executing tail sweeping work;LT i is an electric ground service vehicleiLoading resources takes time;UT i is an electric ground service vehicleiUnloading resources takes time;
vehicle charging constraints:
in the method, in the process of the invention,CD i is an electric ground service vehicleiThe current distance that the electric quantity can travel;DB iP to be from electric ground service vehicleiCurrent location to parking lotPIs a distance of (2);DB jP to be from the flightjFrom the location to the parking areaPIs a distance of (2);VST i Pk electric ground service vehicle for starting from parking lotiServicing flights to be serviced next timekTime to start occupying;S i is an electric ground service vehicleiIs a running speed of the vehicle;CD i max is electrically poweredGround service vehicleiThe distance that can be travelled under full battery conditions;CR i is an electric ground service vehicleiThe ratio of the amount of charge per unit time to the distance that the amount of charge per unit time can travel;TC i max is an electric ground service vehicleiThe amount of time it takes to charge from 0 until full;CD ' i is an electric ground service vehicleiEnding the distance that the occupied electric quantity can travel;D ij is an electric ground service vehicleiService flightsjTotal distance travelled;DB ij is a ground service vehicleiFor flightsjDistance travelled when providing service;TT ij is an electric ground service vehicleiService flightsjTotal travel time;C i is an electric ground service vehicleiCharging judgment function as electric ground service vehicleiWhen chargedC i =1Otherwise, the device can be used to determine whether the current,C i =0；TC i f is an electric ground service vehicleiThe time for charging when idle;TC i m is an electric ground service vehicleiThe time it takes from the current charge until full;TC i is an electric ground service vehicleiIs set to be a battery charging time;kthe electric ground service vehicle charging type judging function is used for judging the charging type of the electric ground service vehicle, wherein when the charging time of the electric ground service vehicle is the interval time of different flights served by the electric ground service vehiclekWhen the charging time of the electric ground service vehicle is the time spent by the electric ground service vehicle from the current electric quantity to fullk=0；
0-1 variable constraint:
vehicle number constraint:
in the method, in the process of the invention,n vs for the number of uses of the electric ground service vehicle,n v the total number of the electric ground service vehicles.
Priority calculation formula:
in the method, in the process of the invention,WD i is an electric ground service vehicleiα is a trade-off parameter;W i is an electric ground service vehicleiEach invocation will cause the weight of the electric ground service vehicle to be added with 1;PD i is an electric ground service vehicleiThe distance that the preliminary operation needs to travel is performed;WD i the smaller the value of (c), the higher the priority of the vehicle.
The calculation formula of the correlation in the correlation-based destruction operator is as follows:
in the method, in the process of the invention,R ml the correlation between flight m and flight l; alpha 1 The weight in the formula of the time window difference between two flights is set to be alpha 1 =0.5；α 2 Servicing a vehicle decision function for a flight, if the flightm、lIs serviced by the same vehicle, then alpha 2 =0, otherwise α 2 = 1；R ml The smaller the value of (c), the greater the correlation between the two flights.
The calculation formula of the deletion proportion in the correlation-based destruction operator is as follows:
in the method, in the process of the invention,ris a deletion proportion;roundown()is a downward integer. Deleting the proportion;
congestion distance calculation formula:
in the method, in the process of the invention,CD iw is shown in the firstwThe first objective functioniThe crowded distance of the individual(s),ithe value of (2) is at least 2,ithe maximum value of (2) is 1 minus the total number of individuals in the current population;f w (i+1)represents the (i+1) th individual of the current populationwA plurality of objective function values;f w (i-1)representing the first of the current populationi-the first of 1 individualwA plurality of objective function values;fmax windicating the relationship to the first among all individuals in the current populationwMaximum value of the individual objective functions;fmin wrepresenting the relationship between the first and second individuals in the current populationwA minimum value of the individual objective functions; l is the set of individuals of the current population;
formulas (1) and (2) are two optimization targets, namely the total distance D travelled by the service flights of the electric ground service vehicles and the standard deviation of the occupied time of each service flight of the vehiclesST. Wherein the method comprises the steps ofSTAnd also represents the degree of balance of task allocation. Constraints (3) and (4) represent electric ground service vehiclesiService flightsjThe end occupation time is longer than the start occupation time, and the next flight to be servicedmIs longer than the last service flightlIs the end occupation time of (c). Equations (5) and (6) are the start occupancy time and the end occupancy time of the electric ground service vehicle i facing the departure flight and the arrival flight, respectively. The electric ground service vehicle (7)iService flightsjThe time it takes to perform the preliminary work. The electric ground service vehicle (8)iService flightsjThe time it takes to perform the tail-sweeping operation. Constraints (9) and (10) represent charging constraints that the vehicle uses in order to maintain normal operation, meaning that when an electric ground service vehicle has served a flight, it has sufficient power to support the distance from the current location to the parking lot for charging, but the ground service vehicle is not at this time charged enough to serve the next flight to go to the parking station for charging. At this time, the car is charged in the parking lot. Constraints (11) and (12) represent constraints for the vehicle to charge with idle time. Wherein the constraint (11) represents that the vehicle is in a larger idle time,the operation of charging by using the idle time indicates that when the electric ground service vehicle finishes servicing one flight, the time from servicing the next flight exceeds 20 minutes, and the electric ground service vehicle goes to a parking lot for charging; constraint (12) indicates that since the new energy electric car battery is charged at 20% -30% with minimum battery loss, idle charging is considered only when the amount of electricity is less than 20% for battery health. Electric ground service vehicle represented by formula (13)iThe ratio of the amount of electricity charged in a unit time to the distance that the amount of electricity charged in the unit time can travel. Equation (14) represents the amount of electricity that the vehicle undergoes after a series of operations. Equation (15) represents the total distance traveled by the vehicle through a series of operations. Electric ground service vehicle represented by formula (16)iAnd the time for charging when idle. Electric ground service vehicle represented by formula (17)iFrom the current charge until it takes time to fill. Electric ground service vehicle represented by formula (18)iIs provided. Constraint (19) indicates that for a flightjIt is necessary and only one electric ground service vehicle is providing service. Constraint (20) represents an electric ground service vehicle charge judgment function. The constraint (21) is a ground service vehicle number constraint. Equation (22) is a vehicle priority calculation equation. Equation (23) represents a flight correlation calculation formula. Equation (24) represents a calculation formula of the deletion ratio. Equation (25) is a congestion distance calculation equation.
S2, solving the mixed integer programming model by using real flight data and electric ground service vehicle operation parameters and adopting an improved NSGA2 algorithm to obtain the pareto optimal solution set containing various scheduling schemes.
As shown in fig. 2, the improved NSGA2 algorithm is adopted for solving, and the method specifically comprises the following steps:
s2.1, presetting parameters, wherein the set parameters comprise the scale of the external populationMNumber of iterationsGen；
S2.2, initializing a population: initializing a population by adopting a real number coding mode, coding chromosomes in the population by adopting a double-layer coding mode, wherein the first layer of coding is used for arranging flights waiting to be allocated with electric ground service vehicles according to the arrival time or departure time sequence, and the second layer of coding is used for allocating the electric ground service vehicles to corresponding flights, wherein the electric ground service vehicles are allocated to the flights according to a dynamic priority scheduling algorithm;
s2.3, calculating the objective function value of each chromosome in the populationDAndST；
s2.4, carrying out genetic operation comprising binary crossover and polynomial variation by adopting individuals in the population to generate a offspring population;
s2.5, sequentially adopting a correlation-based destruction operator and a priority-based optimal greedy inserted repair operator to process chromosomes in the offspring population;
s2.6, merging child parent population to generate 2NThe population of the individual chromosomes, and carrying out rapid non-dominant sorting and crowding distance calculation;
s2.7, selecting chromosomes with low pareto grade and large crowding distance from the population combined by the child and the parent by using elite retention strategy until selectingNThe individual chromosomes form a new generation population;
and S2.8, repeating the steps S2.3-S2.7, and jumping out of the current loop and returning to the pareto optimal solution set if the current genetic algebra reaches the target genetic algebra.
The strategies for generating chromosomal genomic codes for the initial population using the dynamic priority scheduling algorithm are as follows:
as shown in fig. 3, the assignment of ground vehicles to the first four flights of eight flights using a dynamic priority scheduling algorithm is illustrated. From the drop, vehicle2 provides service for flight 1, vehicle1 provides service for flight 2, at this time, the weights of both flights are 1, when the ground service Vehicle is allocated to flight 3, the priorities are calculated, and because the ground service Vehicle weights are the same, vehicle2 is closer, vehicle2 has higher priority, so that Vehicle2 provides service for flight 3. When the ground service is allocated to the flight 4, the weight and the distance are different, the priority is calculated, and the Vehicle1 is judged to provide service for the flight 4 according to the result. The different vectors will eventually return to the drop. Therefore, the dynamic priority scheduling algorithm is used for generating the chromosome genome codes of the initial population, and the balance of task allocation can be realized by dynamically adjusting the priority of the electric ground service vehicles while pursuing the minimum total distance, so that the result maturation speed is increased, the allocation of the electric ground service vehicles to flights for providing services is effectively realized, and the requirements of two objective functions are balanced.
The method adopts a dynamic priority scheduling algorithm to generate chromosome genome codes of an initial population, and specifically comprises the following steps:
s2.2.1 traversing the vehicle numbering the vehicle as 1,2.n v Creating an available vehicle queue consisting of currently idle vehicles according to the number, and arranging vehicles in the available vehicle queue according to the priority order;
s2.2.2 all flights waiting for allocation of electric ground service are ordered according to the arrival time or departure time of the flights and numbered 1,2.n f Creating an allocation set;
s2.2.3 selecting a flight of unassigned electric ground service vehicles from the allocation set, selecting an electric ground service vehicle with highest priority from the available vehicle queues to serve the flight, and if no suitable vehicle exists, randomly selecting a vehicle to allocate to the flight;
s2.2.4, updating the allocation set, namely updating the situation that each flight in the allocation set allocates electric ground service vehicles, updating the vehicle state and the priority, and subtracting 1 from the value of the priority if the number of times a certain vehicle waits for allocation in the available vehicle queue exceeds a set allocation number threshold, wherein the allocation number threshold is set as the total number of electric ground service vehicles in the embodiment; if a certain vehicle is continuously and repeatedly called in the distribution set, adding 1 to the value of the priority of the vehicle for 1 time in each continuous and repeated call;
s2.2.5 repeating S2.2.3, S2.2.4 operations assigns a ground cart to each flight in the assignment set, generating a chromosome genome.
If the population number is m, then cycling m times S2.2.3, S2.2.4, S2.2.5 can result in an initial assigned flight population with a population size of m.
The strategies for chromosome processing by the correlation-based destruction operator and the priority-based optimal greedy-inserted repair operator are as follows:
as shown in fig. 4, the chromosome to be processed is first divided into a Remove set and a Remaining set by correlation, then the Remove set is corrected based on dynamic priority, and finally the Remaining set is inserted by an optimal greedy insertion method to obtain a new chromosome. By processing the chromosome using a correlation-based destruction operator and a priority-based optimal greedy inserted repair operator, more reasonable results can be obtained. This is because crossover and mutation operations performed on parent chromosomes in a population often lack explicit orientation, potentially leading to the introduction of invalid solutions. Therefore, the offspring generated after only performing the crossover and mutation operations are highly likely to be out of compliance with the model constraints, and thus become an infeasible solution. However, by applying a correlation-based destruction operator and a priority-based optimal greedy inserted repair operator to process the chromosome, these shortcomings can be remedied and a more rational scheduling scheme can be obtained.
The method adopts a destruction operator based on correlation to process the chromosome, and specifically comprises the following steps:
s2.5.1 randomly selecting a flight from the chromosome genomeiAnd will take flightsiThe ground service vehicles which are correspondingly distributed are added into the set Remove, and the Remaining genes in the chromosome form a set Remaining;
s2.5.2 randomly selecting a flight from a collection RemovelAnd calculates the correlation between the flight and each of the flights in the collectionR ml ；
S2.5.3 selecting the flight with the greatest relevance to flight l (i.e. relevanceMinimum flight), adding the flight and the ground service vehicles corresponding to the flight to the set Remove, and deleting the flight and the ground service vehicles corresponding to the flight from the set Remove;
s2.5.4 checking the number of removed flights of the collection Remaining; if the removenum reaches the set deletion threshold, the deletion process is completed, otherwise, the process goes to step2.5.2; the present embodiment sets the deletion threshold as the quotient of the total number of flights divided by the deletion ratio;
the method for processing the chromosome by adopting the optimal greedy inserted repair operator based on the priority comprises the following steps:
s2.5.5 traversing flights in the set Remove and checking corresponding assigned vehicles of the flights; in the traversal process, for a certain flight in the set Remove, if the vehicle corresponding to the flight can complete the service of the flight under all constraint conditions, deleting the flight from the set Remove, adding the flight back to the set Remove, and finally, reserving the flight which does not meet the constraint in the set Remove;
s2.5.6 traversing flights in the collection Remove, recalculating vehicles meeting constraints in all vehicles for the traversed flights, and comparing priorities of the vehicles meeting constraintsWD i SelectingWD i And (3) assigning the vehicle with the minimum value to the flight and transferring the flight from the set Remove to the set Remaining, wherein the set Remaining is the adjusted chromosome after the traversing, if the vehicle with the minimum value of the flight does not meet the constraint condition is found in the traversing process and the flight is not traversed at the moment, the traversing is finished, the chromosome keeps the mutated result, and the chromosome is marked as an infeasible solution, so that the chromosome is eliminated as the infeasible solution in the subsequent evolution process.
S3, screening the obtained pareto optimal solution sets by using the equivalent importance degree of the two scheduling targets, and selecting the points of the middle section as a scheduling scheme, namely, a scheduling scheme for processing the airport electric ground service vehicles.
In particular, because in multi-objective optimization, the selection of solutions is often a complex and trade-off process. Pareto optimal solution set refers to the set of all solutions that are not dominated by other solutions in the multi-objective optimization problem. What is meant by dominant is that one solution dominates another solution if it is not worse than the other solution on all objective functions and is better than the other solution on at least one objective function. Solutions in the pareto optimal solution set are considered pareto optimal because they are relatively excellent in all metrics and no other solutions can go beyond them in all metrics at the same time. The solutions in the pareto optimal solution set cannot be simply judged by the traditional good-bad relationship, because they can have trade-offs between different targets. It is necessary for the decision maker to determine which party is more important. The present embodiment interprets the obtained solution with a comparable degree of importance for both scheduling objectives, since both objectives are equally important, then the point of the middle segment is chosen as the scheduling scheme for handling airport electric ground service vehicles.
Three specific examples of the flight scheduling by the electric ground service vehicle for three airports, namely, an electric passenger ladder vehicle, an electric food vehicle and an electric sewage vehicle, will be described below. The improved NSGA2 algorithm is programmed by using Python, and the computer running environment is Win10system withCPUi5-7300hq.
Since we divide flights into inbound flights and outbound flights according to reality. Therefore, in the research of airport new energy ground service vehicles, we divide electric ground service vehicles into ground service vehicles which can serve inbound flights, only outbound flights and only inbound flights. The passenger elevator car, the food car and the sewage car are selected as representatives for research, and the result can be conveniently popularized in the electric ground service cars with the same service flight type.
The embodiment verifies the effect of the algorithm on electric ground service vehicle scheduling of different flights;
because efficient operation of electric passenger vehicles is critical to maintaining on-time operation of flights and providing a comfortable boarding experience for passengers, and both passengers and crewmembers need to get on and off the aircraft by the passenger vehicles. The electric ground service vehicle is selected as a vehicle representative that serves inbound flights and outbound flights. And the electric passenger elevator car is mainly used for assisting the boarding and disembarking process of the inbound and outbound flights so as to ensure that passengers can smoothly board and disembark through the electric passenger elevator car. It is therefore necessary to take into account the different service time windows for the service inbound and outbound flights to meet the needs of passengers and service personnel to get on and off the elevator. Therefore, the passenger elevator car dispatching needs to be considered reasonably, and the arrival and departure flight tasks can be distributed to the electric passenger elevator cars with specific numbers during dispatching, so that the optimal scheme is more specific and practical. The electric passenger ladder vehicle adopts an east wind passenger ladder vehicle EV350, and the endurance mileage and charging time thereof are shown in Table 1. The number of electric passenger ladders, the travel speed, and the time for passengers and service personnel to get on and off the passenger ladders are shown in Table 2.
Electric food vehicles play an important role in supporting outbound flights, so electric food vehicles are selected as representatives of electric ground service vehicles that serve only outbound flights. They are responsible for providing dining services for loading food onto an aircraft prior to take-off of the flight. The electric food cart takes food from the food processing center before servicing the outbound flight and provides the food to the flight while servicing the flight, so that the electric food cart needs to work taking into account the time to go to the food center and the time to load the food, and the time to arrive at the outbound flight from the food center and the time to unload the food. Efficient operation of the food cart ensures that passengers can enjoy high quality and varied dining options while in flight. The electric food cart used the zhi lan light truck one-compartment cart BJ5045XXYEVJ, whose endurance mileage and charging time are shown in table 1. The number of electric food carts, the travel speed, the time period for loading food into the electric food carts at the food processing center, and the time period for loading food from the electric food carts to the aircraft are shown in table 2.
Electric lagoons are vehicles that are critical in the operation of inbound flights, so electric lagoons are selected as representative of electric ground service vehicles that serve only outbound flights. They are responsible for the treatment and cleaning of waste water and waste on board the aircraft and for transporting these sewage and waste to a refuse treatment centre for treatment in order to maintain the cleanliness and hygiene of the aircraft. Therefore, the electric sewage car needs to work in consideration of the sewage suction time, the time for going to the garbage disposal center and the sewage discharge time in the garbage disposal center. The electric sewage car is helpful for keeping the airport clean, and simultaneously ensures that the airplane is in a clean state after falling and before taking off. The electric sewage vehicle adopts a Tianyi aircraft sewage vehicle JSTY5060GWSE, and the endurance mileage and the charging time are shown in table 1. The number of electric sewage cars, the running speed, the sewage suction period and the sewage discharge period are shown in table 2.
Table 1 specific parameter table of new energy ground service vehicle
Table 2 model parameter table
The FCFS method used for MOEA/D, RVEA, NSGA2 and manual scheduling was used to compare with the modified NSGA2 algorithm. The parameters of the four evolutionary algorithms are set as follows: the total iteration algebra of the modified NSGA2, MOEA/D, RVEA and NSGA2 was set to 400 times, the crossover rate was set to 0.85, and the mutation rate was set to 0.3.
To fairly compare the performance of the four algorithms, the algorithm was run 10 times for each of the three electric ground vehicles, resulting in their graphs, as shown in fig. 5-7. It can be observed that the center value and the minimum value obtained by the improved NSGA2 algorithm are smaller than those obtained by other algorithms, and the solution quality of the improved NSGA2 algorithm can be proved to be superior to that of the other three algorithms.
For three electric ground service vehicles, each of which runs the improved NSGA2 algorithm, MOEA/D algorithm, RVEA algorithm and NSGA2 algorithm 10 times, 4 pareto optimal solution sets are obtained for each ground service vehicle. Since in the present embodiment, the obtained solution is explained with the two scheduling target importance degrees equivalent, it is necessary to select the point of the middle segment as the scheduling scheme for comparison. Then, the best near-optimal solution which can be obtained by each algorithm can be obtained by screening the intermediate section points of the 4 pareto optimal solution sets respectively. And comparing the average value of the near-optimal solutions obtained by the four methods with the average value obtained by running the FCFS method adopted by manual scheduling for 10 times. Given by tables 3 and 4.
Table 3 comparison table of average value of electric ground service vehicle total distance optimal solution
Table 4 comparison table of average value of electric ground service vehicle occupancy time standard deviation optimal solutions
Table 3 examines the comparison of the optimum values of the 5 algorithms for the total distance objective function, and table 4 examines the comparison of the optimum values of the standard deviation objective function for the occupation time for the 5 algorithms. As can be seen from table 3, table 4, the improved NSGA2 algorithm reduces the optimal solution of the MOEA/D algorithm, which yields the most excellent results with respect to total distance for electric passenger car scheduling, by 7.96% over total distance compared to the other 4 algorithms, while the NSGA2 algorithm, which has the optimal occupied time standard deviation for electric passenger car scheduling, reduces the occupied time standard deviation by 3.54% over occupied time standard deviation for the other 4 algorithms. The improved NSGA2 algorithm was reduced by 0.0936% over the total distance compared to the optimal solution of the MOEA/D algorithm that gave the most excellent results for the total distance for the electric food cart schedule among the other 4 algorithms, while the RVEA algorithm with the optimal occupancy time standard deviation for the electric food cart schedule was reduced by 9.52% over the occupancy time standard deviation for the other 4 algorithms. The improved NSGA2 algorithm was reduced by 0.20% over the total distance compared to the optimal solution of the RVEA algorithm that gave the most excellent results for the total distance for electric sewer vehicle scheduling among the other 4 algorithms, while the MOEA/D algorithm with the optimal occupancy time standard deviation for electric sewer vehicle scheduling among the other 4 algorithms was reduced by 27.03% over the occupancy time standard deviation. The improved algorithm was found to be superior to the optimal algorithm out of the other 4 algorithms by comparison.
In summary, the proposed improved NSGA2 algorithm has a strategy of generating chromosome genome codes of an initial population, correlation-based destruction operators and optimal greedy-insertion-based repair operators for chromosome processing by a dynamic priority scheduling algorithm, and shows excellent performance in solving the airport electric ground service vehicle scheduling problem. The improved NSGA2 algorithm optimization result can provide a plurality of alternative optimal electric ground service vehicle schemes for a decision maker, and an effective way is provided for the decision maker to easily select an electric ground service vehicle distribution plan.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present invention disclosed in the embodiments of the present invention should be covered by the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (7)
1. The dispatching method of the airport electric ground service vehicle is characterized by comprising the following steps of:
s1, establishing a mixed integer programming model of an airport electric ground service vehicle scheduling problem by taking the total distance travelled by the electric ground service vehicle service flights and the standard deviation of the occupied time of each vehicle as targets, wherein the mixed integer programming model comprises an objective function and constraint conditions;
s2, solving the mixed integer programming model by using real flight data and electric ground service vehicle operation parameters to obtain a pareto optimal solution set containing various scheduling schemes;
s3, establishing a pareto optimal solution set screening strategy, and obtaining an optimal scheduling scheme for processing the airport electric ground service vehicles;
wherein the objective function includes:
；
；
in the middle of，DService the total distance travelled by the flight for the electric ground service vehicle;STthe standard deviation of the flight occupation time is served for all electric ground service vehicles;D ij is an electric ground service vehicleiService flightsjTotal distance travelled;OT i is an electric ground service vehicleiTotal time taken up by the service flight;x ij is an electric ground service vehicleiService flightsjStatus judging function, wherein, electric ground service vehicleiService flightsjThenx ij =1, otherwisex ij ＝0；iNumbering the electric ground service vehicles;n v the total number of the electric ground service vehicles;jfor sailingA class number;n f is the total number of flights;
the constraint conditions include:
and (4) occupying time constraint:
；
in the method, in the process of the invention,VST ij is an electric ground service vehicleiService flightsjTime to start occupied;VET ij is an electric ground service vehicleiService flightsjEnding the occupied time;landmelectric ground service vehicle for being driven successivelyiA flight for the service;EVthe method is an electric ground service vehicle set;AFis a collection of inbound flights;DFa set of flights for departure;AT j for flightsjTime of entering port;DT j for flightsjTime of departure;ST ij is an electric ground service vehicleiFor flightsjTime of providing the service;PT ij is an electric ground service vehicleiService flightsjThe time taken to perform the preliminary work;CT ij is an electric ground service vehicleiService flightsjThe time it takes to perform the tail sweeping work;TTP ij is an electric ground service vehicleiService flightsjTravel time for performing the preliminary work;TTC ij is an electric ground service vehicleiService flightsjRunning time for executing tail sweeping work;LT i is an electric ground service vehicleiLoading resources takes time;UT i is an electric ground service vehicleiUnloading resources takes time;
vehicle charging constraints:
；
in the method, in the process of the invention,CD i is an electric ground service vehicleiThe current distance that the electric quantity can travel;DB iP to be from electric ground service vehicleiCurrent position to stopField of technologyPIs a distance of (2);DB jP to be from the flightjFrom the location to the parking areaPIs a distance of (2);VST i Pk electric ground service vehicle for starting from parking lotiServicing flights to be serviced next timekTime to start occupying;S i is an electric ground service vehicleiIs a running speed of the vehicle;CD i max is an electric ground service vehicleiThe distance that can be travelled under full battery conditions;CR i is an electric ground service vehicleiThe ratio of the amount of charge per unit time to the distance that the amount of charge per unit time can travel;TC i max is an electric ground service vehicleiThe amount of time it takes to charge from 0 until full;CD ' i is an electric ground service vehicleiEnding the distance that the occupied electric quantity can travel;D ij is an electric ground service vehicleiService flightsjTotal distance travelled;DB ij is a ground service vehicleiFor flightsjDistance travelled when providing service;TT ij is an electric ground service vehicleiService flightsjTotal travel time;C i is an electric ground service vehicleiCharging judgment function as electric ground service vehicleiWhen chargedC i =1Otherwise, the device can be used to determine whether the current,C i =0；TC i f is an electric ground service vehicleiThe time for charging when idle;TC i m is an electric ground service vehicleiThe time it takes from the current charge until full;TC i is an electric ground service vehicleiIs set to be a battery charging time;kthe electric ground service vehicle charging type judging function is used for judging the charging type of the electric ground service vehicle, wherein when the charging time of the electric ground service vehicle is the interval time of different flights served by the electric ground service vehiclekWhen the charging time of the electric ground service vehicle is the time spent by the electric ground service vehicle from the current electric quantity to fullk=0；
0-1 variable constraint:
；
vehicle number constraint:
；
in the method, in the process of the invention,n vs for the number of uses of the electric ground service vehicle,n v the total number of the electric ground service vehicles.
2. The method of scheduling airport electric ground service vehicles of claim 1, wherein the step S2 of solving the mixed integer programming model using a modified second generation non-dominant ordered genetic algorithm comprises the steps of:
s2.1, presetting parameters, wherein the set parameters comprise the scale of the external populationMNumber of iterationsGen；
S2.2, initializing a population: the chromosomes in the population are encoded in a double-layer encoding mode, wherein the first-layer encoding is that flights waiting for the distribution of electric ground service vehicles are arranged according to the arrival time or departure time sequence, and the second-layer encoding is that the numbers of the electric ground service vehicles distributed to the flights corresponding to the first-layer encoding are used;
s2.3, calculating the objective function value of each chromosome in the populationDAndST；
s2.4, carrying out genetic operation comprising binary crossover and polynomial variation by adopting individuals in the population to generate a offspring population;
s2.5, sequentially adopting a destruction operator and a repair operator to process chromosomes in the offspring population;
s2.6, merging child parent population to generate 2NThe population of the individual chromosomes, and carrying out rapid non-dominant sorting and crowding distance calculation;
s2.7, selecting chromosomes with low pareto grade and large crowding distance from the population combined by the child and the parent by using elite retention strategy until selectingNThe individual chromosomes form a new generation population;
and S2.8, repeating the steps S2.3-S2.7, and jumping out of the current loop and returning to the pareto optimal solution set if the current genetic algebra reaches the target genetic algebra.
3. The method for dispatching electric ground service vehicles at airports according to claim 2, wherein in step S2.2, electric ground service vehicles are assigned to flights according to a dynamic priority dispatching algorithm, comprising the steps of:
s2.2.1 traversing electric ground service number the vehicle1, 2.n v Creating an available vehicle queue consisting of currently idle vehicles according to the number; sorting vehicles in the queue of available vehicles according to the priority order;
s2.2.2 all flights waiting for allocation of electric ground service are ordered according to the arrival time or departure time of the flights and numbered 1,2.n f Creating an allocation set;
s2.2.3 selecting a flight of an unassigned electric ground service vehicle from the allocation set, selecting an electric ground service vehicle with highest priority from the queue of available vehicles to serve the flight of the unassigned electric ground service vehicle, and if there is no suitable vehicle, randomly selecting a flight of a vehicle allocated to the unassigned electric ground service vehicle;
s2.2.4, updating the situation of each flight in the allocation set for allocating electric ground service vehicles, updating the vehicle state and the priority, and subtracting 1 from the value of the priority if the number of times a certain vehicle waits for allocation in the available vehicle queue exceeds a set allocation number threshold; if a vehicle is continuously and repeatedly called in the distribution set, adding 1 to the value of the priority of the vehicle for 1 time in each continuous and repeated call;
s2.2.5 repeating S2.2.3, S2.2.4 operations to assign an electric ground vehicle to each flight in the set of assignments, generates a chromosome genome.
4. A method of scheduling airport electric ground service vehicles according to claim 3, wherein the priority of the vehicles is calculated as:
；
in the method, in the process of the invention,WD i is an electric ground service vehicleiα is a trade-off parameter;W i is an electric ground service vehicleiEach time the electric ground service vehicle is callediCan lead to an electric ground service vehicleiThe weight of (2) is added with 1;PD i is an electric ground service vehicleiThe distance that needs to be travelled to perform the preparatory operation.
5. The method of scheduling airport electric ground vehicles according to claim 2, wherein the processing of chromosomes in said offspring population using correlation-based disruption operators in step S2.5 comprises the following operations:
s2.5.1 randomly selecting a flight from the chromosome genomeiAnd will take flightsiAdding the electric ground service vehicles which are correspondingly distributed to the chromosome into a set Remove, and forming a set Remaining by the genes left in the chromosome;
s2.5.2 randomly selecting a flight from the collection RemovelAnd calculate the flightlCorrelation with each flight in the collection RemainingR ml ；
S2.5.3, selection and flightslAdding the flight with the largest correlation and the electric ground service vehicle corresponding to the flight with the largest correlation into the set Remove, and deleting the flight with the largest correlation and the electric ground service vehicle corresponding to the flight with the largest correlation from the set Remove;
s2.5.4 checking the number of removed flights of the collection Remaining; if removenum reaches the set deletion threshold, the deletion process is completed, otherwise, go to step S2.5.2.
6. The method for scheduling airport electric ground service vehicles according to claim 5, wherein the correlationR ml The formula of (2) is as follows:
；
in the method, in the process of the invention,R ml for flightsmAnd flightslIs a correlation of (2); alpha 1 The weight in the formula of the time window difference between two flights; alpha 2 Servicing a vehicle decision function for a flight, if the flightm、lIs serviced by the same vehicle, then alpha 2 =0, otherwise α 2 = 1。
7. The method of scheduling airport electric ground vehicles according to claim 6, wherein the step S2.5 of processing the chromosomes in the child population with repair operators based on optimal greedy insertion based on priority, comprises the following operations:
s2.5.5 traversing flights in the set Remove and checking for corresponding assigned vehicles for the flights; if the vehicle corresponding to a certain flight can complete the service of the flight under all constraint conditions, deleting the flight from the set Remove, and adding the flight back to the set Remaining;
s2.5.6 traversing flights in the set Remove, recalculating vehicles meeting constraints in all vehicles for the traversed flights, comparing priorities of the vehicles meeting the constraints, selecting the vehicle with the highest priority to be allocated to the flights, transferring the flights from the set Remove to the set remain after traversing, and after traversing, the set remain is an adjusted chromosome, if a certain flight is found to not meet the constraint conditions in the traversing process and the flight is not traversed at the moment, ending the traversing, keeping the result after the chromosome is mutated, and marking the chromosome as an infeasible solution, so that the chromosome is eliminated as an infeasible solution in the following evolution process.
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