CN114330842A - Airport ferry vehicle scheduling method based on two-stage optimization - Google Patents
Airport ferry vehicle scheduling method based on two-stage optimization Download PDFInfo
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Abstract
The invention discloses a two-stage optimization-based airport ferry vehicle scheduling method, which is used for acquiring parameter information of a ferry vehicle and an aircraft in an airport flight area; constructing a ferry vehicle scheduling model, establishing a visual scheduling window, and simulating a ferry vehicle scheduling process; performing first-stage optimization, and determining the optimal using quantity of the ferry vehicles by taking the delay time less than the maximum delay time as a constraint condition; performing second-stage optimization, and determining the optimal departure time and the average running speed of the ferry vehicle under the set and adjusted step length and range by taking the minimum stand waiting time as an objective function and the optimal use number and the delay time less than the maximum delay time as constraint conditions; and displaying the dispatching process and the optimization result of the ferry vehicle through a visual interface. The invention reduces the quantity cost and potential safety risk possibility of the fleet, meets the requirement of the flight punctuality rate, and has certain reference significance for the safety scheduling of the ferry vehicles in large airports.
Description
Technical Field
The invention belongs to the field of airport special guarantee vehicle scheduling, and particularly relates to an airport ferry vehicle scheduling method based on two-stage optimization.
Background
With the continuous development of the civil aviation industry, the increase of aircrafts and the number of guaranteed vehicles, the operation of airport scenes is increasingly complex, and the potential safety risk potential of the vehicles operating under the complex scenes is increased. Flight ground support service delays have also become one of the important causes of flight delays. The airport needs a scientific computer method to provide scientific guidance for the production management of the airport apron.
The current guarantee scheduling research mainly solves an approximate optimal solution by establishing a mathematical model and an intelligent algorithm. And the airport is used as a complex giant system, and some details are difficult to directly establish a mathematical model for solving. The invention can establish system simulation actual production scheduling by the similarity principle.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a two-stage optimization-based airport ferry vehicle scheduling method, which reduces the fleet quantity cost and the potential safety risk possibility and meets the requirement of the flight punctuality rate.
The technical scheme is as follows: the invention relates to an airport ferry vehicle scheduling method based on two-stage optimization, which comprises the following steps of:
(1) acquiring parameter information of a ferry vehicle and an aircraft in an airport flight area;
(2) constructing a ferry vehicle scheduling model, establishing a visual scheduling window, and simulating a ferry vehicle scheduling process;
(3) determining the optimal number of the ferry vehicles by taking the delay time less than the maximum delay time as a constraint condition;
(4) determining the optimal departure time and the average running speed of the ferry vehicle under the set and adjusted step length and range by taking the minimum stand waiting time as a target function and the optimal vehicle using number obtained in the step (3) and the time less than the maximum delay time as constraint conditions;
(5) and displaying the dispatching process and the optimization result of the ferry vehicle through a visual interface.
Further, the parameter information in the step (1) includes a ferry vehicle path set in an airport flight area, aircraft attributes, the number of ferries and aircraft, the distance from each ferry vehicle to each stand, the starting time of the aircraft receiving the earliest ferry service and the ending time of the latest ferry service.
Further, the step (2) is realized as follows:
dividing a ferry vehicle into 10 vehicle sub-states, wherein a state1 is an initial state, states 2-6 are service departure flight ferry vehicle state transfer flows, and states 7-10 are service arrival flight ferry vehicle state transfer flows; the initial state1 of the vehicle indicates that the vehicle is idle in the parking lot, when the transition condition of the task requirement is received, the state1 enters a bunch diamond judgment box, and the next vehicle state is judged according to the basic attribute of the flight;
if the flight attribute is an departure flight type1, the State of the dispatching vehicle is changed into State2, in the State2 State, the ferrying vehicle waits for departure in the parking lot, waits for a fixed time after waiting, generates a transition after simulating the transition of the passenger boarding task, and the State of the vehicle is changed into State3 when the vehicle departs from the parking lot; setting the running speed of the vehicle through the embedded java language during the running process of the state3 vehicle, wherein the destination of the vehicle is the transition condition of state 3; state4 is the State that the ferry vehicle provides the guarantee service for the aircraft at the stop position, and the ferry vehicle enters the queuing model system to wait for the service according to the rules, and after the service of the ferry vehicle is finished, whether the operation is delayed is judged according to the starting event and the ending event of the service; the State5 State represents the process that the ferry vehicle returns to the yard from the dispatching task destination, if there is an inbound flight dispatching task at the same time, the vehicle in the State5 State is dispatched preferentially to go directly to the parking lot destination of the inbound flight, otherwise, the vehicle enters the State6 State and returns to the yard to enter the State1 initial State;
if the flight attribute is the inbound flight type2, the ferry in the state1 state or the state5 state is changed to the state 7; for a ferry vehicle serving inbound flights, the ferry vehicle does not need to wait for a fixed time in a parking lot to simulate the process of boarding passengers and directly starts to go to a destination, so the state7 is in a state that the vehicle goes to the task destination and does not need to stay in the parking lot to wait for simulating boarding passengers; when the speed of the arriving destination system is judged to be false, the transition is generated, and the state of the ferry vehicle is changed into state 8; the states 8 of a plurality of ferry vehicles trigger a queuing event, a queuing model is entered, and the state is changed into a state9 when the service is completed; when the ferry vehicle returns to the yard, the vehicle state is changed to state 10; the ferry vehicle state10 state represents waiting at the yard for a fixed time value to simulate ferry vehicle passenger service, and when passenger service is complete, the ferry vehicle transitions to an initial idle state1 to receive the next scheduled task.
Further, the step (3) is realized as follows:
optimizing the number of ferry fleets and reducing the cost of the number of vehicles, taking the minimum used vehicle as an optimization target, wherein the scheduling result of the minimum number of the fleets needs to meet the requirement of the precision point rate of the flights, reducing and expanding the fleets on the premise that the number of the fleets meets the guarantee requirement of the aircrafts, repeating the operation model for multiple times under an independent condition, analyzing the guarantee results of different fleets, and determining the use number of the minimum ferry vehicles.
Further, the step (4) comprises the steps of:
(41) adding an interval _ time _ x for adjusting departure time in the ferry vehicle scheduling model, and when the vehicle receives the task demand information, the vehicle does not go to the task destination immediately, but goes to the destination after waiting for the time specified by the parameters in the yard;
(42) subdividing vehicle speed parameters, on the basis of the original vehicle speed parameters, subdividing the speed into four classes according to the types of flights served by the vehicles and whether the service tasks are completed or not, serving the ferrying vehicle average speed parameter speedddp when the departing flights are served, serving the ferrying vehicle average speed parameter speeddap 1 when the departing flights are served and the entering flights are directly continued to be served, ferrying vehicle average speed parameter speeddap 2 when the ferrying vehicles return;
(43) setting parameters, adjusting step length and upper and lower speed boundaries, and setting a minimum parking space waiting time target function:
wherein the content of the first and second substances,
is the ith vehicle stand waiting time, alpha is the stand waiting time weight, beta is the travel time weight, mumIs a travel time correction coefficient that corrects the difference in the amount between the two data,
is the i-th vehicle travel time.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: the invention reduces the quantity cost and potential safety risk possibility of the fleet, meets the requirement of the flight punctuality rate and has certain reference for the safety scheduling of the ferry vehicles in the large airport.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a vehicle dispatch state transition diagram;
FIG. 3 is a diagram illustrating a vehicle dispatching state transition by introducing a new speed stage parameter and a time adjustment parameter;
FIG. 4 is a graph showing the results of an experiment using the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The invention provides an airport ferry vehicle scheduling method based on two-stage optimization, which specifically comprises the following steps as shown in figure 1:
step 1, acquiring parameter information of a ferry vehicle and an aircraft in an airport flight area, wherein the parameter information comprises: the method comprises the steps of collecting ferry vehicle paths in an airport flight area, aircraft attributes, the number of ferry vehicles and aircraft, the distance from each ferry vehicle to each stand, the starting time of the aircraft receiving the earliest ferry service and the ending time of the latest ferry service.
The aircraft attributes include two categories, departure flight and inbound flight. The inbound flight is a flight landing at the local airport, and the inbound flight data category includes: flight number, planned landing time, actual landing time, stop order number, model, and model loading number. The departure flight data includes flights departing from an airport, and the departure flight data categories include: flight number, planned takeoff time, actual takeoff time, model, stop order number and model loading number. The data adopts a 19-day schedule of the target airport 2020-4-month, and the day is 78 flights entering and leaving the remote gate. Part of the flight data is shown in table 1.
TABLE 1 target airport part data sheet
Step 2: and constructing a ferry vehicle scheduling model, establishing a visual scheduling window, and simulating the ferry vehicle scheduling process.
And (3) constructing model parameters, wherein the parameters to be set in the airport ferry vehicle scheduling model mainly comprise a ferry vehicle object, an airport road object, a node object and an aircraft type object.
Ferry vehicles are scheduling objects and entity objects herein. The ferry vehicle conveys passengers to the cabin door position of the aircraft on the designated parking space according to the task requirements. As shown in Table 2, vehicle behavior in the model class pre-formulated in the Intelligent object library, ferry vehicle library, of Anylogic. When the ferry vehicle is in an idle state, the ferry vehicle is parked on an attractor object _ x in a matrix node of matrix x code 33.
TABLE 2 Ferry vehicle object parameter settings
| Name (R) | Speed (km/h) | Number of | Parking position | Task without execution | |
| Vehicle | |||||
| 30 | 10 | MatrixNode33 | Returned to a parked position | First come first serve |
In order to record data of the ferry vehicle in the scheduling process and support subsequent evaluation and analysis, a user-defined variable needs to be added into the ferry vehicle object. In the running process of the ferry vehicle object, the user-defined variable is changed according to the change of the state of the ferry vehicle, all changed results are input into an excel table so as to achieve the effect of recording related data, and the creating parameters are shown in a table 3:
table 3 custom variable parameter settings
| Name of variable | Function of |
| Distance | Recording vehicle scheduled travel distance |
| Moving_time | Recording travel time of a vehicle |
| Distance1 | Recording the operating time of a vehicle |
| Time | Recording the time of arrival of the vehicle at the destination |
| Stay_time | Recording the time at which the vehicle is waiting for service to begin at a stand |
| Moving_time | Recording travel time of a vehicle |
| Idle_time | Recording idle time of a vehicle |
The road object is a passage path of the physical object, and connects two node objects so that the vehicle can be transferred from one node to another. The road objects are divided into two types of objects, namely main traffic lanes and service vehicles according to actual conditions. According to the method and the device, the result which accords with the actual running condition is obtained for the running condition of the ferry vehicle on the actual real road. The road object is not only geometrically consistent with the real road, but also limits the road vehicle operation according to the actual road operation rule, and the specific parameter setting is shown in table 4.
Table 4 road object parameter settings
The node object is a connection point of each road. There are two types of nodes in the model, common nodes at road junctions and stand destination nodes.
For a common road Node, the Node has the basic function of enabling a ferry vehicle to pass through the Node. If the node is connected with a plurality of paths, the ferry vehicle has the function of selecting different paths to the destination at the node, and further the function of the shortest path for the vehicle to travel in the dispatching process is realized.
For the destination Node _ a of the aircraft stop location, the Node corresponds to the cabin door position of the aircraft in the actual production operation. When the ferry vehicle arrives at the destination node, the ferry vehicle stops near the hatch and simulates the passenger job task with waiting for a specified time. When the service is completed, the ferry vehicle leaves from the destination node and goes to the next destination or returns to the yard attractor object attractor.
Constructing a ferry vehicle scheduling process by taking analog software as a platform, wherein the process comprises the following steps of; in an initial state, all ferry vehicles wait for dispatching in a parking lot; generating a scheduling service demand according to a predetermined flight schedule and the number of load people of the corresponding machine type; after the service requirement of one aircraft is generated, determining the number of ferry vehicles required by the aircraft according to the number of load personnel of the required flight type; judging flight attributes, if the flight is an inbound flight, searching for a ferry vehicle of an outbound task which is executing or has completed the task, and selecting a proper ferry vehicle to go to a designated parking space according to the remaining time of the current task of the ferry vehicle and the time required by a driving destination parking space under the limitation of a time window; otherwise, the ferry vehicles with the specified number are sequentially distributed according to the serial numbers of the vehicles in the parking lot. If the flight is an departure flight, sequentially distributing a specified number of ferry vehicles to an assigned parking space attractor directly from the yard according to the serial numbers of the ferry vehicles; and the ferry vehicle finishes ferry operation, joins in an idle vehicle team and executes circularly until all ferry tasks are finished.
The logic of the ferry vehicle dispatching system refers to behavior logic, operation rules and system program flow of an object. In the ferry vehicle intelligent vehicle, the change of the scheduling process event can cause the change of the vehicle state, and the ferry vehicle is divided into 10 vehicle sub-states according to the scheduling process, as shown in fig. 2. The state1 is an initial state, the states 2 to 6 are the state transition flows of the service departure flight ferry car, and the states 7 to 10 are the state transition flows of the service arrival flight ferry car. Described according to the scheduling flow, the vehicle initial state of state1 indicates that the vehicle is idle in the parking lot. And after receiving the transition condition of the task requirement, the state1 enters a bunch diamond judgment box and judges the next vehicle state according to the basic attribute of the flight.
If the flight attribute is the departure flight type1, the scheduled vehicle status becomes state 2. In the State2 State, the ferry vehicle waits for departure in a parking lot, waits for a fixed time after finishing waiting, simulates the transition of a passenger task, generates the transition, and the vehicle departs from the parking lot, and the vehicle State is changed to the State 3. During the running of the vehicle at the state3, the running speed of the vehicle is set by the embedded java language, and the vehicle reaches the transition condition destined for the state 3. State4 is the State that the ferry vehicle provides the guarantee service for the aircraft at the stand, and the ferry vehicle enters the queuing model system to wait for the service according to the rule, and after the ferry vehicle service is finished, whether the operation is delayed or not can be judged according to the starting event and the ending event of the service. The State5 State represents the process that the ferry vehicle returns to the yard from the dispatching task destination, if the inbound flight dispatching task exists at the same time, the vehicle in the State5 State is dispatched preferentially to go directly to the parking lot destination of the inbound flight, otherwise, the vehicle enters the State6 State and returns to the yard to enter the State1 initial State.
If the flight attribute is inbound flight type2, the ferry in state1 state or state5 state transitions to state 7. According to practical situations, for a ferry vehicle serving an inbound flight, the ferry vehicle does not need to wait for a fixed time in a parking lot to simulate the process of getting on passengers and directly departs to a destination, so the state7 is in a state that the vehicle goes to a task destination, and does not need to stay in a parking lot to wait for the simulated passengers to get on passengers. When the speed is determined to be false in the destination system, a transition occurs, and the state of the ferry vehicle is changed to state 8. The states 8 of multiple ferry vehicles trigger queuing events, the queuing model is entered, and the state is changed to state9 when the service is completed. When the ferry returns to the yard, the vehicle state transitions to state 10. The ferry vehicle state10 state represents waiting at the yard for a fixed time value to simulate ferry vehicle passenger service, and when passenger service is complete, the ferry vehicle transitions to an initial idle state1 to receive the next scheduled task.
Setting a dynamic event SendMissioneeven, being responsible for issuing scheduling task demand information to a ferry vehicle, and embedding a Java statement create _ SendMissioneEvent (0, MINUTE, s, i); the departure time interval between two vehicles on the same task is controlled.
And step 3: a first stage optimization is performed: and determining the optimal using quantity of the ferry vehicles by taking the delay time less than the maximum delay time as a constraint condition. Changing the number of the fleets, performing contraction editing and expanding editing on the fleets on the premise that the number of the fleets meets the guarantee requirement of the aircrafts in a simulation experiment, repeating the operation of the simulation system for multiple times under an independent condition, analyzing the guarantee result of using different fleets, and determining the use number of the minimum ferry vehicles.
Optimizing ferry fleet number reduces vehicle quantity cost. Optimization objectives are introduced, with minimal use of vehicles. The scheduling result of the minimum fleet number needs to meet the requirement of flight punctuality rate, namely the maximum delay time cannot exceed 15 min.
Min num_vehicle
S.t.delaytime≤15min
On the premise that the number of the fleets meets the guarantee requirement of the aircrafts, the fleets are contracted, expanded and compiled, the model is repeatedly operated for many times under independent conditions, guarantee results of different fleets are analyzed, and the use number of the minimum ferry vehicles is determined.
According to the analysis of the current simulation result, the target airport can completely meet the flight requirements by using 10 ferry vehicles at a daily time according to 50% of the utilization rate of the ferry vehicles, even the utilization rate of some vehicles is very low, and the fleet has the possibility of further contraction and edition. In order to research the optimal number of the ferry vehicles at the target airport, the number of the ferry vehicles is respectively changed into 9, 8 and 7, the system is independently and repeatedly operated, the flight service operation condition and the ferry vehicle operation condition under the condition of different ferry vehicle numbers are investigated, and comprehensive comparison and analysis are carried out, so that the optimal number of the ferry vehicles at the target airport is determined.
TABLE 5 simulation results for different ferry vehicle quantities
As shown in table 5, the number of fleets was changed, and the system was operated independently and repeatedly a plurality of times to obtain flight service data for different numbers of ferry fleets. And by combining the analysis of the ferry efficiency change diagram and the flight service relation diagram, when the number of the ferry vehicles in the system is 8, the average daily working time of the ferry vehicles is increased by 102.57 minutes, the total driving distance is reduced by 14058 meters, the longest delay time is not more than 15 minutes, and the requirement of flight punctuality is met. And the results show that the vehicle number optimization method also reduces the possibility of potential risks. Although the value is reduced more in the aspect when the number of the ferry vehicles is 7, more delay is caused in the aspect of flight service, and the maximum delay time exceeds 15 minutes, so that the flight destination requirement is not met. The number of the minimum ferry vehicles is 8.
And 4, step 4: and (3) carrying out second-stage optimization: and (3) determining the optimal departure time and the average running speed of the ferry vehicle under the set and adjusted step length and range by taking the minimum stand waiting time as an objective function and the optimal vehicle use number obtained in the step (3) and the time less than the maximum delay time as constraint conditions.
Newly adding departure delay time input parameters. And adding an adjusted departure time parameter interval _ time _ x, wherein when the vehicle receives the task demand information, the vehicle does not go to the task destination immediately, but goes to the destination after waiting for the time specified by the parameters in the yard. In the state transition diagram, an interval time state is newly added, after the vehicle receives task information at an initial state1, the vehicle enters the interval time state to read the adjustment time for adjusting departure parameters, the adjustment time is increased on the basis of the original departure time, and the ferrying vehicle is scheduled according to the newly obtained departure time.
On the basis of newly adding a departure delay time parameter, the speed of the vehicle in executing tasks in different stages is additionally ensured. On the basis of original vehicle speed parameters, the speeds are divided into four categories according to the types of flights served by the vehicles and whether service tasks are completed or not, speed is an average speed parameter of the vehicles when the vehicles leave the airport, speed 1 serves the vehicles entering the airport from the yard, speed 2 serves the departure flights and directly continues to serve the vehicles entering the airport, speed dback is an average speed parameter when the vehicles return, and the unit of the newly added speed parameter is km/h.
In order to apply the new speed parameters to the model, specific setting needs to be performed in the flow state transition diagram. As shown in fig. 3, the average speed of the ferry service departure flight is set, and the action command line is input when entering the action state3 state (get _ Main (). speed, KPH); setting the average speed of the inbound flight vehicles served from the yard, and inputting this. set speed (get _ Main (). speed 1, KPH) in the state7 transition7 action command line; setting the average speed of the vehicle when the departure flight is finished and the departure flight is directly continued to serve the arrival flight, and inputting this, set speed (get _ Main (). speed 2, KPH) into a state5 transition6 action command line; (ii) a Setting the average speed during the return trip, this is required to be input into the state3 and state8 transition action command lines.
And inputting a parameter optimizing experimental environment. In analog simulation software, an experiment optimization model can be established in a simulation experiment environment to find the optimal parameter value in a parameter range meeting the target condition. The model mainly comprises a target conditional expression based on Java and parameter setting. In order to find the optimal value of the newly added parameter, the search range and step length of each parameter and the optimal objective function need to be set before the experiment. Specific parameter settings are shown in table 6.
Table 6 optimization of experimental parameter adjustment settings
A minimum stand wait time objective function is set.
Wherein the content of the first and second substances,
is the ith vehicle stand waiting time, alpha is the stand waiting time weight, beta is the travel time weight, mumIs a travel time correction coefficient that corrects the difference in the amount between the two data,
is the i-th vehicle travel time. And finally, displaying the dispatching process and the optimization result of the ferry vehicle through a visual interface.
The target condition of the simulation optimization experiment is the minimum stand waiting time TsAnd a minimum travel time TiThe sum of (1). When the objective function is set, the running distance time is far longer than the stand waiting time, and the quantities of the two parameter data are not appropriate, so that the optimization result is interfered. For this purpose, a travel distance correction factor μ is introduced into the objective functionmSetting the following correction parameter calculation formula:
in the formula ofmFor the correction factor of the distance travelled, TsTo a minimum stall waiting time staytime, TmIs the minimum driving time movingtime.
And 8 ferry vehicle operation data obtained by the first-stage optimization are adopted for operation, and one decimal is reserved in a calculation result. Obtaining a correction coefficient mumAbout 0.2.
Setting an optimization target in a simulation optimization experiment, circularly calculating the numerical value of each vehicle in the simulation scheduling system by using a for loop statement, and setting an objective function in a Java parameter row of an event of the simulation system: for (vehicle i: vehicle) { value + (i.static/60.0 × 1.5+ i.movingtime/60.0 × 0.2 }; and introducing a correction coefficient of 0.2 and a weight of 1.5, and summing the stand waiting time and the running time of each vehicle to obtain an optimized objective function.
And (4) taking the optimized airport fleet number 8 as a ferry vehicle number simulation input parameter, and inputting the parameter into an operation optimization experiment. The result after optimization adjustment is shown in fig. 4, the iteration number of the optimization experiment is set to 100, and the optimization experiment obtains the optimal value of the parameter in the set range at the 78 th time. Adjusting the optimal solution of departure time within the range of [1,10] to 7 min; the optimal solution that the average speed speedddp of the vehicles going to the departure flight parking lot is within the adjusting range of [10,30] is 25 km/h; the optimal solution of the average speed speedstop 1 of the vehicles going to the airport parking lot of the inbound flights within the adjusting range of [10,30] is 30 km/h; the optimal solution of the average speed speedstop 2 of the vehicles of the outgoing flight and the incoming flight which are continuously served within the adjusting range of [1,30] is 20 km/h.
Claims (5)
1. A method for dispatching airport ferry vehicles based on two-stage optimization is characterized by comprising the following steps:
(1) acquiring parameter information of a ferry vehicle and an aircraft in an airport flight area;
(2) constructing a ferry vehicle scheduling model, establishing a visual scheduling window, and simulating a ferry vehicle scheduling process;
(3) determining the optimal number of the ferry vehicles by taking the delay time less than the maximum delay time as a constraint condition;
(4) determining the optimal departure time and the average running speed of the ferry vehicle under the set and adjusted step length and range by taking the minimum stand waiting time as a target function and the optimal vehicle using number obtained in the step (3) and the time less than the maximum delay time as constraint conditions;
(5) and displaying the dispatching process and the optimization result of the ferry vehicle through a visual interface.
2. The airport ferry vehicle scheduling method based on two-stage optimization of claim 1, wherein the parameter information in step (1) comprises airport ferry vehicle path set, aircraft attribute, number of ferry vehicles and aircraft, distance from ferry vehicle to each stand, earliest ferry service start time and latest ferry service end time of aircraft.
3. The airport ferry vehicle scheduling method based on two-stage optimization of claim 1, wherein the step (2) is implemented as follows:
dividing a ferry vehicle into 10 vehicle sub-states, wherein a state1 is an initial state, states 2-6 are service departure flight ferry vehicle state transfer flows, and states 7-10 are service arrival flight ferry vehicle state transfer flows; the initial state1 of the vehicle indicates that the vehicle is idle in the parking lot, when the transition condition of the task requirement is received, the state1 enters a bunch diamond judgment box, and the next vehicle state is judged according to the basic attribute of the flight;
if the flight attribute is an departure flight type1, the State of the dispatching vehicle is changed into State2, in the State2 State, the ferrying vehicle waits for departure in the parking lot, waits for a fixed time after waiting, generates a transition after simulating the transition of the passenger boarding task, and the State of the vehicle is changed into State3 when the vehicle departs from the parking lot; setting the running speed of the vehicle through the embedded java language during the running process of the state3 vehicle, wherein the destination of the vehicle is the transition condition of state 3; state4 is the State that the ferry vehicle provides the guarantee service for the aircraft at the stop position, and the ferry vehicle enters the queuing model system to wait for the service according to the rules, and after the service of the ferry vehicle is finished, whether the operation is delayed is judged according to the starting event and the ending event of the service; the State5 State represents the process that the ferry vehicle returns to the yard from the dispatching task destination, if there is an inbound flight dispatching task at the same time, the vehicle in the State5 State is dispatched preferentially to go directly to the parking lot destination of the inbound flight, otherwise, the vehicle enters the State6 State and returns to the yard to enter the State1 initial State;
if the flight attribute is the inbound flight type2, the ferry in the state1 state or the state5 state is changed to the state 7; for a ferry vehicle serving inbound flights, the ferry vehicle does not need to wait for a fixed time in a parking lot to simulate the process of boarding passengers and directly starts to go to a destination, so the state7 is in a state that the vehicle goes to the task destination and does not need to stay in the parking lot to wait for simulating boarding passengers; when the speed of the arriving destination system is judged to be false, the transition is generated, and the state of the ferry vehicle is changed into state 8; the states 8 of a plurality of ferry vehicles trigger a queuing event, a queuing model is entered, and the state is changed into a state9 when the service is completed; when the ferry vehicle returns to the yard, the vehicle state is changed to state 10; the ferry vehicle state10 state represents waiting at the yard for a fixed time value to simulate ferry vehicle passenger service, and when passenger service is complete, the ferry vehicle transitions to an initial idle state1 to receive the next scheduled task.
4. The airport ferry vehicle scheduling method based on two-stage optimization of claim 1, wherein the step (3) is implemented as follows:
optimizing the number of ferry fleets and reducing the cost of the number of vehicles, taking the minimum used vehicle as an optimization target, wherein the scheduling result of the minimum number of the fleets needs to meet the requirement of the precision point rate of the flights, reducing and expanding the fleets on the premise that the number of the fleets meets the guarantee requirement of the aircrafts, repeating the operation model for multiple times under an independent condition, analyzing the guarantee results of different fleets, and determining the use number of the minimum ferry vehicles.
5. The airport ferry vehicle scheduling method based on two-stage optimization of claim 1, wherein the step (4) comprises the following steps:
(41) adding an interval _ time _ x for adjusting departure time in the ferry vehicle scheduling model, and when the vehicle receives the task demand information, the vehicle does not go to the task destination immediately, but goes to the destination after waiting for the time specified by the parameters in the yard;
(42) subdividing vehicle speed parameters, on the basis of the original vehicle speed parameters, subdividing the speed into four classes according to the types of flights served by the vehicles and whether the service tasks are completed or not, serving the ferrying vehicle average speed parameter speedddp when the departing flights are served, serving the ferrying vehicle average speed parameter speeddap 1 when the departing flights are served and the entering flights are directly continued to be served, ferrying vehicle average speed parameter speeddap 2 when the ferrying vehicles return;
(43) setting parameters, adjusting step length and upper and lower speed boundaries, and setting a minimum parking space waiting time target function:
wherein the content of the first and second substances,
is the ith vehicle stand waiting time, alpha is the stand waiting time weight, beta is the travel time weight, mumIs a travel time correction coefficient that corrects the difference in the amount between the two data,
is the i-th vehicle travel time.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114927003A (en) * | 2022-05-12 | 2022-08-19 | 华设设计集团北京民航设计研究院有限公司 | Civil airport apron intelligent vehicle scheduling method and system |
| CN116485107A (en) * | 2023-03-21 | 2023-07-25 | 国网宁夏电力有限公司信息通信公司 | Operation and maintenance operation management and control system, method and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114927003A (en) * | 2022-05-12 | 2022-08-19 | 华设设计集团北京民航设计研究院有限公司 | Civil airport apron intelligent vehicle scheduling method and system |
| CN116485107A (en) * | 2023-03-21 | 2023-07-25 | 国网宁夏电力有限公司信息通信公司 | Operation and maintenance operation management and control system, method and storage medium |
| CN116485107B (en) * | 2023-03-21 | 2023-11-24 | 国网宁夏电力有限公司信息通信公司 | Operation and maintenance operation management and control system, method and storage medium |
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