CN113570247A - Multi-objective optimization method for distribution of stand-to-halt positions based on resource limited condition - Google Patents

Multi-objective optimization method for distribution of stand-to-halt positions based on resource limited condition Download PDF

Info

Publication number
CN113570247A
CN113570247A CN202110856814.5A CN202110856814A CN113570247A CN 113570247 A CN113570247 A CN 113570247A CN 202110856814 A CN202110856814 A CN 202110856814A CN 113570247 A CN113570247 A CN 113570247A
Authority
CN
China
Prior art keywords
aircraft
stand
allocation
delay
flight
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110856814.5A
Other languages
Chinese (zh)
Other versions
CN113570247B (en
Inventor
赵征
胡莉
冯事成
宋梅雯
李昌城
江斌
杨磊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Aeronautics and Astronautics
Original Assignee
Nanjing University of Aeronautics and Astronautics
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Aeronautics and Astronautics filed Critical Nanjing University of Aeronautics and Astronautics
Priority to CN202110856814.5A priority Critical patent/CN113570247B/en
Publication of CN113570247A publication Critical patent/CN113570247A/en
Application granted granted Critical
Publication of CN113570247B publication Critical patent/CN113570247B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Tourism & Hospitality (AREA)
  • Development Economics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Educational Administration (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • Primary Health Care (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Traffic Control Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a multi-objective optimization method for parking stall allocation based on a resource limited condition, which comprises the following steps: (1) researching a scene stand allocation process, and determining key resources in the stand allocation process; researching comprehensive performance indexes in the process of parking space allocation, and carrying out detailed analysis on key indexes; constructing an aircraft stop allocation optimization model based on the multi-resource limited condition based on the selected key efficiency index; (2) solving the aircraft stop allocation optimization model established in the step (1); (3) in order to enable the experimental result to be more in line with the actual scene operation, a simulation model is constructed to solve the flight apron delay, and the queuing theory is adopted to solve the flight runway delay; (4) and (5) applying flight data to carry out example verification, and comparing the experimental result with the actual operation condition of the airport. The invention fully considers the actual operation situation of the scene, optimizes the parking space allocation strategy and provides reference for the decision of an airport manager.

Description

Multi-objective optimization method for distribution of stand-to-halt positions based on resource limited condition
Technical Field
The invention relates to the technical field of airport scene traffic management and planning, in particular to a parking space allocation multi-objective optimization method based on a resource limited condition.
Background
The parking space allocation refers to the step of comprehensively considering various resource constraints and matching constraints and allocating an optimal boarding gate to the flight so as to improve the operation efficiency of the airport and the passenger satisfaction degree. A series of guarantee services are generally required to be carried out during the period that the flight passes the station in the airport, and a reasonable stop allocation strategy also has certain influence on optimizing the scheduling of the scene guarantee resources and saving the operation cost, so that the problem of stop allocation is one of the keen research contents of broad trainees. The current domestic and foreign research on the problem of gate allocation is quite extensive and can be broadly categorized into three categories, passenger-oriented targets, airport-oriented targets, and airline-oriented targets. The assignment of parking spaces affects the quality of service provided by airlines or airports to passengers, and for outbound passengers they generally want to be able to board the aircraft with a minimum walking distance, while for arriving passengers the passenger satisfaction is expressed as the distance from the gate to the baggage claim, the shorter the distance the better the passenger will be, and most of the passenger-oriented parking space assignment problems are currently aimed at minimizing walking distance/time. For airports and airlines, in addition to the efficient use of the aircraft stand, the cost of ground support operations is also associated with the allocation of the aircraft stand. The process of moving a flight from one stand to another stand by using a tractor is called towing, when the flight is divided into two different stands at different time periods of the same airport, the flight needs to be moved between the two stands through towing operation, which is an expensive operation, so that towing activity is necessary to be reduced as much as possible. In the actual operation process of an airport, in order to maximize the utilization rate of a near airport, flights which have a long station-passing time are usually towed to a far airport for parking, so that towing is a link which has to be considered when the problem of allocation of the airport parking spaces is researched; the flight entering and leaving process and the towing process need to be directed by controllers, but the number of airport controllers is limited, which limits the entering and leaving of the flight and towing and taxiing to a certain extent, and further delays the flight.
Disclosure of Invention
The invention aims to solve the technical problem of providing a parking space allocation multi-objective optimization method based on a resource limited condition, fully considering the actual operation condition of a scene, optimizing a parking space allocation strategy and providing reference for an airport manager to make a decision.
In order to solve the technical problem, the invention provides a multi-objective optimization method for the allocation of stand-offs based on the condition of limited resources, which comprises the following steps:
(1) researching a scene stand allocation process, and determining key resources in the stand allocation process; researching comprehensive performance indexes in the process of parking space allocation, and carrying out detailed analysis on key indexes; constructing an aircraft stop allocation optimization model based on the multi-resource limited condition based on the selected key efficiency index;
(2) solving the aircraft stop allocation optimization model established in the step (1), firstly sequencing flights according to a first-come-first-serve principle, and carrying out initial assignment on the flights; designing a tabu search heuristic algorithm to optimize the initial assignment scheme, and determining required selection method elements of an adaptation value function, a neighborhood structure, a tabu object, a stopping criterion and a candidate solution;
(3) in order to enable the experimental result to be more in line with the actual scene operation, a simulation model is constructed to solve the flight apron delay, and the queuing theory is adopted to solve the flight runway delay;
(4) and (5) applying flight data to carry out example verification, and comparing the experimental result with the actual operation condition of the airport.
Preferably, in the step (1), the comprehensive performance index in the parking space allocation process is researched, and the detailed analysis of the key index specifically comprises the following steps: flight delay is one of key indexes for judging the distribution efficiency of the stand, and means whether a flight can take off according to the appointed time or not, the flight capable of taking off according to the appointed time is a normal flight, otherwise, the flight delay is a delayed flight, the situation of flight delay in the stand distribution process is analyzed, and the flight delay is divided into apron delay and runway delay; and analyzing other key performance indexes including the towing times and the bridge approach rate in the parking space allocation process, and finding out the relationship between the towing times and the bridge approach rate.
Preferably, in the step (1), the step of constructing the parking space allocation optimization model based on the multi-resource limited condition based on the selected key performance index specifically includes the following steps:
(1-1) establishing an objective function based on the selected key performance indexes:
constructing an aircraft parking position optimization objective function by taking the early peak departure aircraft delay time, the bridge approach rate and the towing times as targets:
Figure BDA0003184429320000021
wherein f (y)ik,tit,cic,Wkm) Representing the time delay of an aircraft apron caused by the mutual limiting factors between the airports and the resource constraints of a controller and a tractor, wherein the time delay is obtained through a subsequently constructed apron delay simulation model, and N represents the number of the aircraft;
Figure BDA0003184429320000022
the runway delay time of the aircraft is represented by the following calculation formula:
Figure BDA0003184429320000031
μ＝1/t
Figure BDA0003184429320000032
Figure BDA0003184429320000033
wherein,
Figure BDA0003184429320000034
representing the mean time delay, lambda, of the runway of an aircraftiRepresenting the runway arrival rate for the ith hour of the aircraft,
Figure BDA0003184429320000035
represents the average runway arrival rate in T hours, T is the runway service duration of a single aircraft, and mu is the runway service rate;
x 'in formula (1)'i(X″i、X″′i) The definition of (1) is the station where the flight enters (stops, departs), if the flight stops at the near station, the station is 1, otherwise, the station is 0; y'ik(y″ikY') is a decision variable of 0-1, the aircraft i is 1 when the aircraft i enters a port (parking and departure) and is distributed to a stand k, otherwise, the aircraft i belongs to F; c. CicThe decision variable is 0-1, the controller C is 1 when providing service for the aircraft i, otherwise, the decision variable is 0; t is titThe decision variable is 0-1, the number of the tractors t providing service for the aircraft i is 1, otherwise, the number is 0; wkmFor mutual limiting parameters between stations, wkmE to {0,1}, wherein the machine position k and the machine position m are mutually limited to be 1, and otherwise, the machine position k and the machine position m are 0; n iskTo represent the position parameter of a machine position k, k belongs to G, nkThe element belongs to {0,1}, and represents a near machine position when the element is 1, and represents a far machine position when the element is 0; diRepresenting the number of towing times of the aircraft i; then:
Figure BDA0003184429320000036
Figure BDA0003184429320000037
(1-2) considering the airplane position constraint in the airplane position allocation process, and establishing the airplane position constraint condition comprises the following steps:
(1-2-1) the constraint that each aircraft can only be arranged at one stand per phase is:
Figure BDA0003184429320000038
wherein F is the set of aircraft, F ═ { i | i ═ 1,2, …, n }; g is a stand set, G { k ═ 1,2, …, m };
(1-2-2) the constraint condition that the aircraft in the same stand has at most one subsequent aircraft is as follows:
Figure BDA0003184429320000041
wherein ZijkThe decision variable is 0-1, the aircraft i, j is distributed to the airplane stand k, the aircraft j is 1 when following the aircraft i, and otherwise, the aircraft j is 0, i, j belongs to F;
(1-2-3) the constraint condition that the aircraft in the same stand has at most one forerunner aircraft is as follows:
Figure BDA0003184429320000042
(1-2-4) when the aircraft i is distributed to the airport k, the constraint condition that the driver of the aircraft i and the driver of the airport k are the same is that:
ciyik＝ckyik，i∈F,k∈G
wherein C is a set of controllers, C ∈ { C ═ 1,2, … f }; c. CiFor the owner of the aircraft i, i ∈ F，ciE {1,2,3} corresponds to three different navigation departments respectively;
(1-2-5) when the aircraft i is allocated to the stand k, the constraint condition that the model size of the aircraft i and the size of the stand k are matched is satisfied:
vi≤tk+(1-yik)M
wherein v isiRepresenting the model size of an aircraft i, i ∈ F, viThe epsilon {1,2,3 and 4} corresponds to four types of aircraft models C \ D \ E \ F respectively; t is tkIs the size of a machine position k, k belongs to G and tkThe epsilon {1,2,3 and 4} respectively corresponds to four sizes of machine positions C \ D \ E \ F; m represents a large number;
(1-2-6) the fixed buffer time constraint between two adjacent aircraft allocated to the same stand is as follows:
EAi+(1-Zijk)M≤EDi+o
wherein EAiPlanning the time of arrival of the aircraft i, wherein i belongs to F; eDiI belongs to F as the planned time of the aircraft i to enter the airport;
(1-3) considering the resource constraints of a controller and a tractor in the parking space allocation process, establishing corresponding constraint conditions:
(1-3-1) the constraint that the same tractor can only be occupied by one aircraft at the same time is as follows:
Figure BDA0003184429320000051
wherein t isitThe decision variable is 0-1, when the tractor t provides service for the aircraft i, the decision variable is 1, otherwise, the decision variable is 0;
Figure BDA0003184429320000052
indicating the moment when the aircraft i finishes occupying the controller C;
Figure BDA0003184429320000053
represents the moment at which the aircraft i starts to occupy the controller C;
(1-3-2) represents the constraint that the same tractor can only be occupied by one aircraft at a time:
Figure BDA0003184429320000054
wherein t isitThe decision variable is 0-1, the number of the tractors t providing service for the aircraft i is 1, otherwise, the number is 0;
Figure BDA0003184429320000055
represents the moment when the aircraft i finishes occupying the tractor t;
Figure BDA0003184429320000056
representing the moment at which the aircraft i starts to engage the tractor t.
Preferably, in the step (2), solving the aircraft stand allocation optimization model established in the step (1), firstly ordering the flights according to a first-come-first-serve principle, and initially assigning the flights specifically includes the following steps:
(2-1) sorting the aircrafts in the order of approach time from small arrival;
(2-2) preferentially selecting a near airplane position meeting the constraint condition for the approach aircraft i to stop, and if no idle near airplane position exists, arranging the aircraft to a far airplane position meeting the constraint condition to stop;
(2-3) dividing the aircraft with the station-crossing time of more than 6 hours into the stages of entering, parking and leaving, and respectively arranging the stations;
(2-4) judging whether stop positions are allocated to all aircrafts or not, and if the allocation is finished, obtaining an initial solution; otherwise, repeating the steps (2-3) and (2-4) until all the aircrafts are assigned to the stand.
Preferably, in the step (2), a tabu search heuristic algorithm is designed to optimize the initial assignment scheme, and elements of a selection method of a required adaptation value function, a neighborhood structure, a tabu object, a stopping criterion and a candidate solution are determined, wherein the determination method of each element specifically comprises the following steps:
(a) the fitness value function determines:
selecting a target function as an adaptation value function, and evaluating the state of the candidate solution;
(b) neighborhood structure:
all moves from the current solution are collectively referred to as neighborhoods, which are also the only way to generate a new solution. Neighborhood can be generated by inserting mobile and exchanging mobile search, and a neighborhood solution meeting constraint conditions is searched to form a candidate solution set;
(c) contraindicated subjects:
selecting an aircraft in which a moving operation (an inserting movement or an exchanging movement) occurs each time as a contraindication object;
(d) the selection method of the candidate solution comprises the following steps:
randomly extracting a plurality of fields, and selecting a plurality of fields with the minimum adaptation values as candidate solution sets;
(e) stopping criterion:
given the maximum number of iterations, the algorithm terminates when this number is exceeded.
Preferably, in the step (2), the implementation of the tabu search heuristic algorithm specifically includes the following steps:
(2-5) firstly, judging whether the iteration times are greater than the iteration times given in the stop criterion, if so, terminating the optimization, and outputting the current optimal solution and an adaptive value function; otherwise, turning to (2-6);
(2-6) exchanging the serial numbers of any two flights in the initial solution sequence to obtain a plurality of neighborhood solutions, and selecting P solutions with smaller adaptation values as candidate solutions;
(2-7) judging whether scofflaw criteria are met; recording the minimum adaptation value function in the candidate solution, if the adaptation value function is smaller than the adaptation value function corresponding to the current optimal solution, taking the candidate solution as the optimal solution (scofflaw criterion), replacing the taboo object which enters the taboo list earliest by the corresponding taboo object, and turning to (2-9); otherwise, turning to (2-8);
(2-8) judging the tabu attribute of each object corresponding to the candidate solution, selecting a neighborhood solution with the minimum adaptation value from the non-tabu objects as a current solution set, and replacing the corresponding tabu object with the tabu object which enters a tabu table at the earliest time;
(2-9) adding 1 to the iteration number, and turning to (2-6).
Preferably, in the step (3), in order to make the experimental result more conform to the actual scene operation, a simulation model is constructed to solve the flight apron delay, and the method for solving the flight runway delay by using the queuing theory specifically comprises the following steps:
(3-1) dividing the parking position distribution process into an entrance airplane position applying module, an after-navigation dragging module, an off-field dragging module and an off-field module;
(3-2) constructing an internal logic relationship of each module and a resource sharing mechanism among the modules;
and (3-3) building a apron delay simulation model based on Python to obtain an aircraft apron delay value under a certain parking space allocation strategy, so as to provide a basis for evaluation and optimization of subsequent parking space allocation strategies.
The invention has the beneficial effects that: (1) the invention considers the dragging operation in the parking space distribution process, so that the constructed optimization model is more in line with the actual operation condition of the scene parking space distribution; (2) considering the influence of factors such as controller resources, tractor resources and the like on the model distribution process and the operation process, establishing a parking space distribution multi-objective optimization model based on the resource limited condition; (3) the method comprises the steps that a apron delay simulation model diagram in the process of parking space allocation is constructed, and is used for solving an aircraft apron delay value under a certain parking space allocation strategy and providing an evaluation basis for subsequent parking space optimization; (4) a heuristic intelligent algorithm for solving the problem of stand allocation under the condition of resource limitation is designed, and the constructed stand allocation optimization model is solved.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a schematic view of the aircraft departure procedure of the present invention.
Fig. 3 is a diagram of an aircraft push-out collision within an estuary of the present invention.
Fig. 4 is a schematic diagram of an aircraft runway head queuing model according to the invention.
FIG. 5 is a scatter plot of the departure towing times versus the bridge approach rate of the present invention.
FIG. 6 is a schematic diagram of a simulation model of apron delay according to the present invention.
FIG. 7(a) is a diagram of neighborhood structure interpolation shifts in the tabu search algorithm of the present invention.
FIG. 7(b) is a diagram of the neighborhood structure exchange shift in the tabu search algorithm of the present invention.
FIG. 8 is a statistical chart of the amount of incoming aircraft obtained by the tabu search algorithm of the present invention.
Fig. 9 is a diagram of statistical images of the departure aircraft obtained by counting the actual operation conditions of the airport and the tabu search algorithm adopted in the invention.
Fig. 10 is a statistical chart of tractor usage of the present invention.
FIG. 11 is a statistical chart of the controller usage according to the present invention.
Detailed Description
As shown in fig. 1, a multi-objective optimization method for aircraft stand allocation based on resource-constrained conditions includes the following steps:
step 1, researching a scene stand allocation process, and determining key resources in the stand allocation process; researching comprehensive performance indexes in the process of parking space allocation, and carrying out detailed analysis on key indexes; and constructing an aircraft stop allocation optimization model based on the multi-resource limited condition based on the selected key efficiency index.
In the step (1), a scene stand allocation flow is researched, a basic flow of flight operation on the scene in the stand allocation process and key resources in the stand allocation process need to be clarified, links involved in departure push-out of the aircraft are shown in fig. 2, when the aircraft is pushed out from a near stand, a tractor is needed to assist operation until the aircraft nose is adjusted, and meanwhile, the process from gear withdrawing to sliding to a runway of the aircraft needs to be instructed by a controller; when an aircraft departs from a remote airport, the process of taxiing from the airport to the runway requires the direction of a controller. The towing vehicle belongs to special equipment, the cost is high, in addition, the towing vehicle needs specific workers to operate, when the towing vehicle resources are insufficient, and the workload of a controller is high, the aircraft cannot smoothly complete the departure operation, and delay is generated, so that the workload of the towing vehicle and the controller is one of important factors influencing the distribution efficiency of the parking spaces. The mutual restriction of the operation between the stations is also a factor affecting the operation efficiency of the stations, as shown in fig. 3, for example, in a bay type terminal, when a certain aircraft needs to be pushed out from a current near station, due to the mutual restriction between the stations, the pushing out of the aircraft may collide with the sliding in or pushing out of the aircraft in the adjacent station, and in order to avoid the collision, the affected aircraft has to wait in place until the collision is released, thereby causing a delay.
In the step (1), the comprehensive efficiency index in the parking space allocation process is researched, and the detailed analysis of the key index specifically comprises the following steps: flight delay is one of key indexes for judging the distribution efficiency of the stand, and means whether a flight can take off according to the appointed time, the flight which can take off according to the appointed time is a normal flight, otherwise, the flight is delayed, the situation of flight delay in the stand distribution process is analyzed, and the flight delay is divided into apron delay and runway delay, wherein a flight runway head queuing model is shown in fig. 4; other key performance indicators in the parking lot assignment process, including the towing times and the bridge approach rate, are analyzed, and the relationship between the towing times and the bridge approach rate is found out, as shown in fig. 5.
In the step (1), the method for constructing the parking space allocation optimization model based on the multi-resource limited condition comprises the following steps of:
(1-1) establishing an objective function based on the selected key performance indexes:
constructing an aircraft parking position optimization objective function by taking the early peak departure aircraft delay time, the bridge approach rate and the towing times as targets:
Figure BDA0003184429320000081
wherein f (y)ik,tit,cic,Wkm) Representing the time delay of an aircraft apron caused by the mutual limiting factors between the airports and the resource constraints of a controller and a tractor, wherein the time delay is obtained through a subsequently constructed apron delay simulation model, and N represents the number of the aircraft;
Figure BDA0003184429320000082
the runway delay time of the aircraft is represented by the following calculation formula:
Figure BDA0003184429320000083
μ＝1/t
Figure BDA0003184429320000084
Figure BDA0003184429320000085
wherein,
Figure BDA0003184429320000086
representing the mean time delay, lambda, of the runway of an aircraftiRepresenting the runway arrival rate for the ith hour of the aircraft,
Figure BDA0003184429320000087
represents the average runway arrival rate in T hours, T is the runway service duration of a single aircraft, and mu is the runway service rate;
x 'in formula (1)'i(X″i、X″′i) The definition of (1) is the station where the flight enters (stops, departs), if the flight stops at the near station, the station is 1, otherwise, the station is 0; y'ik(y″ikY') is a decision variable of 0-1, the aircraft i is 1 when the aircraft i enters a port (parking and departure) and is distributed to a stand k, otherwise, the aircraft i belongs to F; c. CicThe decision variable is 0-1, the controller C is 1 when providing service for the aircraft i, otherwise, the decision variable is 0; t is titThe decision variable is 0-1, the number of the tractors t providing service for the aircraft i is 1, otherwise, the number is 0; wkmFor mutual limiting parameters between stations, wkmE to {0,1}, wherein the machine position k and the machine position m are mutually limited to be 1, and otherwise, the machine position k and the machine position m are 0; n iskTo represent the position parameter of a machine position k, k belongs to G, nkIs e {0,1} and represents when being 1Is a near machine position, and 0 represents a far machine position; diRepresenting the number of towing times of the aircraft i; then:
Figure BDA0003184429320000091
Figure BDA0003184429320000092
(1-2) considering the airplane position constraint in the airplane position allocation process, and establishing the airplane position constraint condition comprises the following steps:
(1-2-1) the constraint that each aircraft can only be arranged at one stand per phase is:
Figure BDA0003184429320000093
wherein F is the set of aircraft, F ═ { i | i ═ 1,2, …, n }; g is a stand set, G { k ═ 1,2, …, m };
(1-2-2) the constraint condition that the aircraft in the same stand has at most one subsequent aircraft is as follows:
Figure BDA0003184429320000094
wherein ZijkThe decision variable is 0-1, the aircraft i, j is distributed to the airplane stand k, the aircraft j is 1 when following the aircraft i, and otherwise, the aircraft j is 0, i, j belongs to F;
(1-2-3) the constraint condition that the aircraft in the same stand has at most one forerunner aircraft is as follows:
Figure BDA0003184429320000101
(1-2-4) when the aircraft i is distributed to the airport k, the constraint condition that the driver of the aircraft i and the driver of the airport k are the same is that:
ciyik＝ckyik，i∈F,k∈G
wherein C is a set of controllers, C ∈ { C ═ 1,2, … f }; c. CiIs the affiliated navigation department of the aircraft i, i belongs to F, ciE {1,2,3} corresponds to three different navigation departments respectively;
(1-2-5) when the aircraft i is allocated to the stand k, the constraint condition that the model size of the aircraft i and the size of the stand k are matched is satisfied:
vi≤tk+(1-yik)M
wherein v isiRepresenting the model size of an aircraft i, i ∈ F, viThe epsilon {1,2,3 and 4} corresponds to four types of aircraft models C \ D \ E \ F respectively; t is tkIs the size of a machine position k, k belongs to G and tkThe epsilon {1,2,3 and 4} respectively corresponds to four sizes of machine positions C \ D \ E \ F; m represents a large number;
(1-2-6) the fixed buffer time constraint between two adjacent aircraft allocated to the same stand is as follows:
EAi+(1-Zijk)M≤EDi+o
wherein EAiPlanning the time of arrival of the aircraft i, wherein i belongs to F; eDiI belongs to F as the planned time of the aircraft i to enter the airport;
(1-3) considering the resource constraints of a controller and a tractor in the parking space allocation process, establishing corresponding constraint conditions:
(1-3-1) the constraint that the same tractor can only be occupied by one aircraft at the same time is as follows:
Figure BDA0003184429320000102
wherein t isitThe decision variable is 0-1, when the tractor t provides service for the aircraft i, the decision variable is 1, otherwise, the decision variable is 0;
Figure BDA0003184429320000103
indicating the moment when the aircraft i finishes occupying the controller c;
Figure BDA0003184429320000104
represents the moment at which the aircraft i starts to occupy the controller c;
(1-3-2) represents the constraint that the same tractor can only be occupied by one aircraft at a time:
Figure BDA0003184429320000111
wherein t isitThe decision variable is 0-1, the number of the tractors t providing service for the aircraft i is 1, otherwise, the number is 0;
Figure BDA0003184429320000112
represents the moment when the aircraft i finishes occupying the tractor t;
Figure BDA0003184429320000113
representing the moment at which the aircraft i starts to engage the tractor t.
Step 2, solving the aircraft stop allocation optimization model established in the step 1, firstly sequencing flights according to a first-come-first-serve principle, and carrying out initial assignment on the flights; then, a tabu search heuristic algorithm is designed to optimize the initial assignment scheme, and required selection method elements of an adaptation value function, a neighborhood structure, a tabu object, a stopping criterion and a candidate solution are determined.
In the step (2), solving the aircraft stand allocation optimization model established in the step (1), firstly ordering flights according to the principle of 'first come first serve', and then initially assigning the flights specifically comprises the following steps:
(2-1) sorting the aircrafts in the order of approach time from small arrival;
(2-2) preferentially selecting a near airplane position meeting the constraint condition for the approach aircraft i to stop, and if no idle near airplane position exists, arranging the aircraft to a far airplane position meeting the constraint condition to stop;
(2-3) dividing the aircraft with the station-crossing time of more than 6 hours into the stages of entering, parking and leaving, and respectively arranging the stations;
(2-4) judging whether stop positions are allocated to all aircrafts or not, and if the allocation is finished, obtaining an initial solution; otherwise, repeating the steps (2-3) and (2-4) until all the aircrafts are assigned to the stand.
In step (2), a tabu search heuristic algorithm is designed to optimize the initial assignment scheme, and selection method elements of a required adaptation value function, a neighborhood structure, a tabu object, a stop criterion and a candidate solution are determined, wherein the determination method of each element specifically comprises the following steps:
(a) the fitness value function determines:
selecting a target function as an adaptation value function, and evaluating the state of the candidate solution;
(b) neighborhood structure:
all moves from the current solution are collectively referred to as neighborhoods, which are also the only way to generate a new solution. Neighborhood may be generated by search for insert and exchange moves, which are shown in fig. 7(a) and 7(b), finding neighborhood solutions satisfying constraints to form a candidate solution set;
(c) contraindicated subjects:
selecting an aircraft in which a moving operation (an inserting movement or an exchanging movement) occurs each time as a contraindication object;
(d) the selection method of the candidate solution comprises the following steps:
randomly extracting a plurality of fields, and selecting a plurality of fields with the minimum adaptation values as candidate solution sets;
(e) stopping criterion:
given the maximum number of iterations, the algorithm terminates when this number is exceeded.
The implementation of the taboo search heuristic algorithm specifically comprises the following steps:
(2-5) firstly, judging whether the iteration times are greater than the iteration times given in the stop criterion, if so, terminating the optimization, and outputting the current optimal solution and an adaptive value function; otherwise, turning to (2-6);
(2-6) exchanging the serial numbers of any two flights in the initial solution sequence to obtain a plurality of neighborhood solutions, and selecting P solutions with smaller adaptation values as candidate solutions;
(2-7) determining whether scofflaw criteria are satisfied. Recording the minimum adaptation value function in the candidate solution, if the adaptation value function is smaller than the adaptation value function corresponding to the current optimal solution, taking the candidate solution as the optimal solution (scofflaw criterion), replacing the taboo object which enters the taboo list earliest by the corresponding taboo object, and turning to (2-9); otherwise, turning to (2-8);
(2-8) judging the tabu attribute of each object corresponding to the candidate solution, selecting a neighborhood solution with the minimum adaptation value from the non-tabu objects as a current solution set, and replacing the corresponding tabu object with the tabu object which enters a tabu table at the earliest time;
(2-9) adding 1 to the iteration number, and turning to (2-6).
Step 3, in order to enable the experimental result to be more in line with the actual scene operation, a simulation model is constructed to solve the flight apron delay, and the method for solving the flight runway delay by adopting the queuing theory specifically comprises the following steps:
(3-1) dividing the parking position distribution process into an entrance airplane position applying module, an after-navigation dragging module, an off-field dragging module and an off-field module;
(3-2) constructing an internal logic relationship of each module and a resource sharing mechanism among the modules;
(3-3) building a apron delay simulation model based on Python to obtain an aircraft apron delay value under a certain parking space allocation strategy, so as to provide a basis for evaluation and optimization of subsequent parking space allocation strategies, wherein the specific apron delay simulation model is shown in FIG. 6. Wherein the pseudo code for solving the delay time of departure of the aircraft in the departure module is shown as an algorithm 1, LkrRepresenting the undisturbed taxi time from the k-station to the r-runway, and dt represents the time for the aircraft to be pushed out of the station using the tractor when approaching the station.
Figure BDA0003184429320000121
Figure BDA0003184429320000131
And 4, selecting aircraft operation data from 12 hours on a typical day of 11 months to 12 hours on the next day (24 hours in total) of Beijing Daxing International airport 2019 for analysis, wherein the number of the aircraft involved is 370, the number of the aircraft involved in the next day of the morning peak departure is 114, and part of aircraft information is shown in Table 1. The current stage of the great airport, the airport positions, the runway and the scene related resource use conditions are shown in the table 2. The maximum iteration times of the experimental parameters are set to be 2000, the size of the candidate set is 100, and the taboo length is 15.
TABLE 1 partial aircraft information Table
Figure BDA0003184429320000132
Figure BDA0003184429320000141
TABLE 2 Daxing airport gate positions, runways and related resource statistics
Figure BDA0003184429320000142
TABLE 3 statistical table of actual operation conditions and experimental results
Figure BDA0003184429320000143
The experimental results obtained by performing the above-described aircraft planning are shown in fig. 8, 9, 10, and 11. In table 3, the number of minutes of delay of a scene, the number of towing times, and the number of bridges, which are obtained by counting the actual operation condition of the airport in the early peak time period, are recorded, the data source is the scene operation data in table 1, the data is the statistical result of an a-CDM system in the airport, corresponding parking spaces are allocated to flights based on the first-come first-serve and model, driver and other matching principles, the limitation of resources such as tractors, controllers and the like is not considered, wherein the scene delay value covers the airport delay value and the runway delay value, the calculation formula is formula (13), DstaIndicating actual operation of an airportTime of minutes delay of scene, Eari_DiRepresenting the actual departure time of a flight in the course of the actual operation of the airport, Aair_DiRepresenting the scheduled departure time of the surface flight during the actual operation of the airport:
Dsta＝Eari_Di-Aair_Di (13)
the statistics of the experimental results comprise statistics of initial solutions and statistics of heuristic algorithms, and specifically comprise objective function values, scene delay minutes (apron delay/runway delay), towing times and bridge approach rates, wherein the bridge approach rate is calculated by dividing the bridge approach rate by the total number of departing flights in a statistical time period. As can be seen from table 3, the field delay in the initial solution is 180 minutes, the towing times is 16, the number of the bridges is 72, and the actual operation condition of the airport is relatively close, and the field delay is 139 minutes after the heuristic algorithm provided by the present invention is adopted, which is reduced by 26.1% compared with the actual operation condition of the airport; meanwhile, under the condition that the towing times are basically unchanged, the bridge approach rate (approaching the airport) of the aircraft is 70.2%, and the bridge approach rate is increased by 5.3%; the objective function is 236, which is better than the objective function 278 obtained for actual airport operation. Comparing the initial solution statistical result with the heuristic statistical result, the scene delay is obviously reduced, because the heuristic algorithm is adopted, the optimal stop allocation strategy enables the dispatching of the resources such as the tractor, the controller and the like to be more reasonable, and the flight delay caused by the resource limitation is relieved. In conclusion, the parking space allocation strategy provided by the invention can well reduce scene delay and improve the bridge approach rate of the aircraft.

Claims (7)

1. A multi-objective optimization method for aircraft stand allocation based on resource limitation condition is characterized by comprising the following steps:
(1) researching a scene stand allocation process, and determining key resources in the stand allocation process; researching comprehensive performance indexes in the process of parking space allocation, and carrying out detailed analysis on key indexes; constructing an aircraft stop allocation optimization model based on the multi-resource limited condition based on the selected key efficiency index;
(2) solving the aircraft stop allocation optimization model established in the step (1), firstly sequencing flights according to a first-come-first-serve principle, and carrying out initial assignment on the flights; designing a tabu search heuristic algorithm to optimize the initial assignment scheme, and determining required selection method elements of an adaptation value function, a neighborhood structure, a tabu object, a stopping criterion and a candidate solution;
(3) in order to enable the experimental result to be more in line with the actual scene operation, a simulation model is constructed to solve the flight apron delay, and the queuing theory is adopted to solve the flight runway delay;
(4) and (5) applying flight data to carry out example verification, and comparing the experimental result with the actual operation condition of the airport.
2. The multi-objective optimization method for aircraft stand allocation based on resource-constrained conditions as claimed in claim 1, wherein in the step (1), the comprehensive performance index in the aircraft stand allocation process is studied, and the detailed analysis of the key indexes is specifically as follows: flight delay is one of key indexes for judging the distribution efficiency of the stand, and means whether a flight can take off according to the appointed time or not, the flight capable of taking off according to the appointed time is a normal flight, otherwise, the flight delay is a delayed flight, the situation of flight delay in the stand distribution process is analyzed, and the flight delay is divided into apron delay and runway delay; and analyzing other key performance indexes including the towing times and the bridge approach rate in the parking space allocation process, and finding out the relationship between the towing times and the bridge approach rate.
3. The multi-objective optimization method for aircraft stand allocation under the resource-constrained condition as claimed in claim 1, wherein in the step (1), the step of constructing the aircraft stand allocation optimization model under the multi-resource-constrained condition based on the selected key performance index specifically comprises the following steps:
(1-1) establishing an objective function based on the selected key performance indexes:
constructing an aircraft parking position optimization objective function by taking the early peak departure aircraft delay time, the bridge approach rate and the towing times as targets:
Figure FDA0003184429310000011
wherein f (y)ik,tit,cic,wkm) Representing the time delay of an aircraft apron caused by the mutual limiting factors between the airports and the resource constraints of a controller and a tractor, wherein the time delay is obtained through a subsequently constructed apron delay simulation model, and N represents the number of the aircraft;
Figure FDA0003184429310000021
the runway delay time of the aircraft is represented by the following calculation formula:
Figure FDA0003184429310000022
μ＝1/t
Figure FDA0003184429310000023
Figure FDA0003184429310000024
wherein,
Figure FDA0003184429310000025
representing the mean time delay, lambda, of the runway of an aircraftiRepresenting the runway arrival rate for the ith hour of the aircraft,
Figure FDA0003184429310000026
represents the average runway arrival rate in T hours, T is the runway service duration of a single aircraft, and mu is the runway service rate;
x 'in formula (1)'i(X″i、X″′i) Is defined as flight entranceThe berth of harbor (parking, leaving) is 1 if the berth is near, otherwise is 0; y'ik(y″ikY') is a decision variable of 0-1, the aircraft i is 1 when the aircraft i enters a port (parking and departure) and is distributed to a stand k, otherwise, the aircraft i belongs to F; c. CicThe decision variable is 0-1, the controller C is 1 when providing service for the aircraft i, otherwise, the decision variable is 0; t is titThe decision variable is 0-1, the number of the tractors t providing service for the aircraft i is 1, otherwise, the number is 0; wkmFor mutual limiting parameters between stations, wkmE to {0,1}, wherein the machine position k and the machine position m are mutually limited to be 1, and otherwise, the machine position k and the machine position m are 0; n iskTo represent the position parameter of a machine position k, k belongs to G, nkThe element belongs to {0,1}, and represents a near machine position when the element is 1, and represents a far machine position when the element is 0; diRepresenting the number of towing times of the aircraft i; then:
Figure FDA0003184429310000027
Figure FDA0003184429310000028
(1-2) considering the airplane position constraint in the airplane position allocation process, and establishing the airplane position constraint condition comprises the following steps:
(1-2-1) the constraint that each aircraft can only be arranged at one stand per phase is:
Figure FDA0003184429310000031
wherein F is the set of aircraft, F ═ { i | i ═ 1,2, …, n }; g is a stand set, G { k ═ 1,2, …, m };
(1-2-2) the constraint condition that the aircraft in the same stand has at most one subsequent aircraft is as follows:
Figure FDA0003184429310000032
wherein ZijkThe decision variable is 0-1, the aircraft i, j is distributed to the airplane stand k, the aircraft j is 1 when following the aircraft i, and otherwise, the aircraft j is 0, i, j belongs to F;
(1-2-3) the constraint condition that the aircraft in the same stand has at most one forerunner aircraft is as follows:
Figure FDA0003184429310000033
(1-2-4) when the aircraft i is distributed to the airport k, the constraint condition that the driver of the aircraft i and the driver of the airport k are the same is that:
ciyik＝ckyik，i∈F,k∈G
wherein C is a set of controllers, C ∈ { C ═ 1,2, … f }; c. CiIs the affiliated navigation department of the aircraft i, i belongs to F, ciE {1,2,3} corresponds to three different navigation departments respectively;
(1-2-5) when the aircraft i is allocated to the stand k, the constraint condition that the model size of the aircraft i and the size of the stand k are matched is satisfied:
vi≤tk+(1-yik)M
wherein v isiRepresenting the model size of an aircraft i, i ∈ F, viThe epsilon {1,2,3 and 4} corresponds to four types of aircraft models C \ D \ E \ F respectively; t is tkIs the size of a machine position k, k belongs to G and tkThe epsilon {1,2,3 and 4} respectively corresponds to four sizes of machine positions C \ D \ E \ F; m represents a large number;
(1-2-6) the fixed buffer time constraint between two adjacent aircraft allocated to the same stand is as follows:
EAi+(1-Zijk)M≤EDi+o
wherein EAiPlanning the time of arrival of the aircraft i, wherein i belongs to F; eDiI belongs to F as the planned time of the aircraft i to enter the airport;
(1-3) considering the resource constraints of a controller and a tractor in the parking space allocation process, establishing corresponding constraint conditions:
(1-3-1) the constraint that the same tractor can only be occupied by one aircraft at the same time is as follows:
Figure FDA0003184429310000041
wherein t isitThe decision variable is 0-1, when the tractor t provides service for the aircraft i, the decision variable is 1, otherwise, the decision variable is 0;
Figure FDA0003184429310000042
indicating the moment when the aircraft i finishes occupying the controller C;
Figure FDA0003184429310000043
represents the moment at which the aircraft i starts to occupy the controller C;
(1-3-2) represents the constraint that the same tractor can only be occupied by one aircraft at a time:
Figure FDA0003184429310000044
wherein t isitThe decision variable is 0-1, the number of the tractors t providing service for the aircraft i is 1, otherwise, the number is 0;
Figure FDA0003184429310000045
represents the moment when the aircraft i finishes occupying the tractor t;
Figure FDA0003184429310000046
representing the moment at which the aircraft i starts to engage the tractor t.
4. The multi-objective aircraft stand allocation optimization method based on resource limitation conditions as claimed in claim 1, wherein in the step (2), solving the aircraft stand allocation optimization model established in the step (1), firstly ordering flights according to the principle of first-come-first-serve, and initially assigning the flights specifically comprises the following steps:
(2-1) sorting the aircrafts in the order of approach time from small arrival;
(2-2) preferentially selecting a near airplane position meeting the constraint condition for the approach aircraft i to stop, and if no idle near airplane position exists, arranging the aircraft to a far airplane position meeting the constraint condition to stop;
(2-3) dividing the aircraft with the station-crossing time of more than 6 hours into the stages of entering, parking and leaving, and respectively arranging the stations;
(2-4) judging whether stop positions are allocated to all aircrafts or not, and if the allocation is finished, obtaining an initial solution; otherwise, repeating the steps (2-3) and (2-4) until all the aircrafts are assigned to the stand.
5. The multi-objective optimization method for parking space allocation based on resource-constrained conditions as claimed in claim 1, wherein in step (2), a tabu search heuristic algorithm is designed to optimize the initial assignment scheme, and select method elements of the required adaptation value function, neighborhood structure, tabu object, stopping criterion, and candidate solution are determined, and the determination method for each element specifically comprises:
(a) the fitness value function determines:
selecting a target function as an adaptation value function, and evaluating the state of the candidate solution;
(b) neighborhood structure:
all the movements performed from the current solution are collectively called neighborhoods, the neighborhood movement is also the only way for generating a new solution, the neighborhoods can be generated by inserting movement and exchanging movement search, and the neighborhood solutions meeting the constraint conditions are searched to form a candidate solution set;
(c) contraindicated subjects:
selecting an aircraft which is subjected to moving operation each time as a contraindication object;
(d) the selection method of the candidate solution comprises the following steps:
randomly extracting a plurality of fields, and selecting a plurality of fields with the minimum adaptation values as candidate solution sets;
(e) stopping criterion:
given the maximum number of iterations, the algorithm terminates when this number is exceeded.
6. The multi-objective optimization method for stand allocation under the resource-constrained condition as claimed in claim 1, wherein in the step (2), the implementation of the tabu search heuristic algorithm specifically comprises the following steps:
(2-5) firstly, judging whether the iteration times are greater than the iteration times given in the stop criterion, if so, terminating the optimization, and outputting the current optimal solution and an adaptive value function; otherwise, turning to (2-6);
(2-6) exchanging the serial numbers of any two flights in the initial solution sequence to obtain a plurality of neighborhood solutions, and selecting P solutions with smaller adaptation values as candidate solutions;
(2-7) judging whether scofflaw criteria are met; recording the minimum adaptation value function in the candidate solution, if the adaptation value function is smaller than the adaptation value function corresponding to the current optimal solution, taking the candidate solution as the optimal solution, replacing the taboo object which enters the taboo table at the earliest time with the corresponding taboo object, and turning to (2-9); otherwise, turning to (2-8);
(2-8) judging the tabu attribute of each object corresponding to the candidate solution, selecting a neighborhood solution with the minimum adaptation value from the non-tabu objects as a current solution set, and replacing the corresponding tabu object with the tabu object which enters a tabu table at the earliest time;
(2-9) adding 1 to the iteration number, and turning to (2-6).
7. The multi-objective optimization method for aircraft stand allocation under the resource-constrained condition as claimed in claim 1, wherein in the step (3), in order to make the experimental result more conform to the actual scene operation, a simulation model is constructed to solve flight apron delays, and the method for solving flight runway delays by using the queuing theory specifically comprises the following steps:
(3-1) dividing the parking position distribution process into an entrance airplane position applying module, an after-navigation dragging module, an off-field dragging module and an off-field module;
(3-2) constructing an internal logic relationship of each module and a resource sharing mechanism among the modules;
and (3-3) building a apron delay simulation model based on Python to obtain an aircraft apron delay value under a certain parking space allocation strategy, so as to provide a basis for evaluation and optimization of subsequent parking space allocation strategies.
CN202110856814.5A 2021-07-28 2021-07-28 Multi-objective stand allocation optimization method based on resource limitation condition Active CN113570247B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110856814.5A CN113570247B (en) 2021-07-28 2021-07-28 Multi-objective stand allocation optimization method based on resource limitation condition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110856814.5A CN113570247B (en) 2021-07-28 2021-07-28 Multi-objective stand allocation optimization method based on resource limitation condition

Publications (2)

Publication Number Publication Date
CN113570247A true CN113570247A (en) 2021-10-29
CN113570247B CN113570247B (en) 2024-07-23

Family

ID=78168400

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110856814.5A Active CN113570247B (en) 2021-07-28 2021-07-28 Multi-objective stand allocation optimization method based on resource limitation condition

Country Status (1)

Country Link
CN (1) CN113570247B (en)

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114358446A (en) * 2022-03-21 2022-04-15 北京航空航天大学 Robust optimization method for airport resource scheduling
CN114648895A (en) * 2022-05-12 2022-06-21 苏州数智源信息技术有限公司 Airport apron safety early warning method and system capable of reducing false alarm rate
CN115049100A (en) * 2022-04-24 2022-09-13 四川大学 Airport resource allocation optimization method, device, equipment and readable storage medium
CN115115097A (en) * 2022-06-10 2022-09-27 中国民航大学 Combined optimization method for airport parking space and aircraft sliding path
CN115187093A (en) * 2022-07-04 2022-10-14 四川大学 Airport scene operation optimization method, device, equipment and readable storage medium
CN115239026A (en) * 2022-09-22 2022-10-25 珠海翔翼航空技术有限公司 Method, system, device and medium for optimizing parking space allocation
CN116168566A (en) * 2023-02-15 2023-05-26 南京航空航天大学 Combined scheduling method, device and equipment for aircraft scene and storage medium
CN116993137A (en) * 2023-09-28 2023-11-03 民航成都信息技术有限公司 Method and device for determining stand, electronic equipment and medium
CN117077981A (en) * 2023-10-16 2023-11-17 四川大学 Method and device for distributing stand by fusing neighborhood search variation and differential evolution
CN117725662A (en) * 2024-02-07 2024-03-19 青岛瑞源工程集团有限公司 Engineering construction simulation method and system based on municipal engineering
CN117933490A (en) * 2024-03-14 2024-04-26 中国民航大学 Airport scene dragging scheduling optimization method, electronic equipment and storage medium
CN118195101A (en) * 2024-05-13 2024-06-14 中国民航大学 Multi-agent-based machine position distribution method, electronic equipment and storage medium

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160203722A1 (en) * 2015-01-09 2016-07-14 General Electric Company Method and system for robust network planning optimization of airline flight operations
CN107230392A (en) * 2017-06-08 2017-10-03 大连交通大学 Optimizing distribution method based on the hub aircraft gate for improving ACO algorithms
CN109859532A (en) * 2019-02-28 2019-06-07 深圳市北斗智能科技有限公司 A kind of the break indices method and relevant apparatus of multi-constraint condition
CN111563636A (en) * 2020-07-13 2020-08-21 北京富通东方科技有限公司 Three-stage meta-heuristic parking space allocation optimization method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160203722A1 (en) * 2015-01-09 2016-07-14 General Electric Company Method and system for robust network planning optimization of airline flight operations
CN107230392A (en) * 2017-06-08 2017-10-03 大连交通大学 Optimizing distribution method based on the hub aircraft gate for improving ACO algorithms
CN109859532A (en) * 2019-02-28 2019-06-07 深圳市北斗智能科技有限公司 A kind of the break indices method and relevant apparatus of multi-constraint condition
CN111563636A (en) * 2020-07-13 2020-08-21 北京富通东方科技有限公司 Three-stage meta-heuristic parking space allocation optimization method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
刘君强;雷凡;王英杰;左洪福;: "基于航班延误的机场滑行道停机位分配模型研究", 武汉理工大学学报(交通科学与工程版), no. 04 *
袁媛;翟好鑫;吴宏元;闫萍;: "民用机场停机位分配问题研究综述", 沈阳航空航天大学学报, no. 06 *

Cited By (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114358446A (en) * 2022-03-21 2022-04-15 北京航空航天大学 Robust optimization method for airport resource scheduling
CN114358446B (en) * 2022-03-21 2022-05-27 北京航空航天大学 Robust optimization method for airport resource scheduling
CN115049100A (en) * 2022-04-24 2022-09-13 四川大学 Airport resource allocation optimization method, device, equipment and readable storage medium
CN114648895A (en) * 2022-05-12 2022-06-21 苏州数智源信息技术有限公司 Airport apron safety early warning method and system capable of reducing false alarm rate
CN115115097A (en) * 2022-06-10 2022-09-27 中国民航大学 Combined optimization method for airport parking space and aircraft sliding path
CN115187093A (en) * 2022-07-04 2022-10-14 四川大学 Airport scene operation optimization method, device, equipment and readable storage medium
CN115187093B (en) * 2022-07-04 2023-12-15 四川大学 Airport scene operation optimization method, device, equipment and readable storage medium
CN115239026A (en) * 2022-09-22 2022-10-25 珠海翔翼航空技术有限公司 Method, system, device and medium for optimizing parking space allocation
CN115239026B (en) * 2022-09-22 2022-12-20 珠海翔翼航空技术有限公司 Method, system, device and medium for optimizing parking space allocation
CN116168566B (en) * 2023-02-15 2023-11-17 南京航空航天大学 Combined scheduling method, device and equipment for aircraft scene and storage medium
CN116168566A (en) * 2023-02-15 2023-05-26 南京航空航天大学 Combined scheduling method, device and equipment for aircraft scene and storage medium
CN116993137A (en) * 2023-09-28 2023-11-03 民航成都信息技术有限公司 Method and device for determining stand, electronic equipment and medium
CN116993137B (en) * 2023-09-28 2023-12-05 民航成都信息技术有限公司 Method and device for determining stand, electronic equipment and medium
CN117077981A (en) * 2023-10-16 2023-11-17 四川大学 Method and device for distributing stand by fusing neighborhood search variation and differential evolution
CN117077981B (en) * 2023-10-16 2024-02-02 四川大学 Method and device for distributing stand by fusing neighborhood search variation and differential evolution
CN117725662A (en) * 2024-02-07 2024-03-19 青岛瑞源工程集团有限公司 Engineering construction simulation method and system based on municipal engineering
CN117725662B (en) * 2024-02-07 2024-04-26 青岛瑞源工程集团有限公司 Engineering construction simulation method and system based on municipal engineering
CN117933490A (en) * 2024-03-14 2024-04-26 中国民航大学 Airport scene dragging scheduling optimization method, electronic equipment and storage medium
CN118195101A (en) * 2024-05-13 2024-06-14 中国民航大学 Multi-agent-based machine position distribution method, electronic equipment and storage medium
CN118195101B (en) * 2024-05-13 2024-07-19 中国民航大学 Multi-agent-based machine position distribution method, electronic equipment and storage medium

Also Published As

Publication number Publication date
CN113570247B (en) 2024-07-23

Similar Documents

Publication Publication Date Title
CN113570247A (en) Multi-objective optimization method for distribution of stand-to-halt positions based on resource limited condition
Yu et al. An adaptive large neighborhood search heuristic for solving a robust gate assignment problem
CN111915046B (en) Method and device for outputting information
Filar et al. How airlines and airports recover from schedule perturbations: a survey
Soltani et al. An eco-friendly aircraft taxiing approach with collision and conflict avoidance
CN109726917B (en) Freight flight scheduling method and device based on four-dimensional track
CN109741638B (en) Integrated collaborative operation method for entering and leaving field management system
Manasra et al. Optimization-based operations control for public transportation service with transfers
CN114358446B (en) Robust optimization method for airport resource scheduling
CN114132363A (en) Train operation diagram compiling method based on passenger flow space-time state fine analysis
CN112562377A (en) Passenger vehicle real-time scheduling method based on random opportunity constraint
van Oosterom et al. Dispatching a fleet of electric towing vehicles for aircraft taxiing with conflict avoidance and efficient battery charging
Petrouš et al. Modeling of passenger demand using mixture of Poisson components
Wang et al. Enabling In‐Depot Automated Routing and Recharging Scheduling for Automated Electric Bus Transit Systems
Gołda et al. Elements of the model positioning of aircraft on the apron
CN113095543B (en) Distribution method and system for airport stand and taxiway
CN113034060B (en) Matching method of flight ferry vehicle
Liu et al. Modeling and heuristic algorithm of ground ferry vehicle scheduling in large airports
CN114819510A (en) Multi-objective flight time optimization method
Bubalo et al. Reducing airport emissions with coordinated pushback processes: A case study
Liu et al. A branch-and-price algorithm for the airport gate assignment problem considering the trade-off between robustness and efficiency
Hsu et al. Flight-delay propagation, allowing for behavioural response
Zhang et al. Dynamic optimal scheduling model of multi runway flight arrival and departure based on improved genetic algorithm
Davydov et al. Optimal rescheduling for the mixed passenger and freight line
CN110334723A (en) A kind of pair of increased luggage of dynamic fetches and delivers the luggage that order is scheduled and fetches and delivers dispatching method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant