A luggage dolly optimization method for reducing airport operating costs

Abstract

The present application relates to the technical field of airport ground vehicle scheduling, and discloses a luggage trolley optimization method for reducing airport operation cost. In view of the heavy workload faced by the luggage trolley in large airports under high passenger flow conditions, which may lead to cargo transportation delay and potential operation conflict, the present application comprehensively considers the dynamic scheduling of the luggage trolley and the overall coordination of the luggage trolley. The luggage trolley optimization method for reducing airport operation cost comprises the following steps: basic data input, basic data processing, path optimization solving, and result visualization output. Compared with the prior art, the present application can effectively improve the scheduling efficiency of large-scale airport luggage trolley, realize cost reduction and timeliness of passenger service, meet the demand of airport daily luggage transportation, and provide a scientific basis for the optimization of airport luggage trolley scheduling.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of airport ground service vehicle scheduling, and in particular to a luggage towboard vehicle optimization method for reducing airport operation cost. BACKGROUND

[0002] With the continuous growth of air travel demand, airports around the world have become more busy and crowded, resulting in serious flight delays and economic losses. Airport ground service, as a key link in the operation of the entire airport system, its operation efficiency directly determines the efficiency of flight circulation. However, the ground service of many airports is complex and lacks overall optimization plan, resulting in low efficiency of cargo loading and unloading and high operation cost of ground service vehicles. At present, a feasible strategy is to use flight schedule data to improve the efficiency of airport ground service and achieve scientific cargo loading and unloading service.

[0003] At present, there is little research on the optimal scheduling of airport ground service vehicles. Most researches are based on the classical vehicle routing problem and time window model (VRPTW) to construct a mathematical programming model. However, the traditional method takes too long to solve, and can only be optimized by heuristic algorithm. The ground service vehicle scheduling optimization problem can be naturally expressed as VRP, but the problem scale is large, resulting in slow solving speed. Therefore, the research on ground service vehicle scheduling mostly focuses on constructing scheduling optimization model through VRP and developing heuristic algorithm to optimize the solution, and few researches start from the perspective of space-time network to construct scheduling optimization model.

[0004] Therefore, the present application provides a luggage towboard vehicle optimization method for reducing airport operation cost, which can dynamically meet the scheduling demand of ground service vehicles, thereby improving the efficiency of ground service and reducing operation cost. SUMMARY

[0005] The present application provides a luggage towboard vehicle optimization method for reducing airport operation cost, which aims to allocate ground service for aircraft loading and unloading tasks and output ground service vehicle scheduling path.

[0006] The object of the present application can be achieved by the following technical solutions:

[0007] The luggage towboard vehicle optimization method for reducing airport operation cost provided by the present application comprises the following steps:

[0008] S1, basic data input:

[0009] a1, reading airport basic data;

[0010] a2, initializing the number of luggage tow trucks and the number of luggage towboards of each garage;

[0011] S2, basic data processing:

[0012] b1, identify potential conflicts in the cargo transport task;

[0013] b2, generate an aircraft cargo transport schedule;

[0014] b3, construct an airport cargo transport space-time network;

[0015] b4, generate a set of driving arcs and a set of static arcs;

[0016] S3, path optimization solution:

[0017] Construct a mixed integer linear programming model for airport baggage trolley scheduling optimization based on space-time network model, the optimization goal is to minimize the operation cost of baggage trolley and the delay penalty of cargo transport task, and output the optimal solution, including baggage trolley service path and the number of baggage trolleys and trolleys allocated to each path;

[0018] S4, result visualization output:

[0019] Display the optimized baggage trolley service path in the airport space-time network diagram, and mark the paths that require more baggage trolleys.

[0020] Preferably, in step S1, the airport basic data to be read includes terminal location information, apron location information, baggage trolley garage location information, airport flight service schedule, the amount of cargo transported with each flight, the departure-destination of the cargo transport task, the allowed loading time and the expected and the latest end time of the cargo transport task.

[0021] Preferably, in step S2, the basic data processing specifically includes:

[0022] Identify potential conflicts in the cargo transport task: when the baggage trolley performs the task, there is a theoretical shortest transport time from the starting point to the destination. If the completion time specified by the cargo transport task is lower than this shortest transport time, the task is considered unfeasible in actual operation;

[0023] Generate an aircraft cargo transport schedule and anchor the start-end time of each task: generate an aircraft cargo transport schedule between the origin and destination of the cargo transport task according to the allowed loading time, expected end time and latest end time of the cargo transport task, to prepare for subsequent path optimization selection;

[0024] Construct an airport cargo transport space-time network: according to the airport basic data, construct a specific cluster of parking spaces into an apron node, and the aircraft capacity of each apron node is the number of parking spaces, while including the terminal node and the baggage trolley garage node, to construct an airport cargo transport space-time network;

[0025] Generating the set of travel arcs and the set of stationary arcs: stationary arcs represent the process of the luggage tug standing still, each stationary arc is vertically upward, spanning one time step, the set of stationary arcs is generated by using double for loop, travel arcs represent the movement of the luggage tug and the luggage between the stand nodes and the terminal nodes, each travel arc points to the oblique upper side, the time span is related to the distance between nodes, the set of travel arcs is generated by using double for loop with judgment.

[0026] Preferably, in the step S3 of solving the path optimization, the objective function of the path optimization model comprises:

[0027] The mixed integer linear programming model of airport luggage tug scheduling optimization based on the space-time network model is constructed, and the optimization objective is to minimize the operation cost of the ground service vehicle and the delay penalty of the freight task:

[0028]

[0029]

[0030]

[0031]

[0032] wherein, represents the cost of the luggage tug traveling per unit time on the travel arc a, represents the cost of the luggage tug traveling per unit time on the travel arc a when dragging a single luggage board, represents the cost of the luggage tug traveling per unit time on the travel arc a when dragging a unit luggage package;

[0033] represents the cost of the luggage tug staying per unit time on the stationary arc a, represents the cost of the luggage board staying per unit time on the stationary arc a;

[0034] represents the delay cost of task j on the travel arc a, if the end of the travel arc a is after the expected arrival time of task j, , otherwise .

[0035] Preferably, in the step S3 of solving the path optimization, the constraint condition of the path optimization model comprises:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] where, is the set of garages (designated storage points for luggage trailers and luggage flatbeds),

[0043] is the set of terminals;

[0044] is the set of tarmacs;

[0045] is the union of the two arcs (for simplicity, the working arc can be ignored, assuming that loading and unloading is done very quickly);

[0046] is the set of stationary arcs;

[0047] is the set of driving arcs;

[0048] is the set of loading and unloading tasks;

[0049] is the number of packages formed by task j (each package holds exactly one flatbed);

[0050] is the set of departure and arrival points for task j, and denotes loading; and denotes unloading;

[0051] is the earliest start time (allowing for loading), the expected end time (exceeding which results in a delay penalty), and the latest end time (exceeding which is not acceptable) for task j, and, , and should exceed the time taken to cross the driving arc between the two locations;

[0052] is the number of luggage trailers and flatbeds initially stored in the garage l ;

[0053] is the maximum number of flatbeds that can be towed by a lead vehicle;

[0054] Wherein, the constraint condition (1) ensures to meet the flow conservation of the ground service vehicle at the warehouse space-time node, (2) ensures to meet the flow conservation of the flat car at the warehouse space-time node, (3) ensures to meet the flow conservation of the ground service vehicle at the terminal and the parking apron space-time node, (4) ensures to meet the flow conservation of the flat car at the terminal and the parking apron space-time node, (5) ensures to meet the flow conservation of the luggage at the terminal and the parking apron space-time node, and (6) is to ensure that the ground service vehicle drags at most p flat cars on the driving arc, and each flat car carries one piece of luggage.

[0055] Preferably, in the step S4, the result visualization output specifically includes:

[0056] d1, output the path optimization result, including the objective function result, the optimized ground service vehicle and flat car path;

[0057] d2, display the optimized luggage trailer path and luggage flat car path in the airport space-time network diagram.

[0058] Compared with the related art, the luggage flat car optimization method for reducing the airport operation cost provided by the application has the following beneficial effects:

[0059] The luggage flat car optimization method for reducing the airport operation cost provided by the application constructs a luggage flat car scheduling optimization model from the perspective of ensuring the operation of airport flight loading and unloading tasks and reducing the operation cost of the luggage flat car, generates a luggage flat car service path in an airport space-time network diagram, provides overall overall allocation for luggage flat car scheduling, helps to ensure the normal operation of flight loading and unloading, and improves the operation efficiency of luggage consignment service. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 A flowchart of the luggage flat car optimization method for reducing the airport operation cost provided by the application is provided.

[0061] Figure 2 An airport overhead view of the specific embodiment of the application is provided, including the path optimization result.

[0062] Figure 3 The driving path of the head car in the space-time network diagram of the specific embodiment of the application is provided.

[0063] Figure 4 The driving path of the trailer in the space-time network diagram of the specific embodiment of the application is provided.

[0064] Figure 5 The service path of each task in the space-time network diagram of the specific embodiment of the application is provided.

[0065] Figure 6 The driving arc set and the static arc set generated in the specific embodiment of the application are provided. DETAILED DESCRIPTION

[0066] The application will be further described below in conjunction with the drawings and embodiments.

[0067] As Figure 1 shown, a luggage trolley optimization method for reducing airport operation cost includes the following steps:

[0068] S1, basic data input: read the airport basic data, including terminal location information, apron location information, luggage trolley garage location information, airport flight service timetable, the amount of cargo transported by each flight, the departure point-destination of the cargo transportation task, the allowed loading time and the expected and the latest end time of the cargo transportation task, and initialize the number of vehicles and trolleys in each garage;

[0069] S2, basic data processing: identify potential conflicts in the cargo transportation task; generate an aircraft cargo transportation timetable and anchor the start-end time of each task; based on the space-time network model, construct an airport cargo transportation space-time network, and according to the travel arc and static arc classification algorithm, generate the corresponding travel arc set and static arc set between each pair of terminal space-time node and apron space-time node, apron space-time node and garage space-time node, and garage space-time node and terminal space-time node;

[0070] S3, path optimization solution: construct a mixed integer linear programming model for airport luggage trolley scheduling optimization based on the space-time network model, the optimization goal is to minimize the luggage trolley operation cost and the delay penalty of the cargo transportation task, and output the optimal solution, including the luggage trolley service path and the number of luggage trolleys and trolleys allocated to each path;

[0071] S4, result visualization output: display the optimized luggage trolley service path in the airport space-time network diagram, and mark the paths that require more luggage trolleys.

[0072] In step S1, the basic data input has the following specific process:

[0073] Step 1.1: Read the road network data of Shanghai Pudong International Airport, including terminal nodes, apron nodes, and luggage trolley garage nodes; read the Shanghai Pudong International Airport flight service timetable, including the amount of cargo transported by each flight, the departure point-destination of the cargo transportation task, and the allowed loading time and the expected and the latest end time of the cargo transportation task;

[0074] Step 1.2: Initialize the number of luggage trolleys and trolleys in each garage of the garage node, here initialize 20 luggage trolleys and 60 luggage trolleys in each garage;

[0075] Step 1.3: A plurality of position-proximal parking spaces are constructed into a parking apron node, the aircraft capacity of each parking apron node is the number of parking spaces, and the number of arrival flights of each parking apron node is the sum of the number of flights of each parking space; an airport loading and unloading network is constructed; and the example data used in the present application is shown in Tables 1 and 2.

[0076] Table 1

[0077]

[0078] Table 2

[0079]

[0080] In step S2, the basic data is processed, and the specific process is as follows:

[0081] Step 2.1: Identify potential conflicts in the cargo transportation task: when the baggage trolley performs the task, there is a theoretical shortest transportation time from the starting point to the destination. If the completion time specified by the cargo transportation task is lower than this shortest transportation time, the task is considered unfeasible in actual operation;

[0082] Step 2.2: Generate an aircraft cargo transportation schedule: according to the allowed loading time, the expected end time, and the latest end time of the cargo transportation task, generate an aircraft cargo transportation schedule between the origin and destination of the cargo transportation task, to prepare for subsequent path optimization selection;

[0083] Step 2.3: Construct an airport cargo transportation space-time network: according to the airport basic data, construct the parking spaces of a specific cluster into a parking apron node, the aircraft capacity of each parking apron node is the number of parking spaces, and the airport terminal node and the ground service garage node are also included, to construct an airport cargo transportation space-time network, as shown in Figure 3 .

[0084] Step 2.4: Generate a set of driving arcs and a set of stationary arcs: stationary arcs represent the process of stationary parking of ground service vehicles, each stationary arc is vertically upward and spans one time step, and a double for loop is used to generate the set of stationary arcs; driving arcs represent the movement of ground service vehicles, trolleys, and baggage between parking apron nodes and terminal nodes, each driving arc points to the upper oblique direction, and the time span is related to the node distance, and a double for loop with judgment is used to generate the set of driving arcs, as shown in Figure 6 .

[0085] In step S3, the path optimization is solved, and the specific process is as follows:

[0086] Step 3.1: The objective function of the path optimization model includes the following contents,

[0087] A mixed integer linear programming model for airport baggage trolley scheduling optimization based on space-time network model is constructed, and the optimization objective is to minimize the ground service vehicle operating cost and the delay penalty of cargo tasks:

[0088]

[0089]

[0090]

[0091]

[0092] wherein, represents the cost of the baggage trolley driving on the driving arc a per unit time, represents the cost of the baggage trolley driving on the driving arc a per unit time when dragging a single baggage trolley, represents the cost of the baggage trolley driving on the driving arc a per unit time when dragging a single baggage trolley;

[0093] represents the cost of the baggage trolley staying on the static arc a per unit time, represents the cost of the baggage trolley staying on the static arc a per unit time;

[0094] represents the delay cost of task j on the driving arc a, if the end of the driving arc a is after the expected arrival time of task j, , otherwise .

[0095] Step 3.2: The constraint conditions of the path optimization model include the following contents:

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] wherein, is the set of garages (designated storage points for baggage trolleys and baggage trolleys),

[0103] is the set of terminals;

[0104] Collecting at the ramp;

[0105] Collecting at the ramp;

[0106] Collecting at the ramp;

[0107] Collecting at the ramp;

[0108] Collecting at the ramp;

[0109] The number of packages formed by packing task j (each package contains exactly one flat);

[0110] The departure point and arrival point of task j, and Indicates loading; and Indicates unloading;

[0111] The earliest start time (allowing loading) of task j, the expected end time (exceeding which will result in a delay penalty), the latest end time (exceeding which will not be accepted), and, , and Should exceed the time spanned by the driving arc between the two locations;

[0112] The number of luggage trailers initially stored in the garage l The number of luggage trailers initially stored in the garage

[0113] The maximum number of flatbeds that a lead vehicle can tow;

[0114] Where constraint (1) ensures that the flow conservation of luggage trailers at the space-time nodes in the garage is satisfied, (2) ensures that the flow conservation of luggage flatbeds at the space-time nodes in the garage is satisfied, (3) ensures that the flow conservation of luggage trailers at the space-time nodes in the terminal and the ramp is satisfied, (4) ensures that the flow conservation of luggage flatbeds at the space-time nodes in the terminal and the ramp is satisfied, (5) ensures that the flow conservation of luggage packages at the space-time nodes in the terminal and the ramp is satisfied, and (6) ensures that on the driving arc, the luggage trailer can at most tow p luggage flatbeds, each containing one piece of luggage.

[0115] In step S4, the results are visualized and output, and the specific process is as follows:

[0116] Step 4.1: Output the optimization results of the luggage flatbed vehicle scheduling, including the objective function results and the optimized service path of the luggage flatbed vehicle;

[0117] Step 4.2: Display the optimized baggage trolley service path in the airport space-time network diagram, including the travel path of the baggage trolley to the apron to serve the aircraft and the optimized path of the baggage trolley following different baggage trolleys;

[0118] Attached Figure 3 、 4 , 5 shows the path optimization result diagram of the example in the present application, which describes the baggage trolley service path and the optimized path of the baggage trolley in detail. These paths are calculated by the space-time network model, aiming to provide accurate planning time for airport baggage handling services;

[0119] The above describes the preferred embodiments of the present application in detail. It should be understood that those skilled in the art can make many modifications and changes without creative labor based on the concept of the present application. Therefore, any technical solution obtained by logical analysis, reasoning or limited experiment based on the existing technology within the scope of the present application should be within the protection scope determined by the claims.

Claims

1. A method of optimizing baggage tugs to reduce airport operating costs, characterized by, Comprise the following steps: S1, basic data input: a1, read the airport basic data; a2, initialize the number of baggage trailers and baggage boards of each garage; S2, basic data processing: b1, identify potential conflicts in cargo transport tasks; b2, generate an aircraft cargo transport schedule; b3, build an airport cargo transport space-time network; b4, generate a set of driving arcs and a set of static arcs; S3, path optimization solution: Construct a mixed integer linear programming model for airport baggage board car scheduling optimization based on space-time network model, the optimization goal is to minimize the operation cost of baggage board car and the delay penalty of cargo transport task, output the optimal solution, including baggage board car service path and the number of baggage board car and baggage board allocated to each path; S4, result visualization output: Display the optimized baggage board car service path in the airport space-time network diagram, and mark the paths with more required baggage board cars; Among them, the step S2, the basic data processing specifically includes: Identify potential conflicts in cargo transport tasks: when the baggage board car executes the task, there is a theoretical shortest transport time from the starting point to the destination, if the completion time specified by the cargo transport task is lower than this shortest transport time, the task is considered infeasible in actual operation; Generate an aircraft cargo transport schedule and anchor the start-end time of each task: generate an aircraft cargo transport schedule between the origin and destination of the cargo transport task according to the allowed loading time and expected end time and the latest end time of the cargo transport task, to prepare for subsequent path optimization selection; Build an airport cargo transport space-time network: according to the airport basic data, build a parking apron node for a specific cluster of parking stands, the aircraft capacity of each parking apron node is the number of parking stands, and the terminal node and baggage board car garage node are also included, to build an airport cargo transport space-time network; Generate a set of driving arcs and a set of static arcs: static arcs represent the process of static parking of baggage board cars, each static arc is vertically upward and spans one time step, a set of static arcs is generated using double for loop, driving arcs represent the movement of baggage board cars and baggage between parking apron nodes and terminal nodes, each driving arc points to the upper right, the time span is related to the node distance, and a set of driving arcs is generated using double for loop with judgment; In the step S3 path optimization solution, the path optimization model objective function includes: Construct a mixed integer linear programming model for airport baggage board car scheduling optimization based on space-time network model, the optimization goal is to minimize the operation cost of ground service vehicle and the delay penalty of cargo transport task: ， ， ， ， wherein, represents the cost of running the luggage trailer per unit of time on the running arc a, represents the cost of running the luggage trailer per unit of time on the running arc a when pulling a single luggage board, represents the cost of running the luggage trailer per unit of time on the running arc a when pulling a unit of luggage wraps; Cp represents the cost of the luggage trailer staying on the stationary arc a per unit time, Cp represents the cost of the luggage trailer staying on the stationary arc a per unit time, denotes the delay cost for task j on driving arc a if the end of driving arc a is after the expected arrival time of task j on driving arc a, , otherwise ; In the step S3 path optimization solution, the path optimization model constraint conditions include: ， ， ， ， ， ， wherein To the garage (designated storage point for luggage trailers and luggage boards) collection, To collect at the terminal; Assemble on the tarmac; is the union of the two arcs (for simplicity, the working arc can be disregarded, considering that loading and unloading are completed very quickly); is a set of stationary arcs; a set of travel arcs; To load and unload a set of tasks; Number of packages formed from task j packing (each package holds exactly 1 tablet); depot and destination for task j, and denotes loading; and denotes unloading; the earliest start time for task j (allowable loading), the desired end time (exceeding this results in a delay penalty), the latest end time (exceeding this is not acceptable), and , and should exceed the time spanned by the travel arc between the two locations; for a garage l the number of luggage trailers, luggage trolleys initially stored in the garage The number of flat cars that can be pulled by a lead locomotive; Wherein, constraint condition (1) ensures to meet the flow conservation of ground service vehicles at the warehouse space-time node, (2) ensures to meet the flow conservation of flat cars at the warehouse space-time node, (3) ensures to meet the flow conservation of ground service vehicles at the terminal and the apron space-time node, (4) ensures to meet the flow conservation of flat cars at the terminal and the apron space-time node, (5) ensures to meet the flow conservation of luggage at the terminal and the apron space-time node, (6) is to ensure that on the driving arc, the ground service vehicle drags at most p flat cars, and each flat car carries one piece of luggage.

2. The method of claim 1, wherein the method further comprises: In the step S1, the airport basic data to be read includes terminal position information, apron position information, luggage flat car warehouse position information, airport flight service timetable, the number of goods transported with each flight, the departure point-destination of the goods transportation task, the allowed loading time, the expected end time and the latest end time of the goods transportation task.

3. The method for optimizing baggage carts to reduce airport operating costs as described in claim 1, characterized in that, In the step S4, the result visualization output specifically includes: d1, output the path optimization result, including the objective function result, the optimized ground service vehicle and flat car path; d2, display the optimized luggage flat car path and the luggage flat car path in the airport space-time network diagram.

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