CN108564219B - Optimization control method for seat price in train running section - Google Patents

Optimization control method for seat price in train running section Download PDF

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CN108564219B
CN108564219B CN201810344901.0A CN201810344901A CN108564219B CN 108564219 B CN108564219 B CN 108564219B CN 201810344901 A CN201810344901 A CN 201810344901A CN 108564219 B CN108564219 B CN 108564219B
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文曙东
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Abstract

The invention relates to a train operation economic benefit control method. The invention discloses a method for optimizing and controlling seat prices in a train running section. a. Dividing sections according to stop stations in a train running interval; b. setting the price grade number of each section, wherein the price grade numbers of the sections are not completely the same; c. establishing an optimized planning model, and setting seat resource constraint conditions; d. determining maximum value of price grade number of each section
Figure DDA0001631573750000011
Price class number of other OD zones
Figure DDA0001631573750000012
When is also set to FMax(ii) a e. And d, establishing an optimized planning model according to the conditions set in the step d, and correcting the model to obtain the equivalent maximum benefit of the train. The invention simplifies the calculation and is convenient for the computer to carry out modeling calculation. The method is very suitable for solving the problem of computer expression of each matrix in a planning model when the price grade quantity is freely set in different sections of the passenger train.

Description

Optimization control method for seat price in train running section
Technical Field
The invention relates to a train operation economic benefit control method, in particular to a seat price optimization control method in a train operation section.
Background
Currently, there are a few countries in the united states, germany, france, etc. that have railways that implement multiple levels of fares. From the published literature, the same multi-gear fare strategy is used simultaneously for each OD (Origin-Destination) segment of a train executing multi-gear fares in the united states, france, etc. In addition, the network type airline company also has a multi-tiered fare for each OD zone. However, the network of the high-speed rail in China is developed, each OD section faces different market competition environments, some OD sections face intense competition of other transportation modes (aviation and coach), and some sections are completely monopolized by the high-speed rail, so that if multi-level fares are executed to attract passenger flow, each OD section should have different fare strategies, for example, the section with market competition should execute multi-level fares to attract price-sensitive passengers, the travel distance is short, and the section with the dominant high-speed rail needs no elasticity in price, so that the fixed fare specified by the state can be directly executed, and the optimal benefit can be obtained.
The train passes through a plurality of stations, the stock resources of the seats of the single train are fixed, and the optimal income can be obtained by a classical operation research model. And uniformly establishing an optimization model of multi-grade prices of each section. For example, the G1 train number has four stations of beijing, jinan, nanjing, shanghai, including the following 6 OD zones:
[ Beijing-Jinan ], [ Beijing-Nanjing ], [ Beijing-Shanghai ]; [ Jinan-Nanjing ], [ Jinan-Shanghai ]; [ Nanjing-Shanghai ].
If each section is set with 3 grades of prices, an operational research optimization model can be established:
calculating the maximum value:
Figure BDA0001631573740000011
the constraint conditions are as follows: a. XODF≤ClFor any single segment l; (2)
0≤XODF≤EDODFfor any ODF; (3)
wherein, the formula (1) represents a total profit maximization expression. ODF represents the F-th gear of the section OD price grade number; f. ofODFPrice of F grade in OD zone; xODFIndicates the number of positions assigned to an ODF; ED (electronic device)ODFRepresenting the predicted value of demand for an ODF.
Equation (2) indicates that the number of passengers on each individual section does not exceed the number of train determinants. A ═ aij) mxn is a correlation matrix representing the relationship of ODF and segment position resources; if a certain product j occupies resource i, then aij1, if resource i is not occupied, then aij0; jth column vector A of matrix AjRepresenting the association condition of the product j and the resource; m is the number of the product j, namely the number of ODFs; n is the number of resources i, namely the number of single sections, and is equal to the number of train stops minus 1;
equation (3) represents that the demand of each ODF is greater than 0 and less than or equal to the demand prediction value.
Such an optimization model is easy to establish, and the formula (1), the formula (2) and the formula (3) can be designed into a matrix and are included in computer modeling calculation.
Still taking the G1 train number as an example, the sites are set to be Beijing 1, Jinan 2, Nanjing 3, Shanghai 4, and f in the formula (1)ODFVariable as a matrix:
[f121 f122 f123 f131 f132 f133 f141 f142 f143 f231 f232 f233 … f341 f342 f343]
also, XODFSimilar transformations can be made, and equations (2) and (3) can also make matrix transformations.
If the section [ Beijing-Shanghai ] is required to set the price of 3 grades, the other 5 sections only have the fixed price of one grade. The above optimization model is then as follows:
calculating the maximum value:
Figure BDA0001631573740000021
constraint conditions are as follows: a. XODF≤ClFor any single segment l; (5)
0≤XODF≤EDODFfor any ODF. (6)
Figure BDA0001631573740000022
Or 3, OD ═ Beijing, Shanghai]Is sometimes 3, the rest is 1 (7)
Wherein, when OD is [ Beijing, Shanghai ], there are three prices, and other sections only have one price, so that the models are inconvenient to convert the formulas (4), (5) and (6) into matrixes. Particularly, if the number of price grades of each section is arbitrarily set when the train passes through a plurality of stations, the expression (4), the expression (5) and the expression (6) are difficult to express by using a matrix, particularly the incidence matrix A in the expression (5) is most difficult, and the computer modeling calculation is influenced.
Disclosure of Invention
The invention mainly aims to provide a method for optimizing and controlling seat prices in a train running section, which simplifies a calculation method and facilitates a computer to carry out modeling calculation.
In order to achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method for controlling optimal seat prices in a train running section, comprising the steps of:
a. dividing sections according to stop stations in a train running interval;
b. setting the price grade number of each section, wherein the price grade numbers of the sections are not completely the same;
c. establishing an optimized planning model, wherein seat resource constraint conditions are as follows:
A·XODF≤Clfor any section l between adjacent stations
The expression indicates that the number of passengers on each single section does not exceed the number of train determinants;
wherein OD (Origin-Destination) represents the Origin-Destination section of the train service passenger; ODF represents the F-th gear of the section OD price grade number; xODFIndicates the number of positions assigned to an ODF; l represents a sector of an adjacent station; clRepresents the total number of positions on segment l; a ═ aij) mxn is a correlation matrix representing the relationship of ODF and segment position resources; if a certain product j occupies resource i, then aij1, if resource i is not occupied, then aij0; jth column vector A of matrix AjRepresenting the association condition of the product j and the resource; m is the number of the product j, namely the number of ODFs; n is the number of resources i, namely the number of single sections, and is equal to the number of train stops minus 1;
d. determining maximum value of price grade number of each section
Figure BDA0001631573740000031
Price class number of other OD zones
Figure BDA0001631573740000032
When is also set to FMax；
e. And d, establishing an optimized planning model according to the conditions set in the step d, and correcting the model to obtain the equivalent maximum benefit of the train.
Further, in step c, the optimization planning model is:
calculating the maximum value:
Figure BDA0001631573740000033
the constraint conditions are as follows: a. XODF≤ClFor any single segment l; (2)
0≤XODF≤EDODFfor any ODF; (3)
wherein, formula (1) represents a total profit maximization expression;
Figure BDA0001631573740000041
the price grade number of the zone OD; f. ofODFPrice of F grade in OD zone; xODFIndicates the number of positions assigned to an ODF; ED (electronic device)ODFRepresenting a predicted value of demand for an ODF; equation (3) represents that the demand of each ODF is greater than 0 and less than or equal to the demand prediction value.
Further, step e specifically comprises:
correction of f in optimization model equation (1)ODFPrice f of the number of price stages of ODF section which does not existODFSet to a negative constant, the ODF demand expected value ED not present in equation (3)ODFSet to any positive value.
Further, step e specifically comprises:
correcting ED in optimization model equation (3)ODFExpectation demand ED for non-existent OD zonesODFSet to 0, the price f of the ODF section price class number not present in equation (1)ODFThe value thereof may be set to an arbitrarily determined value.
The method has the advantages of simplifying the calculation method and facilitating the modeling calculation of the computer. The method ensures that the price grade quantity of each OD section is the same, is convenient for expressing the incidence matrix A by a computer, and is very suitable for solving the problem of computer expression of each matrix in a planning model when the price grade quantity is freely set in different sections of a passenger train. And the further model correction enables the model solution result to be equivalent, and the optimal operation income of the train can be obtained.
The present invention will be further described with reference to the following embodiments. Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Detailed Description
It should be noted that the specific embodiments, examples and features thereof may be combined with each other in the present application without conflict. The present invention will now be described in detail with reference to the following.
In order to make those skilled in the art better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments and the embodiments of the present invention in combination with the embodiments and the examples, and it is obvious that the described examples are only a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments and examples obtained by a person skilled in the art without any inventive step should fall within the protection scope of the present invention.
The invention discloses a method for optimizing and controlling the seat price in a train running section, which comprises two price optimizing and controlling methods, namely a price setting method and a demand setting method. The processing flows of the two methods are described below.
Price one-setting method
Dividing sections according to stop stations in a train running interval;
the price grade number of each section is set, and the price grade numbers of the sections are not identical. Maximum price is
Figure BDA0001631573740000051
Figure BDA0001631573740000052
Price grade number F of other OD zones is less than or equal to FMaxWhen is also set to FMax. Thus, the train profit maximization model becomes:
calculating the maximum value:
Figure BDA0001631573740000053
the constraint conditions are as follows: a. XODF≤ClFor any phaseZone i between adjacent sites; (12)
0≤XODF≤EDODFfor any ODF; (13)
fODFnegative constant when OD zone F<F≤FMax (14)
Thus, only f in the formula (11) needs to be correctedODFPrice f of an ODF section which does not existODFSet to an arbitrary negative constant, non-existent ODF demand expected value EDODFSet to any positive number. When the calculated value of equation (11) is to be maximized, the number of positions X assigned to these non-existent ODFsODFInevitably 0, which corresponds to the absence of ODF. If these non-existent X's are setODFPositive, then obviously multiplied by fODFThe value of equation (11) is lowered and an optimal solution cannot be achieved.
Thus, each OD zone has the same number of price levels, and it is easy to establish a matrix equation in equation (12) as a conventional operational research planning problem to be inputted into a computer for operation.
In particular, some commercial planning model computer software calculates the model when fODFWhen 0, X can be obtained in the same mannerODFAn optimized result of 0 is also within the scope of the present invention.
Second, demand zero setting method
Dividing sections according to stop stations in a train running interval;
setting the price grade number of each section, wherein the maximum price grade number is
Figure BDA0001631573740000061
Price grades F of other zones<FMaxWhen is also set to FMax. Thus, the train profit maximization model becomes:
calculating the maximum value:
Figure BDA0001631573740000062
the constraint conditions are as follows: a. XODF≤ClFor any segment l between adjacent sites; (16)
0≤XODF≤EDODFfor any ODF (17)
EDODF0, when OD zone F<F≤FMax (18)
Thus, f is not present in the setting (15)ODFThe value is set to any positive value, and the OD section expected demand ED that does not exist in equation (17)ODFIs set to 0. When equations (17) and (18) are satisfied, the number of positions X allocated to these non-existent ODFsODFInevitably 0, which corresponds to the absence of ODF.
Thus, for the same reason, each section has the same price grade number, and the matrix equation of the formula (16) is easily established and is input into a computer for operation as a conventional operational research solving problem.
Example 1 (Linear programming model-price setting method)
The high-speed rail D1917 of Xicheng, which was issued in 2017, passes through three stations, namely Xian (station 1), Guangyuan (station 2) and Chengdu (station 3). The section is divided into: 3 segments are total [ xi 'an-Cheng Du ], [ xi' an-Guangyuan ] and [ Guangyuan-Cheng Du ]. The [ xi 'an-Cheng du ] and [ xi' an-Guang Yuan ] are only set with one price, and the [ Guang Yuan-Cheng du ] has big bus competition and is set with two prices.
If the total profit maximization expression is not processed, the total profit maximization expression is as follows:
f121·X121+f131·X131+f231·X231+f232·X232 (19)
the constraint conditions are as follows:
Figure BDA0001631573740000063
0≤XODF≤EDODF (21)
since all sections have only two levels of price, the other sections have only one level of price. For a large number of domestic different train numbers of stop stations, the constraint condition formula (20) is inconvenient to express by using a uniform and regular matrix and is inconvenient to calculate by a computer.
The first method (price setting method) is used for establishing an operational research linear programming model as follows:
determine the maximum price tier for each segment, example FMax＝2；
Establishing an optimization model
Calculating the maximum value:
Figure BDA0001631573740000071
constraint conditions are as follows: a. XODF≤ClFor any segment l between adjacent sites; (23)
0≤XODF≤EDODFfor any ODF; (24)
fODFwhen OD is in the region F ═ 1<F≤FMax (25)
Wherein, the formulas (22) and (25) are merged and converted into matrix expression:
calculating the maximum value:
Figure BDA0001631573740000072
equation (23) is converted to a matrix representation:
Figure BDA0001631573740000073
equation (24) is converted to a matrix representation:
0≤[X121 X122 X131 X132 X231 X231]
≤[ED121 ED122 ED131 ED12 ED231 ED231]
thus, the price grade number of each section of the train is unified, and the maximum value F is takenMax2, only non-existent ODF price fODFSet to negative, non-existent ODF demand expectation EDODFAccording to conventional prediction or estimation. All the formula (22), the formula (23), the formula (24) and the formula (25) are converted into matrix solution, and subscripts of elements in each matrix are regular, so that the solution is convenient for a computer to solve.
Example 2: (integer programming model-demand zero setting method)
Similarly, taking the high-speed rail in western province D1917 as an example, the three stations are Xian (site 1), Guangyuan (site 2) and Chengdu (site 3). The section is divided into: 3 segments are total [ xi 'an-Cheng Du ], [ xi' an-Guangyuan ] and [ Guangyuan-Cheng Du ]. The [ xi 'an-Cheng du ] and [ xi' an-Guang Yuan ] are only set with one price, and the [ Guang Yuan-Cheng du ] has big bus competition and is set with two prices.
Establishing an operational research integer programming model as follows:
determining the maximum number of price levels for each section, in this example FMax＝2；
Establishing an optimization model
Calculating the maximum value:
Figure BDA0001631573740000081
constraint conditions are as follows: a. XODF≤ClFor a single segment l between any adjacent sites; (27)
0≤XODF≤EDODFfor any ODF, XODFIs an integer; (28)
EDODF0, when OD zone F<F≤FMax (29)
Wherein formula (26) is converted to a matrix expression:
Figure BDA0001631573740000082
equation (27) is converted to a matrix representation:
Figure BDA0001631573740000083
and combining the formula (28) and the formula (29) and converting into a matrix expression:
0≤[X121 X122 X131 X132 X231 X231]≤[ED121 0 ED131 0 ED231 ED231]；
wherein, XODFAre integers.
Thus, unifying the trainThe price grade number of each section is taken as the maximum value FMaxSetting the price level demand expectation value which does not exist as 0, the expressions (26), (27), (28) and (29) are converted into matrix solution, and element subscripts in each matrix are regular, so that the computer is convenient to solve.

Claims (4)

1. The optimal control method for the seat price in the train operation section is characterized by comprising the following steps:
a. dividing sections according to stop stations in a train running interval;
b. setting the price grade number of each section, wherein the price grade numbers of the sections are not completely the same;
c. establishing an optimized planning model, wherein seat resource constraint conditions are as follows:
A·XODF≤Clfor any section l between adjacent stations
The expression indicates that the number of passengers on each single section does not exceed the number of train determinants;
wherein OD (Origin-Destination) represents the Origin-Destination section of the train service passenger; ODF represents the F-th gear of the section OD price grade number; xODFIndicates the number of positions assigned to an ODF; l represents a sector of an adjacent station; clRepresents the total number of positions on segment l; a ═ aij) mxn is a correlation matrix representing the relationship of ODF and segment position resources; if a certain product j occupies resource i, then aij1, if resource i is not occupied, then aij0; jth column vector A of matrix AjRepresenting the association condition of the product j and the resource; m is the number of the product j, namely the number of ODFs; n is the number of resources i, namely the number of single sections, and is equal to the number of train stops minus 1;
d. determining maximum value of price grade number of each section
Figure FDA0001631573730000011
Price class number of other OD zones
Figure FDA0001631573730000012
When is also set to FMax；
e. And d, establishing an optimized planning model according to the conditions set in the step d, and correcting the model to obtain the equivalent maximum benefit of the train.
2. The method for controlling optimal seat prices in train operation sections according to claim 1, wherein in the step c, the optimal planning model is:
calculating the maximum value:
Figure FDA0001631573730000013
the constraint conditions are as follows: a. XODF≤ClFor any segment l between adjacent sites; (2)
0≤XODF≤EDODFfor any ODF; (3)
wherein, formula (1) represents a total profit maximization expression;
Figure FDA0001631573730000014
the price grade number of the zone OD; f. ofODFPrice of F grade in OD zone; xODFIndicates the number of positions assigned to an ODF; ED (electronic device)ODFRepresenting a predicted value of demand for an ODF; equation (3) represents that the demand of each ODF is greater than 0 and less than or equal to the demand prediction value.
3. The method for optimizing and controlling the seat prices in the train operation sections according to claim 2, wherein the step e is specifically as follows:
correction of f in optimization model equation (1)ODFPrice f of the number of price stages of ODF section which does not existODFSet to a negative constant, the ODF demand expected value ED not present in equation (3)ODFSet to any positive value.
4. The method for optimizing and controlling the seat prices in the train operation sections according to claim 2, wherein the step e is specifically as follows:
correcting ED in optimization model equation (3)ODFIs not holdingExisting ODF section expectation requirement EDODFSet to 0, the price f of ODFs not present in equation (1)ODFThe value thereof may be set to an arbitrarily determined value.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105279563A (en) * 2015-09-30 2016-01-27 传成文化传媒（上海）有限公司 Vehicle reserving method and system based on television
CN107093110A (en) * 2017-04-25 2017-08-25 杭州纳戒科技有限公司 Carrying pricing method and system
CN107451872A (en) * 2017-08-10 2017-12-08 中国民航信息网络股份有限公司 The management method and device of flight freight rate
CN107657502A (en) * 2017-08-18 2018-02-02 特兰旗（厦门）科技有限公司 The one-stop application system of automobile and method based on internet

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8200514B1 (en) * 2006-02-17 2012-06-12 Farecast, Inc. Travel-related prediction system
CN103955807A (en) * 2014-05-22 2014-07-30 文耀锋 Method for nested management of all train seat OD zones and with social benefits considered
CN105930914A (en) * 2016-04-01 2016-09-07 东南大学 City bus optimal charging structure charge determination method based on origin-destination distance
CN106447095A (en) * 2016-09-14 2017-02-22 中南大学 Urban track traffic line section fare optimizing method and system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105279563A (en) * 2015-09-30 2016-01-27 传成文化传媒（上海）有限公司 Vehicle reserving method and system based on television
CN107093110A (en) * 2017-04-25 2017-08-25 杭州纳戒科技有限公司 Carrying pricing method and system
CN107451872A (en) * 2017-08-10 2017-12-08 中国民航信息网络股份有限公司 The management method and device of flight freight rate
CN107657502A (en) * 2017-08-18 2018-02-02 特兰旗（厦门）科技有限公司 The one-stop application system of automobile and method based on internet

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
A Survey on Energy-Efficient Train Operation for Urban Rail Transit;Xin Yang et al;《 IEEE Transactions on Intelligent Transportation Systems》;20150707;第17卷(第1期);第2-13页 *

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