CN116502850B - Cabin position distribution method, device and equipment - Google Patents
Cabin position distribution method, device and equipment Download PDFInfo
- Publication number
- CN116502850B CN116502850B CN202310479600.XA CN202310479600A CN116502850B CN 116502850 B CN116502850 B CN 116502850B CN 202310479600 A CN202310479600 A CN 202310479600A CN 116502850 B CN116502850 B CN 116502850B
- Authority
- CN
- China
- Prior art keywords
- bunk
- flight
- equivalent
- nested
- sets
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 55
- 230000010006 flight Effects 0.000 claims abstract description 23
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 12
- 241000273930 Brevoortia tyrannus Species 0.000 claims description 54
- 230000008901 benefit Effects 0.000 claims description 35
- 238000004590 computer program Methods 0.000 claims description 20
- 238000012163 sequencing technique Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000013468 resource allocation Methods 0.000 abstract description 3
- 230000006872 improvement Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000007476 Maximum Likelihood Methods 0.000 description 1
- 238000012896 Statistical algorithm Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
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
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
-
- 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
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Mathematical Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Tourism & Hospitality (AREA)
- Pure & Applied Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Educational Administration (AREA)
- Databases & Information Systems (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Algebra (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method, a device and equipment for distributing bilge, wherein the method comprises the following steps: determining a discrete selection model of the flight itinerary; wherein the flight itinerary comprises at least one flight; calculating nested effective boundaries of the discrete selection model; wherein, the nesting property is provided between the sets on the nesting effective boundary; calculating the equivalent selection probability and equivalent freight rate of each cabin in the flight travel according to the nested effective boundaries; calculating the base price of each flight in different flight states by adopting a dynamic programming decomposition method based on the equivalent selection probability, the equivalent freight rate and the total demand of the pre-acquired flight itineraries; when the equivalent freight rate of the berth is larger than the sum of the bottom rates of all flights occupied by the berth in the current state, the state of the berth is set to be an open state, otherwise, the state of the berth is set to be a closed state. By adopting the embodiment of the invention, the competition of the cabin products with different grades to the flight seats can be considered, the cabin can be automatically allocated, and the flight cabin resource allocation efficiency is obviously improved.
Description
Technical Field
The invention relates to the technical field of flight routes, in particular to a method, a device and equipment for cabin allocation.
Background
The airlines provide differentiated services for passengers by dividing the bilges of different grades on the same flight and corresponding different ticket prices for the different grades. These bunkers share the same flight seating and the airlines determine different bunk allocation schemes based on predictions of demand.
In the prior art, the flight space resource allocation process is manually completed by virtue of working experience of an airline operator, namely, a space resource of a flight is adjusted by manually confirming a space adjustment instruction by the airline operator. However, due to the limited effort of airline operators, the number of flights managed is limited, and the flight deck resource allocation is inefficient.
Disclosure of Invention
The invention aims to provide a method, a device and equipment for cabin allocation, which are used for solving the problem of low efficiency of the allocation of the resources of the flight cabin in the prior art.
In order to achieve the above object, an embodiment of the present invention provides a method for allocating a bunk, including:
Determining a discrete selection model of the flight itinerary; wherein the flight itinerary comprises at least one flight;
Calculating nested effective boundaries of the discrete selection model; wherein, the nesting is carried out between the sets on the nesting effective boundary;
Calculating the equivalent selection probability and equivalent freight rate of each berth in the flight journey according to the nested effective boundaries;
Calculating the bottom price of each flight in different flight states by adopting a dynamic programming decomposition method based on the total demand, the equivalent selection probability and the equivalent freight price; wherein the flight status includes time and number of seats remaining;
When the equivalent freight rate of the bunk is larger than the sum of the bottom rates of all flights occupied by the bunk in the current state, the state of the bunk is set to be an open cabin state, otherwise, the state of the bunk is set to be a closed cabin state.
As an improvement to the above, the calculating the nested effective boundaries of the discrete selection model includes:
When the type of the discrete selection model is one of preset model types, enumerating all the bunk sets from the bunk set C 1 to the bunk set C n, taking all the enumerated bunk sets as first bunk sets, and determining a nested effective boundary from all the first bunk sets according to the expected probability and expected benefit of each first bunk set;
When the type of the discrete selection model does not belong to any one of the preset model types, starting iteration, and determining a nested effective boundary from all second cabin level sets according to expected probability and expected benefits of the second cabin level sets generated in the iteration process;
Wherein C n represents a bunk set including the first n bunks with highest freight rates, n is the total number of bunks in the present flight trip, and the preset model types include: an independent demand model, a price oriented model, a basic appeal model, and a hybrid model of independent demand and price oriented.
As an improvement of the above solution, the determining a nesting effective boundary from all the first bunk sets according to the expected probability and expected benefit of each first bunk set includes:
Starting iteration, calculating expected probability and expected benefit of each first bunk set, and judging whether the first bunk sets meet a first preset condition or not based on the expected probability and expected benefit of each first bunk set;
If any one of the first bunk sets meets the first preset condition, searching a bunk set with the largest ratio of the expected gain difference value to the expected probability difference value from all the first bunk sets meeting the first preset condition, taking the bunk set as a first effective bunk set, and continuing iteration;
if any first cabin level set does not exist, outputting all the first effective cabin level sets;
Wherein the first preset condition includes: q (S) > Q (S i-1) and (R (S) -R (S i-1))/(Q(S)-Q(Si-1)) >0; where Q (S) represents the expected probability of the current iteration 'S bunk set S, R (S) represents the expected benefit of the current iteration' S bunk set S, and i represents the number of iterations.
As an improvement of the above solution, the starting the iteration and determining a nested effective boundary from all the second bunk sets according to the expected probability and expected benefit of the second bunk sets generated in the iteration process includes:
starting iteration, and enabling a second cabin level set of the initial iteration to be i＝1；
If any second cabin level set meets a second preset condition, searching a cabin level set with the largest ratio of the expected gain difference value to the expected probability difference value from all second cabin level sets meeting the second preset condition, taking the cabin level set as a second effective cabin level set, and continuing iteration;
If any second space set does not exist and meets the second preset condition, outputting all second effective space sets when judging that the type of the discrete selection model is a general attraction model, taking all second effective space sets as the first space sets when judging that the discrete selection model is the model of the rest type, and determining a nested effective boundary from all first space sets according to the expected probability and expected income of each first space set;
Wherein the second preset condition includes:
The elements in the current iteration bilge set at least comprise all elements in the last iteration bilge set, the number of the elements in the current iteration bilge set is one more than the number of the elements in the last iteration bilge set, and Q (S) > Q (S i-1) and (R (S) -R (S i-1))/(Q(S)-Q(Si-1)) >0;
where Q (S) represents the expected probability of the current iteration 'S bunk set S, R (S) represents the expected benefit of the current iteration' S bunk set S, and i represents the number of iterations.
As an improvement to the above, the expected probability for each bunk set is calculated according to the following equation:
the expected revenue for each bunk set is calculated according to the following equation:
Where p j (S) is the probability that passenger selects bunk j e bunk set S, and r j represents the freight rate of bunk j.
As an improvement of the above solution, the calculating the equivalent selection probability and equivalent freight rate of each bunk in the flight trip according to the nested effective boundaries includes:
Determining all effective bunk sets and the sequence of adding each bunk into the effective bunk sets according to the nested effective boundaries;
sequencing the bilges according to the sequence, sequencing the bilges in the same sequence according to the freight rates of the bilges, and marking the bilges obtained after final sequencing as bilges sequenced in the nested sequence;
enumerating all bunkers from bunkers set G 1 to bunkers set G n based on the bunkers ordered in the nested order; wherein G n represents a set of the top n bunkers ordered in nested order;
Ordering all the effective bin sets according to the number of elements in the sets, and marking the kth effective bin set as E k;
Starting a first round of traversal, for a bunk j, if G j＝Ek, the equivalent selection probability of the bunk j is Q (E k)-Q(Ek-1), the equivalent freight rate of the bunk j is (R (E k)-R(Ek-1))/(Q(Ek)-Q(Ek-1)), if G j is not equal to any E k, the equivalent selection probability of the bunk j is 0, and the equivalent freight rate of the bunk j is temporarily recorded as U;
Starting a second round of traversal, if a third berth which is positioned behind the berth j U in a nested order exists and the third berth has equivalent freight rate, the equivalent freight rate of the berth j U is the equivalent freight rate of the third berth closest to the berth j U, and if the third berth which is positioned behind the berth j U in a nested order does not exist, the equivalent freight rate of the berth j U is 0; wherein, bunk j U is the bunk of equivalent freight temporary record U.
As an improvement of the above solution, the calculating the base price of each flight in different flight states by using a dynamic programming decomposition method based on the equivalent selection probability, the equivalent freight rate and the pre-acquired total demand of the flight itineraries includes:
calculating the equivalent demand of each cabin according to the equivalent selection probability and the pre-acquired demand total of the flight travel;
calculating the shadow price of each flight;
For each flight i, dividing its booking period into T time periods T, calculating the maximum expected return in state (T, x) as V t (x):
Where a ij =1 denotes that the bunk j occupies the seat of the flight i, λ tj denotes the demand occurrence probability at each time period t, λ tj＝dj/T,dj denotes the equivalent demand amount of the bunk j, Representing the equivalent freight rate of bunk j, Δv t-1(x)＝Vt-1(x)-Vt-1 (x-1), x representing the number of seats remaining, z k representing the shadow price of flight k, a kj =1 if bunk occupies flight k; when t=0 or x=0, V t (x) =0;
let DeltaV t-1 (x) in state (t, x) be the base price, obtain the base price of each flight in all states (t, x).
As an improvement of the scheme, the shadow price of each flight is obtained by solving a linear programming model which is built in advance;
wherein the linear programming model is:
wherein, Representing the equivalent freight rate of bunk j, y j is the theoretical allocation number of bunk j, M represents the set of all flights in the flight itinerary, c i represents the current available seat number for flight i e M, a ij =1 if bunk j occupies the seat of flight i, a ij =0 if bunk j does not occupy the seat of flight i.
In order to achieve the above object, an embodiment of the present invention further provides a bunk distribution device, including:
The model determining module is used for determining a discrete selection model of the flight journey; wherein the flight itinerary comprises at least one flight;
A first calculation module for calculating nested effective boundaries of the discrete selection model; wherein, the nesting is carried out between the sets on the nesting effective boundary;
the second calculation module is used for calculating the equivalent selection probability and equivalent freight rate of each cabin in the flight journey according to the nested effective boundaries;
The third calculation module is used for calculating the bottom price of each flight in different flight states by adopting a dynamic programming decomposition method based on the total demand, the equivalent selection probability and the equivalent freight price; wherein the flight status includes time and number of seats remaining;
And the bunk state setting module is used for setting the bunk state to be an open cabin state when the equivalent freight rate of the bunk is larger than the sum of the bottom rates of all flights occupied by the bunk in the current state, and setting the bunk state to be a closed cabin state otherwise.
To achieve the above object, an embodiment of the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor implements the above-mentioned method for allocating a cabin level when executing the computer program.
Compared with the prior art, the method, the device and the equipment for distributing the cabin space provided by the embodiment of the invention have the following beneficial effects:
(1) The embodiment of the invention solves the problem that the non-independent requirement is difficult to process in the problem of the allocation of the aviation network/the flight space;
(2) The embodiment of the invention allows any discrete selection model to be assumed for the bilge product of each flight trip;
(3) The embodiment of the invention can automatically calculate the equivalent demand and equivalent freight rate of each flight journey space;
(4) If the discrete selection model accords with certain specific forms, the embodiment of the invention can ensure that the cabin allocation scheme of the equivalent problem is consistent with the original problem;
(5) The embodiment of the invention can carry out real-time cabin-position marketability control in a navigation network by adopting a BP control method, and can meet the requirement of real-time calculation.
Drawings
FIG. 1 is a flow chart of a method for bunk allocation provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of an effective boundary provided by an embodiment of the present invention;
FIG. 3 is a block diagram of a bunk distribution device according to an embodiment of the present invention;
Fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart of a method for allocating a bunk according to an embodiment of the present invention, where the method for allocating a bunk includes:
S1, determining a discrete selection model of a flight journey; wherein the flight itinerary comprises at least one flight;
S2, calculating a nested effective boundary of the discrete selection model; wherein, the nesting is carried out between the sets on the nesting effective boundary;
S3, calculating the equivalent selection probability and equivalent freight rate of each bunk in the flight journey according to the nested effective boundaries;
s4, calculating the base price of each flight in different flight states by adopting a dynamic programming decomposition method based on the equivalent selection probability, the equivalent freight rate and the pre-acquired total demand of the flight journey; wherein the flight status includes time and number of seats remaining;
S5, when the equivalent freight rate of the bunk is larger than the sum of the bottom rates of all flights occupied by the bunk in the current state, setting the bunk to be in a cabin opening state, otherwise, setting the bunk to be in a cabin closing state.
It will be appreciated that in step S1, if only one flight is included in a flight itinerary, a discrete selection model is determined for the flight, and if at least two flights are included in a flight itinerary and are available for engagement, a discrete selection model is determined for the flight itinerary, a discrete selection model is determined for each flight itinerary, and a plurality of flight itineraries form the airline network.
For the discrete selection model, it describes the total N number of bunkers in a flight trip, if the marketable bunkers are set NAt this time, the passenger selects a probability p j (S) of a certain bunk j ε S. For convenience of description, the discrete selection model is assumed not to change with time, and if the discrete selection model is a parameterized discrete selection model conforming to a specific form, specific parameters of the model need to be determined, and the specific parameters can be automatically obtained based on historical data by technologies such as maximum likelihood estimation or specified by manual experience; if the discrete selection model does not belong to the specific form described above, it is necessary to enumerate the selection probabilities for all the billboards in all the sets. Further, the parameterized discrete selection model of a particular form includes:
Independent demand model: each bunk j has a parameter q j, and the sum of the parameters of all bunks cannot exceed 1; if j is in set S, it selects probability p j(S)＝qj, otherwise it is 0.
Price guide model: each bunk j has a parameter l j not exceeding 1, and the lower the freight rate, the greater l j of the bunk; if j is exactly the lowest freight in the set S, it selects the probability p j(S)＝lj, otherwise it is 0.
Basic attraction model: each bunk j has a parameter V j, and the sum of the parameters of all bunks in S is recorded as V (S) = Σ i∈ Svi; if j is in set S, it selects probability p j(S)＝vj/(V (S) +1), otherwise it is 0.
General attraction model: each bunk j has a parameter V j, and the sum of the parameters of all bunks in S is recorded as V (S) = Σ i∈ Svi; each bunk j has a parameter w j≤vj, and the sum of all bunks not in S isIf j is in set S, it selects probability/>Otherwise, 0.
A hybrid model of any two of the above models: the probability of selecting the bin j in the model 1 is recorded asThe probability of selection in model 2 is/>The proportion parameter is phi, and the selection model of the mixed model is/>
Specifically, the calculating the nested valid boundary of the discrete selection model in step S2 includes:
When the type of the discrete selection model is one of preset model types, enumerating all the bunk sets from the bunk set C 1 to the bunk set C n, taking all the enumerated bunk sets as first bunk sets, and determining a nested effective boundary from all the first bunk sets according to the expected probability and expected benefit of each first bunk set;
When the type of the discrete selection model does not belong to any one of the preset model types, starting iteration, and determining a nested effective boundary from all second cabin level sets according to expected probability and expected benefits of the second cabin level sets generated in the iteration process;
Wherein C n represents a bunk set including the first n bunks with highest freight rates, n is the total number of bunks in the present flight trip, and the preset model types include: an independent demand model, a price oriented model, a basic appeal model, and a hybrid model of independent demand and price oriented.
The nesting effective boundary has the following characteristics:
effective boundaries, which are defined by (Q (S), R (S)) corresponding to all possible bunk sets S of a discrete selection model Wherein q is more than or equal to 0 and less than or equal to 1, Q (S) = Σ j∈Spj(S),R(S)＝∑j∈Srjpj (S),/>And for any S, alpha (S) is more than or equal to 0. As shown in fig. 2, intuitively, the effective boundary is an incremental convex broken line sequentially connecting corresponding points of a plurality of sets on the (Q, R) plane, and these sets are called an effective bunk set, as C 1、C2 in fig. 2 is an effective bunk set, and C 3 is not an effective bunk set.
Nesting the effective boundaries means that the nestability is satisfied between the sets S i on the effective boundaries, that is: q (S j)≤Q(Sk) is the same as
The nesting approximate effective boundary refers to an approximate fold line which is next calculated to meet the required property when the effective boundary of a discrete selection model does not meet the nesting property, namely an incremental upward convex fold line which is determined on the premise of ensuring the nesting property and consists of a plurality of sets S corresponding to (Q (S), R (S)). If the effective boundaries are approximate, then there is a loss in quality of the bunk allocation optimization, but there is no impact on processing the subsequent processing steps, so nested effective boundaries are not strictly distinguished from nested approximate effective boundaries herein.
The nesting order refers to the order in which the bunk products are added to the effective bunk set in the nesting effective boundary according to Q (S) from small to large, and if the bunk products are simultaneously added to the effective bunk set, the bunk products are ordered according to the freight rate of the bunks.
In an alternative embodiment, the determining a nested valid boundary from all the first bunk sets according to the expected probability and expected benefit of each first bunk set includes:
Starting iteration, calculating expected probability and expected benefit of each first bunk set, and judging whether the first bunk sets meet a first preset condition or not based on the expected probability and expected benefit of each first bunk set;
If any one of the first bunk sets meets the first preset condition, searching a bunk set with the largest ratio of the expected gain difference value to the expected probability difference value from all the first bunk sets meeting the first preset condition, taking the bunk set as a first effective bunk set, and continuing iteration;
if any first cabin level set does not exist, outputting all the first effective cabin level sets;
Wherein the first preset condition includes: q (S) > Q (S i-1) and (R (S) -R (S i-1))/(Q(S)-Q(Si-1)) >0; where Q (S) represents the expected probability of the current iteration 'S bunk set S, R (S) represents the expected benefit of the current iteration' S bunk set S, and i represents the number of iterations.
Specifically, the ratio of the expected benefit difference to the expected probability difference is (R (S) -R (S i-1))/(Q(S)-Q(Si-1)), where Q (S) represents the expected probability of the bunk set S, R (S) represents the expected benefit of the bunk set S, and i represents the number of iterations.
For example, if the determined discrete selection model is an independent demand model, a price-oriented model, a basic attraction model, or an independent demand and price-oriented hybrid model, enumerating all the bunkers from bunkers set C 1 to bunkers set C n, taking all the enumerated bunkers set as the first bunkers set, wherein C n represents the bunkers set containing k bunkers with the highest freight rate, and n is the total number of bunkers for the present flight.
For example, for all bunkers Y, B, M, K, Q in a journey, the freight rate is Y > B > M > K > Q, when the determined discrete selection model is one of the preset model types, enumeration is performed to obtain a bunkers set C 1 as { Y }, bunkers set C 2 as { Y, B }, bunkers set C 3 as { Y, B, M }, bunkers set C 4 as { Y, B, M, K }, bunkers set C 5 as { Y, B, M, K, Q }, taking the enumerated bunkers set C 1、C2、C3、C4、C5 as a first bunkers set, and determining a nested valid boundary from all the first bunkers set by the property of the discrete selection model at this time:
Calculating Q (S) = Σ j∈spj (S) and R (S) = Σ j∈Srjpj (S) for all candidate sets S;
Order the i＝1；
At the ith iteration, consider the current iteration' S bunk set S satisfying the following conditions: ①Q(S)>Q(Si-1);②(R(S)-R(Si-1))/(Q(S)-Q(Si-1)) is >0. If such S does not exist, returning to S i where all have been found; otherwise, in all the eligible S, the largest set of (R (S) -R (S i-1))/(Q(S)-Q(Si-1)) is found, denoted S i, i is increased by 1, and the iteration is restarted.
It can be understood that the output first valid bunk set constitutes the nested valid boundary of the discrete selection model of this time;
in an alternative embodiment, the starting the iteration and determining a nested effective boundary from all the second bunk sets according to the expected probability and expected benefit of the second bunk sets generated in the iteration process includes:
starting iteration, and enabling a second cabin level set of the initial iteration to be i＝1；
If any second cabin level set meets a second preset condition, searching a cabin level set with the largest ratio of the expected gain difference value to the expected probability difference value from all second cabin level sets meeting the second preset condition, taking the cabin level set as a second effective cabin level set, and continuing iteration;
If any second space set does not exist and meets the second preset condition, outputting all second effective space sets when judging that the type of the discrete selection model is a general attraction model, taking all second effective space sets as the first space sets when judging that the discrete selection model is the model of the rest type, and determining a nested effective boundary from all first space sets according to the expected probability and expected income of each first space set;
Wherein the second preset condition includes:
The elements in the current iteration bilge set at least comprise all elements in the last iteration bilge set, the number of the elements in the current iteration bilge set is one more than the number of the elements in the last iteration bilge set, and Q (S) > Q (S i-1) and (R (S) -R (S i-1))/(Q(S)-Q(Si-1)) >0;
where Q (S) represents the expected probability of the current iteration 'S bunk set S, R (S) represents the expected benefit of the current iteration' S bunk set S, and i represents the number of iterations.
Illustratively, if the determined discrete selection model is not any of an independent demand model, a price oriented model, a basic appeal model, or an independent demand and price oriented hybrid model, then:
Order the i＝1；
At the ith iteration, consider the current iteration' S bunk set S satisfying the following conditions: ① S is one more product than S i-1; ②Q(S)>Q(Si-1);③(R(S)-R(Si-1))/(Q(S)-Q(Si-1)) is >0. If such S does not exist, determining whether the determined selection model is a general attraction model; otherwise, in all the eligible S, the largest set of (R (S) -R (S i-1))/(Q(S)-Q(Si-1)) is found, denoted S i, i is increased by 1, and the iteration is restarted.
If the determined selection model is a general attraction model, returning to S i of all found models; if the determined selection model is other models, all S i are used as the first bunk sets, and one nested effective boundary is determined from all the first bunk sets. Specifically, a nesting effective boundary is determined from all the first bunk sets, which is described above and will not be described herein.
It can be understood that the second valid bunk set or the first valid bunk set output constitutes the nested valid boundary of the discrete selection model of this time;
specifically, the expected probability for each bunk set is calculated according to the following equation:
the expected revenue for each bunk set is calculated according to the following equation:
Where p j (S) is the probability that passenger selects bunk j e bunk set S, and r j represents the freight rate of bunk j.
It will be appreciated that the freight rate r j for each bunk j may be given by a certain statistical algorithm based on the bunk historical sales settlement data or real-time freight rate data, or specified based on human experience.
In an alternative embodiment, the calculating the equivalent selection probability and the equivalent freight rate of each bunk in the flight itinerary according to the nested valid boundary in step S3 includes:
Determining all effective bunk sets and the sequence of adding each bunk into the effective bunk sets according to the nested effective boundaries;
sequencing the bilges according to the sequence, sequencing the bilges in the same sequence according to the freight rates of the bilges, and marking the bilges obtained after final sequencing as bilges sequenced in the nested sequence;
enumerating all bunkers from bunkers set G 1 to bunkers set G n based on the bunkers ordered in the nested order; wherein G n represents a set of the top n bunkers ordered in nested order;
Ordering all the effective bin sets according to the number of elements in the sets, and marking the kth effective bin set as E k;
Starting a first round of traversal, for a bunk j, if G j＝Ek, the equivalent selection probability of the bunk j is Q (E k)-Q(Ek-1), the equivalent freight rate of the bunk j is (R (E k)-R(Ek-1))/(Q(Ek)-Q(Ek-1)), if G j is not equal to any E k, the equivalent selection probability of the bunk j is 0, and the equivalent freight rate of the bunk j is temporarily recorded as U;
Starting a second round of traversal, if a third berth which is positioned behind a berth j U in a nested order exists and the third berth has an equivalent freight rate, the equivalent freight rate of the berth j U is the equivalent freight rate of the third berth closest to the berth j U, and if the third berth which is positioned behind the berth j U in a nested order does not exist, the equivalent freight rate of the berth j U is 0; wherein, bunk j U is the bunk of equivalent freight temporary record U.
Illustratively, in one flight trip, the bilge products are ordered in a nested order, denoted product j=1, 2,., n; the set containing the first n bunkers in this order is denoted as G n; the kth (approximately) valid bunk set is denoted as E k, ordered by the number of elements in its set.
Starting the first round of traversal, for product j, if G j＝Ek, then the equivalent selection probability of j is Q (E k)-Q(Ek-1), the equivalent freight rate is (R (E k)-R(Ek-1))/(Q(Ek)-Q(Ek-1)), and if G j is not equal to any E k, then the equivalent freight rate of j is temporarily noted as U, and the equivalent selection probability is 0.
Starting a second round of traversal, if a third berth which is positioned behind the berth j U in a nested order exists and the third berth has equivalent freight rate, the equivalent freight rate of the berth j U is the equivalent freight rate of the third berth closest to the berth j U, and if the third berth which is positioned behind the berth j U in a nested order does not exist, the equivalent freight rate of the berth j U is 0; wherein, bunk j U is the bunk of equivalent freight temporary record U.
In one embodiment, as shown in Table 1:
TABLE 1 equivalent freight rates
In table 1, the bunkers Y, B, M, K, Q are ordered in the nested order, then bunkers set G 1＝{Y}、G2 = { Y, B }, bunkers set G 3 = { Y, B, M }, bunkers set G 4 = { Y, B, M, K }, bunkers set G 5 = { Y, B, M, K, Q };
Starting a first round of traversal, wherein for a bunk Y, G 1＝{Y}、E1 = { Y }, the equivalent probability of the bunk Y is Q (E 1)-Q(E0), the equivalent freight rate of the bunk Y is (R (E 1)-R(E0))/(Q(E1)-Q(E0)), and for a bunk B, G 2 = { Y }, no valid bunk set exists, the equivalent selection probability of a bunk j is 0, the equivalent freight rate of the bunk j is temporarily recorded as U, and the rest bunks are not repeated, and can be seen in Table 1;
Starting a second round of traversal, for a bunk B with the equivalent freight rate temporarily marked as U, a third bunk M, K with the nested sequence positioned behind the bunk B exists, the equivalent freight rate of the bunk B is the equivalent freight rate of a third bunk M closest to the bunk B, namely 450, and for a bunk Q with the equivalent freight rate temporarily marked as U, the equivalent freight rate of the bunk C is 0 if the third bunk with the nested sequence positioned behind the bunk C does not exist.
In an alternative embodiment, the calculating, in step S5, the base price of each flight in different flight states by using a dynamic programming decomposition method based on the equivalent selection probability, the equivalent freight rate and the pre-acquired total demand of the flight itineraries includes:
calculating the equivalent demand of each cabin according to the equivalent selection probability and the pre-acquired demand total of the flight travel;
calculating the shadow price of each flight;
For each flight i, dividing its booking period into T time periods T, calculating the maximum expected return in state (T, x) as V t (x):
Where a ij =1 denotes that the bunk j occupies the seat of the flight i, λ tj denotes the demand occurrence probability at each time period t, λ tj＝dj/T,dj denotes the equivalent demand amount of the bunk j, Representing the equivalent freight rate of bunk j, Δv t-1(x)＝Vt-1(x)-Vt-1 (x-1), x representing the number of seats remaining, z k representing the shadow price of flight k, a kj =1 if bunk occupies flight k; when t=0 or x=0, V t (x) =0;
let DeltaV t-1 (x) in state (t, x) be the base price, obtain the base price of each flight in all states (t, x).
It will be appreciated that the total amount of demand for a flight trip may be based on historical booking situations or other relevant data for all billboards, given by algorithmic techniques such as time series, or specified based on human experience.
In an alternative embodiment, the shadow price of each flight is obtained by solving a pre-built linear programming model;
wherein the linear programming model is:
wherein, Let y j be the decision variable, let M be the theoretical allocation number of bunk j, M be the set of all flights in the flight itinerary, c i be the current number of seats available for flight i e M, a ij =1 if bunk j occupies the seat of flight i, a ij =0 if bunk j does not occupy the seat of flight i.
It will be appreciated that for a flight itinerary with only one flight i, its bunk j must occupy the seat of that flight i, i.e. there is only a ij =1;
For a flight itinerary comprising at least two flights, there will be a situation where bunk j occupies the seat of flight i, i.e. a ij =1, and there will also be a situation where bunk j does not occupy the seat of flight i, i.e. a ij =0, because bunk j will occupy other flights at this time.
It can be understood that the embodiment of the invention adopts dynamic programming decomposition to determine the bottom price of the flight, and specifically comprises two steps: (1) obtaining a flight shadow price by a linear programming; (2) And taking the shadow price into consideration, and carrying out dynamic planning on each flight to obtain the base price of each state of the flight.
For (2) dynamic planning, consider "flights", where one flight may involve a number of flights of a flight itinerary. For example, calculate flight A-B (location A-location B), which may have flights A-B, flights A-B-C, and the bunkers of flights X-A-B-C. At this time, the shadow prices of other flights need to be subtracted, the 'adjustment freight rate' of the trips is calculated, for example, the adjustment freight rate of A-B-C is the shadow price of the equivalent freight rate subtracted by B-C, X-A-B-C is the shadow price of the equivalent freight rate subtracted by X-A and B-C, A-B does not need to be subtracted, the adjustment freight rate is the equivalent freight rate, and after adjustment, the solution is carried out according to the dynamic programming of a single flight.
Illustratively, the set of all relevant bunkers in the airline network is denoted as N, let the equivalent demand of each bunkers j e N in the airline network be the product of its equivalent selection probability and the total demand of the flight itinerary, denoted as d j; equivalent freight rate is recorded as
All relevant flights in the airlines form a set M, the currently available seat number of the flight i e M is c i, if the seat of i is occupied by the seat product j, a ij =1 is recorded, otherwise, 0 is recorded. Establishing a linear programming model taking y j as a decision variable:
Solving the above problem by using an optimization software package, the shadow price z i corresponding to i can be obtained.
For each flight i e M, the booking cycle is divided into T time periods of sufficient size, i.e., t=t..0, 0 being the departure time. For convenience of description, it is assumed that the demand does not change with time, but the method is easily generalized to the case of time-dependent. Considering the bunk j of a ij =1, let λ tj＝dj/T be the probability of occurrence of demand at each time period T, let x be the number of seats remaining, let V t (x) be the maximum expected benefit in state (T, x), there is the following dynamic programming equation:
Wherein DeltaV t-1(x)＝Vt-1(x)-Vt-1 (x-1). V t (x) =0, V t (x) and Δv t-1 (x) in all states (t, x) are easily calculated from the marginal condition, i.e., when t=0 or x=0.
For each flight, BP of the flight under the state (t, x) is set as DeltaV t-1 (x), and BP tables formed by BP under all states (t, x) are returned.
It is understood that in step S5, the open cabin state is a saleable state, and the closed cabin state is an unperceivable state. In the current state, for any bunk in the flight journey, calculating the sum of the bottom prices of every flight occupied by the bunk according to every bottom price of the bunk in the current state, if the equivalent freight price of the bunk is larger than the sum of the bottom prices of all flights occupied by the bunk in the current state, setting the bunk as an open cabin state, and otherwise, setting the bunk as a closed cabin state.
Referring to fig. 3, fig. 3 is a block diagram of a bunk distribution device 10 according to an embodiment of the present invention, where the bunk distribution device 10 includes:
A model determination module 11 for determining a discrete selection model of the flight itinerary; wherein the flight itinerary comprises at least one flight;
A first calculation module 12 for calculating nested effective boundaries of the discrete selection model; wherein, the nesting is carried out between the sets on the nesting effective boundary;
A second calculation module 13, configured to calculate an equivalent selection probability and an equivalent freight rate of each bunk in the flight trip according to the nested effective boundaries;
A third calculation module 14, configured to calculate a bottom price of each flight in different flight states using a dynamic programming decomposition method based on the total demand, the equivalent selection probability, and the equivalent freight rate; wherein the flight status includes time and number of seats remaining;
The bunk state setting module 15 is configured to set the bunk state to an open cabin state when the equivalent freight rate of the bunk is greater than the sum of the bottom rates of all flights occupied by the bunk in the current state, and set the bunk state to a closed cabin state otherwise.
Preferably, said computing the nested valid boundaries of the discrete selection model comprises:
When the type of the discrete selection model is one of preset model types, enumerating all the bunk sets from the bunk set C 1 to the bunk set C n, taking all the enumerated bunk sets as first bunk sets, and determining a nested effective boundary from all the first bunk sets according to the expected probability and expected benefit of each first bunk set;
When the type of the discrete selection model does not belong to any one of the preset model types, starting iteration, and determining a nested effective boundary from all second cabin level sets according to expected probability and expected benefits of the second cabin level sets generated in the iteration process;
Wherein C n represents a bunk set including the first n bunks with highest freight rates, n is the total number of bunks in the present flight trip, and the preset model types include: an independent demand model, a price oriented model, a basic appeal model, and a hybrid model of independent demand and price oriented.
Preferably, the determining a nested effective boundary from all the first bunk sets according to the expected probability and expected benefit of each first bunk set includes:
Starting iteration, calculating expected probability and expected benefit of each first bunk set, and judging whether the first bunk sets meet a first preset condition or not based on the expected probability and expected benefit of each first bunk set;
If any one of the first bunk sets meets the first preset condition, searching a bunk set with the largest ratio of the expected gain difference value to the expected probability difference value from all the first bunk sets meeting the first preset condition, taking the bunk set as a first effective bunk set, and continuing iteration;
if any first cabin level set does not exist, outputting all the first effective cabin level sets;
Wherein the first preset condition includes: q (S) > Q (S i-1) and (R (S) -R (S i-1))/(Q(S)-Q(Si-1)) >0; where Q (S) represents the expected probability of the current iteration 'S bunk set S, R (S) represents the expected benefit of the current iteration' S bunk set S, and i represents the number of iterations.
Preferably, the starting the iteration and determining a nested effective boundary from all the second bunk sets according to the expected probability and expected benefit of the second bunk sets generated in the iteration process includes:
Let the second bunk set of the initial iteration i＝1；
Starting iteration, if any second cabin level set meets a preset second preset condition, searching a cabin level set with the largest ratio of a desired gain difference value to a desired probability difference value from all second cabin level sets meeting the second preset condition, taking the cabin level set as a second effective cabin level set, and continuing iteration;
If any second space set does not exist and meets the second preset condition, outputting all second effective space sets when judging that the type of the discrete selection model is a general attraction model, taking all second effective space sets as the first space sets when judging that the discrete selection model is the model of the rest type, and determining a nested effective boundary from all first space sets according to the expected probability and expected income of each first space set;
Wherein the second preset condition includes:
The elements in the current iteration bilge set at least comprise all elements in the last iteration bilge set, the number of the elements in the current iteration bilge set is one more than the number of the elements in the last iteration bilge set, and Q (S) > Q (S i-1) and (R (S) -R (S i-1))/(Q(S)-Q(Si-1)) >0;
where Q (S) represents the expected probability of the current iteration 'S bunk set S, R (S) represents the expected benefit of the current iteration' S bunk set S, and i represents the number of iterations.
Preferably, the expected probability for each bunk set is calculated according to the following equation:
/>
the expected revenue for each bunk set is calculated according to the following equation:
Where p j (S) is the probability that passenger selects bunk j e bunk set S, and r j represents the freight rate of bunk j.
Preferably, the calculating the equivalent selection probability and equivalent freight rate of each bunk in the flight trip according to the nested effective boundaries includes:
Determining all effective bunk sets and the sequence of adding each bunk into the effective bunk sets according to the nested effective boundaries;
sequencing the bilges according to the sequence, sequencing the bilges in the same sequence according to the freight rates of the bilges, and marking the bilges obtained after final sequencing as bilges sequenced in the nested sequence;
enumerating all bunkers from bunkers set G 1 to bunkers set G n based on the bunkers ordered in the nested order; wherein G n represents a set of the top n bunkers ordered in nested order;
Ordering all the effective bin sets according to the number of elements in the sets, and marking the kth effective bin set as E k;
Starting a first round of traversal, for a bunk j, if G j＝Ek, the equivalent selection probability of the bunk j is Q (E k)-Q(Ek-1), the equivalent freight rate of the bunk j is (R (E k)-R(Ek-1))/(Q(Ek)-Q(Ek-1)), if G j is not equal to any E k, the equivalent selection probability of the bunk j is 0, and the equivalent freight rate of the bunk j is temporarily recorded as U;
Starting a second round of traversal, if a third berth which is positioned behind a berth j U in a nested order exists and the third berth has an equivalent freight rate, the equivalent freight rate of the berth j U is the equivalent freight rate of the third berth closest to the berth j U, and if the third berth which is positioned behind the berth j U in a nested order does not exist, the equivalent freight rate of the berth j U is 0; wherein, bunk j U is the bunk of equivalent freight temporary record U.
Preferably, calculating the base price of each flight in different flight states by using a dynamic programming decomposition method based on the equivalent selection probability, the equivalent freight rate and the pre-acquired total demand of the flight itinerary comprises:
calculating the equivalent demand of each cabin according to the equivalent selection probability and the pre-acquired demand total of the flight travel;
calculating the shadow price of each flight;
For each flight i, dividing its booking period into T time periods T, calculating the maximum expected return in state (T, x) as V t (x):
Where a ij =1 denotes that the bunk j occupies the seat of the flight i, λ tj denotes the demand occurrence probability at each time period t, λ tj＝dj/T,dj denotes the equivalent demand amount of the bunk j, Representing the equivalent freight rate of bunk j, Δv t-1(x)＝Vt-1(x)-Vt-1 (x-1), x representing the number of seats remaining, z k representing the shadow price of flight k, a kj =1 if bunk occupies flight k;
let DeltaV t-1 (x) in state (t, x) be the base price, obtain the base price of each flight in all states (t, x).
Preferably, the shadow price of each flight is obtained by solving a pre-built linear programming model;
wherein the linear programming model is:
wherein, Representing the equivalent freight rate of bunk j, y j is the theoretical allocation number of bunk j, M represents the set of all flights in the flight itinerary, c i represents the current available seat number for flight i e M, a ij =1 if bunk j occupies the seat of flight i, and a ij =0 if bunk j does not occupy the seat of flight i.
It should be noted that, the working process of each module in the bunk distribution device 10 according to the embodiment of the present invention may refer to the working process of the bunk distribution method according to the above embodiment, which is not described herein.
The embodiment of the invention also provides a computer readable storage medium, which is characterized in that the computer readable storage medium comprises a stored computer program; wherein the computer program, when running, controls the device in which the computer readable storage medium is located to execute the method for allocating a bunk according to the above embodiment.
Referring to fig. 4, fig. 4 is a block diagram of an electronic device 20 according to an embodiment of the present invention, where the electronic device 20 includes: a processor 21, a memory 22 and a computer program stored in said memory 22 and executable on said processor 21. The steps of the above-described embodiments of the bunk allocation method are implemented when the processor 21 executes the computer program. Or the processor 21, when executing the computer program, performs the functions of the modules/units in the above-described device embodiments.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used to describe the execution of the computer program in the electronic device 20.
The electronic device 20 may be a computing device such as a desktop computer, a notebook computer, a palm computer, and a cloud server. The electronic device 20 may include, but is not limited to, a processor 21, a memory 22. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of the electronic device 20 and is not meant to be limiting of the electronic device 20, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device 20 may also include input-output devices, network access devices, buses, etc.
The Processor 21 may be a central processing unit (Central Processing Unit, CPU), other general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor 21 is a control center of the electronic device 20, and connects various parts of the entire electronic device 20 using various interfaces and lines.
The memory 22 may be used to store the computer program and/or module, and the processor 21 may implement various functions of the electronic device 20 by executing or executing the computer program and/or module stored in the memory 22, and invoking data stored in the memory 22. The memory 22 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
Wherein the integrated modules/units of the electronic device 20 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of each of the method embodiments described above when executed by the processor 21. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Compared with the prior art, the method, the device and the equipment for distributing the cabin space provided by the embodiment of the invention have the following beneficial effects:
(1) The embodiment of the invention solves the problem that the non-independent requirement is difficult to process in the problem of the allocation of the aviation network/the flight space;
(2) The embodiment of the invention allows any discrete selection model to be assumed for the bilge product of each flight trip;
(3) The embodiment of the invention can automatically calculate the equivalent demand and equivalent freight rate of each flight journey space;
(4) If the discrete selection model accords with certain specific forms, the embodiment of the invention can ensure that the cabin allocation scheme of the equivalent problem is consistent with the original problem;
(5) The embodiment of the invention can carry out real-time cabin-position marketability control in a navigation network by adopting a BP control method, and can meet the requirement of real-time calculation.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (9)
1. A bunk allocation method, comprising:
Determining a discrete selection model of the flight itinerary; wherein the flight itinerary comprises at least one flight;
Calculating nested effective boundaries of the discrete selection model; the method comprises the steps that a space level set on a nesting effective boundary is nested, all space level sets form an incremental upward convex broken line on a (Q (S), R (S)) plane, Q (S) represents expected probability of the space level set S of the current iteration, and R (S) represents expected benefit of the space level set S of the current iteration;
Calculating the equivalent selection probability and equivalent freight rate of each berth in the flight journey according to the nested effective boundaries;
Calculating the base price of each flight in different flight states by adopting a dynamic programming decomposition method based on the equivalent selection probability, the equivalent freight rate and the pre-acquired total demand of the flight journey; wherein the flight status includes time and number of seats remaining;
When the equivalent freight rate of the bunk is larger than the sum of the bottom rates of all flights occupied by the bunk in the current state, setting the state of the bunk as an open cabin state, otherwise, setting the state of the bunk as a closed cabin state;
the calculating the equivalent selection probability and equivalent freight rate of each bunk in the flight journey according to the nested effective boundaries comprises the following steps:
Determining all effective bunk sets and the sequence of adding each bunk into the effective bunk sets according to the nested effective boundaries;
sequencing the bilges according to the sequence, sequencing the bilges in the same sequence according to the freight rates of the bilges, and marking the bilges obtained after final sequencing as bilges sequenced in the nested sequence;
enumerating all bunkers from bunkers set G 1 to bunkers set G n based on the bunkers ordered in the nested order; wherein G n represents a set of the top n bunkers ordered in nested order;
Ordering all the effective bin sets according to the number of elements in the sets, and marking the kth effective bin set as E k;
Starting a first round of traversal, for a bunk j, if G j＝Ek, the equivalent selection probability of the bunk j is Q (E k)-Q(Ek-1), the equivalent freight rate of the bunk j is (R (E k)-R(Ek-1))/(Q(Ek)-Q(Ek-1)), if G j is not equal to any E k, the equivalent selection probability of the bunk j is 0, and the equivalent freight rate of the bunk j is temporarily recorded as U;
Starting a second round of traversal, if a third berth which is positioned behind a berth j U in a nested order exists and the third berth has an equivalent freight rate, the equivalent freight rate of the berth j U is the equivalent freight rate of the third berth closest to the berth j U, and if the third berth which is positioned behind the berth j U in a nested order does not exist, the equivalent freight rate of the berth j U is 0; wherein, bunk j U is the bunk of equivalent freight temporary record U.
2. The bunk allocation method of claim 1, wherein said calculating nested valid boundaries for said discrete selection model comprises:
When the type of the discrete selection model is one of preset model types, enumerating all the bunk sets from the bunk set C 1 to the bunk set C n, taking all the enumerated bunk sets as first bunk sets, and determining a nested effective boundary from all the first bunk sets according to the expected probability and expected benefit of each first bunk set;
When the type of the discrete selection model does not belong to any one of the preset model types, starting iteration, and determining a nested effective boundary from all second cabin level sets according to expected probability and expected benefits of the second cabin level sets generated in the iteration process;
Wherein C n represents a bunk set including the first n bunks with highest freight rates, n is the total number of bunks in the present flight trip, and the preset model types include: an independent demand model, a price oriented model, a basic appeal model, and a hybrid model of independent demand and price oriented.
3. The bunk allocation method of claim 2, wherein said determining a nested effective boundary from all of said first bunk sets based on expected probabilities and expected profits for each first bunk set comprises:
Starting iteration, calculating expected probability and expected benefit of each first bunk set, and judging whether the first bunk sets meet a first preset condition or not based on the expected probability and expected benefit of each first bunk set;
If any one of the first bunk sets meets the first preset condition, searching a bunk set with the largest ratio of the expected gain difference value to the expected probability difference value from all the first bunk sets meeting the first preset condition, taking the bunk set as a first effective bunk set, and continuing iteration;
if any first cabin level set does not exist, outputting all the first effective cabin level sets;
Wherein the first preset condition includes: q (S) > Q (S i-1) and (R (S) -R (S i-1))/(Q(S)-Q(Si-1)) >0; where Q (S) represents the expected probability of the current iteration 'S bunk set S, R (S) represents the expected benefit of the current iteration' S bunk set S, and i represents the number of iterations.
4. A method of bunk allocation as claimed in claim 3 wherein said starting an iteration and determining a nested effective boundary from all of said second bunk sets based on expected probabilities and expected profits of the second bunk sets generated during the iteration comprises:
starting iteration, and enabling a second cabin level set of the initial iteration to be i＝1；
If any second cabin level set meets a second preset condition, searching a cabin level set with the largest ratio of the expected gain difference value to the expected probability difference value from all second cabin level sets meeting the second preset condition, taking the cabin level set as a second effective cabin level set, and continuing iteration;
If any second space set does not exist and meets the second preset condition, outputting all second effective space sets when judging that the type of the discrete selection model is a general attraction model, taking all second effective space sets as the first space sets when judging that the discrete selection model is the model of the rest type, and determining a nested effective boundary from all first space sets according to the expected probability and expected income of each first space set;
Wherein the second preset condition includes:
The elements in the current iteration bilge set at least comprise all elements in the last iteration bilge set, the number of the elements in the current iteration bilge set is one more than the number of the elements in the last iteration bilge set, and Q (S) > Q (S i-1) and (R (S) -R (S i-1))/(Q(S)-Q(Si-1)) >0;
where Q (S) represents the expected probability of the current iteration 'S bunk set S, R (S) represents the expected benefit of the current iteration' S bunk set S, and i represents the number of iterations.
5. The bunk allocation method of claim 2, wherein the expected probability for each bunk set is calculated according to the following equation:
the expected revenue for each bunk set is calculated according to the following equation:
Where p j (S) is the probability that passenger selects bunk j e bunk set S, and r j represents the freight rate of bunk j.
6. The bunk allocation method according to claim 1, wherein said calculating a base price of each flight in different flight states using a dynamic programming decomposition method based on said equivalent selection probability and said equivalent freight rate and a pre-acquired total demand of said flight itinerary comprises:
calculating the equivalent demand of each cabin according to the equivalent selection probability and the pre-acquired demand total of the flight travel;
calculating the shadow price of each flight;
For each flight i, dividing its booking period into T time periods T, calculating the maximum expected return in state (T, x) as V t (x):
Where a ij =1 denotes that the bunk j occupies the seat of the flight i, λ tj denotes the demand occurrence probability at each time period t, λ tj＝dj/T,dj denotes the equivalent demand amount of the bunk j, Representing the equivalent freight rate of bunk j, Δv t-1(x)＝Vt-1(x)-Vt-1 (x-1), x representing the number of seats remaining, z k representing the shadow price of flight k, a kj =1 if bunk occupies flight k; when t=0 or x=0, V t (x) =0;
let DeltaV t-1 (x) in state (t, x) be the base price, obtain the base price of each flight in all states (t, x).
7. The bunk allocation method according to claim 1, wherein the shadow price of each flight is obtained by solving a linear programming model constructed in advance;
wherein the linear programming model is:
wherein, Representing the equivalent freight rate of bunk j, y j is the theoretical allocation number of bunk j, M represents the set of all flights in the flight itinerary, c i represents the current available seat number for flight i e M, a ij =1 if bunk j occupies the seat of flight i, and a ij =0 if bunk j does not occupy the seat of flight i.
8. A bunk dispensing device, comprising:
The model determining module is used for determining a discrete selection model of the flight journey; wherein the flight itinerary comprises at least one flight;
a first calculation module for calculating nested effective boundaries of the discrete selection model; the method comprises the steps that a space level set on a nesting effective boundary is nested, all space level sets form an incremental upward convex broken line on a (Q (S), R (S)) plane, Q (S) represents expected probability of the space level set S of the current iteration, and R (S) represents expected benefit of the space level set S of the current iteration;
the second calculation module is used for calculating the equivalent selection probability and equivalent freight rate of each cabin in the flight journey according to the nested effective boundaries;
the demand total amount acquisition module is used for acquiring the demand total amount of the flight journey;
The third calculation module is used for calculating the bottom price of each flight in different flight states by adopting a dynamic programming decomposition method based on the total demand, the equivalent selection probability and the equivalent freight price; wherein the flight status includes time and number of seats remaining;
the cabin state setting module is used for setting the state of the cabin to be an open cabin state when the equivalent freight rate of the cabin is larger than the sum of the bottom rates of all flights occupied by the cabin in the current state, and setting the state of the cabin to be a closed cabin state otherwise;
the calculating the equivalent selection probability and equivalent freight rate of each bunk in the flight journey according to the nested effective boundaries comprises the following steps:
Determining all effective bunk sets and the sequence of adding each bunk into the effective bunk sets according to the nested effective boundaries;
sequencing the bilges according to the sequence, sequencing the bilges in the same sequence according to the freight rates of the bilges, and marking the bilges obtained after final sequencing as bilges sequenced in the nested sequence;
enumerating all bunkers from bunkers set G 1 to bunkers set G n based on the bunkers ordered in the nested order; wherein G n represents a set of the top n bunkers ordered in nested order;
Ordering all the effective bin sets according to the number of elements in the sets, and marking the kth effective bin set as E k;
Starting a first round of traversal, for a bunk j, if G k＝Ek, the equivalent selection probability of the bunk j is Q (E k)-Q(Ek-1), the equivalent freight rate of the bunk j is (R (E k)-R(Ek-1))/(Q(Ek)-Q(Ek-1)), if G j is not equal to any E k, the equivalent selection probability of the bunk j is 0, and the equivalent freight rate of the bunk j is temporarily recorded as U;
Starting a second round of traversal, if a third berth which is positioned behind a berth j U in a nested order exists and the third berth has an equivalent freight rate, the equivalent freight rate of the berth j U is the equivalent freight rate of the third berth closest to the berth j U, and if the third berth which is positioned behind the berth j U in a nested order does not exist, the equivalent freight rate of the berth j U is 0; wherein, bunk j U is the bunk of equivalent freight temporary record U.
9. An electronic device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the method of bunk allocation according to any of claims 1-7 when the computer program is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310479600.XA CN116502850B (en) | 2023-04-27 | 2023-04-27 | Cabin position distribution method, device and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310479600.XA CN116502850B (en) | 2023-04-27 | 2023-04-27 | Cabin position distribution method, device and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116502850A CN116502850A (en) | 2023-07-28 |
| CN116502850B true CN116502850B (en) | 2024-04-26 |
Family
ID=87328089
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310479600.XA Active CN116502850B (en) | 2023-04-27 | 2023-04-27 | Cabin position distribution method, device and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116502850B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005316614A (en) * | 2004-04-27 | 2005-11-10 | Univ Nihon | Optimization method and optimization program |
| CN110751520A (en) * | 2019-10-21 | 2020-02-04 | 中国民航信息网络股份有限公司 | Flight cabin opening method and device |
| CN111626482A (en) * | 2020-05-09 | 2020-09-04 | 天津市市政工程设计研究院 | Air freight cabin allocation method and system |
| CN112258273A (en) * | 2020-10-20 | 2021-01-22 | 中国民航信息网络股份有限公司 | Bidding cabin ascending method and device |
| CN113269402A (en) * | 2021-04-28 | 2021-08-17 | 北京筹策科技有限公司 | Flight space control method and device and computer equipment |
| CN113393088A (en) * | 2021-05-19 | 2021-09-14 | 悠桦林信息科技(上海)有限公司 | Method, device, equipment, medium and yield management system for controlling cabin in air transportation |
| CN114298745A (en) * | 2021-12-27 | 2022-04-08 | 中国民航信息网络股份有限公司 | Data processing method and device, electronic equipment and computer storage medium |
| CN115730784A (en) * | 2022-11-08 | 2023-03-03 | 中国南方航空股份有限公司 | Civil aviation flight seat inventory management method |
-
2023
- 2023-04-27 CN CN202310479600.XA patent/CN116502850B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005316614A (en) * | 2004-04-27 | 2005-11-10 | Univ Nihon | Optimization method and optimization program |
| CN110751520A (en) * | 2019-10-21 | 2020-02-04 | 中国民航信息网络股份有限公司 | Flight cabin opening method and device |
| CN111626482A (en) * | 2020-05-09 | 2020-09-04 | 天津市市政工程设计研究院 | Air freight cabin allocation method and system |
| CN112258273A (en) * | 2020-10-20 | 2021-01-22 | 中国民航信息网络股份有限公司 | Bidding cabin ascending method and device |
| CN113269402A (en) * | 2021-04-28 | 2021-08-17 | 北京筹策科技有限公司 | Flight space control method and device and computer equipment |
| CN113393088A (en) * | 2021-05-19 | 2021-09-14 | 悠桦林信息科技(上海)有限公司 | Method, device, equipment, medium and yield management system for controlling cabin in air transportation |
| CN114298745A (en) * | 2021-12-27 | 2022-04-08 | 中国民航信息网络股份有限公司 | Data processing method and device, electronic equipment and computer storage medium |
| CN115730784A (en) * | 2022-11-08 | 2023-03-03 | 中国南方航空股份有限公司 | Civil aviation flight seat inventory management method |
Non-Patent Citations (1)
| Title |
|---|
| 考虑超售的多级运价航空货运容量分配模型;雷丽彩 等;《中国民航大学学报》;20091015;第27卷(第05期);第44-48页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116502850A (en) | 2023-07-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Frizzell et al. | The split delivery vehicle scheduling problem with time windows and grid network distances | |
| US11176492B2 (en) | Training a machine to automate spot pricing of logistics services in a large-scale network | |
| Elhallaoui et al. | Multi-phase dynamic constraint aggregation for set partitioning type problems | |
| US8046319B2 (en) | Methods suitable for optimizing linehaul operations | |
| Poot et al. | A savings based method for real-life vehicle routing problems | |
| Schneider | The vehicle-routing problem with time windows and driver-specific times | |
| CN110334959A (en) | A kind of flight freight space resource allocation methods and device | |
| Russell | Mathematical programming heuristics for the production routing problem | |
| Goodson | A priori policy evaluation and cyclic-order-based simulated annealing for the multi-compartment vehicle routing problem with stochastic demands | |
| Nicola et al. | Total distance approximations for routing solutions | |
| Huang | Solving the multi-compartment capacitated location routing problem with pickup–delivery routes and stochastic demands | |
| Shi et al. | The dynamic fleet management problem with uncertain demand and customer chosen service level | |
| CN111382894B (en) | Cabin booking planning method, device, equipment and storage equipment | |
| CN111192090A (en) | Seat allocation method and device for flight, storage medium and electronic equipment | |
| CN110377829A (en) | Function recommended method and device applied to electronic equipment | |
| CN110544055A (en) | order processing method and device | |
| CN114219276A (en) | Warehouse logistics simulation method, device, equipment and storage medium | |
| CN116502850B (en) | Cabin position distribution method, device and equipment | |
| US20230130643A1 (en) | System and Method for End-to-End Train Trip Management | |
| Alnaggar et al. | Compensation guarantees in crowdsourced delivery: Impact on platform and driver welfare | |
| US8688599B2 (en) | Transportation problem solving device, transportation problem solving method, and program and recording medium therefor | |
| US20050144056A1 (en) | Systems and methods for capacity reservation | |
| CN112214321A (en) | Node selection method and device for newly-added micro service and micro service management platform | |
| Nonås et al. | Optimal and heuristic solutions for a scheduling problem arising in a foundry | |
| Fisher | On a pooling problem from the statistical decision viewpoint |
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 |