CN116502850A - Cabin position distribution method, device and equipment - Google Patents
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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:
enumerating a slave bunk set C when the type of the discrete selection model is one of preset model types 1 To bunk set C n Taking all enumerated bunk sets as first bunk sets, and determining a nested effective boundary from all first bunk sets according to expected probability and expected income 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 is n Representing a bunk set containing the top n bunks with highest freight rate, wherein n is the total number of bunks of the current flight journey, and the preset model type comprises: 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(S i-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 bei＝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 of the cabin level set at least comprise all elements in the cabin level set of the previous iteration, the number of the elements in the cabin level set of the current iteration is one more than the number of the elements in the cabin level set of the previous iteration, and Q (S) > Q (S i-1 ) And (R (S) -R (S) i-1 ))/(Q(S)-Q(S i-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:
wherein p is j (S) probability of selecting a bunk j e bunk set S for a passenger, r j Representing 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 slave bilge set G based on bilges ordered in nested order 1 To the bunk set G n Is a set of all bunks; wherein G is n Cabins representing the first n cabins ordered in nested orderA set of bits;
ordering all the effective cabin level sets according to the number of elements in the sets, and marking the kth effective cabin level set as E k ；
Starting a first round of traversal, for the bunk j, if G j ＝E k Then the probability of equivalent choice of the bunk j is Q (E k )-Q(E k-1 ) The equivalent freight rate of the bunk j is (R (E k )-R(E k-1 ))/(Q(E k )-Q(E k-1 ) If G) j Not equal to any E k The equivalent selection probability of the bin j is 0, and the equivalent freight rate of the bin j is temporarily recorded as U;
starting a second round of traversal, if there is a nested order at the bunk j U A third post, and the third post has equivalent freight rate, then the post j U Equivalent freight rate of (1) is equal to the cabin level j U The nearest third bunk equivalent freight rate, if no nesting order exists at bunk j U The third cabin is then the cabin j U Is 0; wherein, the cabin position j U The equivalent freight rate is temporarily recorded as the cabin position of 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)：
Wherein a is ij Let 1 denote that bunk j occupies the seat of flight i, λ tj Representing at each timeProbability of occurrence of demand, lambda, of interval t tj ＝d j /T，d j Indicating the equivalent demand of the bunk j,representing the equivalent freight rate of the bunk j, deltaV t-1 (x)＝V t-1 (x)-V t-1 (x-1), x represents the number of seats remaining, z k A represents the shadow price of flight k, if the bunk j occupies flight k kj =1; when t=0 or x=0, V t (x)＝0；
DeltaV in state (t, x) t-1 (x) Setting the base price, the base price of each flight in all states (t, x) is obtained.
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 the bunk j, y j Theoretical allocation number for bunk j, M representing the set of all flights in a flight itinerary, c i Representing the current available seat number of flight i e M, if seat j occupies the seat of flight i, then a ij =1, if the bunk j does not occupy the seat of flight i, a ij ＝0。
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 NWhen the passenger selects the probability p of a certain cabin level j epsilon S j (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 parameters of all the bilges cannot exceed 1; if j is in the set S, its selection probability p j (S)＝q j Otherwise, 0.
Price guide model: each bunk j has a parameter l not exceeding 1 j And the lower the freight rate is for the bunk l j The larger; if j is exactly the lowest freight rate bilge in the set S, the probability p is selected j (S)＝l j Otherwise, 0.
Basic attraction model: each bunk j has a parameter v j The sum of all the cabin bits in S is recorded as V (S) = sigma i∈S v i The method comprises the steps of carrying out a first treatment on the surface of the If j is in the set S, its selection probability p j (S)＝v j V (S) +1), otherwise 0.
General attraction model: each bunk j has a parameter v j Recording all the cabin positions in S as the parameterThe sum is V (S) = sigma i∈S v i The method comprises the steps of carrying out a first treatment on the surface of the Each bunk j has a parameter w j ≤v j The sum of the parameters of all the bilges not in S is recorded asIf j is in set S, its selection 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, the selection model of the mixed model is +.>
Specifically, the calculating the nested valid boundary of the discrete selection model in step S2 includes:
enumerating a slave bunk set C when the type of the discrete selection model is one of preset model types 1 To bunk set C n Taking all enumerated bunk sets as first bunk sets, and determining a nested effective boundary from all first bunk sets according to expected probability and expected income 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 is n Representing a bunk set containing the top n bunks with highest freight rate, wherein n is the total number of bunks of the current flight journey, and the preset model type comprises: 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 modelWherein q is more than or equal to 0 and less than or equal to 1, and Q (S) = Σ j∈S p j (S)，R(S)＝∑ j∈S r j p j (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 connecting corresponding points of multiple sets in turn on the (Q, S) plane, and these sets are called effective bunk sets, such as C in fig. 2 1 、C 2 Is an effective bunk set, C 3 Not a valid set of bunkers.
Nesting effective boundaries, meaning the set S on the effective boundaries i The nesting property is satisfied, namely: if Q (S) j )≤Q(S k ) Then there is a need to
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 curve 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(S i-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 gain difference to the expected probability difference is (R (S) -R (S) i-1 ))/(Q(S)-Q(S i-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.
Illustratively, if the determined discrete selection model is an independent demand model, a price oriented model, a basic appeal model, or an independent demand and price oriented hybrid model, enumerating the slave bunk set C 1 To bunk set C n Will be All enumerated bunk sets are used as a first bunk set, wherein C n The bilge collection containing k bilges with highest freight rates is represented, and n is the total number of bilges of the current flight journey.
For example, for all bunks Y, B, M, K, Q in a journey, whose freight rates are Y > B > M > K > Q, enumerating to obtain a bunk set C when the determined discrete selection model is one of the preset model types 1 Is { Y }, the cabin level set C 2 Is { Y, B }, bunk set C 3 Is { Y, B, M }, bunk set C 4 Is { Y, B, M, K }, bunk set C 5 Is { Y, B, M, K, Q }, the enumerated bunk set C 1 、C 2 、C 3 、C 4 、C 5 As a first bunk set, the nestability of the effective boundary is ensured by discrete selection model properties, and one nested effective boundary is determined from all the first bunk sets:
calculate Q (S) = Σfor all candidate sets S j∈S p j (S) and R (S) = Σ j∈S r j p j (S)；
Order thei＝1；
At the ith iteration, consider the current iteration' S bunk set S satisfying the following conditions: (1) q (S) > Q (S i-1 )；②(R(S)-R(S i-1 ))/(Q(S)-Q(S i-1 ) And) 0. If such S does not exist, return to all found S i The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, in all qualified S, find (R (S) -R (S i-1 ))/(Q(S)-Q(S i-1 ) Maximum set, denoted S i Let i increase by 1 and restart the iteration.
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 bei＝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 of the cabin level set at least comprise all elements in the cabin level set of the previous iteration, the number of the elements in the cabin level set of the current iteration is one more than the number of the elements in the cabin level set of the previous iteration, and Q (S) > Q (S i-1 ) And (R (S) -R (S) i-1 ))/(Q(S)-Q(S i-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 thei＝1；
At the ith iteration, consider the current iteration' S bunk set S satisfying the following conditions: (1) s ratio S i-1 One more product; (2) q (S) > Q (S i-1 )；③(R(S)-R(S i-1 ))/(Q(S)-Q(S i-1 ) And) 0. If such S does not exist, determining whether the determined selection model is a general attraction model; otherwise, in all qualified S, find (R (S) -R (S i-1 ))/(Q(S)-Q(S i-1 ) Maximum set, denoted S i Let i increase by 1 and restart the iteration.
If the determined selection model is a general attraction model, returning to all found S i The method comprises the steps of carrying out a first treatment on the surface of the If the determined selection model is other models, all S i As a first bunk set, a nested valid boundary is determined from all of 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:
wherein p is j (S) probability of selecting a bunk j e bunk set S for a passenger, r j Representing the freight rate of bunk j.
It will be appreciated that the freight rate r for each bunk j j The historical sales settlement data or the real-time freight rate data based on the cabin level can be given by a certain statistical algorithm or specified based on manual 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 slave bilge set G based on bilges ordered in nested order 1 To the bunk set G n Is a set of all bunks; wherein G is n Representing a set of top n bunkers ordered in a nested order;
ordering all the effective cabin level sets according to the number of elements in the sets, and marking the kth effective cabin level set as E k ；
Starting a first round of traversal, for the bunk j, if G j ＝E k Then the probability of equivalent choice of the bunk j is Q (E k )-Q(E k-1 ) The equivalent freight rate of the bunk j is (R (E k )-R(E k-1 ))/(Q(E k )-Q(E k-1 ) If G) j Not equal to any E k The equivalent selection probability of the bin j is 0, and the equivalent freight rate of the bin j is temporarily recorded as U;
starting a second round of traversal, if there is a nested order at the bunk j U A third post, and the third post has equivalent freight rate, then the post j U Equivalent freight rate of (1) is equal to the cabin level j U The nearest third bunk equivalent freight rate, if no nesting order exists at bunk j U A third rear bunk, j U Is 0; wherein, the cabin position j U The equivalent freight rate is temporarily recorded as the cabin position of U.
Illustratively, in one flight trip, the bilge products are ordered in a nested order, denoted product j=1, 2,., n; set of first n bunkers in this orderG as G n The method comprises the steps of carrying out a first treatment on the surface of the Ordered by the number of elements in its set, the kth (approximately) valid bunk set is denoted as E k 。
Starting the first round of traversal for product j, if G j ＝E k Then the equivalent selection probability of j is Q (E k )-Q(E k-1 ) Equivalent freight rate is (R (E) k )-R(E k-1 ))/(Q(E k )-Q(E k-1 ) If G) j Not equal to any E k 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 there is a nested order at the bunk j U A third post, and the third post has equivalent freight rate, then the post j U Equivalent freight rate of (1) is equal to the cabin level j U The nearest third bunk equivalent freight rate, if no nesting order exists at bunk j U The third cabin is then the cabin j U Is 0; wherein, the cabin position j U The equivalent freight rate is temporarily recorded as the cabin position of 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 a nested order, then the bunkers set G 1 ＝{Y}、G 2 = { Y, B }, bunk set G 3 = { Y, B, M }, bunk set G 4 = { Y, B, M, K }, bunk set G 5 ＝{Y、B、M、K、Q}；
A first round of traversal is started, G for bunk Y 1 ＝{Y}、E 1 = { Y }, then the equivalent probability of the bunk Y is Q (E 1 )-Q(E 0 ) The equivalent freight rate of the bunk Y is (R (E 1 )-R(E 0 ))/(Q(E 1 )-Q(E 0 ) For bunk B, G thereof 2 = { Y }, if there is no valid bunk set, then the equivalent selection probability of bunk j is 0, the equivalent freight rate of bunk j is temporarily noted as U, and the remaining bunks are no longer validFor details, see 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)：
Wherein a is ij Let 1 denote that bunk j occupies the seat of flight i, λ tj Represents the probability of occurrence of demand, lambda, at each time period t tj ＝d j /T，d j Indicating the equivalent demand of the bunk j,representing the equivalent freight rate of the bunk j, deltaV t-1 (x)＝V t-1 (x)-V t-1 (x-1), x represents the number of seats remaining, z k A represents the shadow price of flight k, if the bunk j occupies flight k kj =1; when t=0 or x=0, V t (x)＝0；
DeltaV in state (t, x) t-1 (x) Setting the base price to obtain the status of each flightBottom price in state (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,,representing the equivalent freight rate of the bunk j, y j For decision variables, represent the theoretical allocation number of the bunk j, M represents the set of all flights in the flight itinerary, c i Representing the current available seat number of flight i e M, if seat j occupies the seat of flight i, then a ij =1, if bunk j does not occupy the seat of flight i, then a ij ＝0。
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 Case of =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 There may also be cases where bunk j does not occupy the seat of flight i, i.e. a ij =0, because at this point the bunk j will occupy other flights.
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 aggregate of all relevant bunkers in the airline network is N, let the equivalent demand of each bunkers j ε N in the airline network be the product of its equivalent selection probability and the total demand of the flight itinerary, denoted d j The method comprises the steps of carrying out a first treatment on the surface of the Equivalent freight rate is recorded as
All relevant flights in the airline network form a set M, and the current available seat number of the flights i epsilon M is c i If the cabin product j occupies the seat of i, a is recorded ij =1, otherwise 0. Build to y j Linear programming model for decision variables:
solving the problem by using an optimization software package to obtain a shadow price z corresponding to i i 。
For each flight i e M, the booking cycle is divided into T time periods of sufficient size, i.e. t=t, …,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. Consider a ij Bilge j, denoted λ, =1 tj ＝d j T is the probability of occurrence of demand in each time period T, x is the number of seats remaining, and the maximum expected benefit in the state (T, x) is V t (x) There are the following dynamic programming equations:
wherein DeltaV t-1 (x)＝V t-1 (x)-V t-1 (x-1). Starting from the marginal condition, i.e. when t=0 or x=0, V t (x) =0, V in all states (t, x) can be easily found t (x) And DeltaV t-1 (x)。
BP of flights under the status (t, x) of each flight is set to DeltaV t-1 (x) The BP table formed by BP in all states (t, x) is 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:
enumerating a slave bunk set C when the type of the discrete selection model is one of preset model types 1 To bunk set C n Taking all enumerated bunk sets as first bunk sets, and determining a nested effective boundary from all first bunk sets according to expected probability and expected income 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 is n Representing a bunk set containing the top n bunks with highest freight rate, wherein n is the total number of bunks of the current flight journey, and the preset model type comprises: 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(S i-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 iterationi＝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 cabin level set at least comprise all elements in the cabin level set of the last iteration and the cabin level set of the current iterationThe number of elements in the combination is one more than the number of elements in the bin set of the last iteration, Q (S) > Q (S i-1 ) And (R (S) -R (S) i-1 ))/(Q(S)-Q(S i-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:
wherein p is j (S) probability of selecting a bunk j e bunk set S for a passenger, r j Representing 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 slave bilge set G based on bilges ordered in nested order 1 To the bunk set G n Is a set of all bunks; wherein G is n Representing a set of top n bunkers ordered in a nested order;
ordering all the effective cabin level sets according to the number of elements in the sets, and marking the kth effective cabin level set as E k ；
Starting a first round of traversal, for the bunk j, if G j ＝E k Then the probability of equivalent choice of the bunk j is Q (E k )-Q(E k-1 ) The equivalent freight rate of the bunk j is (R (E k )-R(E k-1 ))/(Q(E k )-Q(E k-1 ) If G) j Not equal to any E k The equivalent selection probability of the bin j is 0, and the equivalent freight rate of the bin j is temporarily recorded as U;
starting a second round of traversal, if there is a nested order at the bunk j U A third post, and the third post has equivalent freight rate, then the post j U Equivalent freight rate of (1) is equal to the cabin level j U The nearest third bunk equivalent freight rate, if no nesting order exists at bunk j U A third rear bunk, j U Is 0; wherein, the cabin position j U The equivalent freight rate is temporarily recorded as the cabin position of 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)：
Wherein a is ij Let 1 denote that bunk j occupies the seat of flight i, λ tj Represents the probability of occurrence of demand, lambda, at each time period t tj ＝d j /T，d j Indicating the equivalent demand of the bunk j,representing the equivalent freight rate of the bunk j, deltaV t-1 (x)＝V t-1 (x)-V t-1 (x-1), x represents the number of seats remaining, z k A represents the shadow price of flight k, if the bunk j occupies flight k kj ＝1；
DeltaV in state (t, x) t-1 (x) Setting the base price, the base price of each flight in all states (t, x) is obtained.
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 the bunk j, y j Theoretical allocation number for bunk j, M representing the set of all flights in a flight itinerary, c j Representing the current available seat number of flight i e M, if seat j occupies the seat of flight i, then a ij =1, if bunk j does not occupy the seat of flight i, then a ij ＝0。
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. Alternatively, the processor 21 may implement the functions of the modules/units in the above-described device embodiments when executing the computer program.
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), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, 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 Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one 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 (10)
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; 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 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, 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.
2. The bunk allocation method of claim 1, wherein said calculating nested valid boundaries for said discrete selection model comprises:
enumerating a slave bunk set C when the type of the discrete selection model is one of preset model types 1 To bunk set C n Taking all enumerated bunk sets as first bunk sets, and determining a nested effective boundary from all first bunk sets according to expected probability and expected income 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 is n Representing a bunk set containing the top n bunks with highest freight rate, wherein n is the total number of bunks of the current flight journey, and the preset model type comprises: independent demand model, price oriented model, basic appeal model, and independent demand and price oriented hybrid model Type (2).
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(S i-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 bei＝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, and 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(S i-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:
wherein p is j (S) selecting a bunk j E for the passengerProbability of set S, r j Representing the freight rate of bunk j.
6. The bunk allocation method of claim 5, wherein said calculating an equivalent selection probability and an equivalent freight rate for each bunk in said flight itinerary based on said nested validity boundaries comprises:
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 slave bilge set G based on bilges ordered in nested order 1 To the bunk set G n Is a set of all bunks; wherein G is n Representing a set of top n bunkers ordered in a nested order;
ordering all the effective cabin level sets according to the number of elements in the sets, and marking the kth effective cabin level set as E k ；
Starting a first round of traversal, for the bunk j, if G j ＝E k Then the probability of equivalent choice of the bunk j is Q (E k )-Q(E k-1 ) The equivalent freight rate of the bunk j is (R (E k )-R(E k-1 ))/(Q(E k )-Q(E k-1 ) If G) j Not equal to any E k The equivalent selection probability of the bin j is 0, and the equivalent freight rate of the bin j is temporarily recorded as U;
starting a second round of traversal, if there is a nested order at the bunk j U A third post, and the third post has equivalent freight rate, then the post j U Equivalent freight rate of (1) is equal to the cabin level j U The nearest third bunk equivalent freight rate, if no nesting order exists at bunk j U A third rear bunk, j U Is 0; wherein, the cabin position j U The equivalent freight rate is temporarily recorded as the cabin position of U.
7. The bunk allocation method as claimed in claim 6, wherein said calculating a base price for 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 for 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)：
Wherein a is ij Let 1 denote that bunk j occupies the seat of flight i, λ tj Represents the probability of occurrence of demand, lambda, at each time period t tj ＝d j /T，d j Indicating the equivalent demand of the bunk j,representing the equivalent freight rate of the bunk j, deltaV t-1 (x)＝V t-1 (x)-V t-1 (x-1), x represents the number of seats remaining, z k A represents the shadow price of flight k, if the bunk j occupies flight k kj =1; when t=0 or x=0, V t (x)＝0；
DeltaV in state (t, x) t-1 (x) Setting the base price, the base price of each flight in all states (t, x) is obtained.
8. The bunk allocation method according to claim 6, 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 the bunk j, y j Theoretical allocation number for bunk j, M representing the set of all flights in a flight itinerary, c i Representing the current available seat number of flight i e M, if seat j occupies the seat of flight i, then a ij =1, if bunk j does not occupy the seat of flight i, then a ij ＝0。
9. 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; 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 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;
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.
10. 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 1-8 when the computer program is executed.
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