CN109598400B - Global resource allocation method and device based on allocation model - Google Patents
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Abstract
The embodiment of the specification provides a global resource allocation method and device based on an allocation model, wherein the method comprises the following steps: acquiring a plurality of first increasable resources; acquiring a plurality of resource pools; optimizing the allocation model such that respective estimates of the plurality of resource pools at the predetermined date are closer to their respective same-day expected estimates in the case where respective first-incremental resources are currently being respectively injected into the plurality of resource pools based on allocation results of the allocation model after optimization than before optimization; and injecting the plurality of first expandable resources into the plurality of resource pools based on the allocation result of the optimized allocation model.
Description
Technical Field
Embodiments of the present disclosure relate to mixed integer nonlinear programming model optimization, and more particularly, to a global resource allocation method and apparatus based on an allocation model.
Background
In the service of intelligent resource allocation, corresponding value-added resources are allocated to a plurality of resource pools respectively. Wherein the plurality of resource pools correspond to a plurality of service clients. The value-added resources include, for example, bonds and the like, which have corresponding valuations, expiration times, and profitability. Generally, each resource pool will have an expected rate of return when it is established. The goal of intelligent resource allocation is that the benefit of the incremental resources allocated for each resource pool is expected to reach the desired benefit rate without exceeding too much. The problem can be embodied by allocating a plurality of value-added resources to a plurality of resource pools, and simultaneously controlling the yield and the idle rate of the value-added resources. At present, the method is suitable for single variable value resource allocation by carrying out variable value resource allocation through scale prediction of variable value resources and manual parameter adjustment rules and matching different variable value resources and resource pools. Thus, there is a need for a more efficient global resource allocation scheme.
Disclosure of Invention
Embodiments of the present disclosure aim to provide a more efficient global resource allocation method and apparatus based on an allocation model, so as to solve the deficiencies in the prior art.
To achieve the above object, an aspect of the present specification provides a global resource allocation method based on an allocation model, wherein the allocation model is a mixed integer nonlinear programming model, the method comprising:
obtaining a plurality of first renewable resources, wherein the plurality of first renewable resources have respective day-to-day exchange costs and daily valuations, wherein the day-to-day exchange costs of the first renewable resources are determined based on the day valuations of the first renewable resources;
obtaining a plurality of resource pools, wherein each of the resource pools currently includes a plurality of injected second renewable resources and a predetermined number of exchangeable resources, and each of the resource pools has a daily expectation estimate, wherein the plurality of second renewable resources has a respective daily estimate;
optimizing an allocation model based on the respective estimates of the plurality of first increasable resources at a predetermined date after the current day and the current day exchange cost, and the respective exchangeable resources of the plurality of resource pools, the respective estimates of the expected estimates of the plurality of second increasable resources at the predetermined date, such that the respective estimates of the plurality of resource pools at the predetermined date are closer to their respective expected estimates of the same day after the respective first increasable resources are currently injected into the plurality of resource pools, respectively, based on the allocation results of the optimized allocation model, than before the optimization; and
And injecting the plurality of first expandable resources into the plurality of resource pools based on the allocation result of the optimized allocation model.
In one embodiment, the estimate of the pool of resources on the predetermined date is the sum of: the sum of the respective estimates of the respective first and second increasable resources at said predetermined date comprised by the resource pool after the current injection, the total amount of exchangeable resources comprised by the resource pool after the current injection.
In one embodiment, the resource pool obtains an injection of the respective first renewable resources by paying out a predetermined number of exchangeable resources corresponding to a current day exchange cost of the respective first renewable resources.
In one embodiment, optimizing the allocation model includes optimizing the allocation model such that the estimate of a first one of the plurality of resource pools on the predetermined date is closer to its expected estimate on the same day in a case where the plurality of first incremental resources are currently injected into the plurality of resource pools based on the allocation result of the allocation model after optimization than before optimization, wherein the estimate of the first one of the plurality of resource pools on the predetermined date is most distant from its expected estimate on the same day in a case where the plurality of first incremental resources are currently injected into the plurality of resource pools based on the allocation result of the allocation model before optimization.
In one embodiment, the resource pool has a predetermined deadline, and the optimizing the allocation model includes optimizing the allocation model such that a product of a first parameter of the first resource pool and a second parameter of the first resource pool is maximized in a case where the allocation result based on the allocation model before optimization is currently injecting the plurality of first scalable resources into the plurality of resource pools, wherein the estimate of the first resource pool of the plurality of resource pools on the predetermined date is closer to the expected estimate thereof on the same day, and wherein the second parameter is determined based on an influence factor of the correlation in a case where the allocation result based on the allocation model before optimization is currently injecting the plurality of first scalable resources into the plurality of resource pools.
In one embodiment, the relevant influencing factors include the remaining number of days of the first resource pool from its expiration date on the predetermined date.
In one embodiment, the predetermined date is a day subsequent to the current day.
In one embodiment, optimizing the distribution model includes optimizing the distribution model by simulating an annealing algorithm.
In one embodiment, the first increasable resource and the resource pool each have a respective expiration date, and the allocation model includes at least the following constraints:
each of the first increasable resources can be allocated to only one resource pool or not to any one resource pool;
each of the first increasable resources can only be allocated to a resource pool whose expiration date is subsequent; and
each resource pool contains a total amount of exchangeable resources before the current injection that is greater than or equal to the sum of the exchange costs of the respective first incremental resources injected in the current injection.
In one embodiment, acquiring the plurality of first increasable resources includes accumulating the acquired first increasable resources and ending the accumulating when the number of accumulated first increasable resources reaches a predetermined number or the accumulating time exceeds a predetermined time.
In one embodiment, obtaining the plurality of first increasable resources includes equally dividing a batch of first increasable resources into n groups and obtaining a plurality of first increasable resources comprised by one of the n groups, wherein each of the n groups includes a substantially equal number of first increasable resources;
acquiring the plurality of resource pools comprises acquiring a plurality of first resource pools, dividing each first resource pool into n parts uniformly, and acquiring one of the n parts included in each first resource pool to acquire the plurality of resource pools.
Another aspect of the present disclosure provides a global resource allocation apparatus based on an allocation model, where the allocation model is a mixed integer nonlinear programming model, the apparatus including:
a first acquisition unit configured to acquire a plurality of first renewable resources, wherein the plurality of first renewable resources have respective day-to-day exchange costs and daily estimates, wherein the day-to-day exchange costs of the first renewable resources are determined based on the day estimates of the first renewable resources;
a second obtaining unit configured to obtain a plurality of resource pools, wherein each of the resource pools currently comprises a plurality of injected second increasable resources and a predetermined number of exchangeable resources, and each of the resource pools has a daily expected estimate, wherein the plurality of second increasable resources has a respective daily estimate;
an optimizing unit configured to optimize an allocation model based on the respective estimates of the plurality of first increasable resources on a predetermined date after the day and the day exchange cost, and the respective exchangeable resources of the plurality of resource pools, the expected estimates on the predetermined date, and the respective estimates of the plurality of second increasable resources on the predetermined date, such that the respective estimates on the predetermined date of the plurality of resource pools are closer to their respective expected estimates on the same day after the respective first increasable resources are currently injected into the plurality of resource pools, respectively, based on allocation results of the optimized allocation model, than before the optimization; and
And the injection unit is configured to inject the plurality of first expandable resources into the plurality of resource pools based on the allocation result of the optimized allocation model.
In one embodiment, the estimate of the pool of resources on the predetermined date is the sum of: the sum of the respective estimates of the respective first and second increasable resources at said predetermined date comprised by the resource pool after the current injection, the total amount of exchangeable resources comprised by the resource pool after the current injection.
In one embodiment, the resource pool obtains an injection of the respective first renewable resources by paying out a predetermined number of exchangeable resources corresponding to a current day exchange cost of the respective first renewable resources.
In one embodiment, the optimizing unit is further configured to optimize the allocation model such that the estimate of a first resource pool of the plurality of resource pools on the predetermined date is closer to its expected estimate on the same day in case the plurality of first increasable resources are currently injected into the plurality of resource pools based on the allocation result of the allocation model after optimization than before optimization, wherein the difference between the estimate of the first resource pool on the predetermined date and its expected estimate on the same day in case the plurality of first increasable resources are currently injected into the plurality of resource pools based on the allocation result of the allocation model before optimization.
In one embodiment, the resource pool has a predetermined deadline, and the optimizing unit is further configured to optimize the allocation model such that a product of a first parameter of the first resource pool and a second parameter is maximum in the case where the allocation result based on the allocation model before optimization is currently injecting the plurality of first increasable resources into the plurality of resource pools, wherein the first parameter is determined based on a gap between the estimation of the first resource pool at the predetermined date and the estimation of the same day, and the second parameter is determined based on a relevant influencing factor in the case where the allocation result based on the allocation model before optimization is currently injecting the plurality of first increasable resources into the plurality of resource pools.
In an embodiment, the optimization unit is further configured to optimize the distribution model by simulating an annealing algorithm.
In one embodiment, the first increasable resource and the resource pool each have a respective expiration date, and the allocation model includes at least the following constraints:
Each of the first increasable resources can be allocated to only one resource pool or not to any one resource pool;
each of the first increasable resources can only be allocated to a resource pool whose expiration date is subsequent; and
each resource pool contains a total amount of exchangeable resources before the current injection that is greater than or equal to the sum of the exchange costs of the respective first incremental resources injected in the current injection.
In an embodiment, the first acquisition unit is further configured to accumulate the acquired first increasable resources and to end the accumulation when the number of accumulated first increasable resources reaches a predetermined number or the accumulation time exceeds a predetermined time.
In one embodiment, the first obtaining unit is further configured to equally divide a batch of first increasable resources into n groups and obtain a plurality of first increasable resources comprised by one of the n groups, wherein each of the n groups comprises a substantially identical number of first increasable resources;
the second obtaining unit is further configured to obtain a plurality of first resource pools, divide each first resource pool into n parts, and obtain one of the n parts included in each first resource pool to obtain the plurality of resource pools.
Another aspect of the present specification provides a computing device comprising a memory and a processor, wherein the memory has executable code stored therein, and wherein the processor, when executing the executable code, performs any of the methods described above.
According to the intelligent global distribution scheme, the intelligent and automatic asset injection is realized, the scheme does not depend on asset scale prediction and manual parameter adjustment, but performs problem modeling and solving from the perspective of global optimization, the super-parameters are less, manual intervention is not needed, and the intelligent and automatic implementation can be realized; in addition, batch decision of asset injection is realized, and the scheme firstly carries out accumulation processing on the assets and then carries out batch allocation decision, so that global optimization in batches can be realized.
Drawings
The embodiments of the present specification may be further clarified by describing the embodiments of the present specification with reference to the accompanying drawings:
FIG. 1 shows a schematic diagram of a global resource allocation system 100 according to an embodiment of the present description;
FIG. 2 illustrates a global resource allocation method based on an allocation model according to an embodiment of the present description;
FIG. 3 shows a schematic diagram of asset allocation and injection for n fund pools;
FIG. 4 shows a schematic diagram of the multiple sets of global allocations; and
fig. 5 illustrates a global resource allocation apparatus 500 based on an allocation model according to an embodiment of the present description.
Detailed Description
Embodiments of the present specification will be described below with reference to the accompanying drawings.
Fig. 1 shows a schematic diagram of a global resource allocation system 100 according to an embodiment of the present description. The system is for allocating a plurality of increasable resources to a plurality of resource pools, the resource pools including exchangeable resources and a plurality of acquired increasable resources. The increasable resource has a corresponding amount, expiration time, and rate of return, such as an asset, including, for example, a bond, etc. The exchangeable resources may be various resources that can be freely exchanged in the market at a price, such as money, funds, etc. The resource pool is, for example, a fund pool that includes a predetermined number of initial funds and that obtains a plurality of assets by purchasing the assets with a portion of the funds paid out. The description will be made below taking the asset and fund pool as an example, but embodiments may be generalized to other possible incremental resources and exchangeable resources where applicable.
As shown in fig. 1, the system 100 includes an asset accumulation module 11, a first grouping module 12, a second grouping module 13, a distribution model 14, a merge module 15, and a distribution module 16. First, in the asset accumulation module 11, assets acquired by the system 100 are accumulated. The system 100 is, for example, a payment server, which receives, from each client, the business activities of loans, fees, etc. of the clients, and the loan fees and the fees of the fees can be accumulated as assets. In the first grouping module 12, a batch of assets is randomly divided into n groups. In the second grouping module 13, n groups of fund pools are obtained by equally dividing each fund pool into n shares, respectively, wherein each group of fund pools includes one of the n shares of each fund pool. The allocation model 14 includes a plurality of algorithm modules (three are shown schematically), wherein each algorithm module performs asset allocation for one of the n sets of assets and one of the n sets of fund pools. Specifically, each algorithm module assigns a set of assets to a set of fund pools via an optimization model and approximates the daily valuations of the individual fund pools in the set of fund pools after the assignment to their daily expected valuations. The merge module 15 is used to combine the assignments of n groups of assets described above. And an allocation module 16 for allocating the n groups of assets to the n groups of fund pools, respectively, based on allocation results of the allocation model 14.
The system 100 shown in fig. 1 is merely illustrative, and a system according to embodiments of the present disclosure is not limited thereto, e.g., the system 100 may allocate an acquired collection of assets to a plurality of fund pools directly based on allocation results of an allocation model without grouping them separately. For example, n algorithm modules may be included in the allocation model to perform allocation optimization for each pair of asset groups and fund pool groups, respectively.
Fig. 2 shows a global resource allocation method based on an allocation model, where the allocation model is a mixed integer nonlinear programming model, and the method is executed, for example, at a server side, and the method includes:
at step S202, a plurality of first renewable resources are acquired, wherein the plurality of first renewable resources have respective day-of-the-day exchange costs and daily valuations, wherein the day-of-the-day exchange costs of the first renewable resources are determined based on the day valuations of the first renewable resources;
at step S204, a plurality of resource pools are obtained, wherein each of the resource pools currently includes an injected plurality of second increasable resources and a predetermined number of exchangeable resources, and each of the resource pools has a daily expected valuation, wherein the plurality of second increasable resources has a respective daily valuation;
In step S206, optimizing an allocation model based on the respective estimates of the plurality of first increasable resources at a predetermined date after the day and the day swap cost, and the respective exchangeable resources of the plurality of resource pools, the expected estimates at the predetermined date, and the respective estimates of the plurality of second increasable resources at the predetermined date, such that the respective estimates of the plurality of resource pools at the predetermined date are closer to their respective expected estimates at the same day than before the optimization in a case where the respective first increasable resources are currently injected into the plurality of resource pools, respectively, based on the allocation results of the optimized allocation model; and
in step S208, the plurality of first incremental resources are injected into the plurality of resource pools based on the allocation results of the optimized allocation model.
First, at step S202, a plurality of first renewable resources are acquired, wherein the plurality of first renewable resources have respective day-to-day exchange costs and daily valuations, wherein the day-to-day exchange costs of the first renewable resources are determined based on the day valuations of the first renewable resources. As previously described with reference to FIG. 1, a increasable resource is a resource having a corresponding monetary amount, expiration time, and rate of return, as illustrated by way of example as an asset. Here, the asset to be allocated to the fund pool in the allocation of the current round, but not yet allocated, i.e., the first asset, is represented by the first increasable resource. As described above, the server side is, for example, a payment server side, which continuously receives the loan, the cut payment, and the like of the client side, and the loan payment and the cut payment can be regarded as the property. So that the server side can continually receive new assets. The plurality of first assets may be acquired by performing asset accumulation starting from a particular moment. The predetermined number of assets of the batch and the timeout period may be set before starting to accumulate the assets. After starting to accumulate the assets, when the number of accumulated assets reaches the predetermined number or the accumulated time exceeds the timeout period, accumulation of the batch of assets may be ended, so that the plurality of first assets may be acquired. The daily valuation of the first asset is obtained, for example, based on principal of the asset, future total interest, and remaining deadlines of the first asset for the day, for example, as shown in equation (1):
The current day exchange cost of the first asset is, for example, a current day valuation of the asset. As can be seen from equation (1), the daily valuation of a single asset increases progressively with the number of days remaining. That is, the asset's daily valuations after the day are higher than the day valuations.
In step S204, a plurality of resource pools is obtained, wherein each of the resource pools preceded by a plurality of injected second increasable resources and a predetermined number of exchangeable resources, and each of the resource pools has a daily expectation valuation, wherein the plurality of second increasable resources has a respective daily valuation.
As described above with reference to fig. 1, the resource pool is, for example, a fund pool. The fund pools are established, for example, by an investment platform (e.g., a payroll) contracting with certain banks or businesses for which initial funds are provided for the corresponding fund pools, and for which assets are purchased by the investment platform into the corresponding fund pools. Fig. 3 shows a schematic diagram of asset allocation and injection for n fund pools. As shown in fig. 3, the fund pool 1, the fund pools 2 and … fund pool n are, for example, fund pools established between an investment platform and each bank or enterprise, and the assets are acquired by the investment platform and are to be injected into n fund pools in one injection round, and the method of the embodiment of the present disclosure is to optimize the asset allocation of the pair of multiple fund pools. Typically, each fund pool defines an expected rate of return and expiration date at the time of establishment. The method of embodiments of the present description aims to achieve the expected rate of return for the asset purchased for each fund pool on the expiration date, but not so much, and preferably the same.
After the fund pool is established, in order to always bring the rate of return within the control range, the investment platform will preset a rate of return range for each day during the period of the fund pool so that the final rate of return is a controllable amount. And during each day the investment platform will make multiple rounds of asset purchases, e.g., one round every 15 minutes, each round of 2 tens of thousands of assets. Thus, prior to the asset injection of the current round, the fund pool includes a plurality of purchased assets (hereinafter referred to as second assets) and remaining funds. The second assets have respective daily valuations as the first assets. Wherein the daily expected rate of return for each fund pool is obtained by based on the expected rate of return. Based on the daily expected rate of return of the fund pool, a daily expected estimate of the fund pool may be obtained.
In step S206, the allocation model is optimized based on the respective estimates of the plurality of first increasable resources at a predetermined date after the day and the day swap cost, and the respective exchangeable resources of the plurality of resource pools, the expected estimates at the predetermined date, and the respective estimates of the plurality of second increasable resources at the predetermined date, such that the respective estimates of the plurality of resource pools at the predetermined date are closer to their respective expected estimates at the same day than before the optimization in a case where the respective first increasable resources are currently injected into the plurality of resource pools, respectively, based on the allocation results of the optimized allocation model.
The distribution model is a mixed integer nonlinear programming model, and in the model, the decision variable comprises nonlinear programming of integer variable and continuous variable. For example, in this model, assuming that the multiple resource pools are M resource pools, then V i An estimate of the i-th resource pool after the current round of injection at a predetermined date after the current day is represented, where i takes a value of 1 to M. The predetermined date after the current day may be a period set for controlling the profitability of the pool of funds, and may be, for example, one day, two days, one week, etc., and will be described below by way of example as one day, i.e., the predetermined date after the current day is the next day of the current day, i.e., tomorrow. After this round of injection, the estimate of the ith resource pool at tomorrow is shown in equation (2):
V i ＝∑ k AV(p k )+CC i +∑ j X ij *(AV(p j )-C(p j )) (2)
wherein AV (p) j ) The valuation of the jth first asset of the current round of injection at tomorrow is represented, where j has a value of 1 to N, i.e., the current round of asset injection has a total of N assets injected into M fund pools. C (p) j ) Representing the cost of purchasing the jth asset in the current round of asset injection. As previously described, C (p j ) May be equal to the current day valuation of the asset. X is X ij The value is 0 or 1, wherein 0 indicates that the jth asset is not injected into the ith fund pool in the current round of injection, and 1 indicates that the jth asset is injected into the ith fund pool in the current round of injection, i.e. each asset has a choice of whether to put in each fund pool or not, i.e. 0 or 1.AV (p) k ) Representing an estimate of the kth second asset that has been injected in the ith fund pool prior to the current round of injection at tomorrow, where k has a value of 1 to Q, i.e., the ith fund pool has been injected with Q second assets prior to the current round of injection. CC (CC) i Indicating the amount of cash remaining in the ith cash pond prior to the present round of injection. As can be seen from equation (2), after the present round of injection, the valuation of the ith resource pool at tomorrow is the sum of the valuations of the assets (first and second assets) in the pool at tomorrow and the sum of the remaining cash after the present round of injection (the remaining cash before injection minus the cost of the present round of injection).
In addition, for the parameters in the present round of injection described above, there are the following constraints expressed by formulas (3) - (5):
wherein, the formula (3) shows that each asset can be put into only one fund pool or not put into any fund pool; in formula (4), ED i ED representing the expiration date of the ith fund pool j Representing the expiration date of the jth first asset, equation (4) represents that each asset may choose to place into the fund pool following any expiration date; equation (5) shows that each resource pool contains the total amount of remaining cash before the current injection is equal to or greater than the currentThe sum of the exchange costs of the first assets injected in the injection.
The allocation model in the embodiments of the present description is optimized by optimizing X ij To make the estimate of each fund pool at tomorrow after this round of injection closer to its expected estimate E at tomorrow i . Wherein the expected estimate E of the fund pool at tomorrow i The expected annual rate of return for the funding pool may be calculated by a simulation program, for example, by averaging the expected annual rate of return to each day, thereby obtaining an expected daily rate of return.
In one embodiment, the objective function of the allocation model is as shown in equation (6):
as shown in equation (6), the objective function is expressed such that V in M fund pools i And E is connected with i The estimation error of the fund pool with the largest difference is reduced. By this objective function, the estimation error of the pool of funds with the largest difference is continuously reduced over a number of cycles, thereby making the estimation error of each of the M pools smaller, i.e., making the tomorrow estimation of each pool closer to its tomorrow expected estimation.
In one embodiment, the objective function of the allocation model is as shown in equation (7):
wherein W is i Other factors are expressed, including the degree of "urgency" of the fund pool (the fewer and more prioritized the number of days remaining in the fund pool), the priority of asset allocation (the more and more prioritized the number of days remaining in the asset), and the like. W (W) i For example, by formula (8), wherein LD i The remaining days of the fund pool i are represented, and α is the elastic coefficient of influence of the remaining days:
it will be appreciated that the objective function of the allocation model is not limited to that shown in equation (6) or (7), but may be in other forms available in the art, for example, the objective function may be in the form of the sum of squares, the sum of absolute values, etc. of the estimated errors of the respective fund pools.
As can be seen from the formula (2), the distribution model is a mixed integer nonlinear programming model, and the distribution model is optimized, namely the mixed integer nonlinear programming problem is solved. Thus, in optimizing the distribution model based on the objective function, for example, a simulated annealing algorithm (a heuristic algorithm) may be employed to optimize the distribution model. It will be appreciated that the optimization method is not limited to the simulated annealing algorithm described above, but may employ various algorithms available to those skilled in the art for solving mixed integer nonlinear programming problems, such as branch-and-bound methods, ant colony algorithms, particle swarm algorithms, etc., which are not described in detail herein.
Finally, in step S208, the plurality of first incremental resources are injected into the plurality of resource pools based on the allocation results of the optimized allocation model. After the allocation model is optimized in the step S206, N first assets are injected into the M fund pools based on the allocation result of the optimized allocation model, so that the tomorrow valuation of each fund pool is closer to the tomorrow expected valuation thereof, and the global allocation of the assets is optimized.
In one embodiment, in the event that the amount of assets injected in one round is large, the assets injected in one round may be equally divided into multiple groups of assets, and multiple fund pools may also be correspondingly equally divided, so as to perform multiple groups of global allocations. Fig. 4 shows a schematic diagram of the multiple sets of global allocations. The left side of FIG. 4 is a batch of assets included in a round of injection, the first asset described above, which is, for example, randomly divided into n groups of assets, G 1 、G 2 、…G n Wherein each group of assets G i Including substantially the same number of assets.
As shown in FIG. 4, m fund pools are on the right, including fund pool P 1 、P 2 、…P m Each fund pool is covered byIs divided into n parts (q 1 、q 2 、…q n ). Wherein, a fund pool q j Including the remaining cash amount as the corresponding fund pool P i 1/n of the remaining funds amount included, the one fund pool q j The sum of the valuations of the second assets (injected assets) included is the corresponding fund pool P i 1/n of the sum of the valuations of the second assets included, the pool of funds q j Daily expected estimates of (1) are the corresponding fund pool P i 1/n of the daily expected estimate of (1/n). By taking individual pools of funds P i Each fund pool can obtain m q j For example, to obtain individual fund pools P i Each fund pool p 1 A first group of fund pools can be obtained, and each fund pool P can be obtained i Each fund pool p 2 A second set of fund pools may be acquired, and thus, n sets of fund pools may be acquired.
After grouping the assets and fund pools, respectively, as described above, the allocation method shown in FIG. 2 may be implemented for each group, respectively. For example, as shown in FIG. 4, the global resource allocation method shown in FIG. 2 is performed on the first group of assets G1 and the first group of fund pools by the algorithm modules G1-AD, such that each asset included in the first group of assets G1 is injected into each fund pool q included in the first group of fund pools 1 . Similarly, the global resource allocation is made to the second group of assets G2 and the second group of fund pools by the algorithm modules G2-AD, and the global resource allocation is made to the nth group of assets Gn and the nth group of fund pools by the algorithm modules Gn-AD …. Thus, in the comprehensive decision result module, the allocation of each algorithm module is synthesized, and a batch of assets for the round injection is obtained to a plurality of fund pools P i Is allocated to the global allocation of (a).
It will be appreciated that the global allocation scheme shown in fig. 4 is merely illustrative, and is not limited to the global allocation scheme of the embodiment of the present disclosure, for example, the algorithm modules shown in fig. 4 are not limited to n, but may be any number, for example, in the case that only one algorithm module is included, n groups of assets may be allocated in sequence to obtain an allocation result for all the assets.
Fig. 5 illustrates a global resource allocation apparatus 500 based on an allocation model, wherein the allocation model is a mixed integer nonlinear programming model, the apparatus comprising:
a first obtaining unit 51 configured to obtain a plurality of first expandable resources, wherein the plurality of first expandable resources have respective day-of-the-day exchange costs and daily estimates, wherein the day-of-the-day exchange costs of the first expandable resources are determined based on the day estimates of the first expandable resources;
a second obtaining unit 52 configured to obtain a plurality of resource pools, wherein each of the resource pools currently comprises a plurality of injected second renewable resources and a predetermined number of exchangeable resources, and each of the resource pools has a daily expectation estimate, wherein the plurality of second renewable resources has a respective daily estimate;
an optimizing unit 53 configured to optimize an allocation model based on the respective estimates of the plurality of first expandable resources on a predetermined date after the day and the day exchange cost, and the respective exchangeable resources of the plurality of resource pools, the expected estimates on the predetermined date, and the respective estimates of the plurality of second expandable resources on the predetermined date, such that the respective estimates of the plurality of resource pools on the predetermined date are closer to their respective expected estimates on the same day in a case where the respective first expandable resources are currently injected into the plurality of resource pools, respectively, based on the allocation result of the optimized allocation model, than before the optimization; and
An injection unit 54 is configured to inject the plurality of first expandable resources into the plurality of resource pools based on the allocation result of the optimized allocation model.
In one embodiment, the estimate of the pool of resources on the predetermined date is the sum of: the sum of the respective estimates of the respective first and second increasable resources at said predetermined date comprised by the resource pool after the current injection, the total amount of exchangeable resources comprised by the resource pool after the current injection.
In one embodiment, the resource pool obtains an injection of the respective first renewable resources by paying out a predetermined number of exchangeable resources corresponding to a current day exchange cost of the respective first renewable resources.
In an embodiment, the optimizing unit 53 is further configured to optimize the allocation model such that the estimated value of a first resource pool of the plurality of resource pools on the predetermined date is closer to its expected estimated value on the same day in case the first plurality of expandable resources are currently injected into the plurality of resource pools based on the allocation result of the allocation model after optimization than before optimization, wherein the estimated value of the first resource pool on the predetermined date is the largest in distance from its expected estimated value on the same day in case the first plurality of expandable resources are currently injected into the plurality of resource pools based on the allocation result of the allocation model before optimization.
In an embodiment, the resource pool has a predetermined deadline, and the optimizing unit 53 is further configured to optimize the allocation model such that, in a case where the allocation result based on the allocation model after optimization is at present injecting the plurality of first expandable resources into the plurality of resource pools, the estimated value of the first resource pool of the plurality of resource pools at the predetermined date is closer to the same-day expected estimated value thereof than before optimization, wherein, in the plurality of resource pools, in a case where the allocation result based on the allocation model before optimization is at present injecting the plurality of first expandable resources into the plurality of resource pools, the product of the first parameter of the first resource pool and the second parameter is maximum, wherein the first parameter is determined based on a difference between the estimated value of the first resource pool at the predetermined date and the estimated value thereof at the same day expected, and the second parameter is determined based on the relevant influencing factors.
In an embodiment, the optimization unit 53 is further configured to optimize the distribution model by simulating an annealing algorithm.
In one embodiment, the first increasable resource and the resource pool each have a respective expiration date, and the allocation model includes at least the following constraints:
Each of the first increasable resources can be allocated to only one resource pool or not to any one resource pool;
each of the first increasable resources can only be allocated to a resource pool whose expiration date is subsequent; and
each resource pool contains a total amount of exchangeable resources before the current injection that is greater than or equal to the sum of the exchange costs of the respective first incremental resources injected in the current injection.
In an embodiment, the first obtaining unit 51 is further configured to accumulate the obtained first expandable resources and end the accumulation when the number of accumulated first expandable resources reaches a predetermined number or the accumulation time exceeds a predetermined time.
In one embodiment, the first obtaining unit 51 is further configured to equally divide a batch of the first expandable resources into n groups, and obtain a plurality of first expandable resources included in one of the n groups, where each of the n groups includes a substantially identical number of the first expandable resources;
the second obtaining unit 52 is further configured to obtain a plurality of first resource pools, divide each first resource pool into n parts, and obtain one of the n parts included in each first resource pool to obtain the plurality of resource pools.
In another aspect, the present disclosure provides a computing device including a memory and a processor, where the memory stores executable code and the processor implements the method of fig. 2 when executing the executable code.
According to the intelligent global distribution scheme, the intelligent and automatic asset injection is realized, the scheme does not depend on asset scale prediction and manual parameter adjustment, but performs problem modeling and solving from the perspective of global optimization, the super-parameters are less, manual intervention is not needed, and the intelligent and automatic implementation can be realized; in addition, batch decision of asset injection is realized, and the scheme firstly carries out accumulation processing on the assets and then carries out batch allocation decision, so that global optimization in batches can be realized.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Those of ordinary skill would further appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Those of ordinary skill in the art may implement the described functionality using different approaches for each particular application, but such implementation is not to be considered as beyond the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (23)
1. A global resource allocation method based on an allocation model, wherein the allocation model is a mixed integer nonlinear programming model, the method comprising:
obtaining a plurality of first renewable resources, wherein the plurality of first renewable resources have respective day-to-day exchange costs and daily valuations, wherein the day-to-day exchange costs of the first renewable resources are determined based on the day valuations of the first renewable resources;
Obtaining a plurality of resource pools, wherein each of the resource pools currently includes a plurality of injected second renewable resources and a predetermined number of exchangeable resources, and each of the resource pools has a daily expectation estimate, wherein the plurality of second renewable resources has a respective daily estimate;
optimizing an allocation model based on the respective estimates of the plurality of first increasable resources at a predetermined date after the current day and the current day exchange cost, and the respective exchangeable resources of the plurality of resource pools, the expected estimates at the predetermined date, and the respective estimates of the plurality of second increasable resources at the predetermined date, such that the respective estimates of the plurality of resource pools at the predetermined date are closer to their respective expected estimates at the same day than before the optimizing, in the case where the respective first increasable resources are currently injected into the plurality of resource pools, respectively, based on the allocation results of the optimized allocation model; and
and injecting the plurality of first expandable resources into the plurality of resource pools based on the allocation result of the optimized allocation model.
2. The method of claim 1, wherein the estimate of the pool of resources on the predetermined date is a sum of: the sum of the respective estimates of the respective first and second increasable resources at said predetermined date comprised by the resource pool after the current injection, the total amount of exchangeable resources comprised by the resource pool after the current injection.
3. The method of claim 2, wherein the resource pool obtains the injection of the respective first incremental resource by paying out a predetermined number of exchangeable resources corresponding to a current day exchange cost of the respective first incremental resource.
4. The method of claim 1, wherein optimizing the allocation model comprises optimizing the allocation model such that the estimate of a first one of the plurality of resource pools on the predetermined date is closer to its expected estimate on the same day in a case where the plurality of first incremental resources are currently injected into the plurality of resource pools based on the allocation result of the allocation model after optimization than before optimization, wherein the difference between the estimate of the first one of the plurality of resource pools on the predetermined date and its expected estimate on the same day in a case where the plurality of first incremental resources are currently injected into the plurality of resource pools based on the allocation result of the allocation model before optimization is greatest in the plurality of resource pools.
5. The method of claim 1, wherein the resource pool has a predetermined deadline, the optimizing the allocation model comprises optimizing the allocation model such that a product of a first parameter of a first resource pool among the plurality of resource pools, where the estimate of the first resource pool at the predetermined date is closer to its expected daily estimate, and a second parameter of the first resource pool is determined based on a difference between the estimate of the first resource pool at the predetermined date and its expected daily estimate, is maximized in comparison to before the optimizing, where the allocation model based on the allocation result of the allocation model before the optimizing is currently injected into the plurality of resource pools.
6. The method of claim 5, wherein the relevant influencing factors comprise a remaining number of days of the first resource pool from its expiration date on the predetermined date.
7. The method of claim 1, wherein the predetermined date is a day subsequent to the current day.
8. The method of claim 1, wherein optimizing the distribution model comprises optimizing the distribution model by simulating an annealing algorithm.
9. The method of claim 1, wherein the first increasable resource and the resource pool each have a respective expiration date, the allocation model comprising at least the following constraints:
each of the first increasable resources can be allocated to only one resource pool or not to any one resource pool;
each of the first increasable resources can only be allocated to a resource pool whose expiration date is subsequent; and
each resource pool contains a total amount of exchangeable resources before the current injection that is greater than or equal to the sum of the exchange costs of the respective first incremental resources injected in the current injection.
10. The method of claim 1, wherein acquiring the plurality of first increasable resources comprises accumulating the acquired first increasable resources and ending the accumulating when a number of accumulated first increasable resources reaches a predetermined number or an accumulation time exceeds a predetermined time.
11. The method of claim 1, wherein,
obtaining a plurality of first increasable resources includes equally dividing a batch of first increasable resources into n groups, and obtaining a plurality of first increasable resources included in one of the n groups, wherein each of the n groups includes a substantially equal number of first increasable resources;
acquiring the plurality of resource pools comprises acquiring a plurality of first resource pools, dividing each first resource pool into n parts uniformly, and acquiring one of the n parts included in each first resource pool to acquire the plurality of resource pools.
12. A global resource allocation apparatus based on an allocation model, wherein the allocation model is a mixed integer nonlinear programming model, the apparatus comprising:
a first acquisition unit configured to acquire a plurality of first renewable resources, wherein the plurality of first renewable resources have respective day-to-day exchange costs and daily estimates, wherein the day-to-day exchange costs of the first renewable resources are determined based on the day estimates of the first renewable resources;
a second obtaining unit configured to obtain a plurality of resource pools, wherein each of the resource pools currently comprises a plurality of injected second increasable resources and a predetermined number of exchangeable resources, and each of the resource pools has a daily expected estimate, wherein the plurality of second increasable resources has a respective daily estimate;
An optimizing unit configured to optimize an allocation model based on the respective estimates of the plurality of first increasable resources on a predetermined date after the day and the day exchange cost, and the respective exchangeable resources of the plurality of resource pools, the expected estimates on the predetermined date, and the respective estimates of the plurality of second increasable resources on the predetermined date, such that the respective estimates of the plurality of resource pools on the predetermined date are closer to their respective expected estimates on the same day than before the optimization in a case where the respective first increasable resources are currently injected into the plurality of resource pools, respectively, based on allocation results of the optimized allocation model; and
and the injection unit is configured to inject the plurality of first expandable resources into the plurality of resource pools based on the allocation result of the optimized allocation model.
13. The apparatus of claim 12, wherein the estimate of the pool of resources at the predetermined date is a sum of: the sum of the respective estimates of the respective first and second increasable resources at said predetermined date comprised by the resource pool after the current injection, the total amount of exchangeable resources comprised by the resource pool after the current injection.
14. The apparatus of claim 13, wherein the resource pool obtains an injection of the respective first incremental resource by paying out a predetermined number of exchangeable resources, wherein the predetermined number of exchangeable resources corresponds to a current day exchange cost of the respective first incremental resource.
15. The apparatus of claim 12, wherein the optimization unit is further configured to optimize the allocation model such that the estimate of a first one of the plurality of resource pools at the predetermined date is closer to its expected estimate on the same day in a case where the plurality of first incremental resources are currently injected into the plurality of resource pools based on the allocation result of the allocation model after optimization than before optimization, wherein the difference between the estimate of the first one of the plurality of resource pools at the predetermined date and its expected estimate on the same day in a case where the plurality of first incremental resources are currently injected into the plurality of resource pools based on the allocation result of the allocation model before optimization is largest in the plurality of resource pools.
16. The apparatus of claim 12, wherein the resource pool has a predetermined deadline, the optimization unit is further configured to optimize the allocation model such that a product of a first parameter of a first resource pool among the plurality of resource pools, determined based on a gap between the estimate of the first resource pool on the predetermined date and the expected estimate on the same day thereof, is maximized in a case where the allocation model is based on an allocation result before optimization, and a second parameter of the first resource pool, determined based on an associated influencing factor, is more closely related to the expected estimate on the same day in a case where the allocation result based on the allocation model after optimization is currently injecting the first plurality of the first resources into the plurality of resource pools than in a case where the allocation result based on the allocation model after optimization is currently injecting the first plurality of the first resources into the plurality of resource pools.
17. The apparatus of claim 16, wherein the related influencing factors comprise a remaining number of days of the first resource pool from its expiration date on the predetermined date.
18. The apparatus of claim 12, wherein the predetermined date is a day subsequent to the current day.
19. The apparatus of claim 12, wherein the optimization unit is further configured to optimize the distribution model by simulating an annealing algorithm.
20. The apparatus of claim 12, wherein the first increasable resource and the resource pool each have a respective expiration date, the allocation model comprising at least the following constraints:
each of the first increasable resources can be allocated to only one resource pool or not to any one resource pool;
each of the first increasable resources can only be allocated to a resource pool whose expiration date is subsequent; and
each resource pool contains a total amount of exchangeable resources before the current injection that is greater than or equal to the sum of the exchange costs of the respective first incremental resources injected in the current injection.
21. The apparatus of claim 12, wherein the first acquisition unit is further configured to accumulate the acquired first increasable resources and to end the accumulation when the number of accumulated first increasable resources reaches a predetermined number or an accumulation time exceeds a predetermined time.
22. The apparatus of claim 12, wherein,
the first obtaining unit is further configured to equally divide a batch of first expandable resources into n groups, and obtain a plurality of first expandable resources included in one of the n groups, where each of the n groups includes a substantially identical number of first expandable resources;
the second obtaining unit is further configured to obtain a plurality of first resource pools, divide each first resource pool into n parts, and obtain one of the n parts included in each first resource pool to obtain the plurality of resource pools.
23. A computing device comprising a memory and a processor, wherein the memory has executable code stored therein, which when executed by the processor, implements the method of any of claims 1-11.
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