CN110705753A - Vehicle scheduling method and device based on scheduling model, computer equipment and storage medium - Google Patents

Vehicle scheduling method and device based on scheduling model, computer equipment and storage medium Download PDF

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CN110705753A
CN110705753A CN201910841566.XA CN201910841566A CN110705753A CN 110705753 A CN110705753 A CN 110705753A CN 201910841566 A CN201910841566 A CN 201910841566A CN 110705753 A CN110705753 A CN 110705753A
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程克喜
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Ping An Technology Shenzhen Co Ltd
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Abstract

The invention discloses a vehicle scheduling method, a vehicle scheduling device, computer equipment and a storage medium based on a scheduling model, wherein the method comprises the following steps: acquiring specific dimension data of each release point, wherein the specific dimension data comprises vehicle operation data and position data, and the vehicle operation data comprises borrowing amount; calculating optimization data according to the specific dimension data and a pre-constructed vehicle scheduling model, wherein the vehicle scheduling model takes the vehicle renting requirements and vehicle renting income as optimization targets; and generating a vehicle scheduling strategy according to the optimization data, wherein the vehicle scheduling strategy comprises the variation number of the vehicle release amount of each release point. According to the vehicle scheduling method and device based on the scheduling model, the computer equipment and the storage medium, the optimized launching strategy is generated by combining the operation data and the position data of the vehicle launching points through the vehicle scheduling model which is constructed in advance, the utilization rate of launched vehicles can be improved in an auxiliary mode, idling is reduced, a user can rent the vehicle conveniently, and enterprise benefits are improved.

Description

Vehicle scheduling method and device based on scheduling model, computer equipment and storage medium
Technical Field
The invention relates to the field of shared vehicles, in particular to a vehicle scheduling method and device based on a scheduling model, computer equipment and a storage medium.
Background
Shared vehicles such as shared bicycle, shared car have made things convenient for people's life more and more nowadays, but the input of shared vehicle lacks scientific management on the current market, the setting of input point and the input quantity of each input point are all comparatively random, do not put in according to the actual demand for the demand relation of each shared vehicle input point is unbalanced, some input points supply not to meet the demand, some input point vehicle rate of utilization is very low, so the operation cost of enterprise is higher, the income is difficult to realize the maximize.
Disclosure of Invention
In view of this, the invention provides a vehicle scheduling method, a vehicle scheduling device, a computer device and a storage medium based on a scheduling model, which can optimize a release strategy of a release point according to operation data and a position of an existing release point, thereby improving a utilization rate and realizing maximization of income.
Firstly, in order to achieve the above object, the present invention provides a vehicle scheduling method based on a scheduling model, wherein the method comprises:
acquiring specific dimension data, wherein the specific dimension data comprises use data of registered users and operation data of each release point;
calculating optimization data according to the specific dimension data and a pre-constructed vehicle scheduling model, wherein the vehicle scheduling model takes the vehicle renting requirements and vehicle renting income as optimization targets;
and generating a vehicle scheduling strategy according to the optimization data, wherein the vehicle scheduling strategy comprises the variation number of the vehicle release amount of each release point.
Further, the calculating optimization data according to the specific dimension data and a pre-constructed vehicle scheduling model comprises:
calculating a predicted demand interval of the release points according to the specific dimension data;
calculating the demand of the release point corresponding to the minimum cost according to a pre-constructed cost model, wherein the cost model relates to the transformation cost and the allocation and transportation cost of the release point; the cost model is as follows:
Figure BDA0002193879690000021
where P represents cost, A represents the area of land required to be used per added shared vehicle, and RiRepresenting the unit area rent of the ith vehicle release point; y isiRepresents the vehicle throwing amount, y, of the ith vehicle throwing point after optimizationi' represents the vehicle release amount of the current ith vehicle release point; b represents the construction cost required for each increase or decrease of one shared vehicle; ciRepresenting an average per shared vehicle maintenance cost for the ith vehicle drop-in point; diThe number of the dispatching personnel required to be equipped at the ith vehicle drop-in point is shown, and h represents the wage of the dispatching personnel.
Further, the calculating the predicted demand interval of the drop point according to the specific dimension data includes:
calculating a fixed demand interval according to the use data of the registered user;
calculating a floating demand interval according to the operation data of the release points;
and calculating the sum of the fixed demand interval and the floating demand interval to obtain a predicted demand interval.
Further, the calculating optimization data according to the specific dimension data and a pre-constructed vehicle scheduling model further comprises:
solving the scheduling requirement when the scheduling cost is minimum according to the demand of each release point and a preset scheduling cost model; the scheduling cost model is as follows:
Figure BDA0002193879690000022
wherein G represents the dispatching cost, t represents the service period, and one operation period is divided into M sections; n represents the number of drops, cijRepresents the unit mileage allocation and transportation cost from the release point i to the release point j, dijRepresents the distance, x, from drop point i to drop point jijRepresenting the number of shunting vehicles from a release point i to a release point j, wherein i is not equal to j;
calculating a vehicle dispatching path scheme when the profit is maximized according to the dispatching requirement and a preset profit model; the revenue model is as follows:
Figure BDA0002193879690000031
q represents income, i belongs to P and represents that a release point i needs to be transferred away from a vehicle to another station at a certain time; j epsilon is equal to D and represents that the release point j needs to allocate the vehicle to the release point j at a certain time; s is dispatcher number, and S represents the number of dispatchers; rer represents the benefit of each scheduling task; x is the number ofijs1 means that dispatcher s takes the car from pickup point i to delivery point j, otherwise xijs0; o represents a scheduling center; c represents the labor cost per dispatcher per shift.
Further, the generating a vehicle dispatch strategy from the optimization data comprises:
and generating a list of the throwing points with the throwing amount variation index larger than a preset first threshold value according to the variation number of the vehicle throwing amount of each throwing point to serve as a list of the throwing points needing to be modified.
Further, the generating a vehicle dispatching strategy according to the optimization data further comprises:
and generating a list of the release points with the demand quantity lower than a preset second threshold value as a list of the release points to be cancelled.
Further, the operational data includes operational costs and revenue generation, the operational costs include commissioning costs, maintenance costs, and loss costs, the method further comprising:
generating a drop point list with an operating cost higher than revenue generation;
calculating the proportion of the difference between the operating cost and the generated income relative to the generated income;
and bringing the release points with the proportion higher than a preset third threshold value into a release point list to be cancelled.
In order to achieve the above object, the present invention also discloses a vehicle dispatching device for sharing a vehicle, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is suitable for acquiring specific dimension data, and the specific dimension data comprises use data of registered users and operation data of each release point;
the optimization module is suitable for calculating optimization data according to the specific dimension data and a pre-constructed vehicle scheduling model, wherein the vehicle scheduling model takes vehicle renting requirements and vehicle renting income as optimization targets;
and the output module is suitable for generating a vehicle scheduling strategy according to the optimization data, wherein the vehicle scheduling strategy comprises the variation number of the vehicle putting amount of each putting point.
In order to achieve the above object, the present invention also discloses a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the vehicle scheduling method based on the scheduling model when executing the computer program.
In order to achieve the above object, the present invention also discloses a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the above vehicle scheduling method based on a scheduling model.
Compared with the prior art, the vehicle scheduling method, the vehicle scheduling device, the computer equipment and the storage medium based on the scheduling model generate an optimized release strategy by combining the operation data and the position data of the vehicle release points through the vehicle scheduling model which is constructed in advance, can assist in improving the utilization rate of released vehicles, reduce idling, facilitate vehicle renting of users and improve enterprise benefits.
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FIG. 1 is a flow chart of a vehicle scheduling method based on a scheduling model according to an embodiment of the present invention;
FIG. 2 is a flow chart of an embodiment of the present invention for computing optimization data based on dimension-specific data and a pre-constructed vehicle dispatch model;
FIG. 3 is a flow chart of a forecast demand interval according to an embodiment of the present invention;
FIG. 4 is a flow chart of a further embodiment of the present invention for calculating optimization data based on dimension-specific data and a pre-constructed vehicle dispatch model;
FIG. 5 is a flow chart of generating a vehicle dispatch strategy based on optimization data in a further embodiment of the present invention;
fig. 6 is a schematic diagram of program modules of a vehicle dispatching device sharing a vehicle according to a second embodiment of the invention;
fig. 7 is a schematic hardware configuration diagram of a computer device according to a third embodiment of the present invention.
Reference numerals
Figure BDA0002193879690000051
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the description relating to "first", "second", etc. in the present invention is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
Example one
Referring to fig. 1, a flowchart of a vehicle dispatching method based on a dispatching model according to an embodiment of the invention is shown. It is to be understood that the flow charts in the embodiments of the present method are not intended to limit the order in which the steps are performed. The following description is given by way of example with reference to a vehicle scheduling apparatus for a shared vehicle as an execution subject, which may be applied to a server. The method comprises the following specific steps:
step S101, specific dimension data is obtained, wherein the specific dimension data comprises use data of registered users and operation data of each release point;
in this step, the usage data of the registered user includes rental time of the registered user, a release point during rental of the vehicle, a release point during returning of the vehicle, rental duration, consumption amount and the like, and the operation data of each release point includes rental time of each rental vehicle service, rental duration, release point during returning of the vehicle, generation amount and the like.
Referring back to fig. 1, in step S102, calculating optimization data according to the specific dimension data and a pre-constructed vehicle scheduling model, wherein the vehicle scheduling model takes a vehicle renting demand and vehicle renting income as optimization targets;
specifically, referring to fig. 2, step S102 includes the steps of:
step S201, calculating a forecast demand interval of the release points according to the specific dimension data;
referring to fig. 3, in this step, the predicted demand interval may be obtained according to the following steps S301 to S303:
step S301, calculating a fixed demand interval according to the use data of the registered user;
the use data of the registered users can count the use time period, the use frequency, the common release points and other information of the registered users who rent regularly (such as the registered users who rent normally on duty and off duty, the release points of the release points and the release points of the return cars are fixed), and accordingly, the fixed demand interval of each release point, namely the minimum vehicle release number of the release points can be calculated.
In this step, a method for obtaining a fixed demand interval is as follows steps A1-A3:
step A1, obtaining the historical borrowing and returning records of a certain operation period (such as 3 months) of the release point;
step A2, obtaining the borrowing and returning records of the registered users in the history borrowing and returning records, and generating a user data table according to the borrowing and returning records of the registered users; the user usage data table comprises a user ID, the number of usage times of the user in an operation period and a usage distribution condition;
step A3, screening out user data with regular use distribution status according to the user data table, and generating a second user data table;
in this step, it is determined whether the usage distribution status of the user is regular, and the status of the user using the shared vehicle every week can be counted according to the natural week, and it is determined whether the user uses the shared vehicle every week for not less than a set number of times (e.g., 3 times) every week, and the user data satisfying the condition is listed in the second user data table.
And step A4, respectively counting the number of people with the number of times of use being greater than or equal to a first specific value and the number of people with the number of times of use being greater than or equal to a second specific value in the operation period as the upper limit and the lower limit of the fixed demand interval according to a second data table to obtain the fixed demand interval.
In this step, the number of high-frequency users is used as the upper limit of the interval, and the number of higher-frequency users is used as the lower limit, so that the statistical error can be well taken care of, and the obtained demand interval can meet the actual demand situation.
Step S302, calculating a floating demand interval according to operation data of a release point;
according to the operation data of the release points, the use time period, the rental times and other information of the scattered passengers with random rented vehicles can be counted, and then the floating demand interval of each renting point can be counted.
Step S303, calculating the sum of the fixed demand interval and the floating demand interval to obtain a predicted demand interval.
Referring back to fig. 2, in step S202, a demand amount of the drop point corresponding to the minimum cost is calculated according to a pre-constructed cost model, wherein the cost model relates to the transformation cost and the dispatching cost of the drop point.
The transformation cost relates to rent, construction cost and subsequent equipment maintenance cost of a site, the allocation and transportation cost relates to wages of allocation and transportation personnel, allocation and transportation capacity of companies operating car rental business and the like, a cost model is built according to the allocation and transportation cost, and the demand of each corresponding release point meeting a demand interval and meeting the minimum cost is calculated by using a linear programming principle. Here, one cost model is as follows:
Figure BDA0002193879690000071
where P represents cost, A represents the area of land required to be used per added shared vehicle, and RiRepresenting the unit area rent of the ith vehicle release point; y isiRepresents the vehicle throwing amount, y, of the ith vehicle throwing point after optimizationi' represents the vehicle release amount of the current ith vehicle release point; b represents the construction cost required for each increase or decrease of one shared vehicle; ciRepresenting an average per shared vehicle maintenance cost for the ith vehicle drop-in point; diThe number of the dispatching personnel required to be equipped at the ith vehicle drop-in point is shown, and h represents the wage of the dispatching personnel. The above parameter Ri、Ci、DiThe numerical value of (2) can be obtained by statistics according to historical operation data of each vehicle release point.
From the cost model described above, several constraints can be set, such as: the sum of the personnel number cannot exceed a certain value, and the individual limit condition of each vehicle drop point (for example, the number of vehicles which can be dropped by the ith vehicle drop point does not exceed 20 at most according to the field limit of the site); accordingly, the demand of the corresponding drop point at the minimum cost can be obtained by combining the predicted demand interval.
Optionally, referring to fig. 4, step S102 further includes the following steps:
step S401, solving the scheduling requirement when the scheduling cost is minimum according to the demand of each release point and a preset scheduling cost model;
in one embodiment, the preset scheduling cost model is as follows:
Figure BDA0002193879690000081
wherein G represents the dispatching cost, t represents the service period, and one operation period is divided into M sections; n represents the number of drops, cijRepresents the unit mileage allocation and transportation cost from the release point i to the release point j, dijRepresents the distance, x, from drop point i to drop point jijIndicating the number of trucks from drop point i to drop point j, where i ≠ j.
According to the dispatching cost model, a plurality of constraint conditions can be set according to needs, for example, the total number of vehicles is kept constant in the dispatching process, the cruising mileage of the vehicles is greater than the distance between two throwing points, enough parking spaces exist when the dispatched vehicles reach the target throwing point, and the like, and the dispatching requirements among the throwing points can be solved according to the model.
And S402, calculating a vehicle dispatching path scheme when the profit is maximized according to the dispatching requirement and a preset profit model.
In one embodiment, the predetermined revenue model is as follows:
q represents income, i belongs to P and represents that a release point i needs to be transferred away from a vehicle to another station at a certain time; j epsilon is equal to D and represents that the release point j needs to allocate the vehicle to the release point j at a certain time; s is dispatcher number, and S represents the number of dispatchers; rer represents the benefit of each scheduling task; x is the number ofijs1 means that dispatcher s takes the car from pickup point i to delivery point j, otherwise xijs0; o represents a scheduling center; c represents the labor cost per dispatcher per shift.
Several constraints can be set as desired based on the revenue model, such as: each scheduling task can be executed only once, the vehicle endurance of a dispatcher in the scheduling process is greater than the distance between two release points, and one dispatcher can only execute one scheduling task, so that the optimal scheduling path can be obtained.
Referring back to fig. 1, in step S103, a vehicle scheduling policy is generated according to the optimization data, where the vehicle scheduling policy includes the number of variations of the vehicle release amount of each release point.
In a further embodiment, the vehicle dispatching strategy further comprises the vehicle dispatching path scheme found in step S402.
Optionally, the generating a vehicle dispatching strategy according to the optimization data further comprises the following steps:
and generating a list of the throwing points with the throwing amount variation index larger than a preset first threshold value according to the variation number of the vehicle throwing amount of each throwing point to serve as a list of the throwing points needing to be modified.
According to the steps, the throwing points with small variable number of throwing vehicles can be screened out, the transformation cost and the labor cost are saved, and energy is mainly put on the expansion or reduction transformation of the throwing points with large variable number, so that the throwing amount of each throwing point can better meet the requirements of users. The input amount variation index here may be calculated according to a difference between the required amount and the existing input amount, or may be calculated according to a variation percentage of the required amount to the existing input amount, and the first threshold may be set to a number or a percentage according to different calculation modes.
Optionally, the generating a vehicle dispatching strategy according to the optimization data further comprises the following steps:
and generating a list of the release points with the demand quantity lower than a preset second threshold value as a list of the release points to be cancelled.
Accordingly, the supply of which releasing points is far larger than the demand and the value of continuously releasing the vehicle can be judged, and the releasing points can be suggested to be removed, and the operation activity of the releasing points is stopped.
Optionally, the operation cost includes a commissioning cost, a maintenance cost, and a loss cost, referring to fig. 5, the method further includes the following steps S501-S503:
step S501, generating a release point list with operation cost higher than income;
step S502, calculating the proportion of the difference value of the operation cost and the generated income relative to the generated income;
and step S503, bringing the release points with the proportion higher than a preset third threshold value into a release point list to be cancelled.
Therefore, the throwing points with serious loss can be found in time, and the throwing points with serious loss are cancelled, so that the loss is stopped in time.
Example two
Referring to fig. 6, a schematic diagram of program modules of a vehicle dispatching device 600 for sharing a vehicle according to a second embodiment of the invention is shown. In this embodiment, the vehicle dispatching device 600 for sharing vehicles may include or be divided into one or more program modules, and the one or more program modules are stored in a storage medium and executed by one or more processors to implement the present invention and implement the vehicle dispatching method based on the dispatching model. The program module referred to in the embodiment of the present invention refers to a series of computer program instruction segments capable of performing specific functions, and is more suitable for describing the execution process of the vehicle scheduling method based on the scheduling model in the storage medium than the program itself. The following description will specifically describe the functions of the program modules of the present embodiment:
an obtaining module 601, adapted to obtain specific dimension data, where the specific dimension data includes usage data of registered users and operation data of each drop point;
here, the usage data of the registered user includes rental time of the registered user, a release point when renting a car, a release point when returning the car, rental duration, consumption amount and the like, and the operation data of each release point includes rental time of each car rental service, rental duration, release point when returning the car, generation amount and the like.
Referring back to fig. 6, the optimization module 602 is adapted to calculate optimization data according to the specific dimension data and a pre-constructed vehicle scheduling model, wherein the vehicle scheduling model takes the vehicle renting demand and vehicle renting income as optimization targets;
specifically, referring to fig. 2, the optimization module 602 calculating the optimized data according to the specific dimension data and the pre-constructed vehicle dispatching model includes the following steps:
step S201, an optimization module 602 calculates a forecast demand interval of a release point according to the specific dimension data;
referring to fig. 3, in this step, the predicted demand interval may be obtained according to the following steps S301 to S303:
step S301, an optimization module 602 calculates a fixed demand interval according to the use data of the registered user;
the use data of the registered users can count the use time period, the use frequency, the common release points and other information of the registered users who rent regularly (such as the registered users who rent normally on duty and off duty, the release points of the release points and the release points of the return cars are fixed), and accordingly, the fixed demand interval of each release point, namely the minimum vehicle release number of the release points can be calculated.
In this step, a method for obtaining a fixed demand interval is as follows steps A1-A3:
step A1, the optimization module 602 obtains the historical borrowing and returning records of the release point within a certain period of operation (e.g. 3 months);
step a2, the optimization module 602 obtains the borrowing and returning records of the registered users in the history borrowing and returning records, and generates a user data table according to the borrowing and returning records of the registered users; the user usage data table comprises a user ID, the number of usage times of the user in an operation period and a usage distribution condition;
step a3, the optimization module 602 screens out user data with regular usage distribution status according to the user data table, and generates a second user data table;
in this step, it is determined whether the usage distribution status of the user is regular, and the status of the user using the shared vehicle every week can be counted according to the natural week, and it is determined whether the user uses the shared vehicle every week for not less than a set number of times (e.g., 3 times) every week, and the user data satisfying the condition is listed in the second user data table.
In step a4, the optimization module 602 respectively counts the number of people who use the interval with the number of times greater than or equal to the first specific value and the number of people who use the interval with the number of times greater than or equal to the second specific value as the upper limit and the lower limit of the fixed demand interval according to the second data table, so as to obtain the fixed demand interval.
In this step, the number of high-frequency users is used as the upper limit of the interval, and the number of higher-frequency users is used as the lower limit, so that the statistical error can be well taken care of, and the obtained demand interval can meet the actual demand situation.
Step S302, an optimization module 602 calculates a floating demand interval according to operation data of a release point;
according to the operation data of the release points, the use time period, the rental times and other information of the scattered passengers with random rented vehicles can be counted, and then the floating demand interval of each renting point can be counted.
In step S303, the optimization module 602 calculates a sum of the fixed demand interval and the floating demand interval to obtain a predicted demand interval.
Referring back to fig. 2, in step S202, the optimization module 602 calculates a demand amount of a drop point corresponding to a minimum cost according to a pre-constructed cost model, where the cost model relates to a transformation cost and a dispatching cost of the drop point.
The transformation cost relates to rent, construction cost and subsequent equipment maintenance cost of a site, the allocation and transportation cost relates to wages of allocation and transportation personnel, allocation and transportation capacity of companies operating car rental business and the like, a cost model is built according to the allocation and transportation cost, and the demand of each corresponding release point meeting a demand interval and meeting the minimum cost is calculated by using a linear programming principle. Here, one cost model is as follows:
Figure BDA0002193879690000111
where P represents cost, A represents the area of land required to be used per added shared vehicle, and RiRepresenting the unit area rent of the ith vehicle release point; y isiRepresents the vehicle throwing amount, y, of the ith vehicle throwing point after optimizationi' represents the vehicle release amount of the current ith vehicle release point; b represents the construction cost required for each increase or decrease of one shared vehicle; ciIndicating the ith vehicleAverage per shared vehicle maintenance cost for the drop points; diThe number of the dispatching personnel required to be equipped at the ith vehicle drop-in point is shown, and h represents the wage of the dispatching personnel. The above parameter Ri、Ci、DiThe numerical value of (2) can be obtained by statistics according to historical operation data of each vehicle release point.
From the cost model described above, several constraints can be set, such as: the sum of the personnel number cannot exceed a certain value, and the individual limit condition of each vehicle drop point (for example, the number of vehicles which can be dropped by the ith vehicle drop point does not exceed 20 at most according to the field limit of the site); accordingly, the demand of the corresponding drop point at the minimum cost can be obtained by combining the predicted demand interval.
Optionally, referring to fig. 4, the optimization module 602 calculating the optimized data according to the specific dimension data and the pre-constructed vehicle dispatching model further includes the following steps:
step S401, solving the scheduling requirement when the scheduling cost is minimum according to the demand of each release point and a preset scheduling cost model;
in one embodiment, the preset scheduling cost model is as follows:
wherein G represents the dispatching cost, t represents the service period, and one operation period is divided into M sections; n represents the number of drops, cijRepresents the unit mileage allocation and transportation cost from the release point i to the release point j, dijRepresents the distance, x, from drop point i to drop point jijIndicating the number of trucks from drop point i to drop point j, where i ≠ j.
According to the dispatching cost model, a plurality of constraint conditions can be set according to needs, for example, the total number of vehicles is kept constant in the dispatching process, the cruising mileage of the vehicles is greater than the distance between two throwing points, enough parking spaces exist when the dispatched vehicles reach the target throwing point, and the like, and the dispatching requirements among the throwing points can be solved according to the model.
And S402, calculating a vehicle dispatching path scheme when the profit is maximized according to the dispatching requirement and a preset profit model.
In one embodiment, the predetermined revenue model is as follows:
Figure BDA0002193879690000131
q represents income, i belongs to P and represents that a release point i needs to be transferred away from a vehicle to another station at a certain time; j epsilon is equal to D and represents that the release point j needs to allocate the vehicle to the release point j at a certain time; s is dispatcher number, and S represents the number of dispatchers; rer represents the benefit of each scheduling task; x is the number ofijs1 means that dispatcher s takes the car from pickup point i to delivery point j, otherwise xijs0; o represents a scheduling center; c represents the labor cost per dispatcher per shift.
Several constraints can be set as desired based on the revenue model, such as: each scheduling task can be executed only once, the vehicle endurance of a dispatcher in the scheduling process is greater than the distance between two release points, and one dispatcher can only execute one scheduling task, so that the optimal scheduling path can be obtained.
Referring back to fig. 6, the output module 603 is adapted to generate a vehicle scheduling policy according to the optimization data, wherein the vehicle scheduling policy includes the number of variations of the vehicle release amount of each release point.
In a further embodiment, the vehicle dispatching strategy further comprises the vehicle dispatching path scheme found in step S402.
Optionally, the step of generating the vehicle dispatching strategy by the output module 603 according to the optimized data further comprises the following steps:
and generating a list of the throwing points with the throwing amount variation index larger than a preset first threshold value according to the variation number of the vehicle throwing amount of each throwing point to serve as a list of the throwing points needing to be modified.
According to the steps, the throwing points with small variable number of throwing vehicles can be screened out, the transformation cost and the labor cost are saved, and energy is mainly put on the expansion or reduction transformation of the throwing points with large variable number, so that the throwing amount of each throwing point can better meet the requirements of users. The input amount variation index here may be calculated according to a difference between the required amount and the existing input amount, or may be calculated according to a variation percentage of the required amount to the existing input amount, and the first threshold may be set to a number or a percentage according to different calculation modes.
Optionally, the step of generating the vehicle dispatching strategy by the output module 603 according to the optimized data further comprises the following steps:
and generating a list of the release points with the demand quantity lower than a preset second threshold value as a list of the release points to be cancelled.
Accordingly, the supply of which releasing points is far larger than the demand and the value of continuously releasing the vehicle can be judged, and the releasing points can be suggested to be removed, and the operation activity of the releasing points is stopped.
Optionally, the operation cost includes a commissioning cost, a maintenance cost, and a loss cost, and referring to fig. 5, the generating of the vehicle dispatching strategy by the output module 603 according to the optimized data further includes the following steps S501 to S503:
step S501, generating a release point list with operation cost higher than income;
step S502, calculating the proportion of the difference value of the operation cost and the generated income relative to the generated income;
and step S503, bringing the release points with the proportion higher than a preset third threshold value into a release point list to be cancelled.
Therefore, the throwing points with serious loss can be found in time, and the throwing points with serious loss are cancelled, so that the loss is stopped in time.
EXAMPLE III
Fig. 7 is a schematic diagram of a hardware architecture of a computer device 700 according to a third embodiment of the present invention. In the present embodiment, the computer device 700 is a device capable of automatically performing numerical calculation and/or information processing according to an instruction set or stored in advance. As shown, the computer device 700 includes, but is not limited to, at least a memory 701, a processor 702, a network interface 703, and a vehicle dispatching device 704 sharing a vehicle, which may be communicatively coupled to each other via a system bus. Wherein:
in this embodiment, the memory 701 includes at least one type of computer-readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 701 may be an internal storage unit of the computer device 700, such as a hard disk or a memory of the computer device 700. In other embodiments, the memory 701 may also be an external storage device of the computer device 700, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 700. Of course, the memory 701 may also include both internal and external memory units of the computer device 700. In this embodiment, the memory 701 is generally used for storing an operating system and various application software installed in the computer device 700, such as program codes of the vehicle scheduling apparatus 704 of the shared vehicle. In addition, the memory 701 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 702 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 702 is generally configured to control the overall operation of the computer device 700. In this embodiment, the processor 702 is configured to run the program codes stored in the memory 701 or process data, for example, run the vehicle dispatching device 704 of the shared vehicle, so as to implement the vehicle dispatching method based on the dispatching model in the first embodiment.
The network interface 703 may include a wireless network interface or a wired network interface, and the network interface 703 is generally used for establishing a communication connection between the computer apparatus 700 and other electronic devices. For example, the network interface 703 is used to connect the computer device 700 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 700 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.
It is noted that FIG. 7 only shows the computer device 700 having components 701 and 704, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.
In this embodiment, the vehicle dispatching device 704 of the shared vehicle stored in the memory 701 may be further divided into one or more program modules, and the one or more program modules are stored in the memory 701 and executed by one or more processors (in this embodiment, the processor 702) to complete the vehicle dispatching method based on the dispatching model according to the present invention.
Example four
The present embodiment provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements the above-described scheduling model-based vehicle scheduling method.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A vehicle scheduling method based on a scheduling model, the method comprising:
acquiring specific dimension data, wherein the specific dimension data comprises use data of registered users and operation data of each release point;
calculating optimization data according to the specific dimension data and a pre-constructed vehicle scheduling model, wherein the vehicle scheduling model takes the vehicle renting requirements and vehicle renting income as optimization targets;
and generating a vehicle scheduling strategy according to the optimization data, wherein the vehicle scheduling strategy comprises the variation number of the vehicle release amount of each release point.
2. The scheduling model-based vehicle scheduling method of claim 1, wherein said calculating optimization data from the particular dimensional data and a pre-built vehicle scheduling model comprises:
calculating a predicted demand interval of the release points according to the specific dimension data;
calculating the demand of the release point corresponding to the minimum cost according to a pre-constructed cost model, wherein the cost model relates to the transformation cost and the allocation and transportation cost of the release point; the cost model is as follows:
Figure FDA0002193879680000011
where P represents cost, A represents the area of land required to be used per added shared vehicle, and RiRepresenting the unit area rent of the ith vehicle release point; y isiRepresents the vehicle throwing amount, y, of the ith vehicle throwing point after optimizationi' represents the vehicle release amount of the current ith vehicle release point; b represents the construction cost required for each increase or decrease of one shared vehicle; ciRepresenting an average per shared vehicle maintenance cost for the ith vehicle drop-in point; diThe number of the dispatching personnel required to be equipped at the ith vehicle drop-in point is shown, and h represents the wage of the dispatching personnel.
3. The vehicle scheduling method based on the scheduling model of claim 2, wherein the calculating the predicted demand interval of the drop point according to the specific dimension data comprises:
calculating a fixed demand interval according to the use data of the registered user;
calculating a floating demand interval according to the operation data of the release points;
and calculating the sum of the fixed demand interval and the floating demand interval to obtain a predicted demand interval.
4. The scheduling model-based vehicle scheduling method of claim 2, wherein said calculating optimization data from said particular dimensional data and a pre-built vehicle scheduling model further comprises:
solving the scheduling requirement when the scheduling cost is minimum according to the demand of each release point and a preset scheduling cost model; the scheduling cost model is as follows:
Figure FDA0002193879680000021
wherein G represents the dispatching cost, t represents the service period, and one operation period is divided into M sections; n represents the number of drops, cijIndicating delivery fromUnit mileage allocation and transportation cost from point i to point j, dijRepresents the distance, x, from drop point i to drop point jijRepresenting the number of shunting vehicles from a release point i to a release point j, wherein i is not equal to j;
calculating a vehicle dispatching path scheme when the profit is maximized according to the dispatching requirement and a preset profit model; the revenue model is as follows:
Figure FDA0002193879680000022
q represents income, i belongs to P and represents that a release point i needs to be transferred away from a vehicle to another station at a certain time; j epsilon is equal to D and represents that the release point j needs to allocate the vehicle to the release point j at a certain time; s is dispatcher number, and S represents the number of dispatchers; rer represents the benefit of each scheduling task; x is the number ofijs1 means that dispatcher s takes the car from pickup point i to delivery point j, otherwise xijs0; o represents a scheduling center; c represents the labor cost per dispatcher per shift.
5. The scheduling model-based vehicle scheduling method of claim 1, wherein the generating a vehicle scheduling policy from the optimization data comprises:
and generating a list of the throwing points with the throwing amount variation index larger than a preset first threshold value according to the variation number of the vehicle throwing amount of each throwing point to serve as a list of the throwing points needing to be modified.
6. The scheduling model-based vehicle scheduling method of claim 1, wherein the generating a vehicle scheduling policy from the optimization data further comprises:
and generating a list of the release points with the demand quantity lower than a preset second threshold value as a list of the release points to be cancelled.
7. The scheduling model-based vehicle scheduling method of claim 1 wherein the operational data includes operational costs and revenue generation, the operational costs include commissioning costs, maintenance costs, and loss costs, the method further comprising:
generating a drop point list with an operating cost higher than revenue generation;
calculating the proportion of the difference between the operating cost and the generated income relative to the generated income;
and bringing the release points with the proportion higher than a preset third threshold value into a release point list to be cancelled.
8. A vehicle dispatching device for sharing a vehicle, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module acquires specific dimension data, and the specific dimension data comprises use data of registered users and operation data of each release point;
the optimization module is suitable for calculating optimization data according to the specific dimension data and a pre-constructed vehicle scheduling model, wherein the vehicle scheduling model takes vehicle renting requirements and vehicle renting income as optimization targets;
and the output module is suitable for generating a vehicle scheduling strategy according to the optimization data, wherein the vehicle scheduling strategy comprises the variation number of the vehicle putting amount of each putting point.
9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the scheduling model based vehicle scheduling method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the scheduling model-based vehicle scheduling method according to any one of claims 1 to 7.
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Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111553578A (en) * 2020-04-22 2020-08-18 国电南瑞科技股份有限公司 Mobile electric dining car group delivery point selection method, route planning method and system
CN111612358A (en) * 2020-05-25 2020-09-01 北京交通大学 Shared automobile vehicle scheduling and dispatcher path optimization method
CN111915208A (en) * 2020-08-11 2020-11-10 上海钧正网络科技有限公司 Method and device for evaluating intelligent scheduling yield of shared vehicle
CN112101745A (en) * 2020-08-27 2020-12-18 深圳市元征科技股份有限公司 Vehicle release prompting method and device, terminal device and storage medium
CN112418663A (en) * 2020-11-20 2021-02-26 厦门大学 Method and system for configuring road tourism passenger vehicle, electronic equipment and storage medium
WO2021042833A1 (en) * 2019-09-06 2021-03-11 平安科技（深圳）有限公司 Scheduling model-based vehicle scheduling method and apparatus, computer device and storage medium
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CN113344336A (en) * 2021-05-11 2021-09-03 中车唐山机车车辆有限公司 Vehicle scheduling method and device and storage medium
CN114596667A (en) * 2022-03-08 2022-06-07 北京物资学院 Data sharing method, system and computer storage medium
CN114936886A (en) * 2022-07-22 2022-08-23 北京阿帕科蓝科技有限公司 Method, system and storage medium for controlling delivery of shared vehicles based on block estimates

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113888000A (en) * 2021-10-18 2022-01-04 支付宝(杭州)信息技术有限公司 Scheduling method and device for shared bicycle
CN117910783B (en) * 2024-03-19 2024-05-24 中国民用航空总局第二研究所 Ground guarantee personnel scheduling method based on flight ground guarantee task

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100287073A1 (en) * 2009-05-05 2010-11-11 Exxonmobil Research And Engineering Company Method for optimizing a transportation scheme
US20160027307A1 (en) * 2005-12-23 2016-01-28 Raj V. Abhyanker Short-term automobile rentals in a geo-spatial environment
CN107578199A (en) * 2017-08-21 2018-01-12 南京航空航天大学 A kind of method for solving two dimension and loading constraint logistics vehicle dispatching problem
CN108492546A (en) * 2018-03-27 2018-09-04 北京摩拜科技有限公司 Vehicle dispatching method, system, server and bluetooth scanning device
CN108776852A (en) * 2018-06-22 2018-11-09 北京京东金融科技控股有限公司 Without stake vehicle dispatching method and system
CN109800896A (en) * 2018-12-21 2019-05-24 浙江数链科技有限公司 Vehicle dispatching method, device, computer equipment and storage medium
CN110189006A (en) * 2019-05-20 2019-08-30 深圳市众行网科技有限公司 Dispatching method, device, computer equipment and its storage medium of vehicle

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107330586A (en) * 2017-06-16 2017-11-07 东南大学 A kind of public bicycles dynamic dispatching method based on improvement ant group algorithm
CN107657353A (en) * 2017-11-09 2018-02-02 东峡大通（北京）管理咨询有限公司 The dispatching method and system of lease
CN108388999B (en) * 2018-03-09 2021-01-05 汉海信息技术(上海)有限公司 Vehicle scheduling method, server, client and system
CN108563717A (en) * 2018-03-31 2018-09-21 东南大学 A kind of shared bicycle fault identification and application system based on information fusion
CN109583770A (en) * 2018-11-28 2019-04-05 清华四川能源互联网研究院 Vehicle dispatching method and device
CN110705753B (en) * 2019-09-06 2024-05-03 平安科技（深圳）有限公司 Vehicle dispatching method and device based on dispatching model, computer equipment and storage medium

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160027307A1 (en) * 2005-12-23 2016-01-28 Raj V. Abhyanker Short-term automobile rentals in a geo-spatial environment
US20100287073A1 (en) * 2009-05-05 2010-11-11 Exxonmobil Research And Engineering Company Method for optimizing a transportation scheme
CN107578199A (en) * 2017-08-21 2018-01-12 南京航空航天大学 A kind of method for solving two dimension and loading constraint logistics vehicle dispatching problem
CN108492546A (en) * 2018-03-27 2018-09-04 北京摩拜科技有限公司 Vehicle dispatching method, system, server and bluetooth scanning device
CN108776852A (en) * 2018-06-22 2018-11-09 北京京东金融科技控股有限公司 Without stake vehicle dispatching method and system
CN109800896A (en) * 2018-12-21 2019-05-24 浙江数链科技有限公司 Vehicle dispatching method, device, computer equipment and storage medium
CN110189006A (en) * 2019-05-20 2019-08-30 深圳市众行网科技有限公司 Dispatching method, device, computer equipment and its storage medium of vehicle

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王宁;张文剑;刘向;左静;: "电动汽车共享站点间车辆人工调度策略", 同济大学学报(自然科学版), no. 08, 15 August 2018 (2018-08-15), pages 66 - 73 *

Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021042833A1 (en) * 2019-09-06 2021-03-11 平安科技（深圳）有限公司 Scheduling model-based vehicle scheduling method and apparatus, computer device and storage medium
CN111553578A (en) * 2020-04-22 2020-08-18 国电南瑞科技股份有限公司 Mobile electric dining car group delivery point selection method, route planning method and system
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CN111612358B (en) * 2020-05-25 2024-04-12 北京交通大学 Shared automobile vehicle dispatching and dispatcher path optimization method
CN111915208A (en) * 2020-08-11 2020-11-10 上海钧正网络科技有限公司 Method and device for evaluating intelligent scheduling yield of shared vehicle
CN112101745A (en) * 2020-08-27 2020-12-18 深圳市元征科技股份有限公司 Vehicle release prompting method and device, terminal device and storage medium
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CN112418663A (en) * 2020-11-20 2021-02-26 厦门大学 Method and system for configuring road tourism passenger vehicle, electronic equipment and storage medium
CN112580889A (en) * 2020-12-25 2021-03-30 北京嘀嘀无限科技发展有限公司 Service resource pre-estimation method and device, electronic equipment and storage medium
CN113222348A (en) * 2021-04-16 2021-08-06 深圳竹芒科技有限公司 Shared article scheduling method and device, computer equipment and storage medium
CN113344336A (en) * 2021-05-11 2021-09-03 中车唐山机车车辆有限公司 Vehicle scheduling method and device and storage medium
CN114596667A (en) * 2022-03-08 2022-06-07 北京物资学院 Data sharing method, system and computer storage medium
CN114596667B (en) * 2022-03-08 2023-08-11 北京物资学院 Data sharing method, system and computer storage medium
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CN114936886B (en) * 2022-07-22 2023-02-03 北京阿帕科蓝科技有限公司 Method, system and storage medium for controlling shared vehicle delivery based on block estimation

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