CN110705753B - Vehicle dispatching method and device based on dispatching model, computer equipment and storage medium - Google Patents
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Abstract
The invention discloses a vehicle dispatching method, a device, computer equipment and a storage medium based on a dispatching model, wherein the method comprises the following steps: acquiring specific dimension data of each delivery 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 specific dimension data and a pre-constructed vehicle dispatching model, wherein the vehicle dispatching model takes taxi demands and taxi incomes as optimization targets; and generating a vehicle dispatching strategy according to the optimized data, wherein the vehicle dispatching strategy comprises the fluctuation number of the vehicle throwing quantity of each throwing point. According to the vehicle dispatching method, device, computer equipment and storage medium based on the dispatching model, the optimized dispatching strategy is generated by combining the operation data and the position data of the dispatching points of the vehicle through the pre-built vehicle dispatching model, the use rate of the dispatched vehicle can be increased in an auxiliary mode, idle is reduced, the vehicle renting of users is facilitated, and the enterprise benefit is improved.
Description
Technical Field
The present invention relates to the field of shared vehicles, and in particular, to a vehicle scheduling method, apparatus, computer device, and storage medium based on a scheduling model.
Background
Nowadays, sharing vehicles such as sharing bicycles and sharing automobiles are more and more convenient for people to live, but the putting of sharing vehicles on the current market lacks scientific management, the setting of putting points and the putting quantity of each putting point are more random and are not put according to actual demands, so that the demand relation of each sharing vehicle putting point is unbalanced, some putting points are not supplied, some putting point vehicles have low utilization rate, the operation cost of enterprises is higher, and the income is difficult to realize maximization.
Disclosure of Invention
In view of the above, the invention provides a vehicle scheduling method, a device, a computer device and a storage medium based on a scheduling model, which can optimize a delivery strategy of a delivery point according to the operation data and the position of the existing delivery point, thereby improving the utilization rate and realizing the maximization of benefits.
First, in order to achieve the above object, the present invention provides a vehicle dispatching method based on a dispatching model, the method comprising:
acquiring specific dimension data, wherein the specific dimension data comprises use data of registered users and operation data of each delivery point;
Calculating optimization data according to the specific dimension data and a pre-constructed vehicle dispatching model, wherein the vehicle dispatching model takes taxi demands and taxi incomes as optimization targets;
and generating a vehicle dispatching strategy according to the optimized data, wherein the vehicle dispatching strategy comprises the fluctuation number of the vehicle throwing quantity of each throwing point.
Further, the calculating optimization data according to the specific dimension data and a pre-constructed vehicle dispatching model comprises:
Calculating a predicted demand interval of the delivery point according to the specific dimension data;
calculating the demand of a corresponding delivery point when the cost is minimum according to a pre-constructed cost model, wherein the cost model relates to the transformation cost and the allocation cost of the delivery point; the cost model is as follows:
Wherein P represents cost, A represents the area of land required to be used for adding one shared vehicle, and R i represents the unit area lease of the i-th vehicle drop point; y i represents the vehicle delivery amount of the i-th vehicle delivery point after optimization, and y i' represents the vehicle delivery amount of the i-th vehicle delivery point at present; b represents the construction cost required for each increase or decrease of one shared vehicle; c i represents the maintenance cost of the average per shared vehicle for the ith vehicle drop point; d i represents the number of operators that the ith vehicle drop point needs to be equipped with, and h represents the operator's wages.
Further, the calculating the predicted demand interval of the delivery 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 delivery point;
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 dispatching model further comprises:
according to the demand of each delivery point and a preset scheduling cost model, the scheduling demand when the scheduling cost is minimum is obtained; the scheduling cost model is as follows:
Wherein G represents the running cost, t represents the service period, and an operation period is divided into M sections; n represents the number of delivery points, c ij represents the unit mileage dispatching cost from the delivery point i to the delivery point j, d ij represents the distance from the delivery point i to the delivery point j, x ij represents the number of dispatching vehicles from the delivery point i to the delivery point j, wherein i is not equal to j;
calculating a vehicle dispatching path scheme when the benefits are maximized according to the dispatching requirements and a preset benefit model; the benefit model is as follows:
Wherein Q represents the benefit, i epsilon P represents the need of turning off the vehicle to other stations at a certain time; j epsilon D indicates that the delivery point j needs to allocate the vehicle to the site at a certain time; s is the number of the dispatcher, and S represents the number of the dispatcher; rer represents the benefit of each scheduled task; x ijs =1 means that the dispatcher s takes the vehicle from the pick-up point i to the delivery point j, otherwise x ijs =0; o represents a dispatching center; c represents the man-hour cost per dispatcher per class.
Further, the generating a vehicle scheduling policy according to the optimization data includes:
and generating a list of the delivery points with the delivery quantity change indexes larger than a preset first threshold value as a to-be-modified delivery point list according to the change number of the vehicle delivery quantity of each delivery point.
Further, the generating a vehicle scheduling policy according to the optimization data further includes:
And generating a list of the drop points with the demand lower than a preset second threshold value as a list of drop points to be revoked.
Further, the operational data includes operational costs and generated revenue, the operational costs including operational costs, maintenance costs, and loss costs, the method further comprising:
generating a list of delivery points having an operating cost higher than the generated revenue;
calculating the ratio of the difference between the operation cost and the generated income to the generated income;
And the drop points with the proportion higher than a preset third threshold value are included in the to-be-revoked drop point list.
In order to achieve the above object, the present invention also discloses a vehicle scheduling apparatus for a shared vehicle, comprising:
The system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is suitable for acquiring specific dimension data, wherein the specific dimension data comprises use data of registered users and operation data of each delivery 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 taxi demands and taxi incomes as optimization targets;
and the output module is suitable for generating a vehicle dispatching strategy according to the optimized data, wherein the vehicle dispatching strategy comprises the fluctuation number of the vehicle throwing quantity of each throwing point.
In order to achieve the above object, the present invention also discloses a computer device, including 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 stored thereon a computer program which, when executed by a processor, implements the above-mentioned vehicle scheduling method based on a scheduling model.
Compared with the prior art, the vehicle dispatching method, the device, the computer equipment and the storage medium based on the dispatching model provided by the invention have the advantages that the operation data and the position data of the vehicle delivery points are combined through the pre-built vehicle dispatching model, the optimized delivery strategy is generated, the utilization rate of the delivered vehicles can be increased in an auxiliary manner, the idle time is reduced, the vehicle renting is facilitated for users, and the enterprise benefit is improved.
Drawings
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 computing optimization data based on specific dimension data and pre-constructed vehicle dispatch models in accordance with an embodiment of the present invention;
FIG. 3 is a flow chart of a predicted demand interval according to an embodiment of the present invention;
FIG. 4 is a flow chart of computing optimization data based on specific dimension data and pre-built vehicle dispatch models in a further embodiment of an embodiment of the invention;
FIG. 5 is a flow chart of generating a vehicle dispatch strategy based on optimization data in a further embodiment of an embodiment of the invention;
FIG. 6 is a schematic diagram illustrating a program module of a vehicle dispatching device for a shared vehicle according to a second embodiment of the present invention;
Fig. 7 is a schematic hardware structure of a computer device according to a third embodiment of the present invention.
Reference numerals
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that the description of "first", "second", etc. in this disclosure is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implying an indication of the number of technical features being indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
Example 1
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 will be appreciated that the flow charts in the method embodiments are not intended to limit the order in which the steps are performed. The following description will be made exemplarily with respect to a vehicle scheduling apparatus of a shared vehicle, which may be applied to a server. The method comprises the following steps:
step S101, acquiring specific dimension data, wherein the specific dimension data comprises use data of registered users and operation data of each delivery point;
In this step, the usage data of the registered user includes data such as a lease time of the registered user, a point of delivery at the time of a car returning, a lease time, and a consumption amount, and the operation data of each point of delivery includes data such as a lease time, a point of delivery at the time of a car returning, and a generation amount for each single car service.
Referring back to fig. 1, step S102 calculates optimization data according to the specific dimension data and a pre-constructed vehicle dispatching model, wherein the vehicle dispatching model uses a taxi demand and a taxi income as optimization targets;
specifically, referring to fig. 2, step S102 includes the steps of:
step S201, calculating a predicted demand interval of a delivery point according to the specific dimension data;
referring to fig. 3, in this step, the predicted demand interval can be obtained according to the following steps S301-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 information of the use time period, the use frequency, the common use point and the like of the registered users with regular leasing (the registered users normally leased from work and work, and the leased release points are fixed with the release points of the vehicle), and accordingly the fixed demand interval of each release point, namely the minimum vehicle release quantity of the release points, can be calculated.
In this step, a method for obtaining a fixed demand interval is as follows steps A1 to A3:
step A1, acquiring a history borrowing and returning record in a certain operation period (such as 3 months) of a release point;
step A2, obtaining a borrowing record of the registered user in the historical borrowing record, and generating a user data table according to the borrowing record of the registered user; the user use data table comprises a user ID, the use times of the user in the operation period and the use distribution condition;
step A3, screening user data with more regular distribution conditions according to the user data table to generate a second user data table;
In this step, it is determined whether the usage distribution of the user is regular, and the user's weekly usage of the shared vehicle is counted according to the natural week, and it is determined whether the user uses the shared vehicle weekly and the number of times of weekly usage is not less than a set number of times (e.g., 3 times), and the user data satisfying the condition is listed in the second user data table.
And A4, counting the number of people with the use times greater than or equal to a first specific value and the number of people with the use times greater than or equal to a second specific value in the operation period as an upper limit and a lower limit of the fixed demand interval according to the second data table, and obtaining 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 into consideration, and the obtained demand interval can also meet the actual demand situation.
Step S302, calculating a floating demand interval according to operation data of the delivery points;
according to the operation data of the delivery points, the information such as the use time period, the renting times and the like of the scattered guests with randomness of the renting vehicles can be counted, and further 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, step S202 calculates the demand of the corresponding point of delivery with minimum cost according to a pre-constructed cost model, wherein the cost model relates to the modification cost and the allocation cost of the point of delivery.
The improvement cost relates to site renting, construction cost and subsequent equipment maintenance cost, the transportation cost relates to the wages of transportation personnel, the transportation capacity of companies operating taxi service and the like, a cost model is built according to the transportation cost, and the demand of each corresponding delivery point meeting the demand interval and having the minimum cost is calculated by utilizing a linear programming principle. Here, a cost model is as follows:
Wherein P represents cost, A represents the area of land required to be used for adding one shared vehicle, and R i represents the unit area lease of the i-th vehicle drop point; y i represents the vehicle delivery amount of the i-th vehicle delivery point after optimization, and y i' represents the vehicle delivery amount of the i-th vehicle delivery point at present; b represents the construction cost required for each increase or decrease of one shared vehicle; c i represents the maintenance cost of the average per shared vehicle for the ith vehicle drop point; d i represents the number of operators that the ith vehicle drop point needs to be equipped with, and h represents the operator's wages. The numerical value of the parameter R i、Ci、Di can be obtained by statistics according to the historical operation data of each vehicle drop point.
According to the cost model described above, several constraints can be set, such as: the sum of the personnel numbers cannot exceed a certain value, and the individual limiting condition of each vehicle drop point (such as the number of vehicles which can be put in the ith vehicle drop point is not more than 20at most according to the limit of the field); accordingly, the demand of the corresponding delivery point with the minimum cost can be obtained by combining the predicted demand interval.
Optionally, referring to fig. 4, step S102 further includes the steps of:
Step S401, solving the scheduling requirement when the scheduling cost is minimum according to the requirement of each delivery point and a preset scheduling cost model;
In one embodiment, the preset scheduling cost model is as follows:
Wherein G represents the running cost, t represents the service period, and an operation period is divided into M sections; n represents the number of delivery points, c ij represents the unit mileage dispatching cost from the delivery point i to the delivery point j, d ij represents the distance from the delivery point i to the delivery point j, x ij represents the number of dispatching vehicles from the delivery point i to the delivery point j, wherein i is not equal to j.
According to the dispatching cost model, a plurality of constraint conditions can be set as required, such as conservation of total number of vehicles in the dispatching process, the endurance mileage of the vehicles is larger than the distance between two delivery points, sufficient parking spaces exist when the dispatched vehicles reach the target delivery points, and the dispatching requirements among the delivery points can be solved according to the model.
Step S402, calculating a vehicle dispatching path scheme when the benefits are maximized according to the dispatching requirements and a preset benefit model.
In one embodiment, the preset revenue model is as follows:
Wherein Q represents the benefit, i epsilon P represents the need of turning off the vehicle to other stations at a certain time; j epsilon D indicates that the delivery point j needs to allocate the vehicle to the site at a certain time; s is the number of the dispatcher, and S represents the number of the dispatcher; rer represents the benefit of each scheduled task; x ijs =1 means that the dispatcher s takes the vehicle from the pick-up point i to the delivery point j, otherwise x ijs =0; o represents a dispatching center; c represents the man-hour cost per dispatcher per class.
Several constraints can be set as needed according to the revenue model, such as: each scheduling task can be executed only once, a scheduler can continue the journey of the vehicle in the scheduling process to be larger than the distance between two delivery points, one scheduler can execute only one scheduling task, and the like, and accordingly the optimal scheduling path can be obtained.
Referring back to fig. 1, step S103 generates a vehicle scheduling policy according to the optimization data, where the vehicle scheduling policy includes a number of variations in vehicle delivery amounts of each delivery point.
In a further embodiment, the vehicle dispatching policy further includes a vehicle dispatching path scheme obtained in step S402.
Optionally, the generating a vehicle scheduling policy according to the optimization data further includes the following steps:
and generating a list of the delivery points with the delivery quantity change indexes larger than a preset first threshold value as a to-be-modified delivery point list according to the change number of the vehicle delivery quantity of each delivery point.
According to the steps, the delivery points with smaller fluctuation numbers of the delivery vehicles can be screened out, the transformation cost and the labor cost are saved, and energy is mainly put on the extension or reduction transformation of the delivery points with larger fluctuation numbers, so that the delivery quantity of each delivery point can better meet the demands of users. The variation index of the throwing amount can be calculated according to the difference between the required amount and the existing throwing amount, and also can be calculated according to the variation percentage of the required amount to the existing throwing amount, and the first threshold can be set as the number or the percentage according to different calculation modes.
Optionally, the generating a vehicle scheduling policy according to the optimization data further includes the following steps:
And generating a list of the drop points with the demand lower than a preset second threshold value as a list of drop points to be revoked.
According to the method, the supply of the delivery points can be judged to be far greater than the solution, the value of continuously delivering the vehicle is not achieved, the delivery points can be recommended to be removed, and the operation activity of the delivery 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 drop point list with operation cost higher than generated income;
step S502, calculating the proportion of the difference value between the operation cost and the generated income relative to the generated income;
step S503, the drop points with the proportion higher than a preset third threshold value are included in the to-be-revoked drop point list.
Thus, the release points with serious loss can be found out in time, and the release points with serious loss are cancelled and processed in time.
Example two
Referring to fig. 6, a program module diagram of a vehicle dispatching device 600 for a shared vehicle according to a second embodiment of the invention is shown. In this embodiment, the vehicle dispatching apparatus 600 of the shared vehicle 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 complete the present invention, and may implement the dispatching model-based vehicle dispatching method described above. Program modules in accordance with the embodiments of the present invention refer to a series of computer program instruction segments capable of performing particular functions, and are more suitable than programs themselves for describing the execution of a scheduling model-based vehicle scheduling method in a storage medium. The following description will specifically describe functions of each program module of the present embodiment:
The acquiring module 601 is adapted to acquire specific dimension data, where the specific dimension data includes usage data of registered users and operation data of each delivery point;
The usage data of the registered user includes data such as a lease time of the registered user, a point of delivery at the time of a rental car, a point of delivery at the time of a return car, a lease time, and a consumption amount, and the operation data of each point of delivery includes data such as a lease time, a point of delivery at the time of a return car, and a generation amount for each single rental car service.
Referring back to fig. 6, the optimizing module 602 is adapted to calculate optimizing data according to the specific dimension data and a pre-constructed vehicle dispatching model, wherein the vehicle dispatching model takes taxi demands and taxi incomes as optimizing targets;
Specifically, referring to fig. 2, the optimization module 602 calculates optimization data according to the specific dimension data and a pre-constructed vehicle scheduling model, including the following steps:
Step S201, the optimizing module 602 calculates a predicted demand interval of the delivery point according to the specific dimension data;
referring to fig. 3, in this step, the predicted demand interval can be obtained according to the following steps S301-S303:
Step S301, the optimizing module 602 calculates a fixed demand interval according to the usage data of the registered user;
The use data of the registered users can count the information of the use time period, the use frequency, the common use point and the like of the registered users with regular leasing (the registered users normally leased from work and work, and the leased release points are fixed with the release points of the vehicle), and accordingly the fixed demand interval of each release point, namely the minimum vehicle release quantity of the release points, can be calculated.
In this step, a method for obtaining a fixed demand interval is as follows steps A1 to A3:
step A1, the optimizing module 602 obtains a history borrowing and returning record in a certain operation period (for example, 3 months) of the delivery point;
step A2, the optimizing module 602 obtains the borrowing and returning record of the registered user in the history borrowing and returning record, and generates a user data table according to the borrowing and returning record of the registered user; the user use data table comprises a user ID, the use times of the user in the operation period and the use distribution condition;
Step A3, the optimizing module 602 screens user data with more regular distribution conditions according to the user data table to generate a second user data table;
In this step, it is determined whether the usage distribution of the user is regular, and the user's weekly usage of the shared vehicle is counted according to the natural week, and it is determined whether the user uses the shared vehicle weekly and the number of times of weekly usage is not less than a set number of times (e.g., 3 times), and the user data satisfying the condition is listed in the second user data table.
In step A4, the optimizing module 602 counts the number of people with the number of times greater than or equal to the first specific value and the number of people with the number greater than or equal to the second specific value in the operation period 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 into consideration, and the obtained demand interval can also meet the actual demand situation.
Step S302, an optimizing module 602 calculates a floating demand interval according to operation data of a delivery point;
according to the operation data of the delivery points, the information such as the use time period, the renting times and the like of the scattered guests with randomness of the renting vehicles can be counted, and further the floating demand interval of each renting point can be counted.
In step S303, the optimization module 602 calculates 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, the optimization module 602 calculates the demand of the corresponding point of delivery with minimum cost according to a pre-constructed cost model, where the cost model relates to the modification cost and the allocation cost of the point of delivery.
The improvement cost relates to site renting, construction cost and subsequent equipment maintenance cost, the transportation cost relates to the wages of transportation personnel, the transportation capacity of companies operating taxi service and the like, a cost model is built according to the transportation cost, and the demand of each corresponding delivery point meeting the demand interval and having the minimum cost is calculated by utilizing a linear programming principle. Here, a cost model is as follows:
Wherein P represents cost, A represents the area of land required to be used for adding one shared vehicle, and R i represents the unit area lease of the i-th vehicle drop point; y i represents the vehicle delivery amount of the i-th vehicle delivery point after optimization, and y i' represents the vehicle delivery amount of the i-th vehicle delivery point at present; b represents the construction cost required for each increase or decrease of one shared vehicle; c i represents the maintenance cost of the average per shared vehicle for the ith vehicle drop point; d i represents the number of operators that the ith vehicle drop point needs to be equipped with, and h represents the operator's wages. The numerical value of the parameter R i、Ci、Di can be obtained by statistics according to the historical operation data of each vehicle drop point.
According to the cost model described above, several constraints can be set, such as: the sum of the personnel numbers cannot exceed a certain value, and the individual limiting condition of each vehicle drop point (such as the number of vehicles which can be put in the ith vehicle drop point is not more than 20at most according to the limit of the field); accordingly, the demand of the corresponding delivery point with the minimum cost can be obtained by combining the predicted demand interval.
Optionally, referring to fig. 4, the optimizing module 602 calculates optimizing data according to the specific dimension data and the pre-constructed vehicle scheduling model further includes the following steps:
Step S401, solving the scheduling requirement when the scheduling cost is minimum according to the requirement of each delivery point and a preset scheduling cost model;
In one embodiment, the preset scheduling cost model is as follows:
Wherein G represents the running cost, t represents the service period, and an operation period is divided into M sections; n represents the number of delivery points, c ij represents the unit mileage dispatching cost from the delivery point i to the delivery point j, d ij represents the distance from the delivery point i to the delivery point j, x ij represents the number of dispatching vehicles from the delivery point i to the delivery point j, wherein i is not equal to j.
According to the dispatching cost model, a plurality of constraint conditions can be set as required, such as conservation of total number of vehicles in the dispatching process, the endurance mileage of the vehicles is larger than the distance between two delivery points, sufficient parking spaces exist when the dispatched vehicles reach the target delivery points, and the dispatching requirements among the delivery points can be solved according to the model.
Step S402, calculating a vehicle dispatching path scheme when the benefits are maximized according to the dispatching requirements and a preset benefit model.
In one embodiment, the preset revenue model is as follows:
Wherein Q represents the benefit, i epsilon P represents the need of turning off the vehicle to other stations at a certain time; j epsilon D indicates that the delivery point j needs to allocate the vehicle to the site at a certain time; s is the number of the dispatcher, and S represents the number of the dispatcher; rer represents the benefit of each scheduled task; x ijs =1 means that the dispatcher s takes the vehicle from the pick-up point i to the delivery point j, otherwise x ijs =0; o represents a dispatching center; c represents the man-hour cost per dispatcher per class.
Several constraints can be set as needed according to the revenue model, such as: each scheduling task can be executed only once, a scheduler can continue the journey of the vehicle in the scheduling process to be larger than the distance between two delivery points, one scheduler can execute only one scheduling task, and the like, and accordingly 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, where the vehicle scheduling policy includes a number of variations of the vehicle delivery amounts of the delivery points.
In a further embodiment, the vehicle dispatching policy further includes a vehicle dispatching path scheme obtained in step S402.
Optionally, the output module 603 generates the vehicle scheduling policy according to the optimization data further includes the following steps:
and generating a list of the delivery points with the delivery quantity change indexes larger than a preset first threshold value as a to-be-modified delivery point list according to the change number of the vehicle delivery quantity of each delivery point.
According to the steps, the delivery points with smaller fluctuation numbers of the delivery vehicles can be screened out, the transformation cost and the labor cost are saved, and energy is mainly put on the extension or reduction transformation of the delivery points with larger fluctuation numbers, so that the delivery quantity of each delivery point can better meet the demands of users. The variation index of the throwing amount can be calculated according to the difference between the required amount and the existing throwing amount, and also can be calculated according to the variation percentage of the required amount to the existing throwing amount, and the first threshold can be set as the number or the percentage according to different calculation modes.
Optionally, the output module 603 generates the vehicle scheduling policy according to the optimization data further includes the following steps:
And generating a list of the drop points with the demand lower than a preset second threshold value as a list of drop points to be revoked.
According to the method, the supply of the delivery points can be judged to be far greater than the solution, the value of continuously delivering the vehicle is not achieved, the delivery points can be recommended to be removed, and the operation activity of the delivery points is stopped.
Optionally, the operation cost includes a scheduling cost, a maintenance cost, and a loss cost, referring to fig. 5, and the generating, by the output module 603, a vehicle scheduling policy according to the optimized data further includes the following steps S501-S503:
Step S501, generating a drop point list with operation cost higher than generated income;
step S502, calculating the proportion of the difference value between the operation cost and the generated income relative to the generated income;
step S503, the drop points with the proportion higher than a preset third threshold value are included in the to-be-revoked drop point list.
Thus, the release points with serious loss can be found out in time, and the release points with serious loss are cancelled and processed in time.
Example III
Referring to fig. 7, a hardware architecture diagram of a computer device 700 according to a third embodiment of the invention is shown. In this embodiment, the computer device 700 is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction. 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 scheduler 704 that can 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 flash memory, a hard disk, a multimedia card, a card 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 memory 701 may be an internal storage unit of the computer device 700, such as a hard disk or 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 provided on the computer device 700, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like. Of course, the memory 701 may also include both internal storage units of the computer device 700 and external storage devices. In this embodiment, the memory 701 is typically used to store an operating system and various types of application software installed on the computer device 700, such as program code of the vehicle scheduler 704 of the shared vehicle. In addition, the memory 701 can also be used to temporarily store various types of data that have been output or are to be output.
The processor 702 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 702 is generally used to control the overall operation of the computer device 700. In this embodiment, the processor 702 is configured to execute the program code stored in the memory 701 or process data, for example, execute the vehicle dispatching device 704 of the shared vehicle, so as to implement the dispatching model-based vehicle dispatching method in the first embodiment.
The network interface 703 may comprise a wireless network interface or a wired network interface, which network interface 703 is typically used to establish 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 an Intranet (Intranet), the Internet (Internet), a global system for mobile communications (Global System of Mobile communication, GSM), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA), a 4G network, a 5G network, bluetooth (Bluetooth), wi-Fi, or other wireless or wired network.
It should be noted that fig. 7 only shows a computer device 700 having components 701-704, but it should be understood that not all of the illustrated 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 (the processor 702 in this embodiment) to complete the dispatching method of the vehicle based on the dispatching model of the present invention.
Example IV
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 store, etc., on which a computer program is stored, which when executed by a processor, implements the above-described vehicle scheduling method based on a scheduling model.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (7)
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 delivery point;
Calculating optimization data according to the specific dimension data and a pre-constructed vehicle dispatching model, wherein the vehicle dispatching model takes taxi demands and taxi incomes as optimization targets;
Generating a vehicle dispatching strategy according to the optimized data, wherein the vehicle dispatching strategy comprises the fluctuation number of the vehicle throwing quantity of each throwing point;
The use data of the registered user comprises the lease time of the registered user, the release point of the taxi, the lease time and the consumption amount; the operation data of each release point comprises the lease time, lease time length, release point and production amount of each single vehicle renting service;
the generating a vehicle scheduling policy according to the optimized data includes:
generating a list of the delivery points with the demand lower than a preset second threshold value as a list of the delivery points to be revoked;
Wherein 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 delivery point according to the specific dimension data;
calculating the demand of a corresponding delivery point when the cost is minimum according to a pre-constructed cost model, wherein the cost model relates to the transformation cost and the allocation cost of the delivery point; the cost model is as follows:
Wherein P represents cost, A represents the area of land required to be used for adding one shared vehicle, and R i represents the unit area lease of the i-th vehicle drop point; y i represents the vehicle delivery amount of the i-th vehicle delivery point after optimization, and y i' represents the vehicle delivery amount of the i-th vehicle delivery point at present; b represents the construction cost required for each increase or decrease of one shared vehicle; c i represents the maintenance cost of the average per shared vehicle for the ith vehicle drop point; d i represents the number of operators to be equipped for the ith vehicle drop point, and h represents the payroll of operators;
wherein, the calculating optimization data according to the specific dimension data and the pre-constructed vehicle scheduling model further comprises:
according to the demand of each delivery point and a preset scheduling cost model, the scheduling demand when the scheduling cost is minimum is obtained; the scheduling cost model is as follows:
Wherein G represents the running cost, t represents the service period, and an operation period is divided into M sections; n represents the number of delivery points, c ij represents the unit mileage dispatching cost from the delivery point i to the delivery point j, d ij represents the distance from the delivery point i to the delivery point j, x ij represents the number of dispatching vehicles from the delivery point i to the delivery point j, wherein i is not equal to j;
calculating a vehicle dispatching path scheme when the benefits are maximized according to the dispatching requirements and a preset benefit model; the benefit model is as follows:
Wherein Q represents the benefit, i epsilon P represents the need of turning off the vehicle to other stations at a certain time; j epsilon D indicates that the delivery point j needs to allocate the vehicle to the site at a certain time; s is the number of the dispatcher, and S represents the number of the dispatcher; rer represents the benefit of each scheduled task; x ijs =1 means that the dispatcher s takes the vehicle from the pick-up point i to the delivery point j, otherwise x ijs =0; o represents a dispatching center; c represents the labor hour of each dispatcher per class; x ojs denotes the dispatcher s taking his car from the dispatch center to the delivery point j.
2. The scheduling model-based vehicle scheduling method according to claim 1, wherein calculating the predicted demand interval of the delivery 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 delivery point;
and calculating the sum of the fixed demand interval and the floating demand interval to obtain a predicted demand interval.
3. 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 delivery points with the delivery quantity change indexes larger than a preset first threshold value as a to-be-modified delivery point list according to the change number of the vehicle delivery quantity of each delivery point.
4. The scheduling model-based vehicle scheduling method of claim 1, wherein the operational data comprises operational costs and generated revenue, the operational costs comprising operational costs, maintenance costs, and wastage costs, the method further comprising:
generating a list of delivery points having an operating cost higher than the generated revenue;
calculating the ratio of the difference between the operation cost and the generated income to the generated income;
And the drop points with the proportion higher than a preset third threshold value are included in the to-be-revoked drop point list.
5. A vehicle scheduling apparatus for a shared vehicle, comprising:
the system comprises an acquisition module, a storage module and a storage 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 delivery 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 taxi demands and taxi incomes as optimization targets;
The output module is suitable for generating a vehicle dispatching strategy according to the optimized data, wherein the vehicle dispatching strategy comprises the fluctuation number of the vehicle throwing quantity of each throwing point;
The use data of the registered user comprises the lease time of the registered user, the release point of the taxi, the lease time and the consumption amount; the operation data of each release point comprises the lease time, lease time length, release point and production amount of each single vehicle renting service;
The output module is used for generating a vehicle scheduling strategy according to the optimized data and comprises the following steps:
generating a list of the delivery points with the demand lower than a preset second threshold value as a list of the delivery points to be revoked;
Wherein, the optimization module is specifically used for:
Calculating a predicted demand interval of the delivery point according to the specific dimension data;
calculating the demand of a corresponding delivery point when the cost is minimum according to a pre-constructed cost model, wherein the cost model relates to the transformation cost and the allocation cost of the delivery point; the cost model is as follows:
Wherein P represents cost, A represents the area of land required to be used for adding one shared vehicle, and R i represents the unit area lease of the i-th vehicle drop point; y i represents the vehicle delivery amount of the i-th vehicle delivery point after optimization, and y i' represents the vehicle delivery amount of the i-th vehicle delivery point at present; b represents the construction cost required for each increase or decrease of one shared vehicle; c i represents the maintenance cost of the average per shared vehicle for the ith vehicle drop point; d i represents the number of operators to be equipped for the ith vehicle drop point, and h represents the payroll of operators;
Wherein, the optimization module is further used for:
according to the demand of each delivery point and a preset scheduling cost model, the scheduling demand when the scheduling cost is minimum is obtained; the scheduling cost model is as follows:
Wherein G represents the running cost, t represents the service period, and an operation period is divided into M sections; n represents the number of delivery points, c ij represents the unit mileage dispatching cost from the delivery point i to the delivery point j, d ij represents the distance from the delivery point i to the delivery point j, x ij represents the number of dispatching vehicles from the delivery point i to the delivery point j, wherein i is not equal to j;
calculating a vehicle dispatching path scheme when the benefits are maximized according to the dispatching requirements and a preset benefit model; the benefit model is as follows:
Wherein Q represents the benefit, i epsilon P represents the need of turning off the vehicle to other stations at a certain time; j epsilon D indicates that the delivery point j needs to allocate the vehicle to the site at a certain time; s is the number of the dispatcher, and S represents the number of the dispatcher; rer represents the benefit of each scheduled task; x ijs =1 means that the dispatcher s takes the vehicle from the pick-up point i to the delivery point j, otherwise x ijs =0; o represents a dispatching center; c represents the labor hour of each dispatcher per class; x ojs denotes the dispatcher s taking his car from the dispatch center to the delivery point j.
6. A computer device 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 4 when the computer program is executed.
7. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the scheduling model based vehicle scheduling method of any one of claims 1 to 4.
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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 |
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| CN112418663B (en) * | 2020-11-20 | 2022-09-13 | 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 |
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| 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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