Vehicle Configuration Method, Device, Storage Medium and Electronic Device

Abstract

The present disclosure relates to the field of resource allocation, and particularly to a vehicle configuration method, apparatus, storage medium and electronic device. The vehicle configuration method includes: obtaining cargo volume prediction information and vehicle usage parameters; inputting the cargo volume prediction information and the vehicle usage parameters into a pre-constructed vehicle cost optimization model; wherein, the vehicle cost optimization model includes a total vehicle cost objective function constructed based on the number of fixed vehicles, the number of increased vehicles and the number of decreased vehicles; obtaining the target number of fixed vehicles output by the vehicle cost optimization model, and performing vehicle configuration according to the target number of fixed vehicles. The vehicle configuration method provided by the present disclosure can minimize the total vehicle configuration cost in the case of fluctuating cargo volume.

Description

Technical Field

[0001] The present disclosure relates to the field of resource allocation, and particularly to a vehicle configuration method, apparatus, storage medium, and electronic device. Background Art

[0002] In a logistics scenario, when logistics personnel perform vehicle resource allocation, they adopt a method that combines advance procurement and temporary adjustment. At the beginning of each vehicle usage cycle, logistics personnel need to formulate a vehicle configuration plan in advance for the vehicle usage cycle, purchase a fixed number of vehicles, and during the implementation of this vehicle usage cycle, make temporary decisions to increase vehicles, reduce vehicles, or maintain the original plan based on the daily volume of goods.

[0003] In the prior art, when formulating a vehicle configuration plan, it is often formulated based on the volume of goods in the previous month and operation experience, or only considering minimizing the planned cost. However, when allocating vehicle resources, in addition to the planned cost, there are also certain costs for temporarily increasing or decreasing vehicles. Therefore, how to formulate a suitable vehicle configuration plan has a great impact on reducing operating costs.

[0004] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present disclosure is to provide a vehicle configuration method, apparatus, storage medium, and electronic device, aiming to solve the problem of minimizing the total vehicle configuration cost in the case of fluctuating volume of goods.

[0006] Other features and advantages of the present disclosure will become apparent through the following detailed description, or be learned in part through the practice of the present disclosure.

[0007] According to one aspect of the embodiments of the present disclosure, a vehicle configuration method is provided, including: obtaining volume-of-goods prediction information and vehicle usage parameters; inputting the volume-of-goods prediction information and the vehicle usage parameters into a pre-constructed vehicle cost optimization model; wherein, the vehicle cost optimization model includes a total vehicle cost objective function constructed based on the number of fixed vehicles, the number of vehicle increases, and the number of vehicle decreases; obtaining the target number of fixed vehicles output by the vehicle cost optimization model, and performing vehicle configuration according to the target number of fixed vehicles.

[0008] According to some embodiments of the present disclosure, based on the foregoing solution, the method further includes: constructing an objective function for the total vehicle cost, and the constructing of the objective function for the total vehicle cost includes: constructing a planned cost function based on the fixed number of vehicles; and constructing an adjustment cost function based on the number of increased vehicles and the number of decreased vehicles; constructing the total vehicle cost function according to the planned cost function and the adjustment cost function.

[0009] According to some embodiments of the present disclosure, based on the foregoing solution, the constructing of the adjustment cost function based on the number of increased vehicles and the number of decreased vehicles includes: constructing the planned cost function based on the fixed number of vehicles and the vehicle usage cost.

[0010] According to some embodiments of the present disclosure, based on the foregoing solution, the constructing of the adjustment cost function based on the number of increased vehicles and the number of decreased vehicles includes: constructing a temporary reservation cost function based on the number of increased vehicles, and constructing a temporary cancellation cost function based on the number of decreased vehicles; and obtaining a predicted probability coefficient corresponding to the cargo volume prediction information; constructing the adjustment cost function according to the predicted probability coefficient, the temporary reservation cost function, and the temporary cancellation cost function.

[0011] According to some embodiments of the present disclosure, based on the foregoing solution, the constructing of the temporary reservation cost function based on the number of increased vehicles and the constructing of the temporary cancellation cost function based on the number of decreased vehicles includes: constructing the temporary reservation cost function based on the number of increased vehicles, the vehicle usage cost, and the temporary reservation rate; and constructing the temporary cancellation cost function based on the number of decreased vehicles, the vehicle usage cost, and the temporary cancellation rate.

[0012] According to some embodiments of the present disclosure, based on the foregoing solution, the method further includes determining the vehicle usage cost, and the determining of the vehicle usage cost includes: determining the vehicle usage cost based on the vehicle unit cost, the driving distance, and other vehicle expenses.

[0013] According to some embodiments of the present disclosure, based on the foregoing solution, the cargo volume prediction information includes a plurality of predicted cargo volume values within a target vehicle usage cycle; the vehicle cost optimization model further includes constraint conditions, and the constraint conditions include: the sum of the cargo volume upper limits of each vehicle is not less than each of the predicted cargo volume values; the sum of the cargo volume lower limits of each vehicle is not greater than each of the predicted cargo volume values; the sum of the fixed number of vehicles is not greater than the vehicle usage upper limit; when satisfying each of the predicted cargo volume values, the sum of the number of vehicles is not greater than the vehicle usage upper limit; a fixed vehicle can be used only when it is available; when satisfying each of the predicted cargo volume values, a vehicle can be used only when it is available; when satisfying each of the predicted cargo volume values, the number of decreased vehicles is not greater than the fixed number of vehicles; when satisfying each of the predicted cargo volume values, vehicle adjustment can only increase or decrease the number of vehicles.

[0014] According to a second aspect of the embodiments of the present disclosure, a vehicle configuration device is provided, including: an acquisition module, configured to acquire cargo volume prediction information and vehicle usage parameters; an input module, configured to input the cargo volume prediction information and the vehicle usage parameters into a pre-constructed vehicle cost optimization model; wherein, the vehicle cost optimization model includes a vehicle total cost objective function constructed according to the number of fixed vehicles, the number of increased vehicles, and the number of decreased vehicles; an output module, configured to acquire the number of target fixed vehicles output by the vehicle cost optimization model, and perform vehicle configuration according to the number of target fixed vehicles.

[0015] According to a third aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, it implements the vehicle configuration method in the above embodiments.

[0016] According to a fourth aspect of the embodiments of the present disclosure, an electronic device is provided, characterized by including: one or more processors; a storage device, configured to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle configuration method in the above embodiments.

[0017] The exemplary embodiments of the present disclosure may have the following partial or all beneficial effects:

[0018] In the technical solutions provided by some embodiments of the present disclosure, by pre-configuring the vehicle total cost objective function of the vehicle cost optimization model according to the number of fixed vehicles, the number of increased vehicles, and the number of decreased vehicles, and then inputting the acquired cargo volume prediction information and vehicle usage parameters into the vehicle cost optimization model to obtain the number of fixed vehicles output by the vehicle cost optimization model, and further formulating a vehicle configuration plan according to the number of fixed vehicles. For the vehicle configuration method provided by the present disclosure, on the one hand, the number of increased vehicles and the number of decreased vehicles are introduced to construct the vehicle total cost objective function of the vehicle cost optimization model, which is convenient for characterizing the vehicle adjustment cost at each cargo volume level, so that the established vehicle cost optimization model is a linear model and is easy to solve; on the other hand, the cargo volume prediction information is introduced during vehicle configuration, which can consider all the cargo volume prediction information when formulating a reasonable vehicle configuration plan during vehicle configuration, so that the vehicle configuration can adapt to the cargo volume fluctuation situation within the prediction and meet the actual needs, and the configuration plan has high practicability.

[0019] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. Description of the Drawings

[0020] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts. In the drawings:

[0021] Figure 1 Schematically showing a flowchart of a vehicle configuration method in an exemplary embodiment of the present disclosure;

[0022] Figure 2 Schematically showing a diagram of predicting cargo volume information in an exemplary embodiment of the present disclosure;

[0023] Figure 3 Schematically showing a flowchart of a method for constructing an objective function of the total vehicle cost in an exemplary embodiment of the present disclosure;

[0024] Figure 4 Schematically showing a composition diagram of a vehicle configuration system in an exemplary embodiment of the present disclosure;

[0025] Figure 5 Schematically showing a composition diagram of a vehicle configuration device in an exemplary embodiment of the present disclosure;

[0026] Figure 6 Schematically showing a diagram of a computer-readable storage medium in an exemplary embodiment of the present disclosure;

[0027] Figure 7 Schematically showing a structural diagram of a computer system of an electronic device in an exemplary embodiment of the present disclosure. Detailed Embodiments

[0028] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more complete and will fully convey the concept of the example embodiments to those skilled in the art.

[0029] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present disclosure.

[0030] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0031] The flowcharts shown in the drawings are only exemplary descriptions, and do not necessarily include all contents and operations / steps, nor do they necessarily need to be executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined. Therefore, the actual execution order may change according to the actual situation.

[0032] In the prior art, in non-big promotion scenarios, current route planners adopt a method that combines purchasing one month in advance and making temporary adjustments when ordering vehicle resources. At the beginning of each month, based on the average shipment volume of the previous month and the available vehicle resources, vehicles for the current month are ordered in the order of loading large vehicles first and then small vehicles. During the execution of the current month, every morning, dispatchers make temporary decisions to increase vehicles, reduce vehicles, or maintain the original plan according to the predicted shipment volume of the day.

[0033] During the process of vehicle adjustment, the cost of temporarily ordering vehicles is higher than that of advance reservation, generally 1.5 times that of advance reservation. This is mainly because when ordering in advance, vehicle suppliers can formulate plans in advance and arrange round-trip transportation plans for each vehicle. However, when ordering vehicles temporarily, vehicle suppliers cannot arrange the return transportation plan of the vehicles in time. Therefore, a certain cost for the empty return of the vehicles needs to be paid when ordering temporarily. Similarly, temporarily canceling vehicles will also change the vehicle transportation plan of the supplier, resulting in corresponding drivers having no work, so a penalty fee needs to be paid to compensate the drivers, for example, generally 20% of the advance reservation cost.

[0034] It can be seen from this that on the one hand, the existing vehicle configuration plan is formulated based on the average shipment volume of the previous month, and the fluctuations in the daily shipment volume are not fully considered, which increases the cost of adjusting vehicles due to the inconsistency between the daily shipment volume and the average shipment volume, resulting in higher costs; on the other hand, the loading method is in the order of large first and then small, which may not be the optimal vehicle type combination and wastes certain resources.

[0035] Therefore, in view of the shortcomings of the prior art, the present disclosure provides a vehicle configuration method. By considering the planned cost and adjustment cost in the context of shipment volume fluctuations, an integer programming model is established to optimize the vehicle configuration plan and reduce the cost of using transportation resources.

[0036] The implementation details of the technical solutions of the embodiments of the present disclosure are elaborated in detail below.

[0037] Based on this, in the exemplary embodiment of the present disclosure, a vehicle configuration method is provided, which can run on a server, a server cluster, a cloud server, etc.; of course, those skilled in the art can also run the method of the present disclosure on other platforms according to requirements, and no special limitation is made in this exemplary embodiment.

[0038] Figure 1 Schematically showing a flowchart of a vehicle configuration method in an exemplary embodiment of the present disclosure. As Figure 1 shown, the vehicle configuration method includes steps S1 to S3:

[0039] Step S1, obtaining cargo volume prediction information and vehicle usage parameters;

[0040] Step S2, inputting the cargo volume prediction information and the vehicle usage parameters into a pre-constructed vehicle cost optimization model; wherein, the vehicle cost optimization model includes a vehicle total cost objective function constructed according to the number of fixed vehicles, the number of increased vehicles, and the number of decreased vehicles;

[0041] Step S3, obtaining the target number of fixed vehicles output by the vehicle cost optimization model, and performing vehicle configuration according to the target number of fixed vehicles.

[0042] In the technical solution provided by some embodiments of the present disclosure, by pre-configuring the vehicle total cost objective function of the vehicle cost optimization model according to the number of fixed vehicles, the number of increased vehicles, and the number of decreased vehicles, and then inputting the obtained cargo volume prediction information and vehicle usage parameters into the vehicle cost optimization model to obtain the number of fixed vehicles output by the vehicle cost optimization model, and then formulating a vehicle configuration plan according to the number of fixed vehicles. The vehicle configuration method provided by the present disclosure, on the one hand, introduces the number of increased vehicles and the number of decreased vehicles to construct the vehicle total cost objective function of the vehicle cost optimization model, which is convenient for characterizing the vehicle adjustment cost at each cargo volume level, so that the established vehicle cost optimization model is a linear model and is easy to solve; on the other hand, introducing cargo volume prediction information during vehicle configuration can consider covering all cargo volume prediction information when formulating a reasonable vehicle configuration plan, enabling the vehicle configuration to adapt to the cargo volume fluctuation situation within the prediction and conform to the actual requirements, and the configuration plan has high practicality.

[0043] Next, each step of the vehicle configuration method in this exemplary embodiment will be described in more detail with reference to the drawings and embodiments.

[0044] In step S1, obtain cargo volume prediction information and vehicle usage parameters.

[0045] ① Cargo volume prediction information

[0046] Specifically, the cargo volume prediction information refers to the predicted value of the cargo volume within a vehicle usage cycle when performing vehicle configuration.

[0047] In a real-world scenario, demands are uncertain. With the development of technologies such as big data and machine learning, the accuracy of predicting future demands has been continuously improved, making it possible to plan based on predictions. Therefore, before the start of each vehicle usage cycle, dispatchers will obtain the predicted cargo volume information for that cycle based on historical operation conditions.

[0048] The vehicle usage cycle mentioned here can be set according to user needs. For example, if the vehicle configuration is booked monthly, then the vehicle usage cycle is one month. It can also be booked for project completion. If the project completion cycle is two weeks, then the vehicle usage cycle is two weeks.

[0049] Therefore, it is necessary to obtain the predicted cargo volume value within a future vehicle usage cycle, and based on this, the formulated vehicle configuration plan can cover all cargo volumes, making the plan more forward-looking and applicable. The cargo volume prediction information can be obtained through an external prediction interface or the result can be obtained by model prediction based on historical data.

[0050] ② Vehicle usage parameters

[0051] Specifically, vehicle usage parameters refer to all the parameters required by the vehicle cost optimization model during vehicle configuration.

[0052] For example, vehicle usage parameters include the route information of the routes traveled by the vehicle. Because in the specific vehicle dispatching process, there can be different vehicle models, and for different transportation routes, the available normal vehicle models v are also different and need to be obtained from the route information.

[0053] Another example is that vehicle usage parameters include the usage cost information and usage specification information of vehicles of different models. Because the unit usage cost of vehicles of different models is different, or there are differences in the upper and lower limits of the cargo volume during use, these data need to be input into the vehicle cost optimization model.

[0054] In addition, vehicle usage parameters also include the rate information during vehicle adjustment. Because in actual vehicle configuration, according to the change of the daily cargo volume, vehicles need to be adjusted temporarily, and parameters such as the temporary reservation rate and the temporary cancellation rate will affect the calculation of the vehicle cost optimization model.

[0055] It should be noted that vehicle usage parameters can be values that can be determined before model optimization. These parameters can be pre-stored in the database, and then these basic data can be read by calling the database, or the basic data can be added, deleted, modified, and queried through regular operations on the database. The detailed content will be introduced when constructing the vehicle cost optimization model and will not be explained in detail here.

[0056] Steps S2 and S3 are calculated using a pre - constructed vehicle cost optimization model. The inputs of this model are the cargo volume forecast information and vehicle usage parameters, and the input of the model is the value of the fixed number of vehicles.

[0057] In an embodiment of the present disclosure, when logistics personnel perform vehicle resource allocation, the operator and the vehicle supplier first sign a contract based on a fixed number of vehicles, which can also be called the standard strategy of the vehicle configuration plan. The contract can be to purchase vehicles or lease vehicles. Then, during specific scheduling, the dispatcher makes a temporary decision to increase vehicles, reduce vehicles, or maintain the original plan according to the actual delivery cargo volume of the day.

[0058] Therefore, vehicle configuration according to a fixed number of vehicles here is to provide logistics personnel with a fixed number of vehicles for signing a contract.

[0059] In an embodiment of the present disclosure, it is necessary to pre - construct a vehicle cost optimization model. The vehicle cost optimization model includes the total vehicle cost objective function and constraint conditions. Therefore, constructing the vehicle cost optimization model mainly includes the following steps:

[0060] Step (1), configure the decision variables of the model;

[0061] Step (2), construct the total vehicle cost objective function;

[0062] Step (3), construct the constraint conditions.

[0063] In step (1), a total of three decision variables are set, namely the fixed number of vehicles to be used during the vehicle usage period, and the number of increased vehicles and the number of decreased vehicles for vehicle adjustment under fluctuating cargo volume.

[0064] In an embodiment of the present disclosure, when performing vehicle resource allocation, the logistics operator and the vehicle supplier first sign a contract based on a number of vehicles, and this number of vehicles is the fixed number of vehicles to be used during the vehicle usage period.

[0065] During the usage period of the plan implementation, the cargo volume is not necessarily the same every day, and there are certain changes in the cargo volume value. Therefore, in order to cover the cargo volume of the day, the dispatcher needs the actual delivery cargo volume of the day and makes a temporary decision to increase vehicles, reduce vehicles, or maintain the original plan based on the fixed number of vehicles. The number of increased vehicles is the number of vehicles added during adjustment. Then, the number of vehicles used on that day is the sum of the fixed number of vehicles and the number of increased vehicles. Similarly, the number of decreased vehicles is the number of vehicles reduced during adjustment, and the number of vehicles used on that day is the difference between the fixed number of vehicles and the number of decreased vehicles.

[0066] It should be noted that in the specific vehicle scheduling process, vehicles can have different models, and the number of increased vehicles and the number of decreased vehicles are both relative to the same model. Specifically, the fixed number of vehicles corresponding to model v is denoted as x v , where v ∈ V | V = {A, B, C, D, …}, and V is the set of vehicle models. Then, when the cargo volume level is k, the number of increased vehicles corresponding to model v is denoted as The number of decreased vehicles corresponding to model v is denoted as where k ∈ K | K = {1, 2, 3, …}, and K is the set of cargo volume levels. For example, when the cargo volume value is 600, it is set as the cargo volume level 1 with k = 1; when the cargo volume value is 400, it is set as the cargo volume level 2 with k = 2, and so on.

[0067] In addition, for different transportation routes, the available normal vehicle models v are also different. Table 1 shows several examples of route details. Referring to Table 1, it shows the origin city, destination city, and transportation distance of each route, as well as the vehicle information used on each route under a certain cargo volume.

[0068] Table 1 Examples of Route Details

[0069]

[0070] As shown in Table 1, for example, Route L1 is from Beijing to Shenzhen, with a route distance of 2,203 kilometers. The available vehicle models v include models A, B, C, D, and E, and it uses 1 vehicle of model B, 1 vehicle of model C, 6 vehicles of model D, and 2 vehicles of model E. Route L2 is also from Beijing to Shenzhen, with a route distance of 2,247 kilometers, and it uses 3 vehicles of model C, 5 vehicles of model D, and 1 vehicle of model E. For the corresponding route details of other routes, please refer to Table 1.

[0071] In an embodiment of the present disclosure, the fixed number of vehicles x v , the number of increased vehicles and the number of decreased vehicles corresponding to each vehicle model v are used as decision variables to construct a vehicle configuration model.

[0072] For step (ii), a vehicle total cost objective function is constructed. In an embodiment of the present disclosure, the vehicle total cost function includes two parts of the vehicle cost within a vehicle usage cycle. One is the planned cost f(1), and the other is the adjustment cost f(2). That is, f = f(1) + f(2).

[0073] Among them, the planned cost f(1) refers to the cost of using a fixed number of vehicles. Table 2 shows the basic information table of the usage cost for each vehicle type. As can be seen from Table 2, when using vehicles, on the one hand, the line usage cost needs to be paid according to the distance traveled by the vehicle, and on the other hand, other costs such as driver fees and vehicle insurance also need to be paid. Therefore, the planned costs corresponding to vehicles of different models on different lines are different.

[0074] Table 2 Basic Information Table of Usage Cost

[0075] Element Vehicle Model A Vehicle Model B Vehicle Model C Vehicle Model D Vehicle Model E Line Usage Cost: Yuan / km 4 5 7 9 15 Driver Expenses and Vehicle Insurance (Standard Distance): Yuan 10000 13000 18000 22000 27000

[0076] For the adjustment cost f(2), it refers to the cost generated due to temporary vehicle adjustments during the actual implementation of vehicle scheduling. Specifically, when ordering vehicles temporarily, the vehicle supplier cannot arrange the return transportation plan of the vehicle in time. Therefore, a certain vehicle return empty-load cost also needs to be paid during temporary ordering, that is, the temporary reservation cost corresponding to the increased vehicle. Temporarily canceling a vehicle will also change the vehicle transportation plan of the supplier, resulting in corresponding drivers having no work, and a breach fee needs to be paid to compensate the drivers, that is, the temporary cancellation cost corresponding to the reduced vehicle.

[0077] For step (iii), the constraint conditions can be constructed based on the cargo volume prediction information within the vehicle usage cycle and the vehicle usage parameters.

[0078] In an embodiment of the present disclosure, the cargo volume prediction information needs to be considered when constructing the constraint conditions.

[0079] In the real scenario, the demand is uncertain. With the development of technologies such as big data and machine learning, the accuracy of predicting future demand is continuously improving, making it possible to make plans based on predictions. Therefore, before the start of each vehicle usage cycle, the dispatcher will obtain the cargo volume prediction information for this cycle based on the historical operation situation.

[0080] Figure 2 Schematically shows a schematic diagram of predicting cargo volume information in an exemplary embodiment of the present disclosure. Taking one month as a cycle as an example, the predicted cargo volume values for a certain line in the next month are as Figure 2 shown, where the abscissa is time and the ordinate is the predicted cargo volume value corresponding to this time.

[0081] According to Figure 2 it can be seen that the cargo volume in the prediction information is in a fluctuating state, with a highest point and a lowest point. For example, on the 14th of the current month, the predicted cargo volume is 350 cubic meters, while on the 8th of the current month, the predicted cargo volume is 713 cubic meters. When conducting vehicle scheduling, the sum of the volumes of all vehicles during vehicle scheduling needs to be able to cover all cargo volume levels under the cargo volume fluctuations.

[0082] In one embodiment of the present disclosure, vehicle usage rules also need to be considered when constructing the constraint conditions.

[0083] Table 3 shows the upper and lower limits of the cargo volume for different vehicle models, which is equivalent to showing the usage rules of each vehicle model from the perspective of cargo volume. Referring to Table 3, taking vehicle model E as an example, vehicle model E can only be used when the cargo volume loaded by vehicle model E is between 80 cubic meters and 150 cubic meters. The same applies to other vehicle models.

[0084] Table 3 Upper and lower limits of cargo volume for vehicle models

[0085] Vehicle Model Lower Limit of Cargo Volume (cubic meters) Upper Limit of Cargo Volume (cubic meters) A 0 30 B 10 50 C 40 80 D 50 100 E 75 130

[0086] Therefore, when conducting vehicle scheduling, at different cargo volume levels, a vehicle model can only be used when it meets the usage rules.

[0087] In one embodiment of the present disclosure, for step (two), the method further includes: constructing the vehicle total cost objective function. Figure 3 Schematically shows a flowchart of a method for constructing a vehicle total cost objective function in an exemplary embodiment of the present disclosure. Refer to Figure 3 As shown, constructing the vehicle total cost objective function includes:

[0088] Step S301, constructing a planned cost function based on the fixed number of vehicles; and

[0089] Step S302, constructing an adjustment cost function based on the number of vehicle increases and the number of vehicle decreases;

[0090] Step S303, constructing the vehicle total cost function according to the planned cost function and the adjustment cost function.

[0091] Next, the above process will be described in combination with specific parameter representations.

[0092] In step S301, a planned cost function f(1) is constructed based on the fixed number of vehicles. Among them, constructing an adjustment cost function based on the number of vehicle increases and the number of vehicle decreases includes: constructing the planned cost function based on the fixed number of vehicles and the vehicle usage cost, specifically as follows:

[0093] First, the vehicle usage cost c can be calculated based on the vehicle unit cost, the driving distance, and other vehicle expenses v .

[0094] The vehicle unit cost is the line usage cost e of the v-th vehicle model v , the driving distance is d, and other vehicle expenses can be driver expenses and vehicle insurance expenses, which are represented by r under the standard distance v However, rv It takes different values at different line distances d. Therefore, g(d) is introduced as a distance factor for converting the driver's fee and vehicle insurance cost at different distances.

[0095] Then, the vehicle usage cost c of vehicle type v v is calculated as shown in formula (1):

[0096]

[0097] Among them, the value of the distance factor is as shown in formula (2):

[0098]

[0099] Therefore, the planned cost f(1) includes the vehicle usage costs of all vehicles of each vehicle type, as shown in formula (3):

[0100]

[0101] In step S302, an adjustment cost function f(2) is constructed based on the vehicle increase number and the vehicle decrease number.

[0102] In an embodiment of the present disclosure, constructing the adjustment cost function based on the vehicle increase number and the vehicle decrease number includes: constructing a temporary reservation cost function based on the vehicle increase number, and constructing a temporary cancellation cost function based on the vehicle decrease number; and obtaining a prediction probability coefficient corresponding to the cargo volume prediction information; constructing the adjustment cost function according to the prediction probability coefficient, the temporary reservation cost function, and the temporary cancellation cost function.

[0103] To meet different cargo volume levels k, appropriate vehicle adjustments may be made based on the original fixed number of vehicles, such as increasing or decreasing the number of vehicles of a certain vehicle type.

[0104] Specifically, when the number of vehicles increases, the temporary reservation cost function is constructed based on the vehicle increase number, the vehicle usage cost, and the temporary reservation rate.

[0105] That is, based on the vehicle increase number the vehicle usage cost c v and the temporary reservation rate calculate the temporary reservation cost. The vehicle increase number and the vehicle usage cost c v have been introduced before and will not be elaborated here.

[0106] For the temporary reservation rate it means that the temporary reservation cost is v times of the vehicle usage cost c when making an advance reservation. For example, It is shown that the temporary reservation cost corresponding to adding one vehicle is 1.5c v . Since the specific scenarios of each route are different, users can set as a constant according to their actual situations.

[0107] Therefore, for all vehicle increase numbers , the total temporary reservation cost is

[0108] When the number of vehicles decreases, the temporary cancellation cost function is constructed based on the number of vehicle decreases, vehicle usage cost, and temporary cancellation rate.

[0109] That is, based on the number of vehicle decreases vehicle usage cost c v and temporary cancellation rate to calculate the temporary cancellation cost. Similarly, the number of vehicle decreases and vehicle usage cost c v have been introduced before, so no more details will be given here.

[0110] For the temporary cancellation rate , it is shown that the temporary cancellation cost is v times the vehicle usage cost c at the time of advance reservation. For example, if , it is shown that the temporary cancellation cost corresponding to canceling one vehicle is 0.2c v . Since the specific scenarios of each route are different, users can set as a constant according to their actual situations.

[0111] Therefore, for all vehicle decrease numbers , the total temporary cancellation cost is This is because the vehicle usage cost reserved before needs to be refunded, and only the temporary cancellation cost is paid.

[0112] In addition, since vehicle scheduling is performed according to the predicted cargo volume value, a predicted probability coefficient p k is introduced to correct the calculation result. The cargo volume prediction information includes multiple predicted cargo volume values m k under the cargo volume fluctuation within the target vehicle usage cycle, k as well as the predicted probability coefficient p

[0113] Finally, the adjustment cost f(2) is calculated based on the predicted probability coefficient p k , temporary reservation cost, and temporary cancellation cost, as shown in formula (4):

[0114]

[0115] Step S303: Construct the total vehicle cost function according to the planned cost function and the adjustment cost function.

[0116] That is, add the planned cost and the adjustment cost to obtain the total vehicle cost function, as shown in formula (5):

[0117]

[0118] In an embodiment of the present disclosure, for step (iii), it is necessary to construct the constraint conditions of the target model. Therefore, the vehicle cost optimization model further includes constraint conditions, and the specific constraint conditions are as follows:

[0119] The sum of the upper limits of the cargo volumes of each vehicle is not less than each of the predicted cargo volume values;

[0120] The sum of the lower limits of the cargo volumes of each vehicle is not greater than each of the predicted cargo volume values;

[0121] The sum of the fixed vehicle numbers is not greater than the upper limit of vehicle usage;

[0122] When each of the predicted cargo volume values is satisfied, the sum of the vehicle numbers is not greater than the upper limit of vehicle usage;

[0123] A fixed vehicle can be used only when it is available;

[0124] When each of the predicted cargo volume values is satisfied, a vehicle can be used only when it is available;

[0125] When each of the predicted cargo volume values is satisfied, the number of reduced vehicles is not greater than the number of fixed vehicles;

[0126] When each of the predicted cargo volume values is satisfied, vehicle adjustment can only increase or decrease the number of vehicles.

[0127] Table 4 shows the parameters in the vehicle cost optimization model and the meanings of the parameters. Next, the above process will be described in combination with the specific parameter representations in Table 4.

[0128] Table 4 Parameters in the model and their meanings

[0129]

[0130]

[0131] Therefore, the constructed constraint conditions are as shown in formula (6):

[0132]

[0133] Let the number of fixed vehicles used be x vRegarded as the standard strategy for vehicle scheduling, constraint (1) means that at each cargo volume level k, the total cargo volume of the vehicles used is greater than the predicted cargo volume value; constraint (2) means that at each cargo volume level k, the vehicles used should meet the usage rules of the minimum opening standard for cargo volume; constraint (3) means that under the standard strategy, the total number of vehicles used does not exceed the upper limit of vehicle usage; constraint (4) means that at each cargo volume level k, the total number of vehicles used does not exceed the upper limit of vehicle usage; constraint (5) means that under the standard strategy, the vehicle can be used only when vehicle type v is available; constraint (6) means that at each cargo volume level k, the vehicle can be used only when vehicle type v is available; constraint (7) means that at each cargo volume level k, the reduction in the number of vehicles should not exceed the fixed number of vehicles; constraints (8) and (9) mean that at each cargo volume level k, the adjustment of vehicle type v can only be one of increase and decrease, that is and at least one of them is 0; constraint (10) represents the value range of the parameter, that is y v is a 0-1 variable, and its value range is 0 to 1, x v , is an integer variable, and its value is an integer Z, and v and k belong to the value ranges of their respective sets.

[0134] In an embodiment of the present disclosure, obtaining the target fixed number of vehicles output by the vehicle cost optimization model includes: under the constraint conditions, using a preset solver to optimize the minimum value of the vehicle total cost function to output the target fixed number of vehicles.

[0135] Specifically, the optimization goal of the established vehicle cost optimization model is to minimize the vehicle cost f. The vehicle cost optimization model is converted into computer language, and the obtained cargo volume prediction information and vehicle usage parameters are input into the vehicle cost optimization model, and the model is solved using a solver according to the constraint conditions to obtain the result.

[0136] Among them, according to the modeling language used, a preset solver is used to convert the mathematical model into computer language. For example, python can be used to call the open-source solver SCIP for model solving.

[0137] Of course, other solving methods can also be adopted, such as designing heuristic algorithms, such as genetic algorithms, ant colony algorithms, etc. for solving, and the present disclosure does not make specific limitations here.

[0138] For the vehicle cost optimization model established in the present disclosure, the solution result of the model includes the fixed number of vehicles x output when the vehicle total cost function is minimized vThe value, i.e., the target fixed number of vehicles, has the minimum total vehicle configuration cost when vehicle configuration is carried out according to the target fixed number of vehicles. In addition, the model can also output the corresponding vehicle adjustment strategies at different cargo volume levels k, i.e., the number of increased vehicles and the number of decreased vehicles .

[0139] Extract the fixed number of vehicles according to the model solution results, formulate a vehicle configuration plan according to the fixed number of vehicles, and sign a contract with the vehicle supplier. At the same time, during the implementation of vehicle scheduling, in the face of the current different cargo volume levels, the number of increased vehicles in the vehicle adjustment strategy can be referred to and the number of decreased vehicles for vehicle adjustment.

[0140] Next, the optimization results of the vehicle configuration method will be described in combination with a specific scenario case.

[0141] Table 5 shows the cargo volume prediction information. In an embodiment of the present disclosure, it is assumed that in the cargo volume prediction information during the vehicle usage period, the cargo volume distribution in the next month is as shown in Table 5, i.e., the cargo volume value is 580 cubic meters for 20% of the days, 700 cubic meters for 60% of the days, and 845 cubic meters for 20% of the days.

[0142] Table 5 Details of Cargo Volume Distribution

[0143] Level 1 Level 2 Level 3 Cargo Volume Value: cubic meters 580 700 845 Occurrence Probability 20％ 60％ 20％

[0144] Table 6 shows the vehicle information of different vehicle models, including vehicle usage parameters, i.e., the lower cargo volume limit l v and the upper cargo volume limit u v of vehicle model v; and the vehicle usage cost c v of vehicle model v.

[0145] Table 6 Vehicle Information

[0146] Vehicle Model A Vehicle Model B Vehicle Model C Vehicle Model D Vehicle Model E Lower Limit of Cargo Volume: cubic meters 0 10 40 50 75 Upper Limit of Cargo Volume: cubic meters 30 50 80 100 130 Vehicle Usage Cost: Yuan / vehicle 700 800 960 1190 1220

[0147] In addition, the temporary reservation rate and the temporary cancellation rate are also given. By establishing a vehicle cost optimization model through the above method and solving the model, the output result is x D = 1, x E = 5, that is, 1 vehicle of model D and 5 vehicles of model E are configured under the standard strategy.

[0148] Next, the vehicle configuration plan obtained by solving the model will be compared with other vehicle configuration plans formulated manually.

[0149] Table 7 shows the average planned cost under 4 vehicle configuration plans when the same volume forecast information is met, as well as the average total cost considering both configuration cost and adjustment cost.

[0150] Table 7 Four vehicle configuration plans formulated manually

[0151]

[0152] As shown in Table 1, Plan 2 is consistent with the model calculation results. According to the results shown in Table 2, it can be seen that the average total cost in Plan 2 is the smallest, which is 7,739.8 yuan per day.

[0153] Comparing with Plan 1, it can be seen that although the average planned cost of Plan 1 is 1,220 yuan per day lower than that of Plan 2, the average total cost after comprehensively considering the adjustment cost is higher than that of Plan 2. This is because Plan 2 needs to temporarily adjust the plan at volume level 1 and volume level 3, while Plan 1 needs to temporarily adjust the plan at volume level 2 and volume level 3. Plan 1 needs to adjust 80% of the time, while Plan 2 only needs to adjust 40% of the time, and the temporary adjustment cost is much lower than that of Plan 1. Therefore, formulating a suitable vehicle configuration plan can not only reduce the overall usage cost, but also reduce the number of adjustments and improve the usage experience of operators.

[0154] At the same time, the model also outputs the adjustment strategies at different volume levels. Table 8 shows the adjustment strategies of Plan 2 at different volume levels.

[0155] Table 8 Adjustment strategies of Plan 2 at each volume level

[0156]

[0157] As shown in Table 8, at volume level 1, one vehicle of type E needs to be reduced based on the standard strategy; at volume level 2, the standard strategy can be met and the original plan is maintained; while at volume level 3, one vehicle of type C and one vehicle of type D need to be added based on the standard strategy.

[0158] It should be noted that all the routes in this disclosure adopt full truckload on the road, but there are two types of routes: full truckload on the road and less-than-truckload on the road. There are slight differences in the calculation methods of their usage costs. The usage cost of full truckload on the road is calculated according to the distance, while the usage cost of less-than-truckload on the road is calculated according to the volume. However, a cost conversion coefficient can be introduced to convert the usage cost of full truckload on the road into the usage cost of less-than-truckload on the road, so as to expand the usage scenarios of this solution.

[0159] Based on the vehicle configuration method provided by the present disclosure, considering the scenario of meeting the demand for fluctuating cargo volume, the sum of the planned cost and the adjustment cost is minimized to optimize the vehicle configuration plan. Compared with the prior art, it can reduce the total usage cost of vehicle scheduling and also reduce the number of vehicle adjustments.

[0160] For the model, by introducing the number of increased vehicles and the number of decreased vehicles These two variables express the temporary adjustment cost at each cargo volume level, so that the established model is a linear model, which is simple, elegant, easy to understand, and easy to solve. This is because if the decision variable is used to represent the actual usage of each vehicle type at each cargo volume level, then the vehicle adjustment cost in the objective function is expressed as shown in formula (7), which will obviously lead to a non-linear objective function.

[0161]

[0162] Figure 4 Schematically shows a schematic diagram of the composition of a vehicle configuration system in an exemplary embodiment of the present disclosure, as Figure 4 shown. The vehicle configuration system includes:

[0163] The vehicle basic database 401 is used to store basic data of vehicles such as route detailed information, usage cost basic information table, and vehicle type cargo volume upper and lower limit information;

[0164] The cargo volume prediction database 402 is used to store cargo volume prediction information during the vehicle usage period;

[0165] The model solving module 403 is used to call the vehicle basic database 401 and the cargo volume prediction database 402 to read relevant data, configure decision variables, construct the vehicle total cost function, construct constraint conditions for automatic model construction, and finally use SCIP to solve the model;

[0166] The result output module 404 is used to convert the solution result of the model solving module 403 into the format required by the user, that is, generate a vehicle configuration plan according to the fixed number of vehicles, and generate guidance information for vehicle adjustment according to the number of increased vehicles and the number of decreased vehicles.

[0167] Figure 5 Schematically shows a schematic diagram of the composition of a vehicle configuration device in an exemplary embodiment of the present disclosure, as Figure 5 shown. The vehicle configuration device 500 may include an acquisition module 501, an input module 502, and an output module 503. Among them:

[0168] The acquisition module 501 acquires cargo volume prediction information and vehicle usage parameters;

[0169] An input module 502 inputs the cargo volume prediction information and the vehicle usage parameters into a pre-constructed vehicle cost optimization model; wherein, the vehicle cost optimization model includes a total vehicle cost objective function constructed based on the number of fixed vehicles, the number of increased vehicles, and the number of decreased vehicles.

[0170] An output module 503 obtains the target number of fixed vehicles output by the vehicle cost optimization model and configures the vehicles according to the target number of fixed vehicles.

[0171] According to an exemplary embodiment of the present disclosure, the vehicle configuration device 500 further includes an objective function module, including a planned cost unit, an adjustment cost unit, and a total cost unit. The planned cost unit is used to construct a planned cost function based on the number of fixed vehicles, the adjustment cost unit is used to construct an adjustment cost function based on the number of increased vehicles and the number of decreased vehicles, and the total cost unit is used to construct the total vehicle cost function according to the planned cost function and the adjustment cost function.

[0172] According to an exemplary embodiment of the present disclosure, the planned cost unit is used to construct the planned cost function based on the number of fixed vehicles and the vehicle usage cost.

[0173] According to an exemplary embodiment of the present disclosure, the adjustment cost unit is used to construct a temporary reservation cost function based on the number of increased vehicles, and a temporary cancellation cost function based on the number of decreased vehicles; and obtain a prediction probability coefficient corresponding to the cargo volume prediction information; construct the adjustment cost function according to the prediction probability coefficient, the temporary reservation cost function, and the temporary cancellation cost function.

[0174] According to an exemplary embodiment of the present disclosure, the adjustment cost unit is further used to construct the temporary reservation cost function based on the number of increased vehicles, the vehicle usage cost, and the temporary reservation rate; and construct the temporary cancellation cost function based on the number of decreased vehicles, the vehicle usage cost, and the temporary cancellation rate.

[0175] According to an exemplary embodiment of the present disclosure, the objective function module further includes a usage cost unit, and the cost unit is used to determine the vehicle usage cost based on the vehicle unit cost, the driving distance, and other vehicle expenses.

[0176] According to an exemplary embodiment of the present disclosure, the cargo volume prediction information includes a plurality of predicted cargo volume values under the cargo volume fluctuation during the target vehicle usage period. The vehicle cost optimization model further includes constraint conditions, and the constraint conditions include: the sum of the cargo volume upper limits of each vehicle is not less than each of the predicted cargo volume values; the sum of the cargo volume lower limits of each vehicle is not greater than each of the predicted cargo volume values; the sum of the fixed vehicle numbers is not greater than the vehicle usage upper limit; when each of the predicted cargo volume values is satisfied, the sum of the vehicle numbers is not greater than the vehicle usage upper limit; the fixed vehicle can be used only when the fixed vehicle to be used is available; when each of the predicted cargo volume values is satisfied, the vehicle can be used only when the vehicle is available; when each of the predicted cargo volume values is satisfied, the number of vehicle reductions is not greater than the number of fixed vehicles; when each of the predicted cargo volume values is satisfied, vehicle adjustment can only increase or decrease the number of vehicles.

[0177] Specific details of each module in the above vehicle configuration device 500 have been described in detail in the corresponding vehicle configuration method, and thus will not be elaborated herein.

[0178] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0179] In an exemplary embodiment of the present disclosure, a storage medium capable of implementing the above method is further provided. Figure 6 A schematic diagram schematically showing a computer-readable storage medium in an exemplary embodiment of the present disclosure is as Figure 6 shown, which describes a program product 600 for implementing the above method according to an embodiment of the present disclosure. It may adopt a portable compact disc read-only memory (CD-ROM) and include program code, and can run on a terminal device, such as a mobile phone. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or device.

[0180] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is further provided. Figure 7 A schematic diagram of the structure of a computer system of an electronic device in an exemplary embodiment of the present disclosure is schematically shown.

[0181] It should be noted that Figure 7 the computer system 700 of the electronic device shown is only an example, and should not bring any limitation to the functions and usage scope of the embodiments of the present disclosure.

[0182] As shown Figure 7 in FIG. Figure 7 , computer system 700 includes a central processing unit (CPU) 701 that can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage section 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for system operations are also stored. The CPU 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0183] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed so that a computer program read from it can be installed into the storage section 708 as needed.

[0184] Specifically, according to an embodiment of the present disclosure, the processes described below with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 709, and / or installed from the removable medium 711. When the computer program is executed by a central processing unit (CPU) 701, various functions defined in the system of the present disclosure are executed.

[0185] It should be noted that the computer-readable medium shown in the embodiments of the present disclosure can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or combined with an instruction execution system, apparatus, or device. In the present disclosure, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or combined with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0186] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks can occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0187] The units involved in the embodiments of the present disclosure can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not constitute a limitation to the unit itself in some cases.

[0188] As another aspect, the present disclosure also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiments.

[0189] It should be noted that although several modules or units of a device for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0190] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented in software or in a manner of software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0191] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed by the present disclosure.

[0192] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A vehicle configuration method, characterized in that, Including: Obtain cargo volume prediction information and vehicle usage parameters; Input the cargo volume prediction information and the vehicle usage parameters into a pre-constructed vehicle cost optimization model; wherein, the vehicle cost optimization model includes a vehicle total cost objective function composed of a planned cost function constructed according to a fixed number of vehicles and an adjustment cost function constructed according to the number of vehicle increases and the number of vehicle decreases; the number of vehicle increases is the number of vehicles increased compared to the fixed number of vehicles in the vehicle configuration, and the number of vehicle decreases is the number of vehicles decreased compared to the fixed number of vehicles in the vehicle configuration; Obtain the target fixed number of vehicles output by the vehicle cost optimization model, and perform vehicle configuration according to the target fixed number of vehicles; Wherein, the construction process of the adjustment cost function includes: constructing a temporary reservation cost function based on the number of vehicle increases, and constructing a temporary cancellation cost function based on the number of vehicle decreases; and obtaining a pre-set prediction probability coefficient corresponding to the cargo volume prediction information; the prediction probability coefficient is used to correct the cargo volume prediction information; multiply the sum of the temporary reservation cost function and the temporary cancellation cost function by the prediction probability coefficient to construct the adjustment cost function.

2. The vehicle configuration method according to claim 1, characterized in that, The method further includes: constructing the vehicle total cost objective function, and the construction of the vehicle total cost objective function includes: Constructing a planned cost function based on the fixed number of vehicles; and Constructing an adjustment cost function based on the number of vehicle increases and the number of vehicle decreases; Constructing the vehicle total cost function according to the planned cost function and the adjustment cost function.

3. The vehicle configuration method according to claim 2, characterized in that, The constructing the planned cost function based on the fixed number of vehicles includes: Constructing the planned cost function based on the fixed number of vehicles and the vehicle usage cost.

4. The vehicle configuration method according to claim 1, characterized in that, The constructing the temporary reservation cost function based on the number of vehicle increases, and constructing the temporary cancellation cost function based on the number of vehicle decreases includes: Constructing the temporary reservation cost function based on the number of vehicle increases, the vehicle usage cost, and the temporary reservation rate; and Constructing the temporary cancellation cost function based on the number of vehicle decreases, the vehicle usage cost, and the temporary cancellation rate.

5. The vehicle configuration method according to any one of claims 3 or 4, characterized in that, The method further includes determining the vehicle usage cost, and the determining the vehicle usage cost includes: Determining the vehicle usage cost based on the vehicle unit cost, the driving distance, and other vehicle expenses.

6. The vehicle configuration method according to claim 1, characterized in that, The cargo volume prediction information includes multiple predicted cargo volume values within a target vehicle usage period; The vehicle cost optimization model further includes constraint conditions, and the constraint conditions include: The sum of the cargo volume upper limits of each vehicle is not less than each of the predicted cargo volume values; The sum of the cargo volume lower limits of each vehicle is not greater than each of the predicted cargo volume values; The sum of the fixed number of vehicles is not greater than the vehicle usage upper limit; When each of the predicted cargo volume values is satisfied, the sum of the number of vehicles is not greater than the vehicle usage upper limit; A fixed vehicle can be used only when it is available; When each of the predicted cargo volume values is satisfied, a vehicle can be used only when it is available; When each of the predicted cargo volume values is satisfied, the number of vehicle decreases is not greater than the fixed number of vehicles; When each of the predicted cargo volume values is satisfied, vehicle adjustment can only increase or decrease the number of vehicles.

7. The vehicle configuration method according to claim 6, characterized in that, Obtaining the target fixed number of vehicles output by the vehicle cost optimization model includes: Under the constraint conditions, using a preset solver to optimize the minimum value of the vehicle total cost function to output the target fixed number of vehicles.

8. A vehicle configuration device, characterized in that, It includes: An acquisition module for acquiring cargo volume prediction information and vehicle usage parameters; An input module for inputting the cargo volume prediction information and the vehicle usage parameters into a pre-constructed vehicle cost optimization model; wherein, the vehicle cost optimization model consists of a planned cost function constructed according to the fixed number of vehicles and an adjustment cost function constructed according to the number of vehicle increases and the number of vehicle decreases, which form a vehicle total cost objective function; the number of vehicle increases is the number of vehicles increased compared to the fixed number of vehicles in the vehicle configuration, and the number of vehicle decreases is the number of vehicles decreased compared to the fixed number of vehicles in the vehicle configuration; An output module for obtaining the target fixed number of vehicles output by the vehicle cost optimization model and configuring the vehicles according to the target fixed number of vehicles; A target function module, including an adjustment cost unit, the adjustment cost unit is used to construct a temporary reservation cost function based on the number of vehicle increases, and construct a temporary cancellation cost function based on the number of vehicle decreases; and obtain the prediction probability coefficient corresponding to the cargo volume prediction information; construct the adjustment cost function according to the prediction probability coefficient, the temporary reservation cost function and the temporary cancellation cost function.

9. A computer-readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the vehicle configuration method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that It includes: One or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enabling the one or more processors to implement the vehicle configuration method according to any one of claims 1 to 7.

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