CN106991555B - Urban vehicle crowdsourcing package transfer method based on incentive mechanism - Google Patents

Urban vehicle crowdsourcing package transfer method based on incentive mechanism Download PDF

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CN106991555B
CN106991555B CN201710194705.5A CN201710194705A CN106991555B CN 106991555 B CN106991555 B CN 106991555B CN 201710194705 A CN201710194705 A CN 201710194705A CN 106991555 B CN106991555 B CN 106991555B
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礼欣
史明明
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Abstract

The invention relates to an incentive mechanism-based urban vehicle crowdsourcing package delivery method, belonging to the technical field of Internet of vehicles; the method comprises the following steps: firstly, establishing a package transfer point according to the historical running track of the urban vehicle; planning a transportation path for each package through a path planning algorithm; then establishing a model: finding the optimal return value of package transportation of each station through the model, and selecting the optimal transportation vehicle to transport the packages; and finally, setting the increase rate of the transportation cost according to the bid throwing value of the bidding vehicle, and realizing the payment of the cost through third-party software. The method of the invention makes full use of the urban running vehicles, thereby not only improving the transmission efficiency of the packages, but also reducing the running cost of the urban vehicles.

Description

Urban vehicle crowdsourcing package transfer method based on incentive mechanism
Technical Field
The invention relates to a method for delivering urban parcels, in particular to a method for delivering urban vehicle crowdsourcing parcels based on an incentive mechanism, and belongs to the technical field of vehicle networking.
Background
With the development of economy and the continuous change of the living demands of people, the urban industry layout is continuously updated and adjusted. Meanwhile, as the production and consumption modes of people are continuously upgraded and changed, the wide application of the electronic commerce technology and the industrial and commercial use of modern cities are gradually diversified, the development requirements of the small-batch, frequent and efficient package distribution, fixed-point distribution and gate-to-gate distribution service are increasingly tense nowadays. The same-city express delivery not only solves the problem of uneven resource allocation between cities and surrounding cities, but also solves the problem of uneven resource allocation inside the cities. In real life, large and medium-sized cities have certain transportation limits on urban trucks, so that large-scale logistics transportation and distribution services cannot be realized by the trucks. Therefore, in the process of transporting the packages, the timeliness is met, and the transportation cost is reduced to become the bottleneck of the current logistics development.
At present, logistics companies in China are numerous, but the management system has problems. Firstly, the logistics informatization construction in the same city is relatively lagged, and the real-time tracking of articles cannot be realized. Secondly, due to regional limitations, the express delivery in the same city cannot fully cover the whole city. For remote places such as cities, countryside and suburbs, express delivery points are not basically arranged or are very sparse. Finally, due to the fact that the same city express system is imperfect, express expenses needed for different areas of the city are unequal. Meanwhile, due to the limitation of transportation cost, the articles cannot be transported in real time, and the articles need to be transported uniformly at a certain time point, so that the timeliness of transmission is greatly reduced. It is because these problems hinder the development of express delivery business in our country.
Disclosure of Invention
The invention aims to solve the problems and provides an incentive mechanism-based urban vehicle crowdsourcing package delivery method to effectively promote the development of domestic and foreign logistics transportation business.
The principle of the invention is based on the crowdsourcing principle, and the package transfer is realized by utilizing urban vehicles to jump the ground. It is different from existing crowdsourcing incentive mechanisms where we consider not only the bid value of the dispatch personnel, but also the dispatch distance. In order to increase the transport efficiency and reduce the transport costs, a route is planned for each parcel in advance according to the previous vehicle trajectory. The invention is provided with a platform and a user module, and in the platform module, a suitable value return value is searched by adopting the Stackelberg game theory; in the user module, a reverse auction algorithm is employed to select the best vehicle user to dispatch the package. If the individual vehicle user cannot deliver the package to the destination because of the travel path limitations, the package is placed on the planned route site and the selected vehicle is auctioned again until the package is delivered to the destination.
The method comprises the following concrete implementation steps:
a method for incentive based crowd-sourced parcel delivery for urban vehicles, the method comprising the steps of:
step one, establishing a package transfer point:
clustering the historical tracks of the vehicles to find out a vehicle track concentrated area and set up a website; preferably, the improved DBSCAN algorithm is adopted for clustering the historical tracks of the vehicles;
step two, package path planning:
through the establishment of the transfer point in the last step, the station calculates and releases the optimal transportation path of the package by adopting an optimal path planning algorithm for the received package according to the historical track of the vehicle;
step three, the site calculates and issues R through the following formula so as to maximize the site profit:
Figure GDA0002427678520000021
wherein P represents the parcel set of the site, | P | represents the parcel number of the site, and k represents the unit transportation cost of the vehicle user for transporting the parcels;
step four, the vehicle user bids for the site package according to the R value issued by the platform and the transportation path of the package and considering the traveling track of the vehicle userip＝(lip,bip) Here bipRepresents the unit cost of transportation of package p by vehicle user i, lipRepresents the transport distance of package p by vehicle user i;
step five, the station selects a proper vehicle transportation package according to the bidding situation of the vehicle user and pays the transportation cost of the user through a third party payment tool:
the station is calculated by adopting the following formula based on the bidding of the vehicle users
Figure GDA0002427678520000031
And is selected such that
Figure GDA0002427678520000032
The largest vehicle user i transports packages p:
Figure GDA0002427678520000033
wherein gamma represents a system parameter, and has a value of 2ln 10; f. ofiRepresenting the revenue for a vehicle user i engaged in the transportation of a package p, is calculated by:
Figure GDA0002427678520000034
wherein the content of the first and second substances,
Figure GDA0002427678520000035
i*＝argmin{bij,i∈V',j＝p}，j*＝argmin{liji belongs to V ', j equals p, and V' represents the vehicle set participating in bidding; etapRepresenting the rate of increase of unit price with package shipping distance, is calculated by:
Figure GDA0002427678520000036
has the advantages that:
the present invention takes into account various traffic characteristics, such as speed, time, location of the vehicle and distance from the station; by utilizing the different traffic characteristic information, the method provided by the invention simultaneously considers the consignor cost, the platform profit and the vehicle user profit. In addition, the method provided by the invention has small time complexity and can quickly converge.
Compared with the existing parcel delivery method, the method of the invention fully utilizes the existing urban operation vehicles, thereby not only improving the parcel transmission efficiency, but also reducing the operation cost of the urban vehicles. The experimental result also shows the effectiveness of the method
Drawings
Fig. 1 is a flow chart illustrating package delivery according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of the composition of the parcel delivery system of the present invention.
Fig. 3 is a schematic diagram of location selection for a city site and path planning for individual packages.
FIG. 4 is a diagram illustrating the relationship between the Number of Vehicles (Number of Delivery Vehicles) and the total transportation Distance (overhead Delivery Distance).
FIG. 5 is a diagram of Platform availability (Platform availability) versus reward value R (reward R).
FIG. 6 is a diagram illustrating the relationship between the Number of participating Vehicles (Number of delivery Vehicles) and the delivery Cost (Sender Cost).
FIG. 7 is a diagram illustrating the relationship between the Delivery distance (Delivery Distances) and the Delivery Cost (Sender Cost).
FIG. 8 is a schematic diagram of Platform availability (Platform availability) versus Number of participating transport Vehicles (Number of particulate Vehicles).
FIG. 9 is a schematic diagram showing the relationship between the Sender's Cost (Sender Cost) and the Number of participating transport Vehicles (Number of medical Vehicles).
FIG. 10 is a schematic diagram showing the relationship between the Success Rate (Success Rate) of package transportation and the Number of Vehicles participating in transportation (Number of transportation Vehicles) according to the present invention.
Detailed Description
In the following, a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings, and as shown in fig. 1, a process of transporting a package by using the method of the present invention is shown, that is, a shipper submits a package transportation request to a central platform, and delivers the package to a nearest package transfer site, the central platform calculates a transportation path and an optimal package transportation vehicle based on a return value R according to the package transportation request submitted by the shipper, and then delivers the calculated transportation vehicle to sequentially deliver the package on the transportation path according to the calculation result, as shown in fig. 2, a schematic diagram of the package transportation system of the present invention is shown, and as can be seen from the figure, the system mainly includes two major parts: the system comprises a central platform model and a user central model, wherein the central platform model is used for designing a transportation path and a return value of a package; the user center model is used for selecting vehicles to realize the transportation of packages, and the specific steps are as follows:
step one, establishing a package transfer point as shown in fig. 3:
the road network is complex for the whole urban area, but in consideration of the singularity and the limitation of the urban vehicle transportation route, it is necessary to set a site for each area to cover the whole vehicle road network. Meanwhile, in order to facilitate package delivery of users, package transfer points need to be designed in the whole urban area and used for releasing and transferring vehicle packages. Various strategies can be adopted for setting the parcel transfer points, for example, according to user concentration, vehicle concentration and the like, the historical tracks of the vehicles participating in parcel delivery are clustered, a vehicle track concentration area is found out, and a station is set, so that the waiting time of parcels at the station can be effectively reduced. Preferably, a DBSCAN algorithm is adopted for the historical track clustering of the vehicles; of course, those skilled in the art are not limited to this method, and other clustering methods, such as Kmeans, AGNES, DIANA, may be used.
Fig. 3 shows package transit points established using the DBSCAN algorithm for an exemplary city, where the grey squares represent transit points.
For the transportation of urban packages, the transportation path of each package from the starting point to the destination is not unique, but the running track of each transportation vehicle is single, and in order to monitor the packages in real time during the transportation process so as to avoid the loss of the packages, the transportation path of the packages needs to be planned.
Step two, planning a parcel transportation path;
through the establishment of the station of the previous step, a transportation route needs to be planned for the package next. The optimal package transportation path is found as the package transportation path by adopting Dijkstra algorithm. Such as the transport path in fig. 3.
Step three, maximizing platform (namely package transfer point) benefits
By planning the previous path, how to find the optimal release value R is the following, the benefit obtained by the platform can be maximized. The optimal transport distance of the user is predicted through the Stackelberg theory, so that the optimal R is found, and the platform profit is maximized.
In this context, |iRepresenting the distance of the user vehicle i to deliver the package. When l isiWhen 0, it means that the vehicle user i is not involved in the transportation of the package. The unit cost k of the vehicles involved in the transportation of the packagesi＞0。
Figure GDA0002427678520000052
Represented in the platform model, the platform assumes that the vehicle user obtains a value of revenue.
For the platform, the revenue formula for each vehicle user is predicted:
Figure GDA0002427678520000051
the optimal transport distance can be determined by sorting the revenue formula of the vehicle user. There is a nash equalization. And the transport distance to each user satisfies the formula (3.2), i.e.
Figure GDA0002427678520000061
According to Nash equilibrium principle, we can simplify the transport distance of the vehicle to obtain a formula (3.3)
Figure GDA0002427678520000062
According to the real life, the charging formula of the shipper can be designed as
Figure GDA00024276785200000610
Wherein s ispIndicating the cost a shipper needs to pay to the platform to send a package p, gamma indicates the impact factor of the system, V*Function log (1+ l) representing the number of vehicles involved in transporting a package p during its transportationi) Reflecting that the increase rate of the distance a vehicle user transports a package is greater than the increase rate of the shipper's cost.
When a site issues packages, the site may receive the benefit of
Figure GDA0002427678520000063
Here, the
Figure GDA0002427678520000064
Indicating that the site can assess itself for revenue based on the number of packages at the site and the shipping distance of each package.
The formulae (3.3) and (3.5) can be collated here
Figure GDA0002427678520000065
Here, the
Figure GDA0002427678520000066
To function
Figure GDA0002427678520000067
Obtaining the result by performing secondary derivation
Figure GDA0002427678520000068
From the formula (3.7) can be obtained
Figure GDA0002427678520000069
So the revenue function of the platform
Figure GDA00024276785200000611
In the interval R ∈ (0, ∞) is a strict convex function, i.e. there must be a return value R so that the platform can obtain a maximum. When the return value R is 0, the obtained platform benefit is also 0 by substituting the formula (3.6); when in useWhen R tends to infinity, the formula is substituted to obtain the platform gain which tends to infinity.
Therefore we can get the equation (3.8) about the issued value R
Figure GDA0002427678520000071
Here, the
Figure GDA0002427678520000072
Since the present embodiment is based on a taxi model, the cost spent by each vehicle is the same, i.e., ki＝kj. Finally, the calculation formula of the release value R can be obtained by sorting
Figure GDA0002427678520000073
Step four, participating in vehicle fee payment
The optimal package release value of the transfer point can be found through the previous steps, and when the platform releases the return value R and the transportation path L of the package, the user i can bid theta according to the actual transportation track of the user iip＝(lip,bip) The following steps are how the payment for the participating bidding vehicle is calculated. Herein is defined a variable ηpWhich is used to indicate the rate of price increase as the distance of transport increases. V' represents a set of vehicles participating in the bidding. Therefore, the temperature of the molten metal is controlled,
Figure GDA0002427678520000074
in equation (4.1), when the transportation distance of successful bidding users is equal to the minimum transportation distance of the bidding vehicle users, then ηp＝1。
For each vehicle user i participating in the package transportation, the revenue formula from the platform can be obtained as
Figure GDA0002427678520000075
From the equation (4.2), when ηpAt a given time, for each vehicle involved in the transportation of a package, their unit cost of transportation is related only to their distance of transportation, and only if the distance of transportation for transporting the package is maximized will the vehicle maximize its own revenue.
Step five, selecting a transport vehicle;
through the previous calculation of the payment fee of the user i, the real profit formula of the user i is
Figure GDA0002427678520000081
Here, the
Figure GDA0002427678520000082
Indicating that a vehicle user i engaged in the transportation of a package can make a net profit,
Figure GDA0002427678520000083
representing the cost of vehicle i after completing the package shipment. For other vehicles not participating in the transport, the resulting profit is 0.
If a site has vehicle user i to complete the shipment of package p, the final profit that can be obtained from package p for that site is formulated as
Figure GDA0002427678520000084
In the formula (5.2), g (. theta.) isip) Representing the real value that a vehicle user i can bring to the site (i.e., platform) after completing the shipment of package p. Wherein gamma represents a system parameter, and has a value of 2ln 10;
Figure GDA0002427678520000085
indicating the net profit that the site can make after completing the shipment of package p. Selecting energy-giving station by formulaThe point brings the most profitable vehicles to transport the package.
Evaluation of
To more effectively evaluate the methods described herein, the model is first theoretically demonstrated here. The advantages of the invention will be illustrated by comparing the method selected by the invention with other methods.
Model evaluation
In order to more effectively evaluate the methods described herein, the following theoretical proof of the model is presented.
Theorem 1 if a vehicle user i wins a bid θ for a package pip＝(lip,bip) And is
Figure GDA0002427678520000086
Then the bidding is
Figure GDA0002427678520000087
Must also win the bid.
And (3) proving that: as can be seen from equation (3.4), when the transport distances are the same, the value generated for the platform is equivalent, so
Figure GDA0002427678520000088
Therefore, the temperature of the molten metal is controlled,
Figure GDA0002427678520000089
according to the principle of greedy algorithm, the platform must choose to obtain more income
Figure GDA00024276785200000810
To ship the package.
Theorem 2 winning a bid thetaip＝(lip,bip) Must be less than a threshold value and if this threshold value is exceeded, it is not possible to win a bid.
And (3) proving that: calculating the user fee for all bidding according to the formula (4.2), and finally paying the fee
Figure GDA0002427678520000091
Next, the expected yield for all user vehicles i is demonstrated as b(i,p)*l(i,p)If the cost b of the user's vehicle iip*lip＞fiVehicle users unwilling to ship package p will be replaced by other users. Assuming that the vehicle users are selected for transportation after M cycles, the formula is satisfied.
Figure GDA0002427678520000092
The theory proves that the whole model can meet the authenticity.
Theorem 3 all users of vehicles participating in transportation must satisfy
Figure GDA0002427678520000093
Here, the
Figure GDA0002427678520000094
Representing the cost of the user i in the entire course of the shipment.
And (3) proving that: the income for a vehicle participating in a transport at a certain site can be known by the formula (4.2)
Figure GDA0002427678520000095
Thus, it is possible to provide
Figure GDA0002427678520000096
Where b isi' means the unit transportation cost that the user can really get from the platform. For each vehicle participating in the transportation, in order to obtain a certain profit, the unit bid price of the vehicle must meet the requirement
Figure GDA0002427678520000097
Finally, the formula (6.2) can be derived
Figure GDA0002427678520000098
The whole model meets the individual rationality through the above proof.
Theorem 4 in the process of transporting parcels, the profit obtained by the platform
Figure GDA0002427678520000099
And (3) proving that: the platform does not select the vehicle to transport the package until M cycles, as evidenced by authenticity. Then can obtain
Figure GDA00024276785200000910
From equation (4.3), it can be seen that for each vehicle involved in the transportation of a package
Figure GDA00024276785200000911
Thus, the formula (4.4) can be obtained
Figure GDA0002427678520000101
From the above demonstration, it can be derived that the model satisfies the feasibility of the excitation mechanism.
Assuming that there are n vehicle users willing to ship packages, the time complexity spent to select the appropriate vehicle user m from the n vehicle users is O (n), and the time complexity required to bid for the site p packages from the m vehicles is O (mp). Therefore, the required algorithm complexity is O (nmp) during the whole algorithm operation. The entire system can determine the best parcel delivery vehicle in a significant amount of time.
Evaluation of Experimental results
As shown in FIG. 4, the abscissa indicates the Number of Vehicles (Number of Delivery Vehicles) participating in the Delivery of a package, and the ordinate indicates the sum of the Delivery distances (all Delivery distances) for all packages. As can be seen from the figure, when the platform issues the return value R of all packages at a certain station, the sum of the transportation distances of all packages increases as the number of participating vehicles increases. With the increase of the number of vehicles, the transportation distance and the increasing speed of all the packages become smaller. This is because the return value R is constant, the unit cost per vehicle participating in the parcel transportation vehicle is constant, the total cost of the transportation vehicle gradually increases as the parcel transportation distance increases, and the vehicle user is not always reduced due to the limitation of the vehicle cost, which leads to the gradual reduction of the vehicle user's own profit and the continuous approach to 0. Are not affordable to the vehicle user. This results in a smaller and smaller rate of increase in the transport distance.
In fig. 5, the abscissa indicates the return value r (reward r) and the ordinate indicates the Platform availability (Platform availability). The graph reflects the relationship between the return value R of a site issued by the platform and the revenue obtained by the platform. Analyzing from the graph, and when the platform release price R is 5, the platform can obtain the maximum profit at the moment; when the return value R issued by the platform is less than 5, the platform gains increasing income gradually, because when R is smaller, the number of vehicles participating in package transportation is limited, the transportation cost of the package is increased, but with the gradual increase of R, the number of vehicle users is gradually increased, and the transportation cost is also gradually reduced; when the issued return value R is larger than 5, the platform profit is gradually reduced along with the increase of the return value R, because the cost of the shipper is in a certain fluctuation interval, and after the R reaches a certain value, the transportation cost of the shipper is almost unchanged along with the increase of the R value, so that the return value R is too large, and the platform profit is negative.
FIG. 6 shows the Number of Vehicles (Number of delivery Vehicles) participating in the package delivery on the abscissa and the Sender's Cost (Sender Cost) on the ordinate. From the relationship in the figure it can be seen that the cost of the shipper is related to the number of vehicles participating in the shipment of the package. When the number of participating vehicles is 1, it means that the package can be directly delivered to the destination, and the cost is the least. As the number of vehicles increases, the transportation cost also increases. However, because the number of vehicles participating in the transportation of the package is limited due to the number of stations, there is a maximum value of the total transportation cost. It can also be seen from the figure that the rate of increase of the shipper's shipping costs is decreasing as the number of participating package delivery vehicles increases. Since the planned transport path of the package is subject to limited stations during the actual transportation of the package, the number of vehicle users involved in the transportation of the package is limited, and when the number of vehicle users involved in the transportation of the package reaches this value, the transportation costs of the package will not increase any more.
FIG. 7 shows the Delivery Distance (Delivery Distance) of a package on the abscissa and the Sender's Cost (Sender Cost) on the ordinate. The distance that the shipper sends the package determines the shipper's shipping cost, and it can be seen from the figure that as the shipping distance of the package increases, the shipper's total shipping cost increases. In real-world applications, to stimulate more shippers to send packages through the platform, the platform sets the shipping costs for the packages for the distance of their shipment. When the distance sent by a sender is shorter, the express delivery cost is relatively less, but the transportation cost of the average unit distance is relatively most; the shipping costs incurred are relatively increased as the shipper sends the package relatively farther apart, but the average unit distance is relatively minimal. The rate of increase in the cost of the shipper is diminishing with increasing distance, which is also the current price rule for all logistics transportation.
In fig. 8, the abscissa represents the Number of transport Vehicles (Number of vertical Vehicles) participating in the bidding in the city, and the ordinate represents the profit (Platform availability) of the Platform. From the figure, it can be seen that as the number of vehicles running in the whole urban area increases, the profit of the platform also gradually increases. However, the incentive scheme provided by the paper allows the platform to gain more benefits than the FIFO algorithm. The vehicles are selected through the FIFO algorithm, the vehicles participating in bidding are relatively independent without internal connection, the bidding prices of other vehicles participating in bidding cannot influence the bidding prices of nearby vehicles, and therefore with the increase of the number of the vehicles, when the number of the vehicles reaches a certain value, the platform profit cannot be changed, and the competition is not facilitated.
In fig. 9, the abscissa represents the Number of all the transportation Vehicles (Number of personal Vehicles) participating in the bidding in the city, and the ordinate represents the Cost of the shipper (Sender Cost). From the figure, it can be analyzed that the incentive scheme algorithm provided herein gradually reduces the transportation cost of the shipper as the number of vehicles increases. And FIFO algorithm, when the number of vehicles is less than 2000 vehicles, with the increase of the number of vehicles, the cost of the shipper increases gradually; the cost of the shipper is reduced only when the number of vehicles exceeds 2000. In the whole process, the driving mechanism algorithm designed in the method is adopted, and the transportation cost of a user is less than that of a shipper in the FIFO algorithm.
Through the comparison, the incentive mechanism algorithm provided by the invention not only can effectively reduce the cost of the shipper, but also can maximize the platform income. Therefore, the incentive mechanism algorithm provided by the invention is adopted to stimulate more vehicle users to participate in the transportation of the packages. The problem of resource waste caused by a large number of urban vehicles is effectively solved, and the dilemma of express delivery in the same city in China is solved.
The abscissa in fig. 10 represents the Number of Vehicles (Number of particulate Vehicles) participating in the bidding, and the ordinate represents the package delivery success Rate (process Rate). It can be observed from the figure that when the number of vehicles is 1000, the vehicles are sparsely distributed and cannot cover the whole city of the cologne, so that the transportation vehicles cannot be used for timely auctioning the packages in the transportation process, the package transportation is easy to fail, and the success rate of the package transportation is low. Along with the increase of the number of the whole urban transport vehicles, the coverage rate of the urban vehicles is higher and higher, and the success rate of the packages is increased. When the entire urban transportation vehicle reaches 7000, the success rate of the parcel is already close to 90%. In real life, the number of vehicles running in cities is far more than 7000 vehicles, so the success rate of wrapping is far more than 90%.
In conclusion, the invention adopts the crowdsourcing principle, realizes parcel delivery by utilizing urban vehicles in a multi-hop manner, improves the transportation efficiency of parcels, reduces the transportation cost of shippers, and has very high parcel success rate.

Claims (1)

1. An incentive mechanism-based urban vehicle crowd-sourced package delivery method is characterized by comprising the following steps of:
step one, establishing a package transfer point:
clustering the historical tracks of the vehicles to find out a vehicle track concentrated area and set up a station, namely a transfer point; the clustering of the historical tracks of the vehicles adopts an improved DBSCAN algorithm;
step two, package path planning:
through the establishment of the transfer point in the last step, the station calculates and releases the optimal transportation path of the package by adopting an optimal path planning algorithm for the received package according to the historical track of the vehicle;
step three, the site calculates and issues R through the following formula so as to maximize the site profit:
Figure FDA0002427678510000011
wherein P represents the parcel set of the site, | P | represents the parcel number of the site, and k represents the unit transportation cost of the vehicle user for transporting the parcels;
step four, the vehicle user bids for the site package according to the R value issued by the platform and the transportation path of the package and considering the traveling track of the vehicle userip＝(lip,bip) Here bipRepresents the unit cost of transportation of package p by vehicle user i, lipRepresents the transport distance of package p by vehicle user i;
step five, calculating the station bidding based on the vehicle users by adopting the following formula
Figure FDA0002427678510000012
And is selected such that
Figure FDA0002427678510000013
The largest vehicle user i transports packages p:
Figure FDA0002427678510000014
wherein gamma represents a system parameter, and has a value of 2ln 10; f. ofiRepresenting the revenue for a vehicle user i engaged in the transportation of a package p, is calculated by:
Figure FDA0002427678510000015
wherein the content of the first and second substances,
Figure FDA0002427678510000016
i*＝argmin{bij,i∈V',j＝p}，j*＝argmin{liji belongs to V ', j equals p, and V' represents the vehicle set participating in bidding; etapRepresenting the rate of increase of unit price with package shipping distance, is calculated by:
Figure FDA0002427678510000021
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