CN111553532A - Method and system for optimizing urban express delivery vehicle path - Google Patents
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Abstract
The invention relates to a method and a system for optimizing a path of an urban express vehicle, which comprises the following steps: s1: acquiring vehicle number information and the like to form a genetic code; s2: initializing a population; s3: calculating the fitness value of each individual in the population, and selecting the individual with the maximum fitness value as the optimal individual; s4: judging whether the current iteration times are larger than or equal to the maximum iteration times, if so, entering S8; otherwise, go to S5; s5: performing cross and variation operation on the current population to obtain a new population; s6: judging whether the current temperature T value is less than the temperature threshold value TendIf yes, let G = G +1, and return to S3; otherwise, go to S7; s7: replacing the old individual by adopting a simulated annealing algorithm, enabling G = G +1, and returning to S3; s8: decoding the genetic codes of the optimal individuals of the current population to obtain the optimal driving path of each vehicle. The method can effectively solve the problem of complex express delivery path and time requirements, and also improves the optimization effect of the genetic algorithm.
Description
Technical Field
The invention relates to the technical field of intelligent path optimization, in particular to a method and a system for optimizing a path of an urban express vehicle.
Background
The express delivery service is a novel logistics service which is started in 1960, and is mainly characterized by taking business letters and small goods as main delivery targets and providing quick, stable and accurate delivery services. With the rapid development of the world economy integration and the change of the demands of customers, the express service is rapidly developed; although the home express industry is relatively late in initial stage, the home express business volume is rapidly increased in recent years, and according to statistics, the total volume of home express comprehensive business is as high as 19.2 hundred million pieces in 2009, and the total income is 478 hundred million yuan. The express delivery industry is the fundamental industry keeping the continuous growth of the electronic business industry, and meanwhile, the explosive expansion of the trade pattern of the electronic business industry provides enough energy for driving the express delivery industry to rapidly develop. Relevant research reports show that the total service volume of the electronic commerce industry in the same year is up to 3.6 trillion yuan RMB, the personal online shopping amount is 2501.1 trillion yuan, basically, each entity commodity needs to be delivered by means of express delivery service delivery to finish the delivery service, and the rapid development of the electronic commerce field enables the express delivery industry in China to have a more extensive growth space. However, the distribution capacity and the service level of the current domestic express industry have a large gap with the actual development requirements of the e-commerce industry, and express has become a bottleneck restricting the continued development of the e-commerce industry.
Disclosure of Invention
In view of this, the invention aims to provide a method and a system for optimizing a path of an urban express delivery vehicle, which can not only effectively solve the problem of complex express delivery path and time requirements, but also improve the optimization effect of a genetic algorithm.
The invention is realized by adopting the following scheme: a method for optimizing a path of an urban express delivery vehicle specifically comprises the following steps:
step S1: acquiring vehicle number information, customer time requirement information and distribution point information, and carrying out combined coding on the information to form genetic codes [ y1, y 2.,. yi.,. yn ], wherein each genetic code corresponds to an individual;
step S2: initializing a population, setting the iteration number G to be 1, and setting a temperature threshold value Tend；
Step S3: calculating the fitness value of each individual in the population, and selecting the fitness value with the maximum fitness valueAs the optimal individual; wherein the fitness value is a converted value T of total travel time of all vehicles in the individualGeneral assemblyThe reciprocal of (a);
step S4: judging whether the current iteration times are larger than or equal to the maximum iteration times, if so, entering the step S8; otherwise, go to step S5;
step S5: performing cross and variation operation on the current population to obtain a new population;
step S6: judging whether the current temperature T value is less than the temperature threshold value TendIf yes, make G ═ G +1, and return to step S3; otherwise, go to step S7;
step S7: replacing the old individual by using a simulated annealing algorithm, enabling T to be kT, wherein k is a preset cooling rate, enabling G to be G +1, and returning to the step S3;
step S8: and decoding the genetic codes of the optimal individuals of the current population to obtain the optimal driving path of each vehicle.
Further, in step S1, in the genetic code [ y1, y2,.. once, yi.. once ] yn ], the value of the element yi represents the number of the vehicle, the vector position of the element represents the number of the virtual customer to be served by the vehicle of the number, one virtual customer number represents the customers at the same delivery point in the same time period, and each element yi is bound with the corresponding customer time requirement information and the delivery point position information, the congestion information of the delivery point, and the weight information of the express delivery required by each customer.
Further, in step S3, the total travel time converted values of all the vehicles
Wherein, Tj is the running time reduced value of the vehicle with the number j, and the calculation of Tj is as follows: tj ═ Tj running+Tj express delivery point serviceWherein T isj runningIndicates the actual travel time, T, of the vehicle numbered jj express delivery point serviceIndicating the sum of the service times of all the vehicles numbered j at the various express points they serve.
Wherein the actual travel time T of the vehicle jj runningAdopt toThe following steps are calculated:
step S31: for the genetic code of an individual, finding all elements with the element value of j, acquiring the positions of the elements in the genetic code, namely all virtual customer numbers to be served by the j, and acquiring the time requirement information, the distribution point position information and the congestion information of a distribution point which are bound and correspond to each other;
step S32: according to the time requirement of a client, the vehicle j sequentially goes from the distribution center to the distribution points corresponding to the virtual clients and finally returns to the distribution center according to the sequence of time from first to last, and the actual running time of the vehicle j is obtained according to the distance between the distribution points and the average speed set by each distance; wherein, the average speed of each distance is determined by the congestion information of the distribution point.
Further, TjAlso includes an additional term, T, in the right hand side of the equationPay for jA converted travel time indicating a time when the delivery time exceeds a time required by the corresponding client; if the delivery time does not exceed the required time of the corresponding client, TClaims paymentIs 0, if the delivery time exceeds the required time of the corresponding client, TPay for jHas a value of k1Excess time, wherein k1Is a first reduction factor.
Further, TjAlso includes an additional term, T, in the right hand side of the equationj penaltyIndicating the converted travel time when the vehicle j returns to the distribution center, and if the vehicle j returns to the distribution center on time, Tj penaltyIs 0, if time out returns to the distribution center, TPunishmentHas a value of k2Excess time, wherein k2The second reduction factor.
Further, TjAlso includes an additional term, T, in the right hand side of the equationj is overweightThe expression represents the converted running time of the vehicle j when the total express weight of the distributed client exceeds the rated load weight, and if the vehicle j is not overweight, T isj is overweightIs 0, if overweight, Tj is overweightHas a value of k3Excess of weight, wherein k3The coefficient is calculated for the third fold.
Further, step S8 is specifically to, for each vehicle number, find all the elements in the genetic code, obtain the positions of the elements in the genetic code, that is, all the virtual customer numbers to be served by the vehicle, obtain the corresponding distribution points according to the virtual customer numbers, and obtain the corresponding bound customer time requirement information; and for the same vehicle, sequentially arranging the vehicle to go to the distribution points corresponding to the virtual customers from the distribution center according to the time sequence required by the customers and then returning to the distribution center.
The invention also provides a path optimization system based on the method for optimizing the urban express delivery vehicle path, which comprises an input module, a map application module, an output module, a storage module and a processor, wherein the input module is used for acquiring the vehicle number information and the customer time requirement information; the memory module stores therein a computer program that can be executed by the processor, which when executing executes the computer program performs the method steps as described above.
Further, the map application module includes, but is not limited to, a Baidu map or a Gade map.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention applies the genetic simulated annealing algorithm to the express vehicle path planning, thereby greatly shortening the express vehicle delivery time.
2. The invention can carry out reasonable task distribution on each express vehicle before delivery, can improve the satisfaction degree of customers, and can ensure that the delivery is completed in a time window provided by the customers as far as possible.
3. The genetic simulated annealing algorithm provided by the invention improves the optimizing capability of a standard genetic algorithm.
Drawings
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention.
Fig. 2 is a route location diagram of an express delivery point according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of an optimization iteration curve according to an embodiment of the present invention.
FIG. 4 is a schematic representation of the genetic code of an optimized individual according to an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the embodiment provides a method for optimizing a route of an urban express delivery vehicle, which specifically includes the following steps:
step S1: acquiring vehicle number information, customer time requirement information and distribution point information, and carrying out combined coding on the information to form genetic codes [ y1, y 2.,. yi.,. yn ], wherein each genetic code corresponds to an individual;
step S2: initializing a population, setting the iteration number G to be 1, and setting a temperature threshold value Tend；
Step S3: calculating the fitness value of each individual in the population, and selecting the individual with the maximum fitness value as the optimal individual; wherein the fitness value is a converted value T of total travel time of all vehicles in the individualGeneral assemblyThe reciprocal of (a);
step S4: judging whether the current iteration times are larger than or equal to the maximum iteration times, if so, entering the step S8; otherwise, go to step S5;
step S5: performing cross and variation operation on the current population to obtain a new population;
step S6: judging whether the current temperature T value is less than the temperature threshold value TendIf yes, make G ═ G +1, and return to step S3; otherwise, go to step S7;
step S7: replacing the old individual by using a simulated annealing algorithm, enabling T to be kT, wherein k is a preset cooling rate, enabling G to be G +1, and returning to the step S3;
step S8: and decoding the genetic codes of the optimal individuals of the current population to obtain the optimal driving path of each vehicle.
In this embodiment, in step S1, in the genetic code [ y1, y2,.. once, yi.. once.. yn ], the value of the element yi represents the number of the vehicle, the vector position of the element represents the customer virtual customer number to be served by the vehicle of the number, one virtual customer number represents customers at the same delivery point in the same time period, and each element yi is bound with the corresponding customer time requirement information and the delivery point position information, the congestion information of the delivery point, and the weight information of the express delivery required by each customer. For example, number [3213], represents a first virtual customer serviced by vehicle number 3, a second virtual customer serviced by vehicle number 2, a third virtual customer serviced by vehicle number 1, and a fourth virtual customer serviced by vehicle number 3.
In the present embodiment, in step S3, the total travel time converted values of all the vehicles
Wherein, Tj is the running time reduced value of the vehicle with the number j, and the calculation of Tj is as follows: tj ═ Tj running+Tj express delivery point serviceWherein T isj runningIndicates the actual travel time, T, of the vehicle numbered jj express delivery point serviceIndicating the sum of the service times of all the vehicles numbered j at the various express points they serve.
Wherein the actual travel time T of the vehicle jj runningThe following steps are adopted for calculation:
step S31: for the genetic code of an individual, finding all elements with the element value of j, acquiring the positions of the elements in the genetic code, namely all virtual customer numbers to be served by the j, and acquiring the time requirement information, the distribution point position information and the congestion information of a distribution point which are bound and correspond to each other;
step S32: according to the time requirement of a client, the vehicle j sequentially goes from the distribution center to the distribution points corresponding to the virtual clients and finally returns to the distribution center according to the sequence of time from first to last, and the actual running time of the vehicle j is obtained according to the distance between the distribution points and the average speed set by each distance; wherein, the average speed of each distance is determined by the congestion information of the distribution point.
In the present embodiment, TjAlso includes an additional term, T, in the right hand side of the equationPay for jA converted travel time indicating a time when the delivery time exceeds a time required by the corresponding client; if the delivery time does not exceed the required time of the corresponding client, TClaims paymentIs 0, if the delivery time exceeds the required time of the corresponding client, TPay for jHas a value of k1Excess time, wherein k1Is a first reduction factor.
In the present embodiment, TjAlso includes an additional term, T, in the right hand side of the equationj penaltyIndicating the converted travel time when the vehicle j returns to the distribution center, and if the vehicle j returns to the distribution center on time, Tj penaltyIs 0, if time out returns to the distribution center, TPunishmentHas a value of k2Excess time, wherein k2The second reduction factor.
In the present embodiment, TjAlso includes an additional term, T, in the right hand side of the equationj is overweightThe expression represents the converted running time of the vehicle j when the total express weight of the distributed client exceeds the rated load weight, and if the vehicle j is not overweight, T isj is overweightIs 0, if overweight, Tj is overweightHas a value of k3Excess of weight, wherein k3The coefficient is calculated for the third fold.
In this embodiment, step S8 is specifically to, for each vehicle number, find all the elements in the genetic code, obtain the positions of the elements in the genetic code, that is, the virtual customer numbers of all the customers to be served by the vehicle, obtain the corresponding distribution points according to the virtual customer numbers, and obtain the corresponding bound customer time requirement information; and for the same vehicle, sequentially arranging the vehicle to go to the distribution points corresponding to the virtual customers from the distribution center according to the time sequence required by the customers and then returning to the distribution center. For example, code [21221211], the time period requirement for virtual client 1 is between 9:00 earliest arrival time to 10:00 latest arrival time, the time period requirement for virtual client 2 is between 12:00 earliest arrival time to 13:00 latest arrival time, the time period requirement for virtual client 3 is 11:00 earliest arrival time to 12:00 latest arrival time, the time requirement for virtual client 4 is 14:00 earliest arrival time to 15:00 latest arrival time, the time requirement for virtual client 5 is 15:00 earliest arrival time to 16:00 latest arrival time, the time requirement for virtual client 6 is 13 earliest arrival time: 00 to latest arrival time 14:00, the time requirement of the virtual client 7 is from earliest arrival time 10:00 to latest arrival time 11:00, the time requirement of the virtual client 8 is from earliest arrival time 9:00 to latest arrival time 10:00, and the route of the vehicle 1 can be decoded to be the distribution center-the virtual client 8-the virtual client 7-the virtual client 2-the virtual client 5-the distribution center; the path of the vehicle 2 is distribution center-virtual customer 1-virtual customer 3-virtual customer 6-virtual customer 4-distribution center;
the embodiment also provides a path optimization system based on the method for optimizing the urban express delivery vehicle path, which comprises an input module, a map application module, an output module, a storage module and a processor, wherein the input module is used for acquiring vehicle number information and customer time requirement information, the map application module is used for acquiring distribution point positions and traffic jam information, and the output module is used for outputting a path optimization result; the memory module stores therein a computer program that can be executed by the processor, which when executing executes the computer program performs the method steps as described above.
In this embodiment, the map application module includes, but is not limited to, a Baidu map or a Gade map.
Preferably, this embodiment is illustrated by taking fig. 2 as an example, and fig. 2 is a route location diagram of an express delivery point exemplified in this embodiment, where O denotes a distribution center, C1, C2, and C3 denote traffic congestion locations, 1, 2, 3, 4, 5, 6, 7, 8, and 9 denote distribution points, each distribution point includes a plurality of customers, each two points are expressed by a distance, and the unit is kilometer, for example, the distance between 1 and 2 points is 0.9 kilometer. Table 1 shows a specific case of customers, and taking the distribution point 1 as an example, the distribution point 1 includes 7 actual customers in total, and customers in the same place and in the same time slot are regarded as the same virtual customer according to the required distribution time of the customer, so that the distribution point 1 has 3 virtual customers in total. The optimization iteration schematic diagram after calculation by the method of the embodiment is shown in fig. 3, and the final output optimal individual genetic code is shown in fig. 4. As can be seen from fig. 4, the virtual customer to be serviced of the coded vehicle 1 is 7-9-11-16-17-25-29; the virtual customers to be served by the vehicle 2 are 2-10-20-23-24-32-35-36-38-39-40; the virtual customers to be served by the vehicle 3 are 1-4-5-8-13-15-26-28-30-31-33-37; the virtual customers to be serviced by the vehicle 4 are 3-6-12-14-16-18-19-21-22-27-34; according to table 1, the virtual clients to be serviced by the vehicle are obtained by sequencing according to the chronological order: vehicle 1: 7-29-11-16-25-17-9; the vehicle 2: 32-10-2-23-20-24-38-39-35-36-40; the vehicle 3: 1-37-4-5-28-33-8-15-30-13-26-31; the vehicle 4: 18-19-27-3-14-6-12-16-34-21-22. The path of the vehicle can be finally determined according to the distribution point where the virtual customer is located as follows: vehicle 1: O-2-7-3-4-6-4-2-O, vehicle 2: O-8-3-1-6-5-6-9-8-9-O, vehicle 3: O-1-9-2-7-8-2-4-7-3-6-7-O, vehicle 4: O-5-7-1-4-2-3-4-8-5-O.
TABLE 1
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (10)
1. A method for optimizing a path of a city express delivery vehicle is characterized by comprising the following steps:
step S1: acquiring vehicle number information, customer time requirement information and distribution point information, and carrying out combined coding on the information to form genetic codes [ y1, y 2.,. yi.,. yn ], wherein each genetic code corresponds to an individual;
step S2: initializing a population, setting the iteration number G to be 1, and setting a temperature threshold value Tend；
Step S3: calculating the fitness value of each individual in the population, and selecting the individual with the maximum fitness value as the optimal individual; wherein the fitness value is a converted value T of total travel time of all vehicles in the individualGeneral assemblyThe reciprocal of (a);
step S4: judging whether the current iteration times are larger than or equal to the maximum iteration times, if so, entering the step S8; otherwise, go to step S5;
step S5: performing cross and variation operation on the current population to obtain a new population;
step S6: judging whether the current temperature T value is less than the temperature threshold value TendIf yes, make G ═ G +1, and return to step S3; otherwise, go to step S7;
step S7: replacing the old individual by using a simulated annealing algorithm, enabling T to be kT, wherein k is a preset cooling rate, enabling G to be G +1, and returning to the step S3;
step S8: and decoding the genetic codes of the optimal individuals of the current population to obtain the optimal driving path of each vehicle.
2. The method for optimizing the path of the urban courier vehicle according to claim 1, wherein in step S1, in the genetic code [ y1, y 2.,. yi.,. yn ], the value of the element yi represents the number of the vehicle, the vector position of the element represents the number of the virtual customer to be served by the vehicle with the number, one virtual customer number represents the customers at the same delivery point in the same time period, and each element yi is bound with the corresponding customer time requirement information and the delivery point position information, the congestion information of the delivery point, and the weight information of the courier required to be delivered by each customer.
3. The method for optimizing the path of the city express delivery vehicle according to claim 1, wherein in step S3, the total travel time of all vehicles is converted into a reduced value
Wherein, Tj is the running time reduced value of the vehicle with the number j, and the calculation of Tj is as follows: tj ═ Tj running+Tj express delivery point serviceWherein T isj runningIndicates the actual travel time, T, of the vehicle numbered jj express delivery point serviceIndicating the sum of the service times of all the vehicles numbered j at the various express points they serve.
4. The method for optimizing the path of the city express delivery vehicle according to claim 3, wherein the actual travel time T of the vehicle j isj runningThe following steps are adopted for calculation:
step S31: for the genetic code of an individual, finding all elements with the element value of j, acquiring the positions of the elements in the genetic code, namely all virtual customer numbers to be served by the j, and acquiring the time requirement information, the distribution point position information and the congestion information of a distribution point which are bound and correspond to each other;
step S32: according to the time requirement of a client, the vehicle j sequentially goes from the distribution center to the distribution points corresponding to the virtual clients and finally returns to the distribution center according to the sequence of time from first to last, and the actual running time of the vehicle j is obtained according to the distance between the distribution points and the average speed set by each distance; wherein, the average speed of each distance is determined by the congestion information of the distribution point.
5. The method of claim 3, wherein T is the distance between two express delivery vehiclesjAlso includes an additional term, T, in the right hand side of the equationPay for jA converted travel time indicating a time when the delivery time exceeds a time required by the corresponding client; if the delivery time does not exceed the required time of the corresponding client, TClaims paymentIs 0, if the delivery time exceeds the required time of the corresponding client, TPay for jHas a value of k1Excess time, wherein k1Is a first reduction factor.
6. The method of claim 3, wherein T is the distance between two express delivery vehiclesjAlso includes an additional term, T, in the right hand side of the equationj penaltyIndicating the converted travel time when the vehicle j returns to the distribution center, and if the vehicle j returns to the distribution center on time, Tj penaltyIs 0, if time out returns to the distribution center, TPunishmentHas a value of k2Excess time, wherein k2The second reduction factor.
7. The method of claim 3, wherein T is the distance between two express delivery vehiclesjAlso includes an additional term, T, in the right hand side of the equationj is overweightThe expression represents the converted running time of the vehicle j when the total express weight of the distributed client exceeds the rated load weight, and if the vehicle j is not overweight, T isj is overweightIs 0, if overweight, Tj is overweightHas a value of k3Excess of weight, wherein k3The coefficient is calculated for the third fold.
8. The method for optimizing the urban express delivery vehicle path according to claim 1, wherein the step S8 is specifically implemented by finding all elements of each vehicle number in the genetic code, obtaining the positions of the elements in the genetic code, that is, all virtual customer numbers to be served by the vehicle, obtaining the corresponding delivery points according to the virtual customer numbers, and obtaining the corresponding bound customer time requirement information; and for the same vehicle, sequentially arranging the vehicle to go to the distribution points corresponding to the virtual customers from the distribution center according to the time sequence required by the customers and then returning to the distribution center.
9. A path optimization system based on the city express delivery vehicle path optimization method of any one of claims 1 to 8, comprising an input module for obtaining vehicle number information and customer time requirement information, a map application module for obtaining distribution point positions and traffic jam information, an output module for outputting path optimization results, a storage module and a processor; the memory module stores a computer program that can be executed by the processor, and the processor implements the method steps according to any one of claims 1 to 7 when executing the computer program.
10. The city courier vehicle path optimization system of claim 9, wherein the map application module includes, but is not limited to, a Baidu map or a Gade map.
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