CN111445094A - Express vehicle path optimization method and system based on time requirement - Google Patents
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Abstract
The invention relates to a method and a system for optimizing express vehicle paths in combination with time requirements. And adopting a roulette method to perform primary selection on the population, adopting triangular probability to perform secondary selection on unselected individuals, and finally decoding the optimal individual to obtain a path after the vehicle is optimized. 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 logistics optimization, in particular to a method and a system for optimizing express vehicle paths in combination with time requirements.
Background
Express delivery is an emerging logistics business that has developed in the last two to thirty years, is particularly rapid in the last ten years, has become an important support for electronic commerce, and is called the "last mile" logistics closest to customers. The express industry has developed from past unordered competition to present ordered competition, and the time, efficiency and service quality of express delivery directly determine the survival of express companies. In the face of intense market competition, express companies increasingly attach importance to improving service quality and reducing operation cost, and the problem of vehicle route is the core problem of improving distribution efficiency of the express companies. The early vehicle path problem is solved by some simple and accurate algorithms, such as a split plane method, a branch and bound method, a network flow algorithm, a dynamic programming method and the like, but the algorithms are only suitable for small-scale vehicle path problems. With the generation and application of intelligent heuristic algorithms in recent years, many scholars at home and abroad adopt various intelligent heuristic algorithms to study the problem.
The method comprises the steps of taking the number of express items, vehicle load, vehicle capacity and the like into consideration in research, optimizing express delivery paths by using a genetic algorithm by taking the influence factors as constraints, optimizing the express delivery paths by using a C-W (computer-to-data) conservation algorithm to research the express delivery paths by using Lilingyu, developing Internet-based express delivery path optimization software by combining a Gaode map, optimizing L BS (bus system) express delivery paths by using a particle swarm algorithm, optimizing the express delivery paths by using a elite ant colony system simulation method by using Leishing, wherein the codes of the optimization problem are that random vehicle numbers are arranged in sequence from small to large according to customer numbers, the total number of vehicles is the upper limit of the vehicle numbers, such as 4 vehicles, the vehicle numbers serving each customer are natural numbers between 1 and 4, the genetic algorithm is optimized by simulating the genetic principle, namely, heredity is completed by changing the base pair of chromosomes, the base pair is A-T, T-A, C-G, G-C4, the codes of the problem are matched with chromosomes in genetics, the genetic algorithm, the chromosome search is used for solving the problem, and the genetic algorithm is finally realizing the chromosome convergence, and the global search algorithm is capable of early-based on the global search, and the global search algorithm is capable of solving the chromosome search.
Disclosure of Invention
In view of this, the present invention aims to provide a method and a system for optimizing an express delivery vehicle route in combination with a time requirement, which can not only effectively solve the problem of complex express delivery route and time requirement, but also improve the optimization effect of a genetic algorithm.
The invention is realized by adopting the following scheme: a method for optimizing express delivery vehicle paths in combination with time requirements 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,
step S3: calculating the fitness value of each individual, and selecting the individual with the minimum fitness value as the optimal individual, wherein the fitness value is the total time cost T of all vehicles in the individualAThe reciprocal of (a);
step S4: judging whether the current iteration number meets the requirement, if so, entering a step S8, otherwise, entering a step S5;
step S5: adopting a roulette method, selecting whether the individual enters the next generation according to the fitness value of each individual and the probability function, wherein the higher the fitness value is, the higher the probability of entering the next generation is; the individuals selected to enter the next generation go to step S6, and the individuals not selected to enter the next generation go to step S7;
step S6: performing crossing operation and mutation operation, and sending the crossed and mutated population to step S8;
step S7: secondly selecting unselected individuals by adopting the triangular probability, sequencing the unselected individuals in the step S4, determining whether to update the unselected individuals according to the triangular distribution probability of the individuals, and sending the updated unselected individuals and the unselected individuals into the step S8 after updating the currently selected individuals;
step S8: obtaining a new population, and enabling the iteration times G to be G + 1;
step S9: 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 where the element is located represents the customer number to be serviced by the vehicle of the number, and each element yi is bound with the corresponding customer time requirement information and the distribution point position information, and the road section information of the distribution point going to another distribution point.
Further, the calculating the fitness value of each individual specifically includes the following steps:
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 client numbers to be served by j, and acquiring the information bound by j;
step S32: according to the time requirement of the client, the vehicle j sequentially goes from the distribution center to the distribution points corresponding to the clients according to the sequence of time from first to last and finally returns to the distribution center to obtain the path of the vehicle j in the individual;
step S33: according to the method from step S31 to step S32, all codes in the individual are traversed to obtain the paths of all vehicles corresponding to one individual, the total time cost of all vehicles in the individual is calculated according to the paths, and the reciprocal value of the total time cost is taken as the fitness value of the individual.
Further, in step S3, the total time cost T of all vehicles in one individualAIs calculated as follows:
in the formula, V is a number set of distribution vehicles, V ═ 1, 2.. multidot.m }, and m is a total number of vehicles; p is a number set of distribution points, P is {1, 2.., l }, and l is a total number of distribution points; t is the number set of the time period of delivery, T ═ T1,T2,...,TD}，TDTotal number of time periods for a day; c is a uniform number set of the clients, C ═ 1, 2.., n }, and n is the total number of the uniform numbers of the clients; t isHIs a morning sicknessSet of late congested period numbers, TLNumbering sets for blocks of noon congestion, THAnd TLAll T subsets; t is tijDenotes the time required from point i to point j, qiIndicating the number of deliveries to the delivery point i,
indicating the additional waiting traffic light time required from delivery point i to delivery point j during the peak hours of commuting,
indicating the additional waiting traffic light time, E, required from delivery point i to delivery point j at noon peak hoursT(i) Represents the penalty time cost, w, incurred by failing to meet the customer time requirementijIndicating the time to wait before reaching the delivery point, theta indicates the average waiting time of each dispatch,
representing the average sign-on time of each express item, α representing the average preparation time before sign-on of each express item, xijtkRepresenting integer variables, x, when the delivery vehicles are from delivery points i to j and within the corresponding planned time period ijtk1, otherwise xijtk＝0；yijtkRepresenting an integer variable, when the delivery vehicle is from delivery point i to j, at a time in the morning and evening rush hour period, y ijtk1, otherwise yijtk＝0；zijtkRepresenting an integer variable, z, when the delivery vehicle is from delivery point i to j, during the mid-day rush hour period ijtk1, otherwise zijtk＝0。
Further, the extra waiting traffic light time required from the i point of the distribution point to the j point of the distribution point during the peak period of going to and from work
Additional waiting traffic light time required from delivery point i to delivery point j at noon peak hours
Average sign-in time of each express
And the average preparation time α before each courier sign-in is estimated from historical data.
Further, the penalty time cost E generated by the failure to meet the customer time requirementT(i) Is calculated as follows:
ET(i) 60 × mis-click per average payroll.
Further, the interleaving operation specifically comprises: setting a first judgment probability P, randomly generating a random number from 0 to 1 for each genetic code on an individual, and if the random number is greater than the first judgment probability P, randomly replacing the original code with the code at the corresponding position of other individuals;
the mutation operation specifically comprises the following steps: and setting a second judgment probability P, judging all elements on each individual, generating a random number between 0 and 1 for each element, and randomly generating a trolley number to replace the element if the random number is greater than the second judgment probability P.
Further, in step S7, the triangular probability formula for each individual is:
Pj＝2(n+1-j)/n(n+1),j＝1,…,n；
in the formula, n is the number of unselected individuals, j is the ranking of the fitness value of the individual in the rest of individuals, the individual j with the highest fitness value is 1, the triangular probability is 2/(n +1), the individual j with the lowest fitness value is n, and the triangular probability is 2/n (n + 1).
Further, the decoding in step S9 specifically includes: aiming at each vehicle number, finding all elements in the genetic code of the vehicle in the optimal individual, acquiring the positions of the elements in the genetic code, namely all customer numbers to be served by the vehicle, acquiring corresponding distribution points according to the customer numbers, and acquiring code binding information; and for the same vehicle, sequentially arranging the vehicle to go to each distribution point from the distribution center and then return to the distribution center according to the time sequence required by the customer.
The invention also provides a path optimization system of the express vehicle path optimization method based on the combination of the time requirement, which comprises an input module for acquiring vehicle number information and customer time requirement information, a map application module for acquiring distribution point positions and road section information, an output module for outputting a path optimization result, a storage module and a processor; 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.
Compared with the prior art, the invention has the following beneficial effects: the invention applies the genetic algorithm added with the local search module to the express vehicle distribution path planning, thereby greatly shortening the express vehicle delivery time. Meanwhile, factors including the driving time of the express delivery vehicle, the traffic jam time, the preparation time for sorting before delivery, the time for the customer to sign in from the delivery point, the time for waiting for the customer to reach the delivery point and the time for waiting in advance from the delivery point are included in the calculation of the adaptability value, so that the satisfaction degree of the customer can be improved to a greater extent by the result of the algorithm.
Drawings
Fig. 1 is a route location diagram of an express delivery point according to an embodiment of the present invention. Where, O denotes a distribution center, C1, C2, and C3 denote traffic congestion positions, 1,2, 3, 4, 5, 6, 7, 8, and 9 denote distribution points, each of which includes a plurality of customers, and each of the two points is expressed by a distance in kilometers, for example, the distance between 1 and 2 points is 0.9 kilometer.
FIG. 2 is a flow chart of a genetic algorithm according to an embodiment of the present invention.
FIG. 3 is a decoding diagram according to an embodiment of the present invention.
FIG. 4 is a comparison graph of the optimized iteration curves of the algorithm of the embodiment of the present invention and the standard genetic algorithm under the same example.
Fig. 5 is a diagram of a path and a node for final optimization of an express delivery vehicle according to an embodiment of the present invention.
FIG. 6 is a table of customer requirements, according to an embodiment of the present invention.
Fig. 7 is a distance table of a region according to an embodiment of the present invention, which includes information on traffic jam sections.
FIG. 8 is a table comparing the optimization results of the algorithm of the present invention and the standard genetic algorithm under the same example.
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. 2, the invention provides a method for optimizing a express delivery vehicle path in combination with a time requirement, which 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,
step S3: calculating the fitness value of each individual, and selecting the individual with the minimum fitness value as the optimal individual, wherein the fitness value is the total time cost T of all vehicles in the individualAThe reciprocal of (a);
step S4: judging whether the current iteration number meets the requirement, if so, entering a step S8, otherwise, entering a step S5;
step S5: adopting a roulette method, selecting whether the individual enters the next generation according to the fitness value of each individual and the probability function, wherein the higher the fitness value is, the higher the probability of entering the next generation is; the individuals selected to enter the next generation go to step S6, and the individuals not selected to enter the next generation go to step S7;
step S6: performing crossing operation and mutation operation, and sending the crossed and mutated population to step S8;
step S7: secondly selecting unselected individuals by adopting the triangular probability, sequencing the unselected individuals in the step S4, determining whether to update the unselected individuals according to the triangular distribution probability of the individuals, and sending the currently selected individuals together with the unselected individuals into the step S8 after updating operation is carried out on the currently selected individuals, wherein the updating operation is as follows;
1) setting an upper limit L for the replacement length that is less than the total length of the code, randomly generating a replacement length for the individual i:
l(i)＝Int(rand×L),i＝1,…,n
2) randomly extracting a section of code from the optimal solution, wherein the code length is l (i), and replacing the code on the corresponding position of the individual i;
3) calculating new individual adaptive values, if the adaptive values are improved, replacing the old individual, otherwise, randomly generating a code to replace the original individual;
step S8: obtaining a new population, and enabling the iteration times G to be G + 1;
step S9: 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 where the element is located represents the customer number to be serviced by the vehicle of the number, and each element yi is bound with the corresponding customer time requirement information and the distribution point position information, and the road section information of the distribution point going to another distribution point.
In this embodiment, the calculating the fitness value of each individual specifically includes the following steps:
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 client numbers to be served by j, and acquiring the information bound by j;
step S32: according to the time requirement of the client, the vehicle j sequentially goes from the distribution center to the distribution points corresponding to the clients according to the sequence of time from first to last and finally returns to the distribution center to obtain the path of the vehicle j in the individual;
step S33: according to the method from step S31 to step S32, all codes in the individual are traversed to obtain the paths of all vehicles corresponding to one individual, the total time cost of all vehicles in the individual is calculated according to the paths, and the reciprocal value of the total time cost is taken as the fitness value of the individual.
In the present embodiment, in step S3, the total time cost T of all vehicles in one individualAIs calculated as follows:
in the formula, V is a number set of distribution vehicles, V ═ 1, 2.. multidot.m }, and m is a total number of vehicles; p is a number set of distribution points, P is {1, 2.., l }, and l is a total number of distribution points; t is the number set of the time period of delivery, T ═ T1,T2,...,TD}，TDThe total number of time periods for a day, e.g. the distribution time period for a day is from 9 am, each time period being one hour, for a total of 11 time periods, T1Then represents a time period of 9:00-10:00, T5Then represents a time period of 14:00-15: 00; c is a uniform number set of the clients, C ═ 1, 2.., n }, and n is the total number of the uniform numbers of the clients; t isHNumbering sets for morning and evening congested periods, TLNumbering sets for blocks of noon congestion, THAnd TLAll T subsets; t is tijDenotes the time required from point i to point j, qiIndicating the number of deliveries to the delivery point i,
to representExtra waiting traffic light time required from the point i to the point j during the peak hours of commuting,
indicating the additional waiting traffic light time, E, required from delivery point i to delivery point j at noon peak hoursT(i) Represents the penalty time cost, w, incurred by failing to meet the customer time requirementijIndicating the time to wait before reaching the delivery point, theta indicates the average waiting time of each dispatch,
representing the average sign-on time of each express item, α representing the average preparation time before sign-on of each express item, xijtkRepresenting integer variables, x, when the delivery vehicles are from delivery points i to j and within the corresponding planned time periodijtk1, otherwise xijtk＝0；yijtkRepresenting an integer variable, when the delivery vehicle is from delivery point i to j, at a time in the morning and evening rush hour period, yijtk1, otherwise yijtk＝0；zijtkRepresenting an integer variable, z, when the delivery vehicle is from delivery point i to j, during the mid-day rush hour periodijtk1, otherwise zijtk＝0。
In this embodiment, the extra waiting time for traffic lights from i point to j point
Additional waiting traffic light time required from delivery point i to delivery point j at noon peak hours
Average sign-in time of each express
And the average preparation time α before each courier sign-in is estimated from historical data.
The required extra waiting traffic light time comprises three peak periods of the road in the morning, the noon and the evening, the extra waiting traffic light time is calculated only in the road section which can generate congestion, the information of the congested road section can be obtained according to historical data statistics, and if the time is in the congestion period and is in the peak period of the road, the extra waiting traffic light time is added to the total consumption time.
In this embodiment, the penalty time cost E generated by the failure to meet the customer time requirementT(i) Is calculated as follows:
ET(i) 60 × mis-click per average payroll.
For example, the error point fee is 30 yuan, the average salary per courier is 30 yuan, the time cost is 60 minutes, and if the time required by two customers is exceeded, the penalty time cost is 2 × 30 to 60 minutes.
Preferably, if it is found that the time finally returned to the distribution center exceeds the time constraint, a time cost with a larger value is added, for example, the time returned to the distribution center is late, which causes the related personnel to be unable to go off duty on time, so that additional subsidies need to be given to the personnel, for example, 120 yuan, and the salary of the courier is 30 yuan, which needs to pay a time cost of 4 hours.
In this embodiment, the crossing operation specifically includes: setting a first judgment probability P, randomly generating a random number from 0 to 1 for each genetic code on an individual, and if the random number is greater than the first judgment probability P, randomly replacing the original code with the code at the corresponding position of other individuals;
the mutation operation specifically comprises the following steps: and setting a second judgment probability P, judging all elements on each individual, generating a random number between 0 and 1 for each element, and randomly generating a trolley number to replace the element if the random number is greater than the second judgment probability P.
In the present embodiment, in step S7, the triangular probability formula for each individual is:
Pj＝2(n+1-j)/n(n+1),j＝1,…,n；
in the formula, n is the number of unselected individuals, j is the ranking of the fitness value of the individual in the rest of individuals, the individual j with the highest fitness value is 1, the triangular probability is 2/(n +1), the individual j with the lowest fitness value is n, and the triangular probability is 2/n (n + 1).
In this embodiment, the decoding in step S9 specifically includes: aiming at each vehicle number, finding all elements in the genetic code of the vehicle in the optimal individual, acquiring the positions of the elements in the genetic code, namely all customer numbers to be served by the vehicle, acquiring corresponding distribution points according to the customer numbers, and acquiring code binding information; and for the same vehicle, sequentially arranging the vehicle to go to each distribution point from the distribution center and then return to the distribution center according to the time sequence required by the customer. The decoding process can obtain the driving path, and also can obtain the time of arriving at the distribution point and the time of leaving the distribution point, namely the driving path can be planned in y time. Fig. 3 is an example of decoding. Wherein, the time requirement of the client is coded into the code of the time period, such as 1 represents 9:00-10:00, 2 represents 10:00-11:00, and so on. And arranging the rest numbers according to the time sequence according to the time requirement of the client to generate the path plan of the express delivery vehicle, wherein the path of the No. 3 vehicle decoded by the example is changed into 3-1-5-10.
The embodiment also provides a route optimization system of the express vehicle route optimization method based on the time requirement, which comprises an input module for acquiring vehicle number information and customer time requirement information, a map application module for acquiring distribution point positions and road section information, an output module for outputting route optimization results, a storage module and a processor; 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.
The map application module can adopt a Baidu map or a Gade map.
As shown in fig. 1, fig. 1 is a route location diagram of an express delivery point according to an embodiment of the present invention, and corresponding customer requirements are shown in fig. 6, where the customer numbers, the delivery points where the customers are located, and the earliest and latest delivery times required by the customers are included, in this embodiment, 78 customer numbers, 9 corresponding delivery points, 4 vehicles responsible for delivery, and information such as traffic jam sections obtained from historical data are shown in fig. 7. The optimal path of the vehicle obtained by decoding the optimal individual obtained by the method of the embodiment is shown in fig. 5, and the calculated total delivery time length is shown in fig. 8, wherein the total time cost obtained by the algorithm of the embodiment is 1581 minutes, while the time cost obtained by the conventional algorithm is 1823.6, and obviously, the algorithm of the embodiment can shorten more time cost. Meanwhile, the optimized iteration curve of the genetic algorithm and the traditional genetic algorithm is shown in fig. 4, and it can be seen that the genetic algorithm of the embodiment has a better effect.
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 express vehicle path optimization in combination with time requirements, comprising the steps of:
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,
step S3: calculating the fitness value of each individual, and selecting the individual with the minimum fitness value as the optimal individualThe fitness value is the total time cost T of all vehicles in an individualAThe reciprocal of (a);
step S4: judging whether the current iteration number meets the requirement, if so, entering a step S8, otherwise, entering a step S5;
step S5: adopting a roulette method, selecting whether the individual enters the next generation according to the fitness value of each individual and the probability function, wherein the higher the fitness value is, the higher the probability of entering the next generation is; the individuals selected to enter the next generation go to step S6, and the individuals not selected to enter the next generation go to step S7;
step S6: performing crossing operation and mutation operation, and sending the crossed and mutated population to step S8;
step S7: secondly selecting unselected individuals by adopting the triangular probability, sequencing the unselected individuals in the step S4, determining whether to update the unselected individuals according to the triangular distribution probability of the individuals, and sending the updated unselected individuals and the unselected individuals into the step S8 after updating the currently selected individuals;
step S8: obtaining a new population, and enabling the iteration times G to be G + 1;
step S9: and decoding the genetic codes of the optimal individuals of the current population to obtain the optimal driving path of each vehicle.
2. The express vehicle path optimization method combined with time requirement as claimed in claim 1, wherein in step S1, in the genetic code [ y1, y 2.. 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 number to be served by the numbered vehicle, and each element yi is bound with the corresponding customer time requirement information and the distribution point position information and the road section information of the distribution point to another distribution point.
3. The express vehicle path optimization method in combination with time requirement as claimed in claim 2, wherein the calculating the fitness value of each individual specifically comprises the following steps:
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 client numbers to be served by j, and acquiring the information bound by j;
step S32: according to the time requirement of the client, the vehicle j sequentially goes from the distribution center to the distribution points corresponding to the clients according to the sequence of time from first to last and finally returns to the distribution center to obtain the path of the vehicle j in the individual;
step S33: according to the method from step S31 to step S32, all codes in the individual are traversed to obtain the paths of all vehicles corresponding to one individual, the total time cost of all vehicles in the individual is calculated according to the paths, and the reciprocal value of the total time cost is taken as the fitness value of the individual.
4. The express vehicle route optimization method combined with time requirement as claimed in claim 2, wherein in step S3, the total time cost T of all vehicles in one individual isAIs calculated as follows:
in the formula, V is a number set of distribution vehicles, V ═ 1, 2.. multidot.m }, and m is a total number of vehicles; p is a number set of distribution points, P is {1, 2.., l }, and l is a total number of distribution points; t is the number set of the time period of delivery, T ═ T1,T2,...,TD}，TDTotal number of time periods for a day; c is a uniform number set of the clients, C ═ 1, 2.., n }, and n is the total number of the uniform numbers of the clients; t isHNumbering sets for morning and evening congested periods, TLNumbering sets for blocks of noon congestion, THAnd TLAll T subsets; t is tijDenotes the time required from point i to point j, qiIndicating the number of deliveries to the delivery point i,
indicating the peak time of going to work from the distribution point i to the distribution point jThe additional wait time required for traffic lights,
indicating the additional waiting traffic light time, E, required from delivery point i to delivery point j at noon peak hoursT(i) Represents the penalty time cost, w, incurred by failing to meet the customer time requirementijIndicating the time to wait before reaching the delivery point, theta indicates the average waiting time of each dispatch,
representing the average sign-on time of each express item, α representing the average preparation time before sign-on of each express item, xijtkRepresenting integer variables, x, when the delivery vehicles are from delivery points i to j and within the corresponding planned time periodijtk1, otherwise xijtk＝0；yijtkRepresenting an integer variable, when the delivery vehicle is from delivery point i to j, at a time in the morning and evening rush hour period, yijtk1, otherwise yijtk＝0；zijtkRepresenting an integer variable, z, when the delivery vehicle is from delivery point i to j, during the mid-day rush hour periodijtk1, otherwise zijtk＝0。
5. The method for express vehicle path optimization incorporating time requirement as claimed in claim 3, wherein the extra waiting traffic light time required from delivery point i to delivery point j during peak hours of work
Additional waiting traffic light time required from delivery point i to delivery point j at noon peak hours
Average sign-in time of each express
And average before each express sign-forThe preparation time α is estimated from historical data.
6. The method for express vehicle path optimization in combination with time requirement of claim 3, wherein the penalty time cost E generated by failing to meet the customer time requirementT(i) Is calculated as follows:
ET(i) 60 × mis-click per average payroll.
7. The express vehicle path optimization method in combination with time requirements of claim 1,
the crossing operation specifically comprises the following steps: setting a first judgment probability P, randomly generating a random number from 0 to 1 for each genetic code on an individual, and if the random number is greater than the first judgment probability P, randomly replacing the original code with the code at the corresponding position of other individuals;
the mutation operation specifically comprises the following steps: and setting a second judgment probability P, judging all elements on each individual, generating a random number between 0 and 1 for each element, and randomly generating a trolley number to replace the element if the random number is greater than the second judgment probability P.
8. The express vehicle path optimization method combined with time requirement as claimed in claim 1, wherein in step S7, the triangular probability formula of each individual is:
Pj＝2(n+1-j)/n(n+1),j＝1,…,n；
in the formula, n is the number of unselected individuals, j is the ranking of the fitness value of the individual in the rest of individuals, the individual j with the highest fitness value is 1, the triangular probability is 2/(n +1), the individual j with the lowest fitness value is n, and the triangular probability is 2/n (n + 1).
9. The express vehicle path optimization method combined with the time requirement as claimed in claim 1, wherein the decoding in step S9 specifically includes: aiming at each vehicle number, finding all elements in the genetic code of the vehicle in the optimal individual, acquiring the positions of the elements in the genetic code, namely all customer numbers to be served by the vehicle, acquiring corresponding distribution points according to the customer numbers, and acquiring code binding information; and for the same vehicle, sequentially arranging the vehicle to go to each distribution point from the distribution center and then return to the distribution center according to the time sequence required by the customer.
10. A route optimization system based on the express vehicle route optimization method combined with the time requirement of any one of claims 1 to 9, which comprises an input module for acquiring vehicle number information and customer time requirement information, a map application module for acquiring distribution point position and road section information, an output module for outputting a route optimization result, 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 9 when executing the computer program.
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