CN111461624A

CN111461624A - Logistics line planning method, device, equipment and storage medium

Info

Publication number: CN111461624A
Application number: CN202010310871.9A
Authority: CN
Inventors: 刘梦超; 陈超
Original assignee: Dongpu Software Co Ltd
Current assignee: Dongpu Software Co Ltd
Priority date: 2020-04-20
Filing date: 2020-04-20
Publication date: 2020-07-28
Anticipated expiration: 2040-04-20
Also published as: CN111461624B

Abstract

The invention relates to the technical field of logistics distribution, and discloses a logistics route planning method, a device, equipment and a storage medium, which are used for realizing the maximum loading and distribution of vehicles, improving the matching degree of people and vehicles and the efficiency of collecting and distributing parts, and reducing the logistics distribution cost of a single salesman. The logistics route planning method comprises the following steps: training logistics history processing information classified according to the service staff through an ant colony algorithm to obtain a path optimization model, extracting corresponding express order information, dispatch addresses and distributable vehicle information from express information to be processed by the service staff in the path planning process, taking the information as constraint conditions for planning the path, calculating matching parameters between the service staff and the vehicle information by using the path optimization model, and planning the optimal path based on the matching parameters.

Description

Logistics line planning method, device, equipment and storage medium

Technical Field

The present invention relates to the field of logistics distribution technologies, and in particular, to a method, an apparatus, a device, and a storage medium for planning a logistics route.

Background

With the continuous development of market economy and logistics technology, logistics distribution is rapidly developed, especially the popularization of internet shopping at present, express delivery is increased year by year, and the increase of workload necessarily influences the overall distribution efficiency of the operators on express.

In the prior art, when a salesman realizes the processing of picking up and dispatching, the salesman seriously depends on personal experience to plan a route, most of the business salesman is processed based on the condition of an order received before the salesman starts, in actual operation, a new processing order is often generated by the salesman in the process of attendance or dispatching, for the condition, the processing is not carried out, namely, the condition of the order of a network point is not combined, the salesman or the dispatching is dynamically sensed to improve the processing efficiency, even some salesmans often have the problems of insufficient load or overlarge redundancy in the process of attendance, and the existing mode can solve the problem of matching of the three types of human, vehicles and goods, but is difficult to realize the cost control of the human, the processing efficiency is low, and even the phenomenon of customer complaints is generated.

Disclosure of Invention

The invention mainly aims to solve the technical problem that the logistics processing efficiency is low due to inaccurate high matching between people, vehicles and goods in the conventional logistics route planning mode.

The first aspect of the present invention provides a method for planning a logistics route, including:

acquiring express mail information to be processed by a current salesman, position information of a distribution center where the current salesman is located and vehicle information of a vehicle currently available by the distribution center;

analyzing all express information in charge of the service staff to obtain corresponding express order information and a delivery address set, and numbering each delivery address in the delivery address set;

the vehicle information, the order information and the dispatch address set are used as constraint conditions for logistics route planning, and matching parameters between the salesman and the available vehicles are calculated by utilizing a path optimization model, wherein the path optimization model is a model obtained by training logistics history processing information through an ant colony algorithm;

and planning an optimal logistics line corresponding to the salesman according to the matching parameters.

Optionally, in a first implementation manner of the first aspect of the present invention, the calculating, by using the vehicle information, the order information, and the dispatch address set as constraints for logistics route planning and using a path optimization model, matching parameters between the service engineer and available vehicles includes:

counting the total delivery volume of the order information to be processed by the salesman;

selecting at least one vehicle from the available vehicles that meets a loading requirement based on the total delivery volume, the loading requirement comprising a maximum load of the vehicle being greater than or equal to the total delivery volume;

taking the distribution center as a starting place, and respectively traversing all the dispatching addresses in the dispatching address set by the at least one vehicle on the basis of the path optimization model to obtain a plurality of logistics routes;

and calculating the unit distribution cost of each logistics line in the plurality of logistics lines, and calculating the matching parameters between the salesman and the corresponding vehicle based on the unit distribution cost.

Optionally, in a second implementation manner of the first aspect of the present invention, the traversing, by using the distribution center as a starting point, the at least one vehicle through all the dispatch addresses in the dispatch address set by the path optimization model respectively to obtain a plurality of logistics routes includes:

setting an origin of each of the at least one vehicle as the location information of the distribution center;

randomly selecting one vehicle from the at least one vehicle as a first vehicle;

selecting a dispatch address from the dispatch address set as a first dispatch address based on the first vehicle;

traversing the path information between the distribution center and the first dispatch address through the path optimization model, and storing the first dispatch address into a preset taboo list;

after traversing the dispatch address, selecting a next dispatch address from the dispatch address set for traversing and storing into the tabu list;

when all dispatch addresses in the dispatch address set are traversed, clearing the taboo list, and planning an optimal logistics route between the salesman and the first vehicle based on all route information obtained by traversal;

and selecting a second vehicle from the rest vehicles to traverse the dispatch address set, and outputting a corresponding optimal logistics route.

Optionally, in a third implementation manner of the first aspect of the present invention, the selecting, based on the first vehicle, one dispatch address from the dispatch address set as a first dispatch address includes:

calculating the probability of the first vehicle passing through each dispatch address according to a preset probability conversion rule;

and selecting one dispatch address with the highest probability as the first dispatch address.

Optionally, in a fourth implementation manner of the first aspect of the present invention, after the planning, according to the matching parameter, an optimal logistics route corresponding to a salesman, the method further includes:

acquiring new order information of a network point passing through the optimal logistics pipeline at fixed time, wherein the new order information is a pickup order or a delivery order;

judging whether the new order information belongs to the order on the optimal logistics line;

and if so, embedding the new order information into the optimal logistics line according to the path optimization model.

Optionally, in a fifth implementation manner of the first aspect of the present invention, after the obtaining new order information of a network node that passes through the optimal logistics route at the fixed time, before the determining whether the new order information belongs to an order on the optimal logistics route, the method further includes:

acquiring the current positioning information of the operator;

judging whether the positioning information is consistent with the position information of the distribution center;

if yes, executing a step of judging whether the new order information belongs to the order on the optimal logistics line;

if not, selecting unprocessed dispatch addresses from the dispatch address set to form a second dispatch address set;

and planning a logistics route for the second dispatch address set by using the positioning information as the origin of the vehicle and utilizing the path optimization model.

Optionally, in a sixth implementation manner of the first aspect of the present invention, the method for planning a logistics route includes training to obtain the path optimization model by:

acquiring n logistics history processing information of the salesman, and analyzing the n logistics history processing information to obtain n delivery addresses and vehicle information of m vehicles;

according to the data analysis principle of the ant colony algorithm, analyzing the records of m vehicles passing through each dispatch address;

and constructing the path optimization model according to the record obtained by analysis.

A second aspect of the present invention provides a logistics route planning apparatus, including:

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring express mail information to be processed by a current salesman, position information of a distribution center where the current salesman is located and vehicle information of a vehicle currently available in the distribution center;

the analysis module is used for analyzing all express mail information in charge of the service staff to obtain corresponding express mail order information and a dispatch address set, and numbering each dispatch address in the dispatch address set;

the model analysis module is used for calculating matching parameters between the service staff and available vehicles by taking the vehicle information, the order information and the dispatch address set as constraint conditions of logistics route planning and utilizing a path optimization model, wherein the path optimization model is a model obtained by training logistics history processing information through an ant colony algorithm;

and the line generation module is used for planning the optimal logistics line corresponding to the salesman according to the matching parameters.

Optionally, in a first implementation manner of the second aspect of the present invention, the model analysis module includes:

the statistical unit is used for counting the total delivery volume of the order information to be processed by the salesman;

a selection unit for selecting at least one vehicle satisfying a loading requirement from the available vehicles according to the total delivery amount, the loading requirement including that a maximum load of the vehicle is greater than or equal to the total delivery amount;

the analysis unit is used for traversing all the dispatching addresses in the dispatching address set by the at least one vehicle based on the path optimization model by taking the distribution center as a starting place to obtain a plurality of logistics routes;

and the calculating unit is used for calculating the unit distribution cost of each logistics line in the logistics lines and calculating the matching parameters between the salesman and the corresponding vehicle based on the unit distribution cost.

Optionally, in a second implementation manner of the second aspect of the present invention, the analysis unit is specifically configured to:

Optionally, in a third implementation manner of the second aspect of the present invention, the analysis unit is specifically configured to:

Optionally, in a fourth implementation manner of the second aspect of the present invention, the logistics route planning apparatus further includes:

the optimization module is to: acquiring new order information of a network point passing through the optimal logistics pipeline at fixed time, wherein the new order information is a pickup order or a delivery order; judging whether the new order information belongs to the order on the optimal logistics line; and if so, embedding the new order information into the optimal logistics line according to the path optimization model.

Optionally, in a fifth implementation manner of the second aspect of the present invention, the optimization module is further configured to: acquiring the current positioning information of the operator; judging whether the positioning information is consistent with the position information of the distribution center; if yes, executing a step of judging whether the new order information belongs to the order on the optimal logistics line; if not, selecting unprocessed dispatch addresses from the dispatch address set to form a second dispatch address set; and planning a logistics route for the second dispatch address set by using the positioning information as the origin of the vehicle and utilizing the path optimization model.

Optionally, in a fifth implementation manner of the second aspect of the present invention, the logistics route planning apparatus further includes:

the model training module is used for: acquiring n logistics history processing information of the salesman, and analyzing the n logistics history processing information to obtain n delivery addresses and vehicle information of m vehicles; according to the data analysis principle of the ant colony algorithm, analyzing the records of m vehicles passing through each dispatch address; and constructing the path optimization model according to the record obtained by analysis.

A third aspect of the present invention provides a logistics route planning apparatus, including: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line; the at least one processor invokes the instructions in the memory to cause the logistics route planning apparatus to perform the logistics route planning method described above.

A fourth aspect of the present invention provides a computer-readable storage medium having stored therein a computer program which, when run on a processor on a computer, causes the computer to execute the logistics route planning method described above.

In the technical scheme provided by the invention, logistics history processing information classified according to operators is trained through an ant colony algorithm to obtain a path optimization model, corresponding express order information, dispatch addresses and distributable vehicle information are extracted from express information to be processed by the operators in the path planning process, the matching parameters between the operators and the vehicle information are calculated by using the path optimization model as constraint conditions for planning paths, and the optimal path planning is carried out based on the matching parameters, so that the learning of path planning experience is realized in a learning mode based on a machine algorithm, the input of manual operation can be reduced, the maximum loading of vehicles can be realized, the matching degree of people and vehicles is improved, and the dispatch efficiency is improved, the logistics distribution cost of a single salesperson is reduced.

Drawings

Fig. 1 is a schematic diagram of a first embodiment of a logistics route planning method in an embodiment of the invention;

FIG. 2 is a schematic flow chart of a path optimization model training in an embodiment of the present invention;

fig. 3 is a schematic diagram of a second embodiment of the logistics route planning method in the embodiment of the invention;

FIG. 4 is a schematic flow chart illustrating a vehicle traversing a dispatch address set according to an embodiment of the present invention;

fig. 5 is a schematic diagram of a third embodiment of the logistics route planning method in the embodiment of the invention;

fig. 6 is a schematic diagram of a fourth embodiment of the logistics route planning method in the embodiment of the invention;

fig. 7 is a schematic diagram of a first embodiment of a logistics route planning device in an embodiment of the invention;

fig. 8 is a schematic diagram of a second embodiment of the logistics route planning apparatus in the embodiment of the invention;

fig. 9 is a schematic structural diagram of a logistics route planning apparatus according to an embodiment of the invention.

Detailed Description

The embodiment of the invention provides a logistics route planning method, a device, equipment and a storage medium, the method carries out route planning aiming at the processing business of each salesman through a route optimization model constructed by an ant colony algorithm, particularly, express mail information to be processed in a period of time and vehicle information of available vehicles existing in a distribution center are obtained based on the salesman, matching parameters between personnel and the vehicles are calculated by utilizing the route optimization model based on the express mail information and the vehicle information, an optimal logistics route is planned based on the matching parameters, as the route planning method utilizes the ant colony algorithm, the characteristics of self-organization, parallel calculation and positive feedback search are provided, the optimal route of picking or dispatching the mails and the optimal matching information of the salesman and the vehicles are automatically and efficiently calculated, the distribution route is determined by adopting a scientific and reasonable method, the satisfaction degree of the customer to the logistics distribution service can be enhanced, the distribution cost of logistics enterprises is reduced, and higher enterprise benefits are obtained.

The logistics planning efficiency can be improved, the logistics economic benefit is realized, the logistics scientization is realized, the urban traffic pressure can be relieved, the energy is saved, the pollution is reduced, the internal unification of the aspects of efficiency, resources, environment and value concept is realized, and the progress of the logistics industry and the sustainable development of social economy are promoted.

Furthermore, the construction of a transfer center in a logistics line can be realized through the planning mode, so that the high-efficiency utilization rate of the transfer center is ensured.

The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

For the sake of understanding, the following describes a specific flow of an embodiment of the present invention, please refer to fig. 1, and fig. 1 is a flow chart of the logistic line planning method according to the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs. The method is mainly applied to a logistics distribution system, particularly to a part of functions aiming at an operator in the system, such as a mobile phone of the operator, wherein the mobile phone is provided with APP software for receiving and dispatching orders, the APP is functional software with logistics line planning and express tracking functions, the system distributes corresponding operators according to dispatching addresses of express, and after receiving order information of the express, the APP software plans dispatching paths of the express, in the embodiment, the order information received by the APP can be orders for dispatching, or orders for picking up the express, and a specific implementation flow is as follows:

101. acquiring express mail information to be processed by a current salesman, position information of a distribution center where the current salesman is located and vehicle information of a vehicle currently available by the distribution center;

in this embodiment, the to-be-processed express mail information may be dispatch information or pickup information; in the process of collecting express mail information, the express mail information is obtained according to classes of the service staff, specifically, information of the service staff needing to carry out logistics route planning or optimization is determined firstly, for example, staff numbers corresponding to the service staff and the like can uniquely identify the service staff, then order information processed by the service staff is called from an order database through an APP according to the information of the service staff, wherein the order information can be unprocessed orders in a range before and after a current time period, and can also be all unprocessed orders related to the service staff in the database.

Further, by locating the location information of the distribution center where the service staff is located through the GPS, in practical applications, the distribution center can be understood as the starting point and the ending point of the distribution activities, which include warehouses, ports and the like with the functions of distributing, stocking and picking, and the attributes of the distribution center to be considered in the VRP problem include the geographic location, the number of available distribution vehicles and the like. Generally divided into single distribution centers and multiple distribution centers. If one distribution center is selected, the express information of the service staff can be sent from one place, and the express can be picked up without crossing different distribution centers.

The method includes the steps that vehicle information of vehicles which can be called to dispatch or pull in the distribution center at the current moment is searched based on the distribution center, and corresponding searching can be performed according to business personnel when the vehicle information is searched, for example, a logistics company provides a logistics team with a plurality of distribution vehicles, the team where the logistics company is located is determined according to the information of the business personnel when searching is performed, and then the vehicle information of vehicles which are not dispatched currently in the team is determined.

In this embodiment, when there are a plurality of determined distribution centers, vehicles of the plurality of distribution centers need to be considered when searching for available vehicles, and it is needless to say that the distribution vehicles need to be acquired from each distribution center, and the route planning process considered at this time needs to be dispatched by replacing the vehicles.

In the present embodiment, when the vehicle information is acquired, specifically, the maximum load, specification, speed, maximum travel distance, and the like of each vehicle that can be called are acquired.

102. Analyzing all express information in charge of the service staff to obtain corresponding express order information and a delivery address set, and numbering each delivery address in the delivery address set;

in the step, when the express information is analyzed, specifically, the express information is analyzed through a character and picture identification technology, the character identification technology extracts editable information in the express information, such as an address item, a remark item and the like, and the picture identification technology identifies an image and a character part in the express information through the picture identification technology when the express information belongs to a picture, and the extracted express order information includes delivery requirements of orders and delivery goods quantity of the express information; after all express information of the service staff is extracted, the extracted express addresses form an express address set, the addresses in the express address set are numbered randomly to obtain an address sequence, and finally the address sequence is used as an allowed access address list of each available vehicle.

103. The vehicle information, the order information and the dispatch address set are used as constraint conditions for logistics route planning, and a path optimization model is utilized to calculate matching parameters between the service staff and available vehicles;

in this embodiment, the path optimization model is a model obtained by training logistics history processing information through an ant colony algorithm, and when a logistics route is planned, vehicle information, order information, and a dispatch address set of available vehicles are used as initial conditions for planning the logistics route, that is, the planned route must be transported by a selected available vehicle, and an address through which the route passes is an address specified in the dispatch address set.

When the matching parameters are calculated by using the path optimization model, different vehicles are sequentially selected from available vehicles, all addresses in the dispatch address set are traversed according to the ant algorithm principle in the path optimization model to obtain a plurality of lines, then the time for transporting express mails to the dispatch addresses through the plurality of lines respectively, the path distance, the cost and other parameters are calculated, whether the parameters meet the requirements in order information and the transportation requirements of the system for the service staff is judged, and one of the plurality of lines is selected as the optimal logistics line of the vehicle based on the judgment result.

For example, the number of the selected available vehicles is 5, the dispatch address is 3, and a vehicle which can meet the transportation requirement and can also meet the dispatch requirement is selected from the 5 vehicles by using the path optimization model, and the specific implementation process may be as follows:

firstly, 5 vehicles and 3 dispatch addresses are numbered, namely { A1, A2, A3, A4, A5} and { D1, D2, D3 };

then, randomly selecting one of the { A1, A2, A3, A4 and A5} as a transport vehicle, traversing the { D1, D2 and D3} to obtain a plurality of lines, and then selecting an optimal one of the plurality of lines as a logistics line of the transport vehicle until the logistics line calculation of 5 vehicles is completed;

for example, firstly, a1 is selected to perform traversal processing on { D1, D2 and D3} to obtain a plurality of routes which are D1 → D2 → D3, D1 → D3 → D3, D3 → D1 → D2, and an optimal route is selected from the routes, wherein the optimal route can be understood as a distribution route which meets the A1 vehicle, the meeting is low in cost, the loading amount of goods and vehicles is large, and the path length is moderate, so that a matching parameter of the current distribution amount of the A1 vehicle and a service staff is obtained. After the traversal of the a1 vehicle is completed, the processing of a2, A3, a4, a5 is continued in the same manner as that of a 1. The final output matching parameters include matching parameters of 5 vehicles with respect to the attendant.

104. And planning an optimal logistics line corresponding to the salesman according to the matching parameters.

In this embodiment, when the matching parameters include matching parameters of a plurality of vehicles and the service engineer, the step further includes selecting an optimal one from the matching parameters, and planning and outputting a logistics route based on the optimal matching parameter.

In this embodiment, the step may also be that all the matching parameters of the multiple vehicles and the servicer are output to the logistics route, and then the output logistics routes are further screened to select an optimal output, and the specific screening may be performed according to the constraint conditions of the mathematical model, for example, the constraint conditions of the mathematical model are route time length, distance, cost, and transportation cargo volume, and the specific screening may be compared and considered according to the priorities in the constraint conditions, for example: and evaluating the plurality of logistics lines according to the sequence of time window > cargo capacity > distance > cost, and directly outputting the corresponding line as the optimal line after the evaluation is passed.

In this embodiment, the output logistics route includes two sections of a going route and a returning route, and the processing of ordinary express items can be placed in the going route for processing, but in practical application, the processing can be distributed in a balanced manner according to actual requirements, so that the step also includes determining the type of the express item information before outputting the optimal route, wherein the type includes picking and dispatching, if picking, the route of the returning route section is preferentially selected to be picked in a concentrated manner, and the going route is output according to the current road condition; if the dispatch is carried out, the route centralized dispatch of the output route is preferentially selected, and the return route is output according to the current road condition.

By the implementation, the express delivery route of the salesperson is planned based on the route optimization model obtained by utilizing ant colony algorithm training, and the planning optimization of the route is realized through the algorithm model.

In this embodiment, the path optimization model is obtained by training through an ant colony algorithm, and the training process is specifically shown in fig. 2.

201. Collecting delivery order information which is delivered completely from a logistics delivery system, and classifying the delivery order information;

in the step, the delivery order information comprises two orders of picking up and sending, the two orders are classified according to the salesmen, and the order of each salesmen is also distinguished by picking up and sending.

202. Initializing the classified order information to obtain a traversal address and vehicle information;

in the embodiment, the initialization refers to resolving order information, extracting the information of dispatch addresses and the information of transport vehicles, and counting the number of times each transport vehicle passes through each address.

203. Traversing all the dispatch addresses through an ant colony algorithm to obtain information left by the vehicle passing through the dispatch addresses;

204. and outputting the optimal path and the optimal path length according to the left information, and training based on the output path to obtain a path optimization model.

In this embodiment, the ant colony algorithm is an algorithm obtained by simulating the movement law of natural real ants foraging. When ants search for food, the shortest path from the food source to the ant hole can be always found, and the ant can adapt to the change of the environment, for example, when obstacles suddenly appear on the path of the ant colony foraging, the ants can quickly avoid and find other paths from the food source to the ant hole again. It is found that this is because, during the course of the ant's movement, pheromone (pheromone), which is a kind of secretion, can be released on the path that the ant passes through, and other ants can sense the existence and intensity of this substance while moving, and select the path with large pheromone amount as their own moving direction. Because more information is left over on shorter paths in the same time, more and more ants select shorter paths, which is a positive feedback phenomenon.

For the above training process, the following analysis is performed from the perspective of ants, and the specific principle is as follows:

step 1, initializing parameters, and setting related parameters: the number n of addresses required to be traversed, antsMaximum value N of iteration times of m ants in total number_maxInitial time, pheromone tau on each path_ij(0) Tau (0), pheromone chance coefficient ρ, pheromone elicitation factor α, self-elicitation factor β, total information amount Q, and the like^kAnd ensures that there is no address in the table at this time.

Step 2, randomly placing m ants on n addresses, distributing one ant at most on each address, and storing the address information of the m ants in a taboo list J^k。

In practical application, m ants send n addresses, and actually, the ants send information left when vehicles pass by each address when dispatching the addresses, so that the information on the n addresses is collected after the ants complete traversal of all the addresses, and in the traversal process, after the ants complete traversal of one address, the address information needs to be stored in a taboo list, so that the situation that the ants repeatedly perform traversal operation on the addresses to influence final information collection is avoided.

In this embodiment, when an ant traverses n addresses, after completing an address traversal, and selects a next address, the ant selects the next address by calculating a probability, where the probability calculation rule is:

wherein the content of the first and second substances,

probability of selecting address j from address i at time t for ant k, k representing the kth ant, τ_ij(t) denotes the pheromone concentration at the time t of the address i, j, η_ijIndicating the visibility from address i to address j, L^kRepresenting the total cost (or distance) of the ant k through the path through a loop (or iteration) lock,

the pheromone for ant k varies from address i to address j.

Further, after a path optimization model is obtained according to historical data training, the delivery path is planned for the delivery of the courier on the same day by using the model, and a specific planning flow is shown in fig. 3 and includes the following steps:

301. acquiring express mail information to be processed by a current salesman, position information of a distribution center where the current salesman is located and vehicle information of a vehicle currently available by the distribution center;

302. analyzing all express information in charge of the service staff to obtain corresponding express order information and a delivery address set, and numbering each delivery address in the delivery address set;

303. counting the total delivery volume of the order information to be processed by the salesman;

in this embodiment, taking a dispatch order as an example, since the cargo volumes of different orders may be different, the specific cargo volume may be calculated according to weight or volume, and the load weight of a vehicle is fixed.

In this step, when counting the total delivery volume of the order of the salesman, optionally, selecting the delivery order of one day to count, firstly classifying the order, for example, if the order is a file type small express, counting according to the volume; if the express items of the large items are express items, counting is carried out according to the volume, and after the volume weight counting is finished, the occupied volume needs to be converted into the occupied volume, so that the counting of the total delivery amount of the order information is finished.

304. Selecting at least one vehicle meeting loading requirements from the available vehicles according to the total delivery amount;

in this embodiment, the loading requirement includes a maximum load of the selected vehicle being greater than or equal to the total delivery amount; in the process of selecting the vehicle according to the total delivery volume, in addition to the loading weight of the vehicle, the method further includes considering the model of the vehicle, for example, the vehicle with the same loading volume, and selecting the vehicle with less fuel consumption.

Of course, it is also possible to choose not only vehicles with a loading weight equal to the total delivery volume, but in practice it is also necessary to leave room for the subsequent addition of delivery orders, which can be chosen by setting the fluctuation range, for example a vehicle with a loading weight of 25 kg can be chosen.

In practical application, the method can also comprise the step of selecting vehicles according to dispatch area information of a salesman, small trucks such as flat trucks can be selected for an address area with convenient traffic, and cars such as minibuses can be selected for an area with inconvenient traffic.

305. Taking the distribution center as a starting place, and traversing all the distribution addresses in the distribution address set by the at least one vehicle through the path optimization model to obtain a plurality of logistics routes;

in this embodiment, the position information of the origin of the selected at least one vehicle is set as the position information of the distribution center, and the distribution address set is traversed based on the position information by using the ant colony algorithm principle to obtain a plurality of logistics routes.

In this embodiment, the dispatch address set may be traversed by at least one vehicle through the following process, as shown in fig. 4:

3051. setting an origin of each of the at least one vehicle as the location information of the distribution center;

3052. randomly selecting one vehicle from the at least one vehicle as a first vehicle;

3053. selecting a dispatch address from the dispatch address set as a first dispatch address based on the first vehicle;

3054. traversing the path information between the distribution center and the first dispatch address through the path optimization model, and storing the first dispatch address into a preset taboo list;

3055. after traversing the dispatch address, selecting a next dispatch address from the dispatch address set for traversing and storing into the tabu list;

3056. when all dispatch addresses in the dispatch address set are traversed, clearing the taboo list, and planning an optimal logistics route between the salesman and the first vehicle based on all route information obtained by traversal;

3057. and selecting a second vehicle from the rest vehicles to traverse the dispatch address set, and outputting a corresponding optimal logistics route. Until all vehicle traversal plans are completed, step 306 is executed.

In practical application, after a vehicle is selected from at least one vehicle, a dispatch address is randomly selected from a dispatch address set allowed by the vehicle for traversal, namely, a route from a distribution center to the selected address is traversed through a route optimization model, namely, a route from the distribution center to the selected address is traversed through a model simulation ant, real-time driving records between the two are traversed through navigation software, the concentration of pheromones on the corresponding route is obtained through traversing the number of the vehicles between two places, so that the optimal route between the two places is calculated, in practical application, the driving records of the truck are counted preferentially during traversal, the reaction of the driving time left by the truck and the driving road conditions is obtained, the conditions of traffic and roads on the corresponding route are calculated, and an optimal section of logistics route is output, and after the output of the path is finished, taking the dispatch address as an initial point of the vehicle, selecting the next dispatch address from the rest dispatch addresses, and recombining the whole logistics route to obtain the final optimal logistics route of the vehicle after the dispatch address set is traversed.

In this step, the plurality of logistics routes that are finally output refer to an optimal logistics route set formed by traversing the dispatch address set for all vehicles selected in step 304, respectively, to obtain an optimal route for each vehicle.

306. Calculating unit distribution cost of each logistics line in the plurality of logistics lines, and determining matching parameters between the salesman and the corresponding vehicle based on the unit distribution cost;

307. and planning an optimal logistics line corresponding to the salesman according to the matching parameters.

In this embodiment, when selecting a next dispatch address and performing a traversal by using a path optimization model, specifically, a next dispatch address is selected from the dispatch address set by using a probability calculation method, optionally, one with the highest probability is selected, or two or three routes are selected, so that one route is selected from the traversed route segments, and in practical applications, among the traversed routes, a constraint condition may be set by using a mathematical model to select a route segment with the shortest time, the shortest route, and the largest delivery quantity, for example.

In this embodiment, for the selection according to the probability, specifically, the probability that the first vehicle passes through each dispatch address is calculated according to a preset probability conversion rule, and one dispatch address with the highest probability is selected as the first dispatch address, where the probability conversion rule is as follows:

in conclusion, the path optimization model constructed through the ant colony algorithm optimizes the parcel capturing path of the salesman, the complex optimization problem with high practicability is solved, and the planned path has higher practicability, so that the requirements of various practical applications are met.

Furthermore, by utilizing the ant colony algorithm, the optimal path of picking or dispatching the parts and the optimal matching information of the salesman and the vehicle are automatically and efficiently calculated, so that the benefit maximization is realized, the matching degree of the vehicle and the salesman is greatly improved, and the logistics distribution cost is reduced.

Referring to fig. 5, a server is taken as a logistics route planning device for detailed description, and another embodiment of the logistics route planning method in the embodiment of the invention includes:

501. acquiring express mail information to be processed by a current salesman, position information of a distribution center where the current salesman is located and vehicle information of a vehicle currently available by the distribution center;

the method comprises the steps that a server receives a dispatch or pickup request triggered by an operator on an APP, express information that the operator needs to dispatch or pickup at the current time period is obtained from a logistics database according to the request, the current position of the operator is obtained through a positioning technology, a distribution center closest to the operator is determined based on the position, the distribution center is used as an initial point of the dispatch or pickup, and then vehicle information of a vehicle which can be called by the operator at present is inquired from a system of the distribution center.

502. Analyzing all express information in charge of the service staff to obtain corresponding express order information and a delivery address set, and numbering each delivery address in the delivery address set;

specifically, the server acquires all express delivery information required to be delivered by the salesman at the current time from the delivery database through a request, extracts order numbers and delivery addresses from the express delivery information through a character and picture recognition technology, and numbers the delivery addresses of all express deliveries. Furthermore, in the numbering process, the navigation software can be used for automatically navigating and calculating the routes, numbering is carried out based on the sequencing of the routes, and then the well-numbered dispatch addresses are formed into a set.

503. The vehicle information, the order information and the dispatch address set are used as constraint conditions for logistics route planning, and a path optimization model is utilized to calculate matching parameters between the service staff and available vehicles;

504. planning an optimal logistics line corresponding to the salesman according to the matching parameters;

505. acquiring new order information of the network points passing through the optimal logistics pipeline at fixed time;

in this embodiment, the new order information is a pickup order or a dispatch order, and for this step, the pickup order or the dispatch order is dynamically sensed by combining with the site order condition to adjust the logistics path of the salesman, so that the planning of an optimal path is achieved, and the control of the reassignment of the salesman to the maximum extent is also achieved.

In this embodiment, when a new order is obtained at regular time, the new order can be obtained by sending location of a salesman, for example, setting a timer, setting regular obtaining for dispatch software of the salesman, and monitoring whether the salesman arrives at a current logistics line to obtain a dispatch address, if so, triggering the timer to obtain whether a new order exists on a website where the dispatch address is located, if so, reading the new order and pushing the new order to the salesman, and if so, determining whether receiving processing is needed by the salesman.

506. Judging whether the new order information belongs to the order on the optimal logistics line;

507. and if so, embedding the new order information into the optimal logistics line according to the path optimization model.

In this embodiment, before outputting the optimal route, determining the type of the express delivery information, where the type includes a pulling piece and a sending piece, and if the pulling piece is a pulling piece, preferentially selecting a route concentrated pulling piece of the return route section to be output, and outputting the route concentrated pulling piece according to the current road condition; if the dispatch is carried out, the route centralized dispatch of the output route is preferentially selected, and the return route is output according to the current road condition.

In practical application, when the current logistics line is adjusted by using the path optimization model, the incomplete part of the current logistics line can be decoded by a genetic algorithm to obtain a dispatch address set, and the

above steps

502 and 503 are repeated to perform the optimization planning process of the path to obtain the current optimal logistics line.

Certainly, the output logistics route may also be evaluated by combining with the mathematical model, specifically, whether the logistics route meets the constraint condition in the mathematical model is judged, and in the process of judging whether the logistics route meets the constraint condition, the comparison consideration may be performed according to the priority in the constraint condition in the mathematical model, for example: and evaluating the conditions in the path according to the sequence of time window > cargo capacity > distance > cost, and directly outputting the corresponding logistics line after the evaluation is passed.

In this embodiment, if the constraint condition is set to include at least one of the following:

the freight volume constraint conditions of the corresponding distribution tasks under various journey levels;

vehicle quantity constraint conditions corresponding to different freight volumes;

the difference constraint condition of the linear distance between the distribution starting point and the distribution terminal point and the total distance of the distribution line is set;

deciding a uniqueness constraint condition of a variable in a distribution process;

a delivery time window constraint;

loading limit constraints on the distribution lines;

transportation cost constraints.

Further, the genetic algorithm is used for extracting gene individuals from incomplete partial lines in the current logistics line to obtain a plurality of dispatch addresses;

and sequentially encoding the extracted dispatch addresses and the dispatch addresses in the new order to form an initial solution, wherein the dispatch addresses in the initial solution and the dispatch addresses are in a neighborhood relationship.

And then, based on an ant colony algorithm, each dispatch address in the initial solution is respectively butted with a dispatch address where the service source is located, one dispatch address with the optimal probability is selected as a next dispatch address through a probability conversion rule, a distribution network between the next dispatch address and the current dispatch address is formed, and the optimal route of the route in the distribution network is traversed through simulated ants.

By implementing the method, the learning of path planning experience based on a learning method of a machine algorithm can be realized, the input of manual operation can be reduced, the matching degree of people and vehicles is improved, meanwhile, orders of network points in the line are monitored in real time to adjust the line in real time, the rescheduling of an operator is realized, the delivery logistics cost of express mails is reduced, the efficiency of picking and dispatching mails is improved, and the complaint of customers is avoided.

In this embodiment, in the process of logistics transportation, express information processed by an attendant may include two types, namely, a pickup type and a delivery type, the logistics route planning method provided by the present application may be used for planning a single delivery route, or for planning a pickup type route, or for performing a hybrid processing, as shown in fig. 6, a mixed service form is described with the delivery type as a main route and the pickup type as an auxiliary, and the processing steps specifically include:

601. acquiring an unprocessed delivery order which is distributed to the salesman by a logistics system on the same day according to the delivery departure time reported by the salesman;

602. extracting a delivery address and delivery time in the delivery order to obtain a delivery address set and a delivery time sequence;

603. obtaining vehicle information which can be allocated by an operator on an originating location according to a distribution originating location selected by the operator;

in the step, the acquired vehicle information comprises the number of vehicles which can be allocated, the maximum loading weight, the traveling time, the traveling distance and the like of each vehicle, in practical application, the vehicle specification and the historical traveling record of each vehicle which is called at the starting place are identified, the loading weight of the vehicle is determined based on the vehicle specification, the maximum traveling distance and the specified traveling time of the vehicle are calculated based on the historical traveling record, in the screening process, whether each vehicle can travel at the distribution time in the distribution order is judged according to the specified traveling time, so that a first vehicle set is selected, whether the starting time of a route and the time of returning to the distribution starting place are within the specified traveling time of the vehicle is judged, and if yes, the vehicle is determined to be the vehicle which can be called by a service provider.

And then further screening is carried out on the basis of the first vehicle set by using the loading weight, and finally, the final screening is carried out on the vehicle-shaped path, wherein in the final screening, whether the oil quantity reserved by the vehicle can complete the maximum path in the outward dispatching task or not can be determined on the basis of the relationship between the driving path and the oil consumption in the driving record of the vehicle, and the maximum path is the path from the dispatching starting address to the farthest dispatching task.

604. Traversing the distribution address set and the distribution time sequence by adopting a path optimization model based on the ant colony algorithm principle, and outputting an optimal distribution line;

in this step, as for the implementation of step 604, an initial route is created, whether the time from the vehicle to each dispatch address meets the delivery time sequence is calculated, if yes, the initial route is used as the basis for planning the delivery route, a path optimization model is used to perform traversal adjustment on each route in the initial route, specifically, the route between each two dispatch addresses is re-planned according to the ant colony traversal principle in the model, and certainly, the time to reach the dispatch address cannot be increased under the constraint condition of traversal planning, on this basis, the pheromone left by ant for vehicle traveling between two places is simulated to determine how to travel best, that is, the cost is low, the route is short, and the like, so as to output the final delivery route.

In this embodiment, the logistics order processing is not received and processed after reaching the task amount, but each network site receives a new order at any time, and at this time, real-time dispatch may not be completed because the salesman starts from the network or misses the network, and to solve this problem, this embodiment provides to schedule the salesman whose periphery is being dispatched to perform adjustment, specifically including the following steps:

605. the logistics route planning device monitors that a new delivery order is received by a network point and acquires the new delivery order;

606. acquiring current positioning information of a salesman;

specifically, the step is to call a GPS positioning system in the new logistics route planning device to traverse the card punching information recorded by the operator through the website where the new order is located, so as to determine whether a suitable operator is called, and if so, execute step 607.

607. Judging whether the positioning information is consistent with the position information of the distribution center;

608. if yes, executing and judging whether the new order information belongs to the order on the optimal logistics line;

and when the order belongs to the order on the optimal logistics line, embedding the new order information into the optimal logistics line according to the path optimization model, executing step 609, and otherwise, returning to step 606.

609. If not, selecting unprocessed dispatch addresses from the dispatch address set to form a second dispatch address set;

610. and planning a logistics route for the second dispatch address set by using the positioning information as the origin of the vehicle and utilizing the path optimization model.

By the steps, a route optimization model obtained by ant colony algorithm training is used for planning express delivery routes of operators, and the planning optimization of the route is realized through the algorithm model, so that the calculated route of picking up or dispatching can meet the matching degree between the operators and vehicles to the greatest extent due to the characteristics of self-organization, parallel calculation and positive feedback search, the distribution route is determined by adopting a scientific and reasonable method, the satisfaction degree of customers to logistics distribution service can be enhanced, the distribution cost of logistics enterprises is reduced, and the distribution efficiency is improved.

Meanwhile, the new order condition of each network point is detected in real time to call the service personnel to dispatch, so that the rescheduling strength of the service personnel is improved, the actual loading traffic of each vehicle is increased, the dispatching times of express are reduced, and the logistics cost is further reduced.

With reference to fig. 7, the method for planning a logistics route in an embodiment of the present invention is described above, and a logistics route planning apparatus in an embodiment of the present invention is described below, where the apparatus in an embodiment of the present invention includes:

the acquisition module 71 is configured to acquire express mail information to be processed by a current salesman, position information of a distribution center where the current salesman is located, and vehicle information of a vehicle currently available in the distribution center;

the analysis module 72 is configured to analyze all express mail information for which the service staff is responsible, obtain corresponding express mail order information and a dispatch address set, and number each dispatch address in the dispatch address set;

the model analysis module 73 is configured to use the vehicle information, the order information, and the dispatch address set as constraint conditions for logistics route planning, and calculate matching parameters between the service engineer and available vehicles by using a path optimization model, where the path optimization model is a model obtained by training logistics history processing information through an ant colony algorithm;

and the route generation module 74 is configured to plan an optimal logistics route corresponding to the salesman according to the matching parameters.

The device trains logistics history processing information classified according to the salesman by adopting an ant colony algorithm to obtain a path optimization model, corresponding express order information, express delivery addresses and distributable vehicle information are extracted from express information to be processed by the salesman in the path planning process, the information is used as constraint conditions for planning a path, matching parameters between the salesman and the vehicle information are calculated by using the path optimization model, the optimal path planning is carried out based on the matching parameters, the logistics line is optimized according to the relation between the vehicle information and the express processed by the salesman, the maximum loading of vehicles is realized, the matching degree of people and vehicles is improved, the efficiency of picking up and delivering is improved, and the logistics distribution cost of a single salesman is reduced.

Based on the same description of the embodiments as the logistics route planning method of the present invention, the contents of the embodiments of the logistics route planning apparatus are not described in detail in this embodiment.

Referring to fig. 8, in another embodiment of the logistics route planning apparatus in the embodiment of the present invention, the apparatus includes:

Wherein the model analysis module 73 comprises:

a counting unit 731, configured to count a total delivery amount of the order information to be processed by the salesman;

a selecting unit 732, configured to select at least one vehicle from the available vehicles according to the total delivery amount, where the vehicle meets a loading requirement, where the loading requirement includes that a maximum load of the selected vehicle is greater than or equal to the total delivery amount;

the analysis unit 733 is configured to traverse all the dispatch addresses in the dispatch address set by the at least one vehicle through the route optimization model with the distribution center as a starting place to obtain a plurality of logistics routes;

the calculating unit 734 is configured to calculate a unit distribution cost of each logistics route in the plurality of logistics routes, and determine a matching parameter between the salesman and the corresponding vehicle based on the unit distribution cost.

Optionally, the analysis unit 733 is specifically configured to:

Optionally, the logistics route planning apparatus further includes an optimization module 75, which is specifically configured to:

Wherein the optimization module 75 is further configured to:

acquiring the current positioning information of the operator;

In this embodiment, the logistics route planning apparatus further includes a model training module 76, specifically configured to:

In practical applications, fig. 7 to 8 describe the logistics route planning apparatus in the embodiment of the present invention in detail from the perspective of a modular functional entity, and describe the logistics route planning apparatus in the embodiment of the present invention in detail from the perspective of hardware processing.

Fig. 9 is a schematic structural diagram of an entity apparatus of a logistics route planning apparatus provided by the logistics route planning method of the present invention, where the logistics route planning apparatus 2000 may generate a relatively large difference according to an actual requirement, an actual configuration, or a difference in performance, and may include, for example, one or more processors (CPUs) 2010 (e.g., one or more processors) and a memory 2020, and one or more storage media 2030 (e.g., one or more mass storage devices) storing an application program 2033 or data 2032. The storage mode used by the memory 1020 and the storage medium 2030 may be a transient storage or a persistent storage. The program stored in the storage medium 2030 may include modules (not shown) of the functionality provided by one or more embodiments, and each module may include a series of instructions operating on the logistics route planning apparatus 2000. Further, the processor 2010 may be configured to communicate with the storage medium 2030, and execute a series of instruction operations in the storage medium 2030 on the logistics route planning apparatus 2000, where the series of instructions correspondingly implement the functions of the logistics route planning method provided in the foregoing embodiments.

The logistics route planning apparatus 2000 can also include one or more power supplies 2040, one or more wired or wireless network interfaces 2050, one or more input/output interfaces 2060, and/or one or more operating systems 2031, such as Windows server, Mac OS X, Unix, L inux, FreeBSD, etc. those skilled in the art will appreciate that the logistics route planning apparatus configuration shown in fig. 9 does not constitute the only limitation of the logistics route planning apparatus, and in practical applications it can also include more or less components than those shown, or combine some of the components, or a different arrangement of components.

The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and which may also be a volatile computer-readable storage medium, wherein a computer program (i.e., instructions) is stored in the computer-readable storage medium, and when the instructions are executed on a computer, the instructions cause the computer to execute the steps of the logistics route planning method, and optionally, the computer program is executed by a processor on the computer.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A logistics route planning method is characterized by comprising the following steps:

2. The method for planning a logistics route according to claim 1, wherein the calculating the matching parameters between the service engineer and the available vehicles by using the vehicle information, the order information and the dispatch address set as constraints for the logistics route planning and using a path optimization model comprises:

3. The method for planning logistics route according to claim 2, wherein the traversing the at least one vehicle through all dispatch addresses in the dispatch address set based on the path optimization model with the distribution center as origin to obtain a plurality of logistics routes comprises:

4. The logistics route planning method of claim 3, wherein said selecting one dispatch address from the dispatch address set as a first dispatch address based on the first vehicle comprises:

5. The logistics route planning method according to any one of claims 1 to 4, wherein after the planning of the optimal logistics route corresponding to the salesman according to the matching parameters, the method further comprises:

6. The logistics route planning method according to claim 5, after the periodically acquiring new order information of the network points passed by the optimal logistics route, before the determining whether the new order information belongs to the order on the optimal logistics route, further comprising:

acquiring the current positioning information of the operator;

7. The logistics route planning method of claim 1, wherein the logistics route planning method comprises training the path optimization model by:

8. A logistics route planning device, characterized in that, the logistics route planning device includes:

9. A logistics route planning apparatus, characterized in that the logistics route planning apparatus comprises: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;

the at least one processor invokes the instructions in the memory to cause the logistics route planning apparatus to perform the logistics route planning method of any one of claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements a logistics route planning method according to any one of claims 1-7.