CN113408775A - Logistics network-based routing planning method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to a method, a device, equipment and a storage medium for routing planning based on a logistics network, wherein the method comprises the following steps: determining the cargo flow of all demand pairs, generating a new routing pool according to a heuristic algorithm, combining the new routing pool and the historical routing pool to determine an integral router pool, wherein the combination of a first distribution center and a last distribution center through which packages pass is called as a demand pair, constructing a mixed integer programming model, and determining a target function; acquiring a demand pair when the value of the target function is minimum to generate a target network; and determining a target routing planning scheme aiming at the target network. Through effective network design, the cost and the timeliness of the whole route are optimized through vehicle arrangement, the loading rate of the vehicle is improved, and the idle return of the vehicle is reduced.
Description
Technical Field
The invention relates to the technical field of logistics distribution, in particular to a method, a device, equipment and a storage medium for routing planning based on a logistics network.
Background
At present, the routing planning is generally carried out by human decision in various large logistics industries, each center arranges the routing, and the cargo flow direction and the vehicle arrangement are arranged based on manual experience. In this case, consideration is given to a local small-scale vehicle plan, which leads to a problem that cost reduction can be achieved from local points, but also leads to cost increase in other associated areas.
In addition, in the related art, global overall planning is lacked, and the method belongs to experience accumulated by manpower, and at the same time, no real technical scheme for landing is technically seen.
Disclosure of Invention
In view of this, a routing planning method, device, equipment and storage medium based on a logistics network are provided to solve the problems of low overall efficiency, high routing cost and failure, low vehicle loading rate and empty return of vehicles in the related art.
The invention adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a routing planning method based on a logistics network, where the method includes:
determining the cargo flow of all demand pairs, generating a new routing pool according to a heuristic algorithm, combining the new routing pool and the historical routing pool to determine an overall router pool, wherein the combination of a first distribution center and a last distribution center through which a package passes is called a demand pair,
constructing a mixed integer programming model and determining a target function;
acquiring a demand pair when the value of the objective function is minimum to generate a target network;
and determining a target routing plan scheme aiming at the target network.
In a second aspect, an embodiment of the present application provides a routing planning apparatus based on a logistics network, where the apparatus includes:
the determining module is used for determining the cargo flow of all demand pairs, generating a new routing pool according to a heuristic algorithm, combining the new routing pool and the historical routing pool to determine an overall router pool, wherein the combination of a first distribution center and a last distribution center through which a package passes is called a demand pair,
the construction module is used for constructing a mixed integer programming model and determining a target function;
the target network generation module is used for acquiring a demand pair when the value of the target function is minimum so as to generate a target network;
and the planning module is used for determining a target routing planning scheme aiming at the target network.
In a third aspect, an embodiment of the present application provides an apparatus, including:
a processor, and a memory coupled to the processor;
the memory is configured to store a computer program, where the computer program is at least configured to execute the method for routing based on a logistics network according to the first aspect of the embodiment of the present application;
the processor is used for calling and executing the computer program in the memory.
In a fourth aspect, an embodiment of the present application provides a storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the route planning method based on a logistics network according to the first aspect are implemented.
By adopting the technical scheme, the goods flow scheme constructed by the data model is constructed based on a certain routing pool, the routing network is constructed by applying the heuristic algorithm, and the cost is optimized by designing the balance strategy. Through effective network design, the cost and the timeliness of the whole route are optimized through vehicle arrangement, the loading rate of the vehicle is improved, and the idle return of the vehicle is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a routing planning method based on a logistics network according to an embodiment of the present invention;
FIG. 2 is a visual effect diagram suitable for use in embodiments of the invention;
FIG. 3 is another visualization effect graph suitable for use in embodiments of the present invention;
FIG. 4 is a schematic diagram of a cost comparison applicable to embodiments of the present invention;
FIG. 5 is a schematic illustration of a comparison of turn numbers applicable in embodiments of the present invention;
fig. 6 is a schematic structural diagram of a routing planning apparatus based on a logistics network according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
Examples
Fig. 1 is a flowchart of a routing method based on a logistics network according to an embodiment of the present invention, where the method may be executed by a routing device based on a logistics network according to an embodiment of the present invention, and the device may be implemented in a software and/or hardware manner. Referring to fig. 1, the method may specifically include the following steps:
s101, determining the cargo flow of all demand pairs, generating a new routing pool according to a heuristic algorithm, and combining the new routing pool and the historical routing pool to determine an overall routing pool, wherein the combination of a first distribution center and a last distribution center through which a package passes is called a demand pair.
In general, a package will pass through at least two distribution centers after passing through a network point, one is an initial transit center for collecting the network point and the other is a destination transit center for the terminal delivery network point. The first hub that the package passes through is denoted by O, which denotes Origin, and the last hub is denoted by D, which denotes Destination. In addition, packages that are identical at the distribution center origin and destination are identical and merged into a stream of packages of OD pairs. Based on the amount of each center-to-center, it is first necessary to determine the walk of all OD pairs, which may also be referred to as the flow of OD pairs.
Optionally, determining the commodity flow of all demand pairs, generating a new routing pool according to a heuristic algorithm, and combining the new routing pool and the historical routing pool to determine the overall router pool, which may specifically be implemented in the following manner: the method comprises the steps that the whole logistics network is fully connected, and lines with the line cost larger than a set cost threshold value in the logistics network are removed, so that a first logistics network is obtained; judging the first logistics network according to the loading rate and the distance, searching by using a shortest path algorithm, and if the line cost is lower than the initial cost or meets the temperature rejection condition, rejecting the corresponding line until the goods flow meeting the requirement pair of the stable condition is obtained; determining a new routing pool comprising different demands for the cargo flow based on different parameters according to a heuristic algorithm; the new routing pool and the historical routing pool are merged to determine an overall router pool.
First, some basic assumptions are made that the capacity of the vehicle is determined by the number of parcels, the transfer fee is calculated as 0.33/ticket, the travel fee is determined by distance and model, and the vehicle is going back and forth between two centers.
Second, a mathematical description is defined: n is all centers, i belongs to N; d is the number of the demand from the center to the center of all the demands; dijRepresenting all the demand from center to center, so the total demand number is N (N-1), D belongs to D; k belongs to K and is all vehicle types, NkThe maximum vehicle number of the kth vehicle type;
cost from center i to j for kth model;
the p-th line from the center i to the center j;
representing the pool of routes through the two centers in all the routing lines in segments i through j.
Then, an algorithm description is made, taking into account
And one of the generation methods is based on actual lines which are run historically at present and based on some service logics, whether the time efficiency is met or not is mainly determined, a part of routing pools are obtained preferentially and are called historical routing pools, then, new routing pools are generated by using a heuristic algorithm, and the two parts are taken as an integral routing pool.
Specifically, a new routing pool is generated based on a heuristic algorithm and is realized by adopting simulated annealing. The general idea is as follows: first, the whole network is fully connected, i.e. all Rij＝DijAnd then continuously removing some high-cost directly-connected lines from the network, preferentially sorting according to loading rate and distance, judging the removed network, and searching for one line, wherein in the scheme, the shortest path algorithm dijkstra is mainly used for searching, once the shortest path algorithm dijkstra is changed, the cost is lower than that of the original network or the original directly-connected line is removed under the condition of receiving a certain temperature, and the steps are repeated to obtain a stable commodity flow of all demand pairs. Based on different parameters, a plurality of groups of such routing pools can be obtained, and then the routing pools are combined with the historical routing pools to obtain an overall routing pool for subsequent integer planning.
S102, constructing a mixed integer programming model and determining a target function.
First, the variables used are explained, FijRepresenting the flow from center i to j, i.e., all demand passing through, then
MaxFi，j＝Max(Fij，Fji) Represents the maximum flow of Pair to center i and center j; i isij∈[0，1]，Iji∈[0，1]Indicating whether Pair is the direction of flow that is greatest for center i and center j;
representing the number of k-th type vehicles arranged from center i to center j; miMaximum flow for each center; a transfer fee Q; m is a positive large number.
And then, explaining each constraint, wherein optionally, the constraint of the objective function comprises that each demand pair selects a unique line, the vehicle capacity of any center is larger than the maximum center pair flow, the quantity of each vehicle type is constrained, and each center processing capacity is constrained.
Representing that each OD demand selects a unique line;
means that the vehicle capacity scheduled for any center pair (i, j) is greater than the maximum center pair (i, j) traffic; fij≤MaxFi，j，Fji≤MaxFi，j；Fij+(1-Iij)M≤MaxFi，j，Fji+(1-Iji)M≥MaxFi，j；Iij+Iji＝1；
The quantity of each vehicle type is restricted, and different quantities of different vehicle types in actual service are considered; sigmajFij≤MiCentral processing power limitations.
Alternatively, the objective function includes the sum of the total transfer fee of the driving cost and the cargo amount, and can be expressed as
The model mainly assumes that the vehicle does not transit, but two pairs of forwarding routes are used, and in order to balance the balance of the forwarding flow as much as possible, a routing route with low forwarding cost is continuously searched. The model can continuously increase more service constraints in the follow-up process, such as the control of long-distance routing, the amount of traffic reaching the model can be distributed in a corresponding way, and the like.
And S103, counting the demand pairs with the minimum value to generate the target network.
S104, determining a target routing planning scheme aiming at the target network.
By adopting the technical scheme, the goods flow scheme constructed by the data model is constructed based on a certain routing pool, the routing network is constructed by applying the heuristic algorithm, and the cost is optimized by designing the balance strategy. Through effective network design, the cost and the timeliness of the whole route are optimized through vehicle arrangement, the loading rate of the vehicle is improved, and the idle return of the vehicle is reduced.
The embodiment of the application also has the following beneficial effects: the reality is continuously approached by continuously increasing the actual service constraint, and meanwhile, the landing property is considered, and some service constraints can be continuously increased in the generated routing pool to reduce or route the pool; the method can be conveniently applied to a plurality of other related fields related to vehicle scheduling by constructing a mathematical model for solving; the scheme can fix part of the goods flow and only optimize the goods flow in a local range, and a network is optimized in a targeted manner by combining services; the scheme can be conveniently combined with a subsequent vehicle dispatching plan.
In addition, the optimal scheme is obtained by combining the current routes and the freight flow generated by the heuristic algorithm through a mixed integer programming model, the current scheme is considered, and meanwhile, new freight flows can be generated, for the freight flow from 0 to 1, the landing is easy, and as the current freight flow can be greatly influenced due to certain shopping festivals in China, the flow can be planned in advance according to certain special solar terms, such as 11, 12, spring festival, and the like, then a freight flow arrangement can be obtained, and then the optimal scheme is considered together with the subsequent traffic flow and the scheduling plan.
On the basis of the above technical solution, the embodiment of the present application further includes: sequencing the flow of each line pair according to the target network, and determining the line pair to be corrected according to the set flow condition; and searching for the demands in the line pair to be corrected, judging whether all routing schemes included in the line pair increase corresponding vehicles after the demands are increased according to each demand, and replacing the line pair to be corrected to adjust the flow of the target network if the demands are not increased.
Based on the obtained target network, some unbalanced lines can be found out, specifically as follows: defining flow difference sequencing of the line pairs, and selecting a line section to be corrected based on a certain threshold value; sequentially searching related demands in the vehicle, removing a line AB to be deleted for each demand based on the current network, searching all lines of AC- > CB based on the current network, judging whether the existing vehicle is increased or not when the demand is increased, and replacing if the demand is not changed; and adjusting the network flow and returning to the previous step. This can reduce the routing cost further, but it can increase the transit cost and subsequent aging, so this solution is an optional step, looking at the traffic demand actually.
In order to make the technical solution of the present application easier to understand, a specific set of examples is described below. For comparison and actual reason difference, the algorithm effect is visualized. Fig. 2 shows a visualization effect diagram showing a route planning from the head of the starting point to the key point Hainan, and fig. 3 shows another visualization effect diagram showing a route planning from the head of the starting point to the key point Sanming. Referring to fig. 2 and 3, the left path is a path planned by the algorithm of the embodiment of the present application, the right path is a baseline route, and in fig. 3, different transit centers are respectively selected for transit.
Fig. 4 shows a schematic diagram of cost comparison, wherein by solving the mixed integer programming problem, the optimal vehicle distribution schemes corresponding to different routing schemes can be solved, and the lowest cost corresponding to the routing scheme can be obtained. The results show that the transfer cost is increased by 3.5%, the vehicle running cost is reduced by 19.7%, and the total cost is reduced by 13.0%. Fig. 5 shows a transit time comparison diagram, in which the weighted average transit time of the existing route by the number of packages is 2.27, and the algorithm route is 2.28. The straight OD pairs and the transit OD pairs in the routing of the algorithm are increased, and the transit OD pairs are obviously reduced. Table 1 shows a transit center traffic comparison table, where due to a change in routing, the number of packages relayed in each center changes correspondingly, that is, the importance of each center in the entire routing network changes. For example, the parcel quantity in Zhengzhou transit is increased by 3 times compared with the current parcel quantity in Guangzhou, which is only 0.73.
The whole process of the embodiment of the present application is summarized as follows:
firstly, based on the existing goods quantity from each center to each center, the routing method of the goods flow is determined, and the complexity of the model is considered at present, so that the goods flow of each center-center is determined to be unique, and the method has the advantages that the arrangement of vehicle gates can be well regulated by operators in actual lines, and the possibility of misdistribution is reduced. The routing cost of the OD requirement is lower from the theory.
The solution for the cargo flow-away method is roughly as follows: based on a historical existing cargo flow pool, cargo flows generated by combining a heuristic algorithm are put into a mixed integer programming model together for solving, the goal is to determine the only routing method required by each OD, so that the overall cost is as low as possible, some business constraints are considered to be added into the model at the same time, the flow of the whole network is determined after the cargo flows are determined, then the vehicle flows are designed, namely, the second step, the design of the vehicle flows considers that the actual vehicles need to return, the current reality shows that the cargos from the center to the center are not equal, and the situation that the back-and-forth pieces similar to Guangzhou, Ivy and Humen are not equal needs to be solved by designing a reasonable vehicle loop line.
Briefly described as follows: a plurality of vehicle rings are generated on the basis of a network, then the optimized vehicle rings are solved by establishing a mathematical model, and then a specific vehicle plan is solved in a modeling mode in the determined vehicle rings, namely, OD cargo flows are carried and connected by specific vehicles. And finally, designing the departure time of each vehicle based on the existing vehicle route and routing arrangement, and aiming at optimizing the time efficiency of the whole cargo. The scheme mainly relates to a heuristic algorithm and mixed integer optimization from an algorithm level, and converts business data into mathematical description, and is mainly realized through python and cplex.
Fig. 6 is a schematic structural diagram of a routing planning apparatus based on a logistics network according to an embodiment of the present invention, where the apparatus is suitable for executing a routing planning method based on a logistics network according to an embodiment of the present invention. As shown in fig. 6, the apparatus may specifically include: a determination module 601, a construction module 602, a target network generation module 603, and a planning module 604.
The determining module 601 is configured to determine the commodity flow of all demand pairs, generate a new routing pool according to a heuristic algorithm, and merge the new routing pool and the historical routing pool to determine an overall router pool, where a combination of a first distribution center and a last distribution center through which a package passes is referred to as a demand pair; a building module 602, configured to build a mixed integer programming model and determine an objective function; a target network generating module 603, configured to obtain a requirement pair when a value of the target function is minimum, so as to generate a target network; a planning module 604, configured to determine a target routing plan for the target network.
By adopting the technical scheme, the goods flow scheme constructed by the data model is constructed based on a certain routing pool, the routing network is constructed by applying the heuristic algorithm, and the cost is optimized by designing the balance strategy. Through effective network design, the cost and the timeliness of the whole route are optimized through vehicle arrangement, the loading rate of the vehicle is improved, and the idle return of the vehicle is reduced.
Optionally, the determining module 601 is specifically configured to:
the method comprises the steps that the whole logistics network is fully connected, and lines with the line cost larger than a set cost threshold value in the logistics network are removed, so that a first logistics network is obtained;
judging the first logistics network according to the loading rate and the distance, searching by using a shortest path algorithm, and if the line cost is lower than the initial cost or meets the temperature rejection condition, rejecting the corresponding line until the goods flow meeting the requirement pair of the stable condition is obtained;
determining a new routing pool comprising different demands for the cargo flow based on different parameters according to a heuristic algorithm;
the new routing pool and the historical routing pool are merged to determine an overall router pool.
Optionally, the system further comprises an optimization module, configured to:
sequencing the flow of each line pair according to the target network, and determining the line pair to be corrected according to the set flow condition;
and searching for the demands in the line pair to be corrected, judging whether all routing schemes included in the line pair increase corresponding vehicles after the demands are increased according to each demand, and replacing the line pair to be corrected to adjust the flow of the target network if the demands are not increased.
Optionally, the vehicles in the mixed integer programming model are paired and do not transit.
Optionally, the constraints of the objective function include that each demand pair selects a unique route, the vehicle capacity of any center is greater than the maximum center pair flow, the number of each vehicle type is constrained, and the processing capacity of each center is constrained.
The routing planning device based on the logistics network provided by the embodiment of the invention can execute the routing planning method based on the logistics network provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
An embodiment of the present invention further provides an apparatus, please refer to fig. 7, fig. 7 is a schematic structural diagram of an apparatus, as shown in fig. 7, the apparatus includes: a processor 710, and a memory 720 coupled to the processor 710; the memory 720 is used for storing a computer program, which is at least used for executing the logistics network-based route planning method in the embodiment of the invention; a processor 710 for invoking and executing the computer program in the memory; the routing planning method based on the logistics network at least comprises the following steps: determining the cargo flow of all demand pairs, generating a new routing pool according to a heuristic algorithm, combining the new routing pool and the historical routing pool to determine an integral router pool, wherein the combination of a first distribution center and a last distribution center through which packages pass is called as a demand pair, constructing a mixed integer programming model, and determining a target function; acquiring a demand pair when the value of the target function is minimum to generate a target network; and determining a target routing planning scheme aiming at the target network.
The embodiment of the present invention further provides a storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, the method implements the following steps in the routing planning method based on the logistics network in the embodiment of the present invention: determining the cargo flow of all demand pairs, generating a new routing pool according to a heuristic algorithm, combining the new routing pool and the historical routing pool to determine an integral router pool, wherein the combination of a first distribution center and a last distribution center through which packages pass is called as a demand pair, constructing a mixed integer programming model, and determining a target function; acquiring a demand pair when the value of the target function is minimum to generate a target network; and determining a target routing planning scheme aiming at the target network.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A routing planning method based on a logistics network is characterized by comprising the following steps:
determining the cargo flow of all demand pairs, generating a new routing pool according to a heuristic algorithm, combining the new routing pool and the historical routing pool to determine an overall router pool, wherein the combination of a first distribution center and a last distribution center through which a package passes is called a demand pair,
constructing a mixed integer programming model and determining a target function;
acquiring a demand pair when the value of the objective function is minimum to generate a target network;
and determining a target routing plan scheme aiming at the target network.
2. The method of claim 1, wherein determining the flow of all demand pairs, generating a new routing pool according to a heuristic algorithm, and merging the new routing pool and the historical routing pool to determine an overall router pool comprises:
the method comprises the steps that the whole logistics network is fully connected, and lines with the line cost larger than a set cost threshold value in the logistics network are removed, so that a first logistics network is obtained;
judging the first logistics network according to the loading rate and the distance, searching by using a shortest path algorithm, and if the line cost is lower than the initial cost or meets the temperature removal condition, removing the corresponding line until the goods flow meeting the requirement pair of the stable condition is obtained;
determining a new routing pool comprising different demands for the cargo flow based on different parameters according to a heuristic algorithm;
and merging the new routing pool and the historical routing pool to determine an overall router pool.
3. The method of claim 1, further comprising:
sequencing the flow of each line pair according to the target network, and determining the line pair to be corrected according to a set flow condition;
and searching the demands in the line pair to be corrected, judging whether all routing schemes included in the line pair increase corresponding vehicles after the demands are increased according to each demand, and replacing the line pair to be corrected to adjust the flow of the target network if the demands are not increased.
4. The method of claim 1, wherein the vehicles in the mixed integer programming model are paired and do not transit.
5. The method of claim 1, wherein the constraints of the objective function include a selection of a unique route per demand pair, a vehicle capacity at any center greater than a maximum center pair flow, a number of vehicles per model constraint, and individual center processing capacity constraints.
6. A routing planning device based on a logistics network is characterized by comprising:
the determining module is used for determining the cargo flow of all demand pairs, generating a new routing pool according to a heuristic algorithm, combining the new routing pool and the historical routing pool to determine an overall router pool, wherein the combination of a first distribution center and a last distribution center through which a package passes is called a demand pair,
the construction module is used for constructing a mixed integer programming model and determining a target function;
the target network generation module is used for acquiring a demand pair when the value of the target function is minimum so as to generate a target network;
and the planning module is used for determining a target routing planning scheme aiming at the target network.
7. The apparatus of claim 6, wherein the determining module is specifically configured to:
the method comprises the steps that the whole logistics network is fully connected, and lines with the line cost larger than a set cost threshold value in the logistics network are removed, so that a first logistics network is obtained;
judging the first logistics network according to the loading rate and the distance, searching by using a shortest path algorithm, and if the line cost is lower than the initial cost or meets the temperature removal condition, removing the corresponding line until the goods flow meeting the requirement pair of the stable condition is obtained;
determining a new routing pool comprising different demands for the cargo flow based on different parameters according to a heuristic algorithm;
and merging the new routing pool and the historical routing pool to determine an overall router pool.
8. The apparatus of claim 6, further comprising an optimization module to:
sequencing the flow of each line pair according to the target network, and determining the line pair to be corrected according to a set flow condition;
and searching the demands in the line pair to be corrected, judging whether all routing schemes included in the line pair increase corresponding vehicles after the demands are increased according to each demand, and replacing the line pair to be corrected to adjust the flow of the target network if the demands are not increased.
9. An apparatus, comprising:
a processor, and a memory coupled to the processor;
the memory is configured to store a computer program for performing at least the method of logistics network based route planning of any of claims 1-5;
the processor is used for calling and executing the computer program in the memory.
10. A storage medium, characterized in that the storage medium stores a computer program, which when executed by a processor, implements the steps of the logistics network based routing method of any one of claims 1-5.
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