CN116402432B - Route planning method, device, equipment and storage medium - Google Patents

Route planning method, device, equipment and storage medium Download PDF

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Publication number
CN116402432B
CN116402432B CN202310678504.8A CN202310678504A CN116402432B CN 116402432 B CN116402432 B CN 116402432B CN 202310678504 A CN202310678504 A CN 202310678504A CN 116402432 B CN116402432 B CN 116402432B
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feasible
paths
path
orders
relaxation
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CN116402432A (en
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安义丹
严良
庄晓天
吴盛楠
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Beijing Jingdong Qianshi Technology Co Ltd
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Beijing Jingdong Qianshi Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0835—Relationships between shipper or supplier and carriers
    • G06Q10/08355—Routing methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The disclosure provides a route planning method, a device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the steps of acquiring static network structure data and order data of a plurality of orders; based on the static network structure data and the order data, obtaining initial feasible paths of a plurality of orders; screening the feasible paths to be processed of the relaxation main problem model, and carrying out iterative solution on the feasible paths to be processed based on the relaxation main problem model to obtain optimal paths of a plurality of orders; and generating a vehicle dispatching strategy corresponding to the optimal path. The method and the device can reduce the number of model variables, reduce the model solving scale and improve the overall solving efficiency of the model.

Description

Route planning method, device, equipment and storage medium
Technical Field
The disclosure relates to the field of computer technology, and in particular, to a route planning method, a route planning device, electronic equipment and a computer readable storage medium.
Background
In current logistics networks, there may be multiple orders at the same time, which need to be sent from the origin to the destination corresponding to the order by means of existing road network capacity resources. How to arrange a reasonable transportation path for each order such that the total cost is minimized or the performance timeliness is minimized is known as a routing problem.
In the related art, when solving the routing problem, a historical empirical manual index, an accurate algorithm, or a genetic algorithm, an ant colony algorithm, a neighborhood search and other meta heuristic algorithms are generally directly used. However, the above routing scheme has the problems of low efficiency, long time consumption and failure to obtain the optimal solution of the routing scheme problem in a short time.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure provides a route planning, a device, an apparatus, and a storage medium, which at least overcome to some extent the problems of low efficiency, long time consumption, and failure to obtain an optimal solution of a route planning problem in a short time in the related art.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to one aspect of the present disclosure, there is provided a route planning method including: acquiring static network structure data and order data of a plurality of orders; based on the static network structure data and the order data, obtaining initial feasible paths of the orders; screening a feasible path to be processed of a relaxation main problem model, and carrying out iterative solution on the feasible path to be processed based on the relaxation main problem model to obtain optimal paths of the orders; and generating a vehicle dispatching strategy corresponding to the optimal path.
In one embodiment of the disclosure, the obtaining the initial viable paths for the plurality of orders based on the static network structure data and the order data includes: acquiring a feasible path set of the orders based on the static network structure data and the order data; searching the feasible paths meeting preset conditions from the feasible path sets of the orders to obtain initial feasible paths of the orders.
In one embodiment of the disclosure, the acquiring the feasible path set of the plurality of orders based on the static network structure data and the order data includes: and determining routing nodes between the initial sorting center and the target sorting center of each order by adopting a dynamic programming algorithm based on the static network structure data to obtain a feasible path set of the orders, wherein one order corresponds to one initial sorting center and one target sorting center.
In one embodiment of the disclosure, the searching the feasible paths meeting the preset condition from the feasible path sets of the orders to obtain the initial feasible paths of the orders includes: and selecting a feasible path with the lowest cost for each order from the feasible path sets of the orders, and obtaining an intermediate feasible path set of the orders.
In one embodiment of the present disclosure, the method further comprises: iteratively adjusting the feasible paths in the middle feasible path set based on a neighborhood search algorithm, and determining the feasible path with the lowest total cost of the network; if the iteration times and/or the search time meet the preset stop conditions, ending the search, and taking the feasible path with the lowest total cost of the iterated network as the initial feasible path of the orders; if the iteration times or the search time do not meet the preset stop conditions, continuing to iteratively adjust the feasible paths in the middle feasible path set, and determining the feasible path with the lowest total cost of the network until the iteration times and/or the search time meet the preset stop conditions.
In one embodiment of the present disclosure, the screening the feasible paths to be processed of the relaxation master problem model, and iteratively solving the feasible paths to be processed based on the relaxation master problem model, to obtain optimal paths of the multiple orders, includes: at least one feasible path in the initial feasible paths is used as a feasible path to be processed, a relaxation main problem model is input for solving, and the number of the test of the rest feasible paths in the initial feasible paths is calculated; and if the feasible paths in the initial feasible paths are all input into the relaxation main problem model, and/or the number of the check numbers of the rest feasible paths is greater than or equal to a preset check number threshold value, judging that the solving result of the relaxation main problem model reaches the optimal, and determining the optimal paths of the orders according to the solving result.
In one embodiment of the present disclosure, the method further comprises: if at least one feasible path in the initial feasible paths does not input a relaxation main problem model, or the check number of at least one feasible path in the rest feasible paths is smaller than a preset check number threshold value, generating a base column of at least one feasible path with the check number smaller than the preset check number threshold value, and adding the base column into the to-be-processed feasible paths; and inputting the feasible paths to be processed after the base entering into the relaxation main problem model for iterative solution until the feasible paths in the initial feasible paths are all input into the relaxation main problem model, and/or the verification number of the rest feasible paths in the initial feasible paths is greater than or equal to a preset verification number threshold value.
In one embodiment of the present disclosure, the relaxed master problem model is constructed by: constructing a transportation cost objective function based on the flow direction decision variables; constructing a sorting cost objective function based on the line decision variables; constructing a minimization cost objective function according to the transportation cost objective function and the sorting cost objective function; based on the flow direction decision variables and the line decision variables, constraints of the minimized cost objective function are constructed.
In one embodiment of the present disclosure, the constraint conditions that minimize the cost objective function include at least one of a flow direction route unique constraint, a capacity limit constraint, a line open standard limit constraint, and a total number of vehicles limit constraint.
In one embodiment of the present disclosure, the vehicle scheduling policy includes a route number schedule for the optimal path and a transportation path for the plurality of orders; the generating the vehicle scheduling policy corresponding to the optimal path includes: obtaining transportation paths of the orders according to the value of the corresponding flow direction decision variable when the solving result of the relaxation main problem model reaches the optimal value; obtaining the line train number scheduling of the optimal path according to the value of the corresponding line decision variable when the solving result reaches the optimal; and outputting the transportation paths of the orders and the line train number scheduling of the optimal path to obtain a vehicle scheduling strategy corresponding to the optimal path.
In one embodiment of the present disclosure, the static network structure data includes one or more of shift data, line data, sort data, load data, vehicle data; the order data for each order includes one or more of order type, order start, order end, order aging requirements.
In another aspect of the present disclosure, there is also provided a route planning apparatus including: the data acquisition module is used for acquiring static network structure data and order data of a plurality of orders; the path acquisition module is used for acquiring initial feasible paths of a plurality of orders based on the static network structure data and the order data; the optimal path determining module is used for screening the feasible paths to be processed of the relaxation main problem model, and carrying out iterative solution on the feasible paths to be processed based on the relaxation main problem model to obtain optimal paths of the orders; and the strategy generation module is used for generating a vehicle dispatching strategy corresponding to the optimal path.
According to another aspect of the present disclosure, there is provided an electronic device including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the route planning method described above via execution of the executable instructions.
According to another aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described route planning method.
According to yet another aspect of the present disclosure, there is provided a computer program product comprising a computer program or computer instructions loaded and executed by a processor to cause the computer to implement the above-described route planning method.
In an embodiment of the disclosure, static network structure data and order data of a plurality of orders are acquired; based on the static network structure data and the order data, obtaining initial feasible paths of a plurality of orders; screening the feasible paths to be processed of the relaxation main problem model, and carrying out iterative solution on the feasible paths to be processed based on the relaxation main problem model to obtain optimal paths of a plurality of orders; and generating a vehicle scheduling strategy corresponding to the optimal path, screening the feasible paths to be processed of the relaxation main problem model through a column generation algorithm before solving the relaxation main problem model, deleting redundant paths, avoiding adding all the initial feasible paths into the relaxation main problem model to solve and calculate, and taking only part of feasible paths which can be taken into consideration, thereby reducing the number of model variables, reducing the model solving scale and improving the overall model solving efficiency.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
Fig. 1 shows a flow chart of a route planning method provided by an embodiment of the present disclosure.
Fig. 2 shows a flowchart of another route planning method provided by an embodiment of the present disclosure.
FIG. 3 illustrates a flow chart of an initial viable path determination method for a plurality of orders provided by embodiments of the present disclosure.
FIG. 4 illustrates a flow chart of a method for optimal path determination for multiple orders provided by embodiments of the present disclosure.
Fig. 5 shows a flowchart of a vehicle scheduling policy generation method provided by an embodiment of the present disclosure.
Fig. 6 shows a flowchart of a specific example of a route planning method provided by an embodiment of the present disclosure.
Fig. 7 shows a schematic structural diagram of a routing device provided in an embodiment of the present disclosure.
Fig. 8 shows a route planning system architecture diagram provided by an embodiment of the present disclosure.
Fig. 9 shows a block diagram of an electronic device provided by an embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
In the related art, when solving the routing problem, most logistics enterprises often directly use historical experience manual assignment, accurate algorithms (such as a business solver Gurobi and the like), or adopt genetic algorithms, ant colony algorithms, neighborhood search and other meta-heuristic algorithms.
Conventional logistics enterprises often adopt a manual assignment mode to conduct routing planning, order routing is arranged by means of historical experience, and small sites are usually hub sites transported to the province or the local area and then are transported to the hub sites of the target area, and then are transferred to the final target sites. However, manual assignment is strongly dependent on historical experience of personnel, and multiple routes cannot be considered simultaneously, resulting in lower overall efficiency.
For the heuristic algorithm, the multiple calculation results of the same heuristic algorithm are inconsistent, an optimal solution cannot be obtained, and a gap between the current solution and the optimal solution cannot be obtained, so that the accuracy of route planning is reduced.
For an accurate algorithm, an edge-based mathematical model is often constructed, the model is large in scale and comprises a large number of redundant variables and complex constraint conditions, when a solver is directly used for solving, the solving speed is slow, the time consumption is long, and the flexible and changeable requirements of a solution cannot be met by a time-limited quick output solution.
Therefore, how to design a reasonable and efficient route planning method to obtain an optimal solution of the route planning problem in a short time becomes a technical problem to be solved.
Based on the above, the technical solution provided by the embodiments of the present disclosure obtains static network structure data and order data of a plurality of orders; based on the static network structure data and the order data, obtaining initial feasible paths of a plurality of orders; screening the feasible paths to be processed of the relaxation main problem model, and carrying out iterative solution on the feasible paths to be processed based on the relaxation main problem model to obtain optimal paths of a plurality of orders; and generating a vehicle scheduling strategy corresponding to the optimal path, screening the feasible paths to be processed of the relaxation main problem model through a column generation algorithm before solving the relaxation main problem model, deleting redundant paths, avoiding adding all the initial feasible paths into the relaxation main problem model to solve and calculate, and taking only part of feasible paths which can be taken into consideration, thereby reducing the number of model variables, reducing the model solving scale and improving the overall model solving efficiency.
It should be noted that the embodiments of the present invention and the technical features in the embodiments may be combined with each other without collision.
The present exemplary embodiment will be described in detail below with reference to the accompanying drawings and examples.
First, a route planning method is provided in the embodiments of the present disclosure, and the method may be performed by any system having computing processing capability. In some embodiments, the route planning method provided in the embodiments of the present disclosure may be performed by a server; in other embodiments, the route planning method provided by the embodiments of the present disclosure may be implemented by the terminal device and the server in an interactive manner.
Fig. 1 shows a flow chart of a route planning method in an embodiment of the present disclosure, as shown in fig. 1, the route planning method provided in the embodiment of the present disclosure includes the following steps:
s102, acquiring static network structure data and order data of a plurality of orders.
It should be noted that the static network structure data includes one or more of shift data, line data, sorting data, loading data, and vehicle data.
Wherein the shift data includes, but is not limited to, a start time, a latest arrival time, an end time, etc. of the sorting center shift. Line data includes, but is not limited to, start point, departure time, end point, arrival time, etc. of the line. The sorting data includes location information of the sorting center such as the area where the sorting center is located, the city where the sorting center is located, the longitude, the latitude, and the like. The load data includes load codes on the lines, the load codes being a coding scheme for representing a set of dots. The vehicle data includes a vehicle type, a maximum bearing capacity, a unit cost, and the like.
For example, in the static network structure, a total of A, B, C, D sorting centers are included, each sorting center is divided into three shifts, the duration of each shift is 8 hours, the starting time and the ending time are set according to practical situations, for example, the three shifts are respectively early shift, middle shift and late shift, and the early shift is 8:00-16:00, the starting time and the ending time of the middle shift and the late shift are sequentially carried forward. The method comprises the steps that a sorting center A sequentially passes through a sorting center B and a sorting center C to obtain line data of the sorting center D, the starting point of the line is the sorting center A, the ending point of the line is the sorting center D, wherein the departure time of the sorting center A is 18:00, the arrival time of the line to the sorting center B is 1:00 on the next day, the departure time of the line to the sorting center B is 10:00 after goods are sorted by the sorting center B, the arrival time of the line to the sorting center C is 17:00, the departure time of the line to the sorting center C is 20:00 after goods are sorted by the sorting center C, and the arrival time of the line to the sorting center D is 4:00 on the next day, so that the timeliness of the line can be calculated according to the line data.
The order data of each order includes one or more of order type, order start point, order end point, order aging requirement.
S104, obtaining initial feasible paths of a plurality of orders based on the static network structure data and the order data.
In one embodiment, each order corresponds to at least one initial feasible route, the initial feasible routes are obtained by searching a network structure network formed by static network structure data based on an order starting point and an order ending point, and the initial feasible routes of a plurality of orders can ensure that the total cost of the network is minimum.
In practice, a static tandem system may be used to obtain a set of viable paths for multiple orders based on a network architecture network. In detail, a dynamic programming algorithm can be used for calculation to obtain all feasible paths of each order, namely a feasible path set of a plurality of orders can be obtained; and then, processing the feasible path set by using a heuristic algorithm combining a greedy algorithm and neighborhood search to obtain initial feasible paths of a plurality of orders.
It should be noted that, the feasible path set or the initial feasible path acquisition mode may also use other search methods or heuristic algorithms, which is not specifically limited in this disclosure.
S106, screening the feasible paths to be processed of the relaxation main problem model, and carrying out iterative solution on the feasible paths to be processed based on the relaxation main problem model to obtain optimal paths of a plurality of orders.
The present disclosure employs a column generation algorithm to screen the feasible paths to be processed of the relaxed master problem model. The column generation algorithm (Column Generation Algorithm) is an improvement over the simplex method in that only one variable base is involved per iteration in the iterative process of determining the base-in and base-out variables, and fewer partial variables are involved in the overall solution process. And converting each target into a non-base variable input base with negative check number by removing a column generation algorithm, deleting redundant paths, avoiding adding all paths into a relaxation main problem model to solve and calculate, only taking the initial feasible paths of part of the workable bases into consideration, reducing the number of decision variables of the relaxation main problem model, reducing the model solving scale, and greatly improving the overall model solving efficiency.
A planning problem requiring that some or all of the decision variables must take integer values is called integer planning (Integer Programming, IP). The Problem of the programming consisting of the remaining objective functions and constraints is called the relaxation Problem (Slack Problem) of the integer programming Problem, irrespective of the integer condition, and if the relaxation Problem is a linear programming, the integer programming is called the integer linear programming (Integer Linear Programming).
The present disclosure defines a routing problem as a relaxation problem to solve. In the actual implementation process, a Dantzig-Wolfe decomposition algorithm is adopted to solve the relaxation problem, the Dantzig-Wolfe divides the constraint condition into two parts, a relaxation main problem model based on a path is constructed, and a check number formula is constructed based on a simplex method. In the process of solving the relaxation problem, solving an initial feasible path input into a relaxation main problem model, calculating a check number of the initial feasible path which is not added into the relaxation main problem model, judging whether the initial feasible path reaches the optimal or not according to a solving result, or generating an entry list and adding the initial feasible path into the relaxation main problem model, further completing one simplex iteration of the relaxation main problem, and continuously carrying out loop iteration until an iteration stopping condition is reached, so as to obtain the optimal paths of a plurality of orders.
Wherein the iteration stop condition may be defined by a number of tests and/or optimality of the solution result.
One base-entering column corresponds to one initial feasible path, one input initial feasible path is newly added in the relaxation main problem model, decision variables are increased, and the calculation difficulty is reduced by gradually increasing the number of the feasible paths for inputting the relaxation main problem model.
S108, generating a vehicle dispatching strategy corresponding to the optimal path.
The vehicle scheduling strategy includes a line number scheduling of the transportation path and the optimal path of the plurality of orders. The transportation path of each order is at least one section of the optimal path and can be the same as the optimal path, and the transportation path comprises information such as a starting sorting center, a routing sorting center, a destination sorting center and the like of goods transportation, wherein the routing sorting center can be omitted in one transportation path; at least one route sorting center may also be included.
The route train number scheduling of the optimal path is used for determining the type, the cargo carrying capacity, the maximum cargo carrying capacity, the departure time, the arrival time and the like of the cargo carrying vehicles on the optimal path.
It should be noted that, the scheduling of the number of line vehicles of the transportation paths and the optimal paths of the orders is determined according to the values of the corresponding decision variables when the solving result reaches the optimal.
In an embodiment of the disclosure, static network structure data and order data of a plurality of orders are acquired; based on the static network structure data and the order data, obtaining initial feasible paths of a plurality of orders; screening the feasible paths to be processed of the relaxation main problem model, and carrying out iterative solution on the feasible paths to be processed based on the relaxation main problem model to obtain optimal paths of a plurality of orders; and generating a vehicle scheduling strategy corresponding to the optimal path, screening the feasible paths to be processed of the relaxation main problem model through a column generation algorithm before solving the relaxation main problem model, deleting redundant paths, avoiding adding all the initial feasible paths into the relaxation main problem model to solve and calculate, and taking only part of feasible paths which can be taken into consideration, thereby reducing the number of model variables, reducing the model solving scale and improving the overall model solving efficiency.
Fig. 2 illustrates another route planning method provided in an embodiment of the present disclosure. Based on the embodiment of fig. 1, S104 is further refined to S1042 to S1044, so as to define the acquisition modes of the initial feasible paths of the multiple orders. As shown in fig. 2, in one embodiment, the route planning method provided in the embodiment of the disclosure includes S102, S1042 to S1044, and S106 to S108. In detail, the method includes:
s1042, acquiring a feasible path set of a plurality of orders based on static network structure data and order data;
s1044, searching a feasible path meeting preset conditions from a feasible path set of a plurality of orders to obtain a plurality of initial feasible paths.
It should be noted that, the implementation manners of S102, S106 to S108 are the same as the specific implementation manners of S102, S106 to S108 in the foregoing embodiments, and are not repeated here.
In one embodiment, S1042 determines routing nodes between the initial sorting center and the destination sorting center of each order by using a dynamic planning algorithm based on the static network structure data, so as to obtain a feasible path set of a plurality of orders, where one order corresponds to one initial sorting center and one destination sorting center.
For all feasible paths for each order, the present disclosure calculates using a dynamic programming algorithm that can be used to solve a problem with some optimal property to find a solution of the optimal value from a plurality of feasible solutions of the problem. Dynamic programming is effective in finding the optimal solution for the case where there are many overlapping sub-problems, and the problems are recombined into sub-problems. To avoid solving these sub-problems multiple times, their results are gradually computed and saved, ranging from simple problems to the whole problem being solved. Thus, dynamic programming preserves the results of recursion and thus does not take unnecessary time in solving the same problem.
Dynamic programming can only be applied to problems with optimal substructures. The optimal substructure means that the locally optimal solution can determine the globally optimal solution (this requirement for some problems is not fully met, so a certain approximation needs to be introduced at times). In short, it can be applied to a problem that can be resolved into sub-problems.
Assuming that all sorting centers to be accessed are waiting_nodes, waiting_nodes are empty sets, the current order starting sorting center is start_nodes, and the current order destination sorting center is end_nodes, the specific implementation process is as follows:
The first step: adding the starting sorting centers start_nodes of orders into the waiting-to-be-accessed sorting centers waiting_nodes, wherein one order corresponds to one starting sorting center;
and a second step of: randomly selecting an initial sorting center from the sorting centers to be accessed, namely the waiting_nodes, and taking the initial sorting center as a current node;
and a third step of: based on a preset network structure diagram, selecting a subsequent path of the current node to obtain a route node corresponding to the current node and a current path formed by the current node to the route node;
fourth step: updating the routing node into a current node, updating a current path, and adding the updated current node (the routing node before updating) into a sorting center to be accessed;
fifth step: if the updated current node is the target sorting center, adding the current path to a feasible path set;
sixth step: repeating the third step and the fourth step until all subsequent lines of the current node in the second step are traversed, and obtaining a feasible path set corresponding to the initial sorting center;
seventh step: repeating the second step to the fifth step until the waiting sorting center waiting_nodes are empty sets, obtaining feasible path sets of a plurality of orders, and outputting the feasible path sets.
In order to facilitate understanding of the above embodiments, a description will be given below with reference to specific examples.
For example: for a simple network, four mesh points are included, A, B, C, D. The circuit is as follows: A-C, A-D, B-D, C-B, C-D. The origin of the order is a and the destination is D. The acquisition steps of the feasible path set are as follows:
1. the node set to be accessed is [ A ];
2. the candidate line is A-C, A-B;
3. selecting an A-B line, wherein B is a routing node;
4. updating the current node to be [ B ], wherein the current path is [ A-B ];
5. candidate lines are B-D;
6. B-D is selected, and the routing node is D;
7. updating the routing node D into a current node [ D ], wherein the current path is [ A-B-D ];
8. the current node D is a target sorting center, and the feasible route set is [ A-B-D ];
9. returning to 5, checking whether other lines exist from the B, and temporarily stopping;
10. returning to 2, checking whether other candidate lines exist, and if so, A-C exists, and continuing to execute 3-9 steps, so that a feasible route set corresponding to the A-C line can be determined.
It should be noted that, the search mode of a-C is the same as a-B, and will not be described here again. Similarly, the method for obtaining the feasible route set of each order and the situation of networks with different network points are similar to the above process, and will not be repeated.
The preset conditions in S1044 are used to screen the path with the lowest cost corresponding to each order and the path with the lowest total cost of the network corresponding to the plurality of orders, and may be pre-configured in the route planning device, and the form, the value, and the like of the preset conditions may be determined according to the actual situation.
According to the route planning method provided by the embodiment of the disclosure, the feasible path sets of a plurality of orders are obtained based on the static network structure data and the order data, and the feasible paths meeting the preset conditions are searched from the feasible path sets to obtain a plurality of initial feasible paths, so that the path combination with the lowest total cost of the network is obtained through screening, the data quantity of the input relaxation main problem model is reduced, and the calculation efficiency is improved.
FIG. 3 illustrates a flow chart of an initial viable path determination method for a plurality of orders provided by embodiments of the present disclosure. In one embodiment, S1044 may be further refined to S302 to define a case of screening the feasible paths with the lowest cost for each order. As shown in fig. 3, S1044 searches for a feasible path satisfying a preset condition from a feasible path set of a plurality of orders, to obtain an initial feasible path of the plurality of orders, including:
S302, selecting a feasible path with the lowest cost for each order from the feasible path sets of the orders to obtain a middle feasible path set of the orders.
In one embodiment, the least costly feasible path is selected for each order based on a greedy principle, and the least costly feasible path is determined by comparing the feasible paths corresponding to each order. For an order, at least one feasible path can be obtained through a static tandem system, and when the feasible path corresponding to the order is one, the feasible path can be determined as the feasible path with the lowest cost corresponding to the order; when at least two feasible paths corresponding to one order form are provided, the cost of the at least two feasible paths is ranked in the order from big to small or from small to big, and the feasible path with the lowest cost corresponding to the order form is determined from the ranking result.
And the set formed by the feasible paths with the lowest cost corresponding to the orders is the middle feasible path set of the orders, so that the data volume is reduced.
With continued reference to fig. 3, in one embodiment, S1044 may be further refined as S302 to S308, so as to iteratively adjust the intermediate feasible path set based on the neighborhood search algorithm, and determine the feasible path set with the lowest total cost of the network. In detail, the route planning method of the embodiment of the present disclosure further includes:
S304, iteratively adjusting feasible paths in the middle feasible path set based on a neighborhood search algorithm, and determining the feasible path with the lowest total cost of the network;
s306, judging whether the iteration times and/or the search time meet the preset stop conditions, if so, executing S308, and if not, executing S304;
and S308, finishing the search, and taking the feasible path with the lowest total cost of the iterated network as an initial feasible path of a plurality of orders.
In one embodiment, the above-mentioned neighbor search algorithm uses a neighbor operator to update a routing node of a current path, and compares the cost of the path updated by the neighbor operator with the cost of the original current path, where the neighbor operator has a mutation operator, for example, the current path is a-B-C, and uses E as the neighbor operator, the updated path is a-E-C, and the path with the lowest cost is reserved in the two paths.
The iteration times or the search time is adopted as the condition for stopping iteration, for example, when the iteration times reach a preset time threshold value and/or the search time reaches a maximum search time threshold value, the condition for stopping iteration is judged; when the iteration number does not reach the preset number threshold or the search time does not reach the maximum search time threshold, judging that the iteration stopping condition is not reached, continuing to iteratively adjust the feasible paths in the middle feasible path set, and determining the feasible path with the lowest total cost of the network until the iteration number and/or the search time meet the preset stopping condition.
It should be noted that, the values of the preset time threshold and the maximum search time threshold may be determined according to actual situations, and the disclosure is not limited specifically.
In the embodiment of the disclosure, the feasible paths are iteratively adjusted through the neighborhood search algorithm, and the feasible path with the lowest total cost of the network is determined and used as the initial feasible path of a plurality of orders, so that the redundancy variable is reduced, the solving speed is improved, and the solving time is shortened.
FIG. 4 illustrates a flow chart of a method for optimal path determination for multiple orders provided by embodiments of the present disclosure. Based on the embodiment of fig. 1, S106 is further refined into S402 to S408, so as to define the solution process of the relaxation master problem model. As shown in fig. 4, in one embodiment, the step S106 of screening the feasible paths to be processed of the relaxation master problem model, performing iterative processing on the feasible paths to be processed based on the relaxation master problem model to obtain optimal paths of a plurality of orders includes:
s402, taking at least one feasible path in the initial feasible paths as a feasible path to be processed, inputting a relaxation main problem model for solving, and calculating the check number of the rest feasible paths in the initial feasible paths;
s404, judging whether all feasible paths in the initial feasible paths are input into a relaxation main problem model, and/or judging whether the number of the checks of the rest feasible paths is greater than or equal to a preset check threshold, if yes, executing S406;
S406, judging that the solving result of the relaxation main problem model reaches the optimal, and determining optimal paths of a plurality of orders according to the solving result.
Optionally, the method further comprises: s408, if at least one feasible path in the initial feasible paths does not input a relaxation main problem model, or the check number of at least one feasible path in the rest feasible paths is smaller than a preset check number threshold value, generating a base column of at least one feasible path with the check number smaller than the preset check number threshold value, and adding the base column into the to-be-processed feasible paths; and inputting the adjusted feasible paths to be processed into the relaxation main problem model for iterative solution until the feasible paths in the initial feasible paths are all input into the relaxation main problem model, and/or the check number of other feasible paths in the initial feasible paths is greater than or equal to a preset check number threshold value.
It should be noted that the preset threshold of the number of tests may be 0. When the relaxation main problem model is solved for the first time, only one feasible path can be included in the feasible paths to be processed, the feasible paths can be any one of the initial feasible paths, and the feasible paths can be selected or designated randomly. When the relaxation main problem model is solved for the first time, the number of the feasible paths can be preconfigured when the feasible paths to be processed comprise a plurality of feasible paths, and the feasible paths can be selected or designated randomly, for example, 2 feasible paths in the initial feasible paths are selected randomly to be used as the feasible paths to be processed.
In one embodiment, the relaxed master problem model is constructed by: constructing a transportation cost objective function based on the flow direction decision variable and the line decision variable; constructing a sorting cost objective function based on the line decision variables; constructing a minimized cost objective function according to the transportation cost objective function and the sorting cost objective function; based on the flow direction decision variables and the line decision variables, constraints are constructed that minimize the cost objective function. The constructed minimized cost objective function and constraints of the minimized cost objective function are used as a relaxed master problem model.
The construction process of the relaxation master problem model is as follows:
determining decision variables of the relaxed master problem model, wherein the decision variables include flow direction decision variablesAnd line decision variable y kl Flow decision variable->Indicating whether the flow direction od selects the path, if so +.>The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, go (L)>I.e. +.>0-1 variable, o and dThe start and end of the flow direction respectively. Line decision variable y kl The integer variable represents the number of vehicles arranged on the line kl, k and l being the start and end points of the line, respectively.
In transportation systems, network costs often include two categories, transportation costs and sorting costs, respectively.
The transportation cost of the whole road vehicle is referred to as the whole vehicle cost, and is the product of the number of vehicles and the unit cost of the whole road vehicle. The transportation cost for a road part in the transportation mode is called the part cost, and is the product of the order volume and the part unit cost.
The transportation cost is the sum of the whole vehicle cost and the spare part cost, and is expressed by the following formula:
wherein C is 1 Representing the unit cost of the line sign, A 1 Representing all transportation types as road whole vehicle lines; c (C) 2 Representing the unit cost of the piece goods, V od Representing the cargo volume corresponding to the flow direction (o, d), A 2 Indicating that all transportation types are on-road routes.
The sorting cost is the product of the single quantity, the sorting times and the unit sorting cost, and is expressed by adopting a formula II:
wherein C is 3 Representing unit sorting cost, SN r The number of sorting centers passing by a path R is represented, R represents a set of feasible paths to be processed, wherein R is A 1 And A is a 2 Is a union of (a) and (b).
Thus, the minimization cost objective function is the lowest overall cost of the logistics network, i.e., the sum of the transportation cost and sorting cost is the lowest, and the minimization cost objective function is available as follows:
where Min is a function that takes a minimum value.
It should be noted that the minimization of the cost objective function includes at least one of a flow direction route unique constraint, a capacity limit constraint, a line opening standard limit constraint, and a total number of vehicles limit constraint.
The flow direction route is only restricted, and only one path can be selected for each flow direction od to complete cargo transportation, so that the flow direction od cannot be split, and the order transportation cannot be completed through multiple paths.
And a capacity limiting constraint for limiting the total amount of cargo transported on each line not to exceed a maximum line load, which is the sum of the full capacity of all vehicles on the line.
The line opening criteria limit constraints, the square of all flow directions (o, d) through the line, is greater than or equal to the sum of the minimum opening cargo volume criteria for all trucks on the line.
The total number of vehicles limits the constraint that the sum of the number of vehicles on all lines does not exceed the existing number of vehicles.
The disclosure derives a corresponding proof-weight RC equation for a flow decision variable based on the principle of the simplex method as follows:
wherein pi 1 Representing the corresponding dual variable value, pi, of the capacity limitation constraint 2 Representing the value of the dual variable corresponding to the constraint of the line opening standard limitation, pi 3 And representing the dual variable value corresponding to the line opening standard limiting constraint.
The dual variables can be obtained directly by a solver, which can adopt CPLEX, gurobi, SCIP and the like.
In the embodiment of the disclosure, on one hand, by considering the unique constraint of the flow direction, a plurality of orders which can be produced in the same shift in the same flow direction are combined, the combined orders can only select the same path, the situation of not allowing the diversion is generated, the operation management of the actual sorting site is facilitated, and the solving scale can be effectively reduced. On the other hand, the vehicle resources are limited in actual operation, and meanwhile, the capacity limit constraint and the vehicle number limit constraint of the line are considered, so that the requirement that all cargoes on the line can be transported is met, the limit constraint of the actual resources is not exceeded, and the full utilization of the resources can be realized.
Fig. 5 shows a flowchart of a vehicle scheduling policy generation method provided by an embodiment of the present disclosure. As shown in fig. 5, in one embodiment, the generating, at S108, a vehicle scheduling policy corresponding to the optimal path includes:
s502, obtaining transportation paths of a plurality of orders according to the value of the corresponding flow direction decision variable when the solving result of the relaxation main problem model reaches the optimal value;
s504, obtaining the line train number scheduling of the optimal path according to the value of the corresponding line decision variable when the solving result reaches the optimal;
s506, outputting the transportation paths of the orders and the line train number scheduling of the optimal path to obtain a vehicle scheduling strategy corresponding to the optimal path.
In one embodiment, a mixed integer programming solver, such as CPLEX, gurobi, SCIP, is used to input a set of to-be-processed feasible paths obtained by solving a column generation algorithm into a relaxation main problem model, and solve the model, so that transportation paths of all orders can be obtained according to the values of flow direction decision variables corresponding to the optimal solution, and scheduling of line train numbers can be obtained according to the values of line decision variables corresponding to the optimal solution, so as to obtain a vehicle scheduling strategy.
Fig. 6 shows a flowchart of a specific example of a route planning method provided by an embodiment of the present disclosure. As shown in fig. 6, the route planning method includes the steps of:
And carrying out static serial calculation on the static network structure data and the order data according to a static serial system to obtain an initial feasible path, wherein the static network structure data comprises shift data, line data, sorting data, loading data, vehicle data and the like, and the static serial system automatically connects all links in series into a full-link transportation path according to a preset rule.
The initial feasible paths are input into a column generation algorithm module (such as the relaxation master problem model mentioned above) which solves the relaxation master problem model to generate a scheduling policy.
The process of solving the relaxation master problem model is as follows:
inputting an initial feasible path;
solving a relaxation main problem;
obtaining a dual variable value;
calculating a test number;
judging whether the solving result reaches the optimal or not;
if yes, outputting an optimal path; if not, the feasible path with negative check number is fed into the base, and the relaxation main problem is solved again until the solving result reaches the optimal value.
It should be noted that, the implementation manner of the above process is the same as that of the foregoing embodiment, and will not be repeated here.
Based on the same inventive concept, a route planning device is also provided in the embodiments of the present disclosure, as described in the following embodiments. Since the principle of solving the problem of the embodiment of the device is similar to that of the embodiment of the method, the implementation of the embodiment of the device can be referred to the implementation of the embodiment of the method, and the repetition is omitted.
Fig. 7 shows a route planning apparatus schematic of an embodiment of the disclosure. As shown in fig. 7, the route planning apparatus of the present embodiment includes a data acquisition module 701, a path acquisition module 702, an optimal path determination module 703, and a policy generation module 704.
The data acquisition module 701 is configured to acquire static network structure data and order data of a plurality of orders;
the path acquisition module 702 is configured to obtain initial feasible paths of a plurality of orders based on the static network structure data and the order data;
the optimal path determining module 703 is configured to screen a feasible path to be processed of the relaxation master problem model, and perform iterative solution on the feasible path to be processed based on the relaxation master problem model to obtain optimal paths of a plurality of orders;
and the policy generation module 704 is configured to generate a vehicle scheduling policy corresponding to the optimal path.
In one embodiment, the path acquisition module 702 is configured to acquire a feasible path set of a plurality of orders based on static network structure data and order data; and searching the feasible paths meeting the preset conditions from the feasible path sets of the orders to obtain initial feasible paths of the orders.
In one embodiment, the path obtaining module 702 is configured to determine, based on the static network structure data, a routing node between a starting sorting center and a destination sorting center of each order by using a dynamic planning algorithm, so as to obtain a feasible path set of a plurality of orders, where one order corresponds to one starting sorting center and one destination sorting center.
In one embodiment, the path obtaining module 702 is configured to select a feasible path with the lowest cost for each order from the feasible path sets of the multiple orders, so as to obtain an intermediate feasible path set of the multiple orders.
In one embodiment, the path obtaining module 702 is further configured to iteratively adjust the feasible paths in the intermediate feasible path set based on a neighborhood search algorithm, and determine the feasible path with the lowest total cost of the network; if the iteration times and/or the search time meet the preset stop conditions, ending the search, and taking the feasible path with the lowest total cost of the iterated network as the initial feasible path of a plurality of orders; if the iteration times or the search time do not meet the preset stop conditions, continuing to iteratively adjust the feasible paths in the middle feasible path set, and determining the feasible path with the lowest total cost of the network until the iteration times and/or the search time meet the preset stop conditions.
It is noted that, the optimal path determining module 703 is configured to input at least one feasible path in the initial feasible paths as a feasible path to be processed, solve the relaxation master problem model, and calculate the number of tests of the remaining feasible paths in the initial feasible paths; if the feasible paths in the initial feasible paths are all input into the relaxation main problem model, and/or the number of the check of the rest feasible paths is greater than or equal to a preset check threshold, judging that the solving result of the relaxation main problem model reaches the optimal, and determining the optimal paths of a plurality of orders according to the solving result.
In one embodiment, the optimal path determining module 703 is further configured to generate, if at least one of the initial feasible paths does not input the relaxation master problem model, or if a number of tests of at least one of the remaining feasible paths is less than a preset number of tests threshold, at least one feasible path with a number of tests less than the preset number of tests threshold into a base column, and add the base column into the feasible path to be processed; and inputting the feasible paths to be processed after the base processing into the relaxation main problem model for iterative solution until the feasible paths in the initial feasible paths are all input into the relaxation main problem model, and/or the verification number of the rest feasible paths in the initial feasible paths is greater than or equal to a preset verification number threshold value.
It should be noted that, the route planning device of the embodiment of the present disclosure further includes a model construction module not shown in the drawings, configured to construct a relaxation master problem model, specifically configured to construct a transportation cost objective function based on the flow direction decision variables; constructing a sorting cost objective function based on the line decision variables; constructing a minimized cost objective function according to the transportation cost objective function and the sorting cost objective function; based on the flow direction decision variables and the line decision variables, constraints are constructed that minimize the cost objective function.
The constraint condition for minimizing the cost objective function includes at least one of a flow direction route unique constraint, a capacity limit constraint, a line opening standard limit constraint, and a total number of vehicles limit constraint.
In one embodiment, the vehicle scheduling policy includes a line number schedule of the transportation path and the optimal path of the plurality of orders; the strategy generation module 704 is configured to obtain transportation paths of multiple orders according to the values of the corresponding flow direction decision variables when the solution result of the relaxation main problem model reaches the optimum; obtaining the line train number scheduling of the optimal path according to the value of the corresponding line decision variable when the solving result reaches the optimal; and outputting the transportation paths of the orders and the line train number scheduling arrangement of the optimal path to obtain the vehicle scheduling strategy corresponding to the optimal path.
It should be noted that the static network structure data includes one or more of shift data, line data, sorting data, loading data, and vehicle data; the order data for each order includes one or more of order type, order start, order end, order aging requirements.
The route planning device provided by the embodiment of the disclosure acquires static network structure data and order data of a plurality of orders; based on the static network structure data and the order data, obtaining initial feasible paths of a plurality of orders; screening the feasible paths to be processed of the relaxation main problem model, and carrying out iterative solution on the feasible paths to be processed based on the relaxation main problem model to obtain optimal paths of a plurality of orders; on the one hand, before solving the relaxation main problem model, the redundant paths are deleted by screening the feasible paths to be processed of the relaxation main problem model, so that all the initial feasible paths are prevented from being added into the relaxation main problem model to solve and calculate, and only part of feasible paths which can be taken into consideration are taken into consideration, so that the number of model variables is reduced, the model solving scale is reduced, and the overall solving efficiency of the model is improved.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
Fig. 8 illustrates an exemplary system architecture 800 to which a route planning method, or route planning apparatus, of embodiments of the present disclosure may be applied.
As shown in fig. 8, system architecture 800 may include a terminal device 810, a network 820, and a server 830.
The network 820 may be a wired network or a wireless network, and is a medium used to provide a communication link between the terminal device 810 and the server 830.
A user may interact with the server 830 through the network 820 using the terminal device 810 to receive or send messages.
Terminal device 810 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, display screens, desktop computers, and the like.
The clients of the applications that may be installed on terminal device 810 are the same or clients of the same type of application based on different operating systems. The specific form of the application client may also be different based on the different terminal platforms, for example, the application client may be a mobile phone client, a PC client, etc.
Illustratively, the terminal device 810 is installed with various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, etc., through which the user's needs can be collected and orders generated according to the needs.
The server 830 may be a server providing various services, such as a background management server providing support for devices operated by the user with the terminal device 810. The background management server may analyze and process the received data (e.g., order data) such as the request, and obtain a processing result (e.g., an optimal path).
Optionally, the server 830 may be a separate physical server, or may be a server cluster or a distributed system formed by a plurality of physical servers, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network ), and basic cloud computing services such as big data and artificial intelligence platforms.
Those skilled in the art will appreciate that the number of terminal devices, networks, and servers in fig. 8 is merely illustrative, and that any number of terminal devices, networks, and servers may be provided as desired. The embodiments of the present disclosure are not limited in this regard.
An electronic device 900 according to such an embodiment of the invention is described below with reference to fig. 9. The electronic device 900 shown in fig. 9 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 9, the electronic device 900 is embodied in the form of a general purpose computing device. Components of electronic device 900 may include, but are not limited to: the at least one processing unit 910, the at least one storage unit 920, and a bus 930 connecting the different system components (including the storage unit 920 and the processing unit 910).
Wherein the storage unit stores program code that is executable by the processing unit 910 such that the processing unit 910 performs steps according to various exemplary embodiments of the present invention described in the above-described "exemplary methods" section of the present specification. For example, the processing unit 910 may execute the order data that obtains static network structure data and a plurality of orders as shown in FIG. 1; based on the static network structure data and the order data, obtaining initial feasible paths of a plurality of orders; screening the feasible paths to be processed of the relaxation main problem model, and carrying out iterative solution on the feasible paths to be processed based on the relaxation main problem model to obtain optimal paths of a plurality of orders; and generating a vehicle dispatching strategy corresponding to the optimal path.
The storage unit 920 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 9201 and/or cache memory 9202, and may further include Read Only Memory (ROM) 9203.
The storage unit 920 may also include a program/utility 9204 having a set (at least one) of program modules 9205, such program modules 9205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The bus 930 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 900 may also communicate with one or more external devices 940 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the system, and/or any devices (e.g., routers, modems, etc.) that enable the electronic device 900 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 950. Also, the system may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 960. As shown in fig. 9, the network adapter 960 communicates with other modules of the electronic device 900 over the bus 930. It should be appreciated that although not shown in fig. 9, other hardware and/or software modules may be used in connection with electronic device 900, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium, which may be a readable signal medium or a readable storage medium, is also provided. In an exemplary embodiment of the present disclosure, there is also provided a computer program product comprising a computer program or computer instructions loaded and executed by a processor to cause the computer to carry out the steps of the method disclosed in the above embodiments.
More specific examples of the computer readable storage medium in the present disclosure may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In this disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Alternatively, the program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
In particular implementations, the program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
From the description of the above embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims (13)

1. A method of route planning, comprising:
acquiring static network structure data and order data of a plurality of orders;
based on the static network structure data and the order data, obtaining initial feasible paths of the orders;
screening a feasible path to be processed of a relaxation main problem model through a column generation algorithm, and carrying out iterative solution on the feasible path to be processed based on the relaxation main problem model to obtain optimal paths of the orders, wherein the column generation algorithm is used for determining a non-base variable base with a negative check number in an iterative process of determining a base-in variable and a base-out variable, and each time iterates a variable base;
generating a vehicle scheduling strategy corresponding to the optimal path;
wherein the relaxation master problem model is constructed by:
constructing a transportation cost objective function based on flow direction decision variablesIndicating whether the flow direction od selects the path, o and d are the start point and the end point of the flow direction, respectively, if so +.>The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, go (L)>；
Constructing a sorting cost objective function based on a line decision variable y kl Is an integer variable representing the number of vehicles arranged on line kl, k and l is the start point and the end point of the line respectively;
constructing a minimization cost objective function according to the transportation cost objective function and the sorting cost objective function;
based on the flow direction decision variables and the line decision variables, constraints of the minimized cost objective function are constructed.
2. The method of claim 1, wherein the deriving the initial viable paths for the plurality of orders based on the static network structure data and the order data comprises:
acquiring a feasible path set of the orders based on the static network structure data and the order data;
searching the feasible paths meeting preset conditions from the feasible path sets of the orders to obtain initial feasible paths of the orders.
3. The method of claim 2, wherein the obtaining a set of viable paths for the plurality of orders based on the static network structure data and the order data comprises:
and determining routing nodes between the initial sorting center and the target sorting center of each order by adopting a dynamic programming algorithm based on the static network structure data to obtain a feasible path set of the orders, wherein one order corresponds to one initial sorting center and one target sorting center.
4. The method of claim 2, wherein searching for a feasible path from the set of feasible paths for the plurality of orders that meets a preset condition results in an initial feasible path for the plurality of orders, comprising:
and selecting a feasible path with the lowest cost for each order from the feasible path sets of the orders, and obtaining an intermediate feasible path set of the orders.
5. The method according to claim 4, wherein the method further comprises:
iteratively adjusting the feasible paths in the middle feasible path set based on a neighborhood search algorithm, and determining the feasible path with the lowest total cost of the network;
if the iteration times and/or the search time meet the preset stop conditions, ending the search, and taking the feasible path with the lowest total cost of the iterated network as the initial feasible path of the orders;
if the iteration times or the search time do not meet the preset stop conditions, continuing to iteratively adjust the feasible paths in the middle feasible path set, and determining the feasible path with the lowest total cost of the network until the iteration times and/or the search time meet the preset stop conditions.
6. The method of claim 1, wherein the screening the feasible paths to be processed for the relaxed master problem model, iteratively solving the feasible paths to be processed based on the relaxed master problem model, obtains optimal paths for the plurality of orders, comprises:
At least one feasible path in the initial feasible paths is used as a feasible path to be processed, a relaxation main problem model is input for solving, and the number of the test of the rest feasible paths in the initial feasible paths is calculated;
and if the feasible paths in the initial feasible paths are all input into the relaxation main problem model, and/or the number of the check numbers of the rest feasible paths is greater than or equal to a preset check number threshold value, judging that the solving result of the relaxation main problem model reaches the optimal, and determining the optimal paths of the orders according to the solving result.
7. The method of claim 6, wherein the method further comprises:
if at least one feasible path in the initial feasible paths does not input a relaxation main problem model, or the check number of at least one feasible path in the rest feasible paths is smaller than a preset check number threshold value, generating a base column of at least one feasible path with the check number smaller than the preset check number threshold value, and adding the base column into the to-be-processed feasible paths;
and inputting the feasible paths to be processed after the base entering into the relaxation main problem model for iterative solution until the feasible paths in the initial feasible paths are all input into the relaxation main problem model, and/or the verification number of the rest feasible paths in the initial feasible paths is greater than or equal to a preset verification number threshold value.
8. The method of claim 1, wherein the constraint that minimizes the cost objective function comprises at least one of a flow direction route unique constraint, a capacity limit constraint, a line open standard limit constraint, and a total number of vehicles limit constraint.
9. The method of claim 1, wherein the vehicle scheduling policy includes a line number schedule of the optimal path and a transportation path of the plurality of orders;
the generating the vehicle scheduling policy corresponding to the optimal path includes:
obtaining transportation paths of the orders according to the value of the corresponding flow direction decision variable when the solving result of the relaxation main problem model reaches the optimal value;
obtaining the line train number scheduling of the optimal path according to the value of the corresponding line decision variable when the solving result reaches the optimal;
and outputting the transportation paths of the orders and the line train number scheduling of the optimal path to obtain a vehicle scheduling strategy corresponding to the optimal path.
10. The method of any of claims 1-9, wherein the static network structure data includes one or more of shift data, line data, sort data, load data, vehicle data; the order data for each order includes one or more of order type, order start, order end, order aging requirements.
11. A route planning device, comprising:
the data acquisition module is used for acquiring static network structure data and order data of a plurality of orders;
the path acquisition module is used for acquiring initial feasible paths of a plurality of orders based on the static network structure data and the order data;
the optimal path determining module is used for screening a feasible path to be processed of a relaxation main problem model through a column generating algorithm, carrying out iterative solution on the feasible path to be processed based on the relaxation main problem model to obtain optimal paths of the orders, wherein the column generating algorithm is used for determining a variable base in each iteration in the process of determining a base-in variable and a base-out variable, and determining a non-base variable base with negative check number;
the strategy generation module is used for generating a vehicle scheduling strategy corresponding to the optimal path;
a model construction module for constructing a relaxed main problem model, in particular for constructing a transportation cost objective function based on flow direction decision variablesIndicating whether the flow direction od selects the path, o and d are the start point and the end point of the flow direction, respectively, if so +.>The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, go (L) >The method comprises the steps of carrying out a first treatment on the surface of the Constructing a sorting cost objective function based on a line decision variable y kl As integer variables, representing the number of vehicles arranged on the line kl, k and l being the start point and the end point of the line, respectively; constructing a minimized cost objective function according to the transportation cost objective function and the sorting cost objective function; constructing a minimized cost objective function based on the flow direction decision variables and the line decision variablesConstraint conditions.
12. An electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the route planning method of any of claims 1-10 via execution of the executable instructions.
13. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements a route planning method according to any one of claims 1-10.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111860991A (en) * 2020-07-13 2020-10-30 清华大学深圳国际研究生院 Unmanned vehicle distribution path planning method
CN112488391A (en) * 2020-11-30 2021-03-12 合肥工业大学 Industrial tobacco logistics scheduling method based on Lagrange relaxation
CN114677087A (en) * 2022-03-31 2022-06-28 上海圆徕科技有限公司 Vehicle combination unmanned aerial vehicle cooperative distribution method
CN115187169A (en) * 2022-07-13 2022-10-14 东北大学 Logistics distribution system and method based on collaborative path planning

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7092894B1 (en) * 1994-09-01 2006-08-15 Harris Corporation Cost reactive scheduler and method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111860991A (en) * 2020-07-13 2020-10-30 清华大学深圳国际研究生院 Unmanned vehicle distribution path planning method
CN112488391A (en) * 2020-11-30 2021-03-12 合肥工业大学 Industrial tobacco logistics scheduling method based on Lagrange relaxation
CN114677087A (en) * 2022-03-31 2022-06-28 上海圆徕科技有限公司 Vehicle combination unmanned aerial vehicle cooperative distribution method
CN115187169A (en) * 2022-07-13 2022-10-14 东北大学 Logistics distribution system and method based on collaborative path planning

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