A method, device and electronic equipment for intelligent scheduling of weekly transportation plan of finished cigarettes

Abstract

The present invention discloses a method, device and electronic device for intelligent scheduling of a weekly plan for the transportation of finished cigarettes. The method includes obtaining weekly planned transportation tasks, merging the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point combined loading tasks in turn, and obtaining a combined loading waybill, so as to maximize the reduction in the transportation distance of the combined total tasks; performing conflict graph degree decomposition on the combined loading waybill based on the daily plan demand conditions to obtain each waybill set, and solving the allocation scheme for each waybill set to obtain the allocation result; integrating all the allocation results to obtain the optimized transportation task. The present invention realizes the unified consideration of the timetable problem and the vehicle path problem, and after the existing weekly plan is combined with single-point tasks based on the vehicle path problem, the combined loading waybill is re-decomposed and allocated based on the specific daily plan requirements, so as to achieve systematic optimization of the entire transportation scheduling from a global perspective, thereby obtaining a better weekly plan scheduling scheme.

Description

Technical Field

[0001] The present application relates to the technical field of cigarette logistics transportation, and in particular to a method, device and electronic equipment for intelligent scheduling of weekly plans for transportation of finished cigarettes. Background Art

[0002] As the scope of industrial and commercial network distribution expands, manual weekly contract assignment is time-consuming and labor-intensive, and relies on the dispatcher's own habits and dispatching experience, and cannot be effectively integrated with the existing daily plan intelligent dispatching model. The daily plan intelligent dispatching model is based on the current daily plan delivery process, and dispatches and loads according to the contracts with confirmed delivery dates pushed by the marketing center, with the main goal of reducing transportation costs. Manual dispatch can only be based on fixed rules and experience, such as transportation cycle, customer importance, key brands, etc., and it is difficult to simultaneously consider the overall contract loading opportunities, so it is impossible to achieve the global optimality of the industrial cigarette transportation plan.

[0003] Through in-depth research on various cigarette factories and combined with the current cutting-edge research status in the field of logistics, the weekly plan scheduling under the industrial and commercial network can be classified as a mixture of the time tabling problem (TTP) and the vehicle routing problem (VRP), referred to as the TTVRP problem. At present, manual weekly plan contract assignment often focuses only on the TTP link, that is, considering the resource constraints such as platform delivery capacity, transportation cycle, warehouse inventory, carrier drivers, and commercial companies, and reasonably assigning the delivery date of the weekly plan contract, and less consideration is given to the VRP problem of the subsequent daily plan. Therefore, it is currently difficult to simultaneously consider the overall loading opportunities of the contract, resulting in a lot of time and energy consumption, reduced delivery efficiency, and high logistics costs, and it is impossible to achieve the global optimal method of industrial cigarette transportation. Summary of the invention

[0004] In order to solve the above problems, the embodiments of the present application provide a method, device and electronic device for intelligent scheduling of weekly plans for transportation of finished cigarettes.

[0005] In a first aspect, an embodiment of the present application provides a method for intelligently scheduling a weekly transportation plan for finished cigarettes, the method comprising:

[0006] Obtain weekly planned transportation tasks, and sequentially merge the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point consolidation tasks to obtain a consolidation waybill, so as to minimize the reduction in the transportation distance of the total tasks after the consolidation;

[0007] Decomposing the degree of the conflict graph of the combined waybill based on the daily plan demand conditions to obtain each waybill set, and solving the allocation scheme for each waybill set to obtain an allocation result;

[0008] All the allocation results are integrated to obtain the optimized transportation task.

[0009] Preferably, the acquiring of the weekly planned transportation tasks, sequentially merging the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point consolidation tasks, and obtaining a consolidation waybill, so as to maximize the reduction in the transportation distance of the total tasks after the consolidation, comprises:

[0010] Obtain weekly planned transportation tasks based on a preset intelligent scheduling model, and determine the actual driving distances between warehouses of various commercial companies based on the weekly planned transportation tasks;

[0011] Pairing the warehouses of each commercial company based on the transportation data of the warehouses of each commercial company and the actual driving distance to obtain an initial optimized grading pairing solution of the warehouses of each commercial company;

[0012] Based on the swarm intelligence algorithm, evolutionary training is performed on the pairing solutions of each of the initial optimization levels to obtain the pairing optimization level;

[0013] Based on the pairing priority level, the single-point direct dispatch tasks in the weekly planned transportation tasks are merged into multi-point joint loading tasks to obtain a joint loading waybill, so as to maximize the reduction in the transportation distance of the total task after the merger.

[0014] Preferably, the step of merging the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point consolidation tasks based on the pairing priority level to obtain a consolidation waybill, so as to maximize the reduction in the transportation distance of the combined total tasks, includes:

[0015] Based on the pairing priority level, the single-point direct dispatch tasks in the weekly planned transportation tasks are merged into multi-point loading tasks to obtain a preliminary scheduling plan;

[0016] Optimizing the preliminary scheduling plan based on a local search algorithm to obtain a preliminary loading plan;

[0017] The access sequence of the preliminary loading scheme is optimized by enumeration method to obtain a loading waybill, so as to maximize the reduction of the total task transportation distance after the merger.

[0018] Preferably, the conflict graph degree decomposition of the combined waybill is performed based on the daily plan requirement condition to obtain each waybill set, and the allocation scheme is solved for each waybill set to obtain the allocation result, including:

[0019] Based on the daily plan requirement conditions, the conflict graph degree decomposition of the combined loading waybill is performed to obtain waybill sets, wherein the waybill sets include conflicting waybill sets and non-conflicting waybill sets;

[0020] For the conflicting waybill set, solving the allocation scheme based on the colored GC decomposition algorithm to obtain a first allocation result;

[0021] For the non-conflicting waybill set, an allocation solution is solved based on the balanced priority best fit descending BFD algorithm to obtain a second allocation result;

[0022] The first allocation result and the second allocation result are integrated to obtain an allocation result.

[0023] Preferably, solving the allocation scheme based on the colored GC decomposition algorithm includes:

[0024] Establishing a time conflict graph between the waybills, the time conflict graph comprising a vertex set and an edge set, the vertex set is used to represent the assembled waybills set, and the edge set is used to represent the conflict relationship between the waybills;

[0025] Divide the vertex set into a colored vertex set and an uncolored vertex set, calculate the degree value of each vertex that has not been colored yet, the degree value is the number of vertices adjacent to each vertex and the uncolored vertex set, and when the degree value is full, it indicates that the delivery days are reduced by a unit natural day;

[0026] Numbering each color in the time conflict graph, selecting a first vertex with the largest current degree value, assigning a first numbered color to the first vertex, and moving the first vertex from the uncolored vertex set to the colored vertex set;

[0027] Sort all vertices in the uncolored vertex set according to saturation, and determine the second vertex with the maximum saturation;

[0028] Assign a second numbered color to each of the second vertices, and move the second vertices from the uncolored vertex set to the colored vertex set, wherein the second numbered color is the color with the smallest number among the currently available colors;

[0029] The step of numbering each color in the temporal conflict graph is repeated until all the vertex sets are the colored vertex sets.

[0030] Preferably, the allocation solution is solved based on the balanced priority best fit descent BFD algorithm, including:

[0031] Define a waybill set and a box set, where the box set is used to represent the delivery days of working days;

[0032] The waybill set is sorted in descending order of conflict values ​​to obtain an allocation set;

[0033] The allocation set is allocated to the box set in sequence, so as to minimize the coefficient of variation of allocation of waybills to boxes.

[0034] Preferably, after solving the allocation scheme for each waybill set and obtaining the allocation result, the method further includes:

[0035] When there is a waybill set that cannot be solved, sort the vehicles that have not been assigned a delivery time in descending order according to the degree value, and obtain the waybill data of the first vehicle in the sequence;

[0036] Extracting the allocated delivery time waybill that meets the preset constraint condition from the box set based on the waybill data of the first vehicle;

[0037] Calculate the cargo variance of each of the waybills with assigned delivery time based on the BFD algorithm, and perform SWAP exchange between the waybill with assigned delivery time with the smallest variance and the waybill data of the first vehicle;

[0038] Insert the exchanged goods into the insertion time window whose overall variance is less than the variance of the current time window;

[0039] The number of vehicles not assigned with delivery time is updated based on the insertion time window, and the step of sorting the vehicles not assigned with delivery time in descending order of degree values ​​is repeated until the number of vehicles is zero.

[0040] In a second aspect, an embodiment of the present application provides an intelligent scheduling device for a weekly transportation plan of finished cigarettes, the device comprising:

[0041] An acquisition module is used to acquire weekly planned transportation tasks, and sequentially merge the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point consolidation tasks to obtain a consolidation waybill, so as to minimize the reduction in the transportation distance of the total task after the merger;

[0042] A decomposition module, used for performing conflict graph degree decomposition on the combined waybill based on the daily plan requirement conditions to obtain each waybill set, and respectively solving the allocation scheme for each waybill set to obtain an allocation result;

[0043] The integration module is used to integrate all the allocation results to obtain an optimized transportation task.

[0044] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method provided in the first aspect or any possible implementation of the first aspect are implemented.

[0045] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect or any possible implementation manner of the first aspect.

[0046] The beneficial effects of the present invention are as follows: the timetable problem and the vehicle path problem are considered in a unified manner, and after the existing weekly plan is combined with single-point tasks based on the vehicle path problem, the combined waybill is re-decomposed and allocated based on the specific daily plan requirements, so as to achieve systematic optimization of the entire transportation scheduling from a global perspective, thereby obtaining a better weekly plan scheduling solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A flow chart of an intelligent scheduling method for a weekly plan of finished cigarette transportation provided in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of the structure of an intelligent scheduling device for weekly planning of finished cigarette transportation provided in an embodiment of the present application;

[0050] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0052] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present application, and different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments recorded. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing one or more of all other possible combinations of A, B, C, and D, although the embodiment may not be clearly recorded in the following text.

[0053] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present application. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted or combined. In addition, features described in some examples may be combined in other examples.

[0054] See also Figure 1 , Figure 1 : is a flow chart of a method for intelligent scheduling of a weekly plan for the transportation of finished cigarettes provided in an embodiment of the present application. In an embodiment of the present application, the method comprises:

[0055] S101, obtaining weekly planned transportation tasks, and sequentially merging the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point combined loading tasks, and obtaining a combined loading waybill, so as to maximize the reduction in the combined total task transportation distance.

[0056] The execution entity of this application can be a cloud server.

[0057] The single-point direct delivery task in the embodiment of the present application can be understood as a task of directly delivering the goods from a single delivery point in the weekly planned transportation task to the destination.

[0058] The multi-point loading task in the embodiment of the present application can be understood as a task of combining different goods arriving at the same location in multiple single-point direct delivery tasks into the same container for transportation.

[0059] In an embodiment of the present application, the cloud server will first obtain the current weekly planned transportation task. The weekly planned transportation task obtained at this time is planned based on the existing scheduling method, that is, it only focuses on the timetable problem (Timetabling problem, TTP) and needs to be optimized in combination with the vehicle routing problem (VRP). Since the VRP aspect is not considered, most of the weekly planned transportation tasks at this time are single-point direct dispatch tasks, so the single-point direct dispatch tasks will be merged to form a multi-point joint loading task, and finally a joint loading waybill is obtained. The reduction in the total task transportation distance of the obtained joint loading waybill should be the largest, that is, the total task transportation distance at this time is the smallest.

[0060] In one possible implementation, step S101 includes:

[0061] Obtain weekly planned transportation tasks based on a preset intelligent scheduling model, and determine the actual driving distances between warehouses of various commercial companies based on the weekly planned transportation tasks;

[0062] Pairing the warehouses of each commercial company based on the transportation data of the warehouses of each commercial company and the actual driving distance to obtain an initial optimized grading pairing solution of the warehouses of each commercial company;

[0063] Based on the swarm intelligence algorithm, evolutionary training is performed on the pairing solutions of each of the initial optimization levels to obtain the pairing optimization level;

[0064] Based on the pairing priority level, the single-point direct dispatch tasks in the weekly planned transportation tasks are merged into multi-point joint loading tasks to obtain a joint loading waybill, so as to maximize the reduction in the transportation distance of the total task after the merger.

[0065] In an embodiment of the present application, the existing weekly planned transportation tasks can be calculated and obtained through an intelligent scheduling model, and the intelligent scheduling model can adopt the HVRPTW model. After preprocessing the relevant data of the weekly planned transportation tasks, the actual driving distance between the warehouses of the commercial company will be obtained first, so as to further determine the transportation distance saved based on the commercial company's guaranteed tonnage and two-point loading, and obtain the initial priority pairing solution for pairing the commercial company's warehouses (i, j) based on these data. Then, the pairing priority of the commercial company's warehouse (i, j) will be autonomously evolved based on probability through the swarm intelligence algorithm (Swarm Intelligence Algorithm) to increase the diversity of the algorithm solution and achieve a balance between global and local optimization. Finally, the construction of the scheduling plan is realized. Specifically, n single-point transportation paths are initialized, and the pairing priorities given after learning the swarm intelligence algorithm are combined. Under the premise of meeting the preset constraints, the two related paths are merged in sequence. The constraints may be that the commercial company warehouses i and j do not appear on the loaded path at the same time, that is, there is at least one point that is not loaded; the unloaded commercial company warehouse i does not appear on the point within the loaded path, that is, the unloaded point must be directly connected to the cigarette factory on the arranged path; the weight of the unloaded commercial company warehouse i shall not be greater than the remaining capacity of the vehicle on the path to be merged; the fusion of the unloaded commercial company warehouse i shall not cause delays in the collision time of other commercial companies; the number of newly loaded path points shall not exceed the maximum number of loadable points; the total logistics weight of the newly loaded path shall not exceed the maximum passable vehicle model of the corresponding commercial company; the newly loaded path must meet the connectivity constraints.

[0066] In one possible implementation, the method of merging the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point consolidation tasks based on the pairing priority level to obtain a consolidation waybill, so as to maximize the reduction in the transportation distance of the combined total tasks, includes:

[0067] Based on the pairing priority level, the single-point direct dispatch tasks in the weekly planned transportation tasks are merged into multi-point loading tasks to obtain a preliminary scheduling plan;

[0068] Optimizing the preliminary scheduling plan based on a local search algorithm to obtain a preliminary loading plan;

[0069] The access sequence of the preliminary loading scheme is optimized by enumeration method to obtain a loading waybill, so as to maximize the reduction of the total task transportation distance after the merger.

[0070] In an embodiment of the present application, after obtaining the preliminary scheduling plan, it is necessary to further optimize and improve the preliminary scheduling plan. Specifically, the preliminary scheduling plan will be calculated through a local search algorithm to optimize the initial solution obtained in the aforementioned process, and finally the access order of the optimized initial solution will be optimized by enumeration method to ensure that the total mileage of the carrier is reduced while meeting the time window.

[0071] S102, based on the daily plan demand conditions, the conflict graph degree decomposition is performed on the combined waybill to obtain each waybill set, and the allocation scheme is solved for each waybill set to obtain an allocation result.

[0072] The daily planning requirement conditions in the embodiment of the present application can be understood as the conditions that need to be considered in the daily planning situation, such as arrival time requirements, transit time, transportation resources, platform resources, etc., that is, specifying the day on which each contract is shipped.

[0073] In an embodiment of the present application, in order to optimize the weekly planned transportation tasks, the combined waybills will be split according to the actual daily planned demand conditions to obtain several waybill sets. The splitting standard can be decomposed by the degree of the conflict graph. The conflict graph is a necessary condition graph used to identify and display the various elements in the conflict and the methods to solve the problem. The graph contains system goals, necessary but not sufficient conditions, and conflict prerequisites. It can be seen that based on the daily planned demand conditions, the waybill set can be decomposed according to the conflict graph. After obtaining each waybill set, the optimized allocation result can be obtained by solving each waybill set separately.

[0074] In one possible implementation, step S102 includes:

[0075] Based on the daily plan requirement conditions, the conflict graph degree decomposition of the combined loading waybill is performed to obtain waybill sets, wherein the waybill sets include conflicting waybill sets and non-conflicting waybill sets;

[0076] For the conflicting waybill set, solving the allocation scheme based on the colored GC decomposition algorithm to obtain a first allocation result;

[0077] For the non-conflicting waybill set, an allocation solution is solved based on the balanced priority best fit descending BFD algorithm to obtain a second allocation result;

[0078] The first allocation result and the second allocation result are integrated to obtain an allocation result.

[0079] In an embodiment of the present application, the waybill set can be decomposed into a conflicting waybill set and a non-conflicting waybill set by decomposing the degree of the conflict graph. The two waybill sets need to be solved in different ways respectively. For the conflicting waybill set, the colored GC decomposition algorithm can be used to solve it. This method selects the node with the largest number of used colors in its field for coloring each time, and the color used for coloring is the available color with the smallest number. Repeat the above process until all nodes are colored, and the maximum number of colors used is the number of colors used. For the non-conflicting waybill set, the balanced priority best fit descent BFD algorithm can be used to solve it. At this time, it is regarded as a packing problem for solution, in which the shipping date is analogous to a box, and the carrier's waybill is regarded as an item. The items (waybill) are assigned to the box (shipping date) in an iterative manner. It is worth noting that the number of boxes here is fixed, and the optimization goal is the balance of shipment rather than the minimum number of boxes in the traditional sense. The coefficient of variation of the internal loading of the box is used to measure the balance.

[0080] In one possible implementation, the allocation solution is solved based on the colored GC decomposition algorithm, including:

[0081] Establishing a time conflict graph between the waybills, the time conflict graph comprising a vertex set and an edge set, the vertex set is used to represent the assembled waybills set, and the edge set is used to represent the conflict relationship between the waybills;

[0082] Divide the vertex set into a colored vertex set and an uncolored vertex set, calculate the degree value of each vertex that has not been colored yet, the degree value is the number of vertices adjacent to each vertex and the uncolored vertex set, and when the degree value is full, it indicates that the delivery days are reduced by a unit natural day;

[0083] Numbering each color in the time conflict graph, selecting a first vertex with the largest current degree value, assigning a first numbered color to the first vertex, and moving the first vertex from the uncolored vertex set to the colored vertex set;

[0084] Sort all vertices in the uncolored vertex set according to saturation, and determine the second vertex with the maximum saturation;

[0085] Assign a second numbered color to each of the second vertices, and move the second vertices from the uncolored vertex set to the colored vertex set, wherein the second numbered color is the color with the smallest number among the currently available colors;

[0086] The step of numbering each color in the temporal conflict graph is repeated until all the vertex sets are the colored vertex sets.

[0087] In the embodiment of the present application, the specific calculation process is as follows: (1) First, based on the result of data preprocessing, a time conflict graph G=(V,E) between waybills is established, where V is a vertex set corresponding to the set of assembled waybills; E is an edge set corresponding to the conflict relationship between waybills. Any edge in the graph Indicates that there is a time conflict between waybills i and j. (2) Then divide the vertex set V into two parts {C;U}, where C is the colored vertex set and U is the uncolored vertex set. Define the full degree as the number of shipping days - 1, and calculate the degree value of each vertex that has not been colored yet. The degree value is the number of adjacent vertices between each vertex and U. (3) Number each color and select the vertex with the largest current degree value , assign color 1 to vertex V. And move it from set U to set C, that is and (4) Arrange all vertices in U from large to small according to their saturation values, where saturation is the maximum number of colors that it and the colored points in the adjacent set can obtain, and select the vertex with the current maximum saturation. , when there are multiple points with equal saturation, select the vertex with the maximum degree value. (5) Assign each vertex the color with the smallest number among the available colors, and move V from set U to set C, that is and (6)If C=V, the algorithm terminates. Otherwise, go to (3).

[0088] In one possible implementation, the allocation solution is solved based on the balanced priority best fit descent BFD algorithm, including:

[0089] Define a waybill set and a box set, where the box set is used to represent the delivery days of working days;

[0090] The waybill set is sorted in descending order of conflict values ​​to obtain an allocation set;

[0091] The allocation set is allocated to the box set in sequence, so as to minimize the coefficient of variation of allocation of waybills to boxes.

[0092] In the embodiment of the present application, the specific calculation process is: (1) Define set I={I1,I2,⋯In} to represent the waybill set, and set B={B1,B2,⋯B5} to represent the box (shipping days from Monday to Friday) set. (2) Arrange the waybills in descending order according to the conflict value to form a new set sequence. It should be noted that even for a set of non-conflicting waybills, each waybill will generate a conflict value during the calculation of the conflict graph degree. (3) Seek the internal optimal allocation plan. Carton allocation is performed based on the principle of minimizing the variance of the boxes allocated, on the basis of satisfying the requirement that there is sufficient remaining capacity and no conflict with the allocated items. Box is the total capacity of the boxes (the platform’s delivery capacity for the day), The amount of goods already in the box (the number of pieces assigned to the waybill), Indicates the packing rate; N is the number of boxes; Place separately Coefficient of variation within the box , and adjust the coefficient of variation to the minimum to achieve distribution solution. X is the average number of waybills placed in the box, .

[0093] In one possible implementation manner, after solving the allocation scheme for each waybill set and obtaining the allocation result, the method further includes:

[0094] When there is a waybill set that cannot be solved, sort the vehicles that have not been assigned a delivery time in descending order according to the degree value, and obtain the waybill data of the first vehicle in the sequence;

[0095] Extracting the allocated delivery time waybill that meets the preset constraint condition from the box set based on the waybill data of the first vehicle;

[0096] Calculate the cargo variance of each of the waybills with assigned delivery time based on the BFD algorithm, and perform SWAP exchange between the waybill with assigned delivery time with the smallest variance and the waybill data of the first vehicle;

[0097] Insert the exchanged goods into the insertion time window whose overall variance is less than the variance of the current time window;

[0098] The number of vehicles not assigned with delivery time is updated based on the insertion time window, and the step of sorting the vehicles not assigned with delivery time in descending order of degree values ​​is repeated until the number of vehicles is zero.

[0099] In the embodiment of the present application, based on the above-mentioned solution steps, a complete solution to the TTVRP problem can be obtained in most cases. However, due to the special requirements of commercial companies, the limited delivery capacity and delivery time of the platform, in the case of serious conflicts, some vehicles may not be able to be loaded, that is, it is impossible to arrange the delivery of all waybills. At this time, it is necessary to perform a SWAP-based exchange repair strategy on the objects that cannot be solved, and repair the local solution by iteratively determining the waybill allocation situation. The specific calculation process is as follows:

[0100] Step 1: Sort the vehicles IO that have not been assigned a delivery time in descending order according to their degree values, and take the first vehicle Ii in the sequence;

[0101] Step 2: Extract the assigned delivery time waybill BO from box B that meets the following constraints:

[0102] a) The consignment quantity on the waybill shall not be less than the consignment quantity on waybill II.

[0103] b) The waybill value is not full.

[0104] c) Satisfy the time constraints of waybill Ii.

[0105] d) The combined load quantity of the waybill is less than or equal to the remaining space within the time window of the corresponding waybill.

[0106] Step 3: Use the BFD algorithm to calculate the variance of each BO product, and select the one with the smallest variance to perform SWAP with Ii.

[0107] Step 4: Insert a time window that makes the overall variance smaller for the exchanged goods.

[0108] Step 5: Update IO; if IO is not empty, return to step 1. Otherwise, the repair is complete.

[0109] S103: Integrate all the allocation results to obtain an optimized transportation task.

[0110] In the embodiment of the present application, after the aforementioned process, each optimized allocation result obtained by redistribution after considering the daily plan demand and the impact of the vehicle path problem can be obtained, and by integrating these allocation results again, the optimized transportation task can be obtained. In the actual transportation process, scheduling is performed according to the optimized transportation task. Since the entire transportation scheduling is systematically optimized from a global perspective, the transportation efficiency is better.

[0111] After implementing this application plan, the number of cigarette shipping orders, freight and mileage savings data for a certain year were randomly selected, and the comparison table is as follows:

[0112] date Weekly Number of waybills cost mileage 20200413 12 weeks 15.91% 5.33% 11.89% 20200525 18 weeks 25.00% 12.98% 20.05% 20200720 26 weeks 24.39% 8.24% 22.70% 20201019 38 weeks 24.07% 8.14% 20.86% 20201116 42 weeks 28.95% 9.81% 20.66%

[0113] As can be seen from the table, according to the optimization plan of this application, the number of waybills, transportation logistics costs, and carrier mileage of the five randomly selected data sets have all been reduced compared with the manual scheduling plan of the same period. The number of waybills has dropped by more than 15%, the transportation costs have dropped by more than 5%, and the mileage has dropped by more than 11%.

[0114] In addition, a comparative analysis of the balance of dispatching plans in a certain year was conducted based on this solution. The historical weekly dispatching data of a certain year was randomly selected, and the balance of dispatching plans under algorithm dispatching and manually arranged dispatching plans were compared and analyzed, as shown in the following table. Among them, the balance is equal to the coefficient of variation of the number of shipments every Sunday.

[0115] date Weekly Algorithm plan balance Artificial planning balance 20200413 12 weeks 0.55 1.45 20200525 18 weeks 0.39 0.45 20200720 26 weeks 0.48 0.71 20201019 38 weeks 0.23 1.35 20201116 42 weeks 0.81 1.42 20201214 46 weeks 0.75 0.89

[0116] As can be seen from the table, the balance of the delivery plan arranged by intelligent scheduling is lower than that of the delivery plan arranged by manual scheduling. By calculating the historical data that has occurred throughout the year, it can be seen that the mean of the balance of the intelligent algorithm is 0.750, and the mean of the manual algorithm is 0.894. Through the T test of the double population means, p=0.037, the test shows that in a statistical sense, the balance of the algorithm is better than the balance of manual allocation, which further verifies the effectiveness of the model.

[0117] By comparing and analyzing the number of waybills, transportation costs, transportation mileage, and delivery plan balance of the historical waybill data that have occurred, manual scheduling and intelligent algorithm scheduling, we found that:

[0118] (1) Compared with the manual scheduling solution, the intelligent optimization solution reduced the number of waybills per week by an average of 18.43%, the total transportation cost by an average of 5.02%, and the total mileage by an average of 15.26%.

[0119] (2) T-test of double population means, p=0.037. The test shows that the balance of the algorithm is statistically better than that of manual allocation.

[0120] The following will be combined with the attached Figure 2 , the finished cigarette transportation weekly plan intelligent scheduling device provided in the embodiment of the present application is introduced in detail. It should be noted that the attached Figure 2 The finished cigarette transportation weekly plan intelligent scheduling device shown is used to implement this application Figure 1 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 1 The embodiment shown.

[0121] See also Figure 2 , Figure 2 Schematic diagram of the structure of an intelligent scheduling device for weekly transportation plan of finished cigarettes provided in the embodiment of the present application. Figure 2 As shown, the device comprises:

[0122] The acquisition module 201 is used to acquire weekly planned transportation tasks, and sequentially merge the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point consolidation tasks to obtain a consolidation waybill, so as to minimize the reduction of the total transportation distance of the merged tasks;

[0123] A decomposition module 202 is used to perform conflict graph degree decomposition on the combined waybill based on the daily plan requirement conditions to obtain waybill sets, and solve the allocation scheme for each waybill set to obtain an allocation result;

[0124] The integration module 203 is used to integrate all the allocation results to obtain an optimized transportation task.

[0125] In one possible implementation, the acquisition module 201 includes:

[0126] A first acquisition unit is used to acquire a weekly planned transportation task based on a preset intelligent scheduling model, and determine the actual driving distance between warehouses of each commercial company according to the weekly planned transportation task;

[0127] A pairing unit, used to pair the warehouses of each commercial company based on the transportation data of the warehouses of each commercial company and the actual driving distance, and obtain an initial optimized level pairing solution of the warehouses of each commercial company;

[0128] An evolution unit, used for performing evolutionary training on each of the initial optimization level pairing solutions based on a swarm intelligence algorithm to obtain a pairing optimization level;

[0129] A merging unit is used to merge the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point joint loading tasks based on the pairing priority level, and obtain a joint loading waybill to maximize the reduction in the transportation distance of the total task after the merger.

[0130] In one possible implementation, the merging unit includes:

[0131] A merging component is used to merge the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point loading tasks based on the pairing priority level to obtain a preliminary scheduling plan;

[0132] A first optimization component is used to optimize the preliminary scheduling plan based on a local search algorithm to obtain a preliminary loading plan;

[0133] The second optimization element is used to optimize the access sequence of the preliminary loading scheme by enumeration method to obtain a loading waybill, so as to maximize the reduction of the total task transportation distance after the merger.

[0134] In one possible implementation, the decomposition module 202 includes:

[0135] A waybill decomposition unit, configured to perform conflict graph degree decomposition on the combined waybill based on the daily plan requirement conditions to obtain waybill sets, wherein the waybill sets include conflict waybill sets and non-conflict waybill sets;

[0136] A first solving unit is used to solve an allocation scheme for the conflicting waybill set based on a colored GC decomposition algorithm to obtain a first allocation result;

[0137] The second solving unit is used to solve the allocation scheme for the non-conflicting waybill set based on the balanced priority best fit descending BFD algorithm to obtain a second allocation result;

[0138] The integration unit is used to integrate the first allocation result and the second allocation result to obtain an allocation result.

[0139] In one possible implementation, the first solving unit includes:

[0140] A time conflict graph establishment component is used to establish a time conflict graph between each waybill, wherein the time conflict graph includes a vertex set and an edge set, wherein the vertex set is used to represent the assembled waybill set, and the edge set is used to represent the conflict relationship between each waybill;

[0141] A partitioning component is used to divide the vertex set into a colored vertex set and an uncolored vertex set, and calculate the degree value of each vertex that has not been colored yet, wherein the degree value is the number of vertices adjacent to each vertex and the uncolored vertex set, and when the degree value is full, it indicates that the delivery days are reduced by a unit natural day;

[0142] A color numbering component is used to number each color in the time conflict graph, select a first vertex with the largest current degree value, assign a first numbered color to the first vertex, and move the first vertex from the uncolored vertex set to the colored vertex set;

[0143] A saturation sorting component, used to sort all vertices in the uncolored vertex set according to saturation, and determine the second vertex with the maximum saturation at present;

[0144] an assignment component, used for assigning a second numbered color to each of the second vertices, and moving the second vertices from the uncolored vertex set to the colored vertex set, wherein the second numbered color is the color with the smallest number among the currently available colors;

[0145] The first repeating element is used to repeat the step of respectively numbering each color in the time conflict graph until all the vertex sets are the colored vertex sets.

[0146] In one possible implementation, the second solving unit includes:

[0147] A definition element is used to define a waybill set and a box set, wherein the box set is used to represent the delivery days of the working day;

[0148] A descending sorting component is used to sort the waybill set in descending order of conflict values ​​to obtain a distribution set;

[0149] The allocation component is used to allocate the allocation set to the box set in sequence, so as to minimize the coefficient of variation of the allocation of the waybill to the box.

[0150] In one possible implementation, the device further comprises:

[0151] A judgment module, used for sorting the vehicles to which delivery time is not assigned in descending order according to degree values ​​when there is a waybill set that cannot be solved, and obtaining the waybill data of the first vehicle in the sequence;

[0152] An extraction module, configured to extract, from the box set, a waybill with an allocated delivery time that satisfies a preset constraint condition based on the waybill data of the first vehicle;

[0153] A variance calculation module, used for calculating the cargo variance of each of the allocated delivery time waybills based on the BFD algorithm, and performing SWAP exchange between the allocated delivery time waybill with the smallest variance and the first vehicle waybill data;

[0154] An insertion module is used to insert the exchanged goods into an insertion time window whose overall variance is less than the variance of the current time window;

[0155] An updating module is used to update the number of vehicles without assigned delivery time based on the insertion time window, and repeat the step of sorting the vehicles without assigned delivery time in descending order according to degree values ​​until the number of vehicles is zero.

[0156] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (FPGA), an integrated circuit (IC), etc.

[0157] Each processing unit and / or module of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0158] See also Figure 3 , which shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application, the electronic device can be used to implement Figure 1 The method in the embodiment shown. Figure 3 As shown, the electronic device 300 may include: at least one central processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0159] The communication bus 302 is used to realize the connection and communication between these components.

[0160] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0161] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0162] Among them, the central processing unit 301 may include one or more processing cores. The central processing unit 301 uses various interfaces and lines to connect various parts of the entire electronic device 300, and executes various functions and processes data of the terminal 300 by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the central processing unit 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The central processing unit 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the central processing unit 301, and it can be implemented separately through a chip.

[0163] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned central processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0164] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the central processor 301 can be used to call the finished cigarette transportation weekly plan intelligent scheduling application stored in the memory 305, and specifically perform the following operations:

[0165] Obtain weekly planned transportation tasks, and sequentially merge the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point consolidation tasks to obtain a consolidation waybill, so as to minimize the reduction in the transportation distance of the total tasks after the consolidation;

[0166] Decomposing the degree of the conflict graph of the combined waybill based on the daily plan demand conditions to obtain each waybill set, and solving the allocation scheme for each waybill set to obtain an allocation result;

[0167] All the allocation results are integrated to obtain the optimized transportation task.

[0168] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0169] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0170] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0171] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0172] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0173] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0174] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0175] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct related hardware, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0176] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for intelligent scheduling of weekly transportation plans for finished cigarettes, characterized in that: The method comprises: Obtain weekly planned transportation tasks, and sequentially merge the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point consolidation tasks to obtain a consolidation waybill, so as to minimize the reduction in the transportation distance of the total tasks after the consolidation; Decomposing the degree of the conflict graph of the combined waybill based on the daily plan demand conditions to obtain each waybill set, and solving the allocation scheme for each waybill set to obtain an allocation result; Integrate all the allocation results to obtain an optimized transportation task; The method of obtaining the weekly planned transportation tasks, merging the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point combined loading tasks in turn, and obtaining a combined loading waybill, so as to minimize the combined total task transportation distance, includes: Obtain weekly planned transportation tasks based on a preset intelligent scheduling model, and determine the actual driving distances between warehouses of various commercial companies based on the weekly planned transportation tasks; Pairing the warehouses of each commercial company based on the transportation data of the warehouses of each commercial company and the actual driving distance to obtain an initial optimized grading pairing solution of the warehouses of each commercial company; Based on the swarm intelligence algorithm, evolutionary training is performed on the pairing solutions of each of the initial optimization levels to obtain the pairing optimization level; Based on the pairing priority level, the single-point direct dispatch tasks in the weekly planned transportation tasks are merged into multi-point joint loading tasks to obtain a joint loading waybill, so as to maximize the reduction of the transportation distance of the total task after the merger; The conflict graph degree decomposition of the combined waybill is performed based on the daily plan demand condition to obtain each waybill set, and the allocation scheme is solved for each waybill set to obtain the allocation result, including: Based on the daily plan requirement conditions, the conflict graph degree decomposition of the combined waybill is performed to obtain waybill sets, wherein the waybill sets include conflict waybill sets and non-conflict waybill sets; For the conflicting waybill set, solving the allocation scheme based on the colored GC decomposition algorithm to obtain a first allocation result; For the non-conflicting waybill set, an allocation solution is solved based on the balanced priority best fit descending BFD algorithm to obtain a second allocation result; Integrate the first allocation result and the second allocation result to obtain an allocation result; The allocation solution based on the coloring GC decomposition algorithm includes: Establishing a time conflict graph between the waybills, the time conflict graph comprising a vertex set and an edge set, the vertex set is used to represent the assembled waybills set, and the edge set is used to represent the conflict relationship between the waybills; Divide the vertex set into a colored vertex set and an uncolored vertex set, calculate the degree value of each vertex that has not been colored yet, the degree value is the number of vertices adjacent to each vertex and the uncolored vertex set, and when the degree value is full, it indicates that the delivery days are reduced by a unit natural day; Numbering each color in the time conflict graph, selecting a first vertex with the largest current degree value, assigning a first numbered color to the first vertex, and moving the first vertex from the uncolored vertex set to the colored vertex set; Sort all vertices in the uncolored vertex set according to saturation, and determine the second vertex with the maximum saturation; Assign a second numbered color to each of the second vertices, and move the second vertices from the uncolored vertex set to the colored vertex set, wherein the second numbered color is the color with the smallest number among the currently available colors; Repeat the step of numbering each color in the time conflict graph until all the vertex sets are the colored vertex sets; The allocation solution based on the balanced priority best suited descent BFD algorithm includes: Define a waybill set and a box set, where the box set is used to represent the delivery days of working days; The waybill set is sorted in descending order of conflict values ​​to obtain an allocation set; The allocation set is allocated to the box set in sequence, so as to minimize the coefficient of variation of allocation of waybills to boxes.

2. The method according to claim 1, characterized in that The step of merging the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point consolidation tasks based on the pairing priority level to obtain a consolidation waybill, so as to maximize the reduction in the transportation distance of the combined total tasks, includes: Based on the pairing priority level, the single-point direct dispatch tasks in the weekly planned transportation tasks are merged into multi-point loading tasks to obtain a preliminary scheduling plan; Optimizing the preliminary scheduling plan based on a local search algorithm to obtain a preliminary loading plan; The access sequence of the preliminary loading scheme is optimized by enumeration method to obtain a loading waybill, so as to maximize the reduction of the total task transportation distance after the merger.

3. The method according to claim 1, characterized in that: After solving the allocation scheme for each waybill set and obtaining the allocation result, the method further includes: When there is a waybill set that cannot be solved, sort the vehicles that have not been assigned a delivery time in descending order according to the degree value, and obtain the waybill data of the first vehicle in the sequence; Extracting the allocated delivery time waybill that meets the preset constraint condition from the box set based on the waybill data of the first vehicle; Calculate the cargo variance of each of the waybills with assigned delivery time based on the BFD algorithm, and perform SWAP exchange between the waybill with assigned delivery time with the smallest variance and the waybill data of the first vehicle; Insert the exchanged goods into the insertion time window whose overall variance is less than the variance of the current time window; The number of vehicles not assigned with delivery time is updated based on the insertion time window, and the step of sorting the vehicles not assigned with delivery time in descending order of degree values ​​is repeated until the number of vehicles is zero.

4. An intelligent scheduling device for weekly transportation plan of finished cigarettes, characterized in that: The device comprises: An acquisition module is used to acquire weekly planned transportation tasks, and sequentially merge the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point consolidation tasks to obtain a consolidation waybill, so as to minimize the reduction in the transportation distance of the total task after the merger; A decomposition module, used for performing conflict graph degree decomposition on the combined waybill based on the daily plan requirement conditions to obtain each waybill set, and respectively solving the allocation scheme for each waybill set to obtain an allocation result; An integration module, used for integrating all the allocation results to obtain an optimized transportation task; Wherein, the acquisition module includes: A first acquisition unit is used to acquire a weekly planned transportation task based on a preset intelligent scheduling model, and determine the actual driving distance between warehouses of each commercial company according to the weekly planned transportation task; A pairing unit, used to pair the warehouses of each commercial company based on the transportation data of the warehouses of each commercial company and the actual driving distance, and obtain an initial optimized level pairing solution of the warehouses of each commercial company; An evolution unit, used for performing evolutionary training on each of the initial optimization level pairing solutions based on a swarm intelligence algorithm to obtain a pairing optimization level; A merging unit, configured to merge the single-point direct dispatch tasks in the weekly planned transportation tasks into multi-point consolidation tasks based on the pairing priority level, and obtain a consolidation waybill, so as to maximize the reduction of the transportation distance of the total tasks after the merger; The decomposition module comprises: A waybill decomposition unit, configured to perform conflict graph degree decomposition on the combined waybill based on the daily plan requirement conditions to obtain waybill sets, wherein the waybill sets include conflict waybill sets and non-conflict waybill sets; A first solving unit is used to solve an allocation scheme for the conflicting waybill set based on a colored GC decomposition algorithm to obtain a first allocation result; The second solving unit is used to solve the allocation scheme for the non-conflicting waybill set based on the balanced priority best fit descending BFD algorithm to obtain a second allocation result; an integration unit, configured to integrate the first allocation result and the second allocation result to obtain an allocation result; The first solving unit comprises: A time conflict graph establishment component is used to establish a time conflict graph between each waybill, wherein the time conflict graph includes a vertex set and an edge set, wherein the vertex set is used to represent the assembled waybill set, and the edge set is used to represent the conflict relationship between each waybill; A partitioning component is used to divide the vertex set into a colored vertex set and an uncolored vertex set, and calculate the degree value of each vertex that has not been colored yet, wherein the degree value is the number of vertices adjacent to each vertex and the uncolored vertex set, and when the degree value is full, it indicates that the delivery days are reduced by a unit natural day; A color numbering component is used to number each color in the time conflict graph, select a first vertex with the largest current degree value, assign a first numbered color to the first vertex, and move the first vertex from the uncolored vertex set to the colored vertex set; A saturation sorting component, used to sort all vertices in the uncolored vertex set according to saturation, and determine the second vertex with the maximum saturation at present; an assignment component, used for assigning a second numbered color to each of the second vertices, and moving the second vertices from the uncolored vertex set to the colored vertex set, wherein the second numbered color is the color with the smallest number among the currently available colors; A first repeating element, configured to repeat the step of respectively numbering each color in the temporal conflict graph until all of the vertex sets are the colored vertex sets; The second solving unit comprises: A definition element is used to define a waybill set and a box set, wherein the box set is used to represent the delivery days of the working day; A descending sorting component is used to sort the waybill set in descending order of conflict values ​​to obtain a distribution set; The allocation component is used to allocate the allocation set to the box set in sequence, so as to minimize the coefficient of variation of the allocation of the waybill to the box.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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