Cargo scheduling method and cargo scheduling system

Abstract

The invention provides a cargo scheduling method and a cargo scheduling system. The method comprises the following steps: acquiring material storage information of each material stacking point; a single-time transportation stowage scheme model is generated, the objective function of the model is F = Q-sigma i belonging to M qi, and the constraint conditions of the model comprise the number of stowage points and vehicle loading capacity constraints; the charging points are divided into stockpiling point groups through which the vehicle passes in single transportation; solving the single-time transportation stowage scheme model based on a genetic algorithm to obtain an optimal stowage point group; updating the storage information of the stockpiling points, obtaining the optimal stockpiling point group again, and circulating the step until the termination condition is reached; all the optimal stockpiling point groups form a cargo scheduling scheme; judging whether the cargo scheduling scheme meets a preset requirement or not, and if yes, determining the scheduling scheme as a final confirmation scheme; if not, the constraint condition is adjusted, and then the single-time transportation stowage scheme model is optimized and solved again until the cargo scheduling scheme meeting the requirements is obtained. According to the invention, the utilization rate of the vehicle and the cargo transfer efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of freight transportation optimization, and in particular to a freight scheduling method and a freight scheduling system. Background Technology

[0002] When multiple devices or production lines are producing / operating goods simultaneously, the accumulation of goods can affect the storage of subsequent goods or waste materials, requiring timely removal. However, the accumulation of some goods is irregular, making it impossible to schedule vehicle operations based on the accumulation speed. Generally, the weight of goods accumulated at a single stacking point will not exceed the vehicle's maximum load capacity. Without a scientific loading and transportation plan, vehicles will blindly shuttle between multiple stacking points, resulting in excessive travel time and low utilization of vehicle load capacity.

[0003] Taking the cold rolling production area of ​​a steel plant as an example, there are nearly a hundred stockpiling points for storing steel scrap. Each stockpiling point has a fixed amount of steel scrap. Once the maximum storage capacity is reached, the scrap steel cutting process at that point will be interrupted. To ensure the continuous operation of the scrap steel cutting process, vehicles need to transport the stockpiled steel scrap to the recycling area in a timely manner. However, the accumulation speed of the steel scrap is unpredictable, making it impossible to schedule vehicle operations based on this speed. Vehicles often wander aimlessly while transporting steel scrap, affecting the efficiency of scrap steel transfer. Therefore, a scheduling method is needed to improve vehicle utilization and achieve efficient transportation of steel scrap. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a cargo scheduling method and a cargo scheduling system to improve the efficiency of vehicle cargo transfer.

[0005] This invention provides a cargo scheduling method, comprising the following steps:

[0006] S1. Obtain the material storage information for each stockpile point;

[0007] S2. Generate a single-transport loading scheme model. The objective function of the single-transport loading scheme model is F = Q - ∑ i∈M q i Where Q is the maximum load capacity of the vehicle, q i Let M be the loading capacity of the i-th stacking point in a single transport loading scheme, and M be the loading point set of the single transport loading scheme; the constraints of the single transport loading scheme model include the number of stacking points and the vehicle loading capacity constraint; the loading point set is the set of stacking points that the vehicle passes through in a single transport.

[0008] S3. Based on the genetic algorithm, optimize the single transport loading scheme model to obtain an optimal stacking point group;

[0009] S4. Update the material storage information at the stacking points and optimize the single transport loading scheme model to obtain the optimal stacking point group again. Repeat this step until the preset termination condition is reached. Based on all the obtained optimal stacking point groups, obtain the cargo scheduling scheme.

[0010] S5. Determine whether the cargo scheduling plan meets the preset requirements. If it does, the scheduling plan is the final confirmed plan. If it does not meet the requirements, adjust the constraints of the single transport loading plan model, and then re-optimize and solve the single transport loading plan model until a cargo scheduling plan that meets the requirements is obtained.

[0011] In this scheme, the sum of the loading weights of the goods at each stacking point in the optimal stacking point group is closest to the maximum loading capacity of the vehicle. The cargo scheduling scheme is composed of all optimal stacking point groups. That is, throughout the scheduling process, the cargo scheduling scheme can match the vehicle with the optimal loading capacity, ensuring that the vehicle fully utilizes its loading capacity and does not overload, thereby improving the efficiency of cargo transportation.

[0012] Preferably, the storage information is the weight of the goods.

[0013] Preferably, the constraint condition for the number of stockpiling points is: v≤4, where v is the number of stockpiling points; the constraint condition for the number of stockpiling points can be determined according to user requirements.

[0014] Preferably, the vehicle loading capacity constraint is: 0.9Q≤∑ i∈M q i ≤Q, where, ∑ i∈M q i This represents the load weight after the vehicle passes through all the stacking points in the stacking point group during a single transport. This setting ensures that the weight of goods loaded by the vehicle in each transport is close to the vehicle's maximum load capacity, thereby maximizing the vehicle's load utilization rate.

[0015] Preferably, a path model is constructed with the goal of minimizing transportation time, taking each stacking point group in the cargo scheduling scheme as the object; the path model is then optimized using the annealing algorithm to obtain the optimal loading path for each stacking point group in the cargo scheduling scheme. The shorter the loading path time, the higher the efficiency of cargo transportation.

[0016] Preferably, the objective function of the path model is: E = ∑ x∈g ∑ y∈g E xy Z xy Where g represents the union of the vehicle's starting point and the set of material stacking points along the desired path, and x and y represent any two material stacking points in the set. xyZ represents the time taken to travel from stockpile point x to stockpile point y. xy This indicates whether the path from x to y is selected during transportation. If the path from x to y is selected, then Z... xy The value of Z is 1; if the path from x to y is not selected, then Z... xy The value is 0.

[0017] Preferably, the method for optimizing the path model using the annealing algorithm includes the following steps:

[0018] N1, Define the initial temperature T0 and the final temperature T end Maximum number of iterations N, and the temperature decrease rate alpha are defined.

[0019] N2. Randomly generate an initial solution S, that is, randomly generate a loading path, and obtain the objective function E for the loading path;

[0020] N3. Change the order of the stockpiling points along the random loading path to generate a new solution S. ′ And calculate the increment ΔE;

[0021] N4. If the increment ΔE < 0, then accept the new solution as the current solution; if the increment ΔE > 0, then decide whether to accept the new solution based on a certain probability P.

[0022] N5. Repeat steps N3 and N4 at the current temperature drop rate until the temperature drops to the termination temperature or the number of iterations is reached, then stop the calculation. At this point, the optimal loading path is obtained.

[0023] The present invention also provides a cargo scheduling system, including a marking module, a material storage information acquisition module, a first objective function construction module, a fitness function construction module, an optimal stacking point group acquisition module, a cargo scheduling scheme acquisition and judgment module, and a cargo scheduling scheme output module;

[0024] The marking module is used to label the stockpiling points;

[0025] The material storage information acquisition module is used to acquire the weight of the stored material at each stacking point;

[0026] The first target construction module is used to construct the target function F = Q - ∑ i∈M q i ;

[0027] The fitness function module is used to construct the fitness function;

[0028] The optimal stockpiling point group acquisition module obtains the optimal stockpiling point group based on a genetic algorithm, and updates the storage weight of each stockpiling point in a loop based on the obtained optimal stockpiling point group to obtain all optimal stockpiling point groups in a loop.

[0029] The cargo scheduling scheme acquisition and judgment module is used to summarize all the best stacking point groups to form a cargo scheduling scheme, and to determine whether there are any stacking points where the cargo has not been fully loaded based on the formed cargo scheduling scheme; if not, the formed cargo scheduling scheme is sent to the cargo scheduling scheme output module; if so, the constraints are adjusted and the cargo scheduling scheme is formed and judged again until a cargo scheduling scheme that meets the requirements is obtained.

[0030] Preferably, the cargo loading path system further includes a location information acquisition module, a second target construction module, and an optimal loading path solution module;

[0031] The location information acquisition module is used to acquire the location of the stacking points in each stacking point group in the cargo scheduling plan;

[0032] The second objective construction module is used to construct the objective function E = ∑ for each stockpile group. x∈g ∑ y∈g E xy Z xy ;

[0033] The optimal loading path solution module obtains the optimal loading path for each stacking point group based on the annealing algorithm.

[0034] As described above, the present invention relates to a cargo scheduling method and system. Based on the weight information of each stacking point, a single-transport loading scheme model is constructed with the goal of maximizing vehicle load utilization. An optimal stacking point group is obtained by iteratively optimizing the single-transport loading scheme model using a genetic algorithm. After updating the weight information, the single-transport loading scheme model is optimized again to obtain a new optimal stacking point group. This process is repeated until a preset termination condition is met. All optimal stacking point groups are then combined to form a cargo scheduling scheme. Next, it is determined whether the obtained cargo scheduling scheme satisfies the condition of transporting all cargo from all stacking points. If not, the constraints are changed to reformulate the cargo scheduling scheme until a satisfactory cargo scheduling scheme is obtained. Because the entire cargo scheduling scheme acquisition process is iteratively optimized with the goal of maximizing vehicle load utilization, it ensures that users can fully utilize vehicle load and improve vehicle utilization when scheduling vehicles based on the acquired cargo scheduling scheme.

[0035] Furthermore, this application takes each stacking point group in the cargo scheduling scheme as the object and constructs a path model with the goal of minimizing transportation time; it optimizes the path model based on the annealing algorithm to obtain the optimal loading path for each stacking point group in the cargo scheduling scheme; that is, this scheme can find all the most suitable stacking point groups for loading in terms of weight, and can also find the most suitable loading path in each stacking point group, thus greatly improving the efficiency of vehicle cargo transportation. Attached Figure Description

[0036] Figure 1 A flowchart for obtaining a cargo scheduling scheme that meets the requirements in the invention.

[0037] Figure 2 A flowchart for obtaining the cargo loading path in the invention.

[0038] Figure 3 This is a schematic diagram of the modules of the cargo scheduling system in the invention.

[0039] Figure 4 This is a schematic diagram of the modules of the cargo loading path system in the invention. Detailed Implementation

[0040] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.

[0041] Please see Figures 1 to 4 It should be understood that the structures, proportions, sizes, etc., illustrated in the accompanying drawings are merely for illustrative purposes to aid those skilled in the art and to facilitate understanding and reading. They are not intended to limit the scope of the invention and therefore have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, without affecting the effectiveness and purpose of the invention, should still fall within the scope of the technical content disclosed in this invention. Furthermore, the terms such as "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity and not intended to limit the scope of the invention. Changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention's implementation.

[0042] This invention proposes a cargo scheduling method and system for scheduling scrap materials in the cold rolling section of a steel plant. The cold rolling section of a steel plant has multiple cold rolling production lines, and each production line has a stockpile point at its end for storing steel scrap such as cut ends generated during production. Once the steel scrap accumulates to a certain extent, it must be cleared to avoid affecting the smooth operation of the cold rolling production lines. However, because the accumulation rate of steel scrap is irregular, it is impossible to schedule vehicles for transportation based on the cumulative rate of goods; only vehicles can be assigned to handle the transfer of steel scrap from specific stockpile points, resulting in long transportation times and low vehicle utilization. Therefore, this application provides a cargo scheduling method and system to improve vehicle utilization and cargo transfer efficiency.

[0043] The cargo scheduling method and scheduling system proposed in this invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] Example 1:

[0045] like Figure 1 As shown, the detailed steps of the cargo scheduling method in this embodiment are as follows:

[0046] S1. Obtain the material storage information for each stockpile point;

[0047] It is understood that the storage information can be either the weight or volume of the goods, and there is no limitation on this; in this embodiment, the storage information is preferably the weight of the goods.

[0048] S2. Generate a single-transport loading scheme model. The single-transport loading scheme model includes constructing the objective function F = Q - ∑ i∈M q i Where Q is the maximum load capacity of the vehicle, q i Let M be the loading capacity of the i-th stacking point in a single transport loading scheme, and M be the loading point set of the single transport loading scheme. The constraints of the single transport loading scheme model include the number of stacking points and the vehicle loading capacity constraint. The loading point set is the set of stacking points that the vehicle passes through in a single transport.

[0049] Among them, the constraint on the number of stacking points refers to the limitation on the number of stacking points a vehicle must pass through in a single transport of goods; the constraint on vehicle loading refers to the constraint on the weight of goods a vehicle must carry in a single transport of goods. Generally, the fewer stacking points a vehicle passes through in a single transport, the less time is spent on the loading route; the closer the vehicle loading constraint is to the vehicle's maximum load capacity, the higher the vehicle's loading utilization rate. In this embodiment, the constraint on the number of stacking points is preferably v≤4, where v is the number of stacking points; the constraint on vehicle loading capacity is preferably: 0.9Q≤∑ i∈M q i ≤Q. Where, ∑ i∈M q i This refers to the load weight after the vehicle passes through all the stacking points in the stacking point group during a single transport.

[0050] S3. Based on the genetic algorithm, optimize the single transport loading scheme model to obtain an optimal stacking point group;

[0051] The steps of a genetic algorithm include: obtaining an initial population, calculating the fitness of individuals in the initial population to obtain multiple individuals with higher fitness, performing mutation operations on the individuals with higher fitness to obtain new individuals, and the new individuals forming a new population; calculating the fitness of individuals in the new population again to obtain multiple individuals with higher fitness... and so on, until the termination condition of the genetic algorithm is reached, and the optimal individual is output.

[0052] Specifically: First, based on the constraints of the single transport loading scheme model, an initial population is randomly generated. Each individual in the initial population represents a candidate solution, which represents a group of material stacking points that the vehicle may pass through during a single transport loading.

[0053] By using roulette wheel selection, several individuals with high fitness are selected from the initial population;

[0054] The selected individuals undergo mutation operations to generate new individuals. Mutation operations refer to modifying the characteristics of individuals with high fitness to prevent the computational results from converging to a local optimum. It should be noted that uniform mutation, boundary mutation, multi-point mutation, exchange mutation, local mutation, and arithmetic mutation are all common mutation operations, and no specific mutation method is limited here. In this embodiment, exchange mutation is used to change the characteristics of individuals with high fitness. Specifically, exchange mutation refers to randomly exchanging the characteristics of two individuals (i.e., randomly exchanging the stockpiles in two stockpiling point groups) to obtain new individuals, which form a new population. Again, a roulette wheel selection is used to select individuals with higher fitness from the new population… This process is repeated until the termination condition of the genetic algorithm is met, at which point the individual (optimal stockpiling point group) is output. In this embodiment, the termination condition of the genetic algorithm is 100 iterations.

[0055] S4. Update the material storage information at the stacking points and optimize the single transport loading scheme model to obtain the optimal stacking point group again. Repeat this step until the preset termination condition is reached. Based on all the obtained optimal stacking point groups, obtain the cargo scheduling scheme.

[0056] In this embodiment, the preset termination condition is that, based on the current constraints, it is impossible to obtain a new stacking point group.

[0057] S5. Determine whether the cargo scheduling plan meets the preset requirements. If it does, the scheduling plan is the final confirmed plan. If it does not meet the requirements, adjust the constraints of the single transport loading plan model, and then re-optimize and solve the single transport loading plan model until a cargo scheduling plan that meets the requirements is obtained.

[0058] In this embodiment, the preset requirement is that the vehicle must load all goods at all stacking points. Adjusting the constraints of the single-transport loading scheme model involves adjusting the constraint on the number of stacking points and / or the constraint on the vehicle loading capacity. For example, the constraint on the number of stacking points can be adjusted to x≤6 or x≤3, etc., and the constraint on the vehicle loading capacity can be adjusted to 0.7Q≤∑ i∈M q i ≤Q or 0.6Q≤∑ i∈M q i≤Q, etc.; to ensure that the cargo scheduling plan meets the requirement of transporting all cargo from the stockpiling point. Adjustments should be made based on the actual situation, such as the remaining cargo quantity, and are not subject to any restrictions.

[0059] Based on the above steps, let's take seven stockpiling points as an example: First, label all stockpiling points with numbers (1, 2, 3, 4, 5, 6, 7). The label of each stockpiling point and the corresponding weight of stored material are shown in Table 1.

[0060] Table 1 lists the labels of each stockpiling point and the corresponding stock weight.

[0061] label 1 2 3 4 5 6 7 Material weight 0.81Q 0.86Q 0.75Q 0.52Q 0.93Q 0.6Q 0.68Q

[0062] First, based on the constraints of the single-transport loading scheme model, an initial population is randomly generated: (1, 2, 3); (1, 2, 5); (1, 4, 6); (1, 6)...(1, 7). Through roulette wheel selection, several excellent individuals are selected from the initial population: (1, 2, 3), (1, 6)...(1, 7). Mutation operations are performed on these excellent individuals to obtain new individuals. In this embodiment, the mutation operation uses exchange mutation, which involves exchanging the characteristics of two individuals to obtain new individuals. For example, exchanging the stockpiling points in (1, 2, 3) and (1, 7) yields new individuals (1, 2, 7) and (1, 3). These new individuals then form a new population. Again, roulette wheel selection is used to select individuals with higher fitness from the new population... This process is repeated 100 times until the calculation terminates. The best individual (best stockpiling point group) is then output. It is understood that exchange mutation is only the mutation method selected in this embodiment, and is not the only limitation. In actual application, appropriate mutation methods should be selected according to the needs.

[0063] It should be noted that if multiple "optimal" stacking point groups (with the same loading weight and all closest to the vehicle's maximum load capacity) appear in a single transport loading plan, a secondary selection is performed on these "optimal" stacking point groups. Specifically, the stacking point group with fewer stacking points is selected as the optimal stacking point group. If the number of stacking points is the same, then any stacking point group is randomly selected as the optimal stacking point group. That is, in the selection of the optimal stacking point group, the loading weight has the highest priority, followed by the number of stacking points. Taking the first obtained single transport loading plan as an example, if the loading weight of stacking point groups (1, 2, 5); (1, 6); and (1, 7) is Q, then stacking point group (1, 2, 5) is eliminated. Since the remaining two stacking point groups have the same number of stacking points, (1, 6) is randomly selected as the optimal stacking point group.

[0064] In summary, the sum of the loading weights of goods at all stacking points in the optimal stacking point group is closest to the maximum loading capacity of the vehicle. The cargo scheduling scheme is composed of all optimal stacking point groups; that is, throughout the entire scheduling process, the optimal loading weight can be matched to the vehicle, ensuring that the vehicle fully utilizes its loading capacity without overloading, thus improving the efficiency of cargo transportation. It is understood that the material storage information of each stacking point mentioned in this invention is obtained in real time, and the method mentioned in this invention can be used to transport goods even when the cold rolling equipment is running.

[0065] Taking each stacking point group in the cargo scheduling scheme as the object, a path model is constructed with the objective of minimizing transportation time. The path model is then optimized using the annealing algorithm to obtain the optimal loading path for each stacking point group in the cargo scheduling scheme. The objective function of the path model is: E = ∑ x∈g ∑ y∈g E xy Z xy Where g represents the union of the vehicle's starting point and the set of material stacking points along the desired path, and x and y represent any two material stacking points in the set. xy Z represents the time taken to travel from stockpile point x to stockpile point y. xy This indicates whether the path from x to y is selected during transportation. If the path from x to y is selected, then Z... xy The value of Z is 1; if the path from x to y is not selected, then Z... xy The value is 0.

[0066] like Figure 2 As shown, the methods for determining the optimal loading path for each stockpile group include:

[0067] N1, Define the initial temperature T0 and the final temperature T end Maximum number of iterations N, and the temperature decrease rate alpha are defined.

[0068] N2. Randomly generate an initial solution S, that is, randomly generate a loading path, and obtain the objective function E for the loading path;

[0069] N3. Change the order of the stockpiling points along the random loading path to generate a new solution S. ′ And calculate the increment ΔE;

[0070] N4. If the increment ΔE < 0, then accept the new solution as the current solution; if the increment ΔE > 0, then decide whether to accept the new solution based on a certain probability P.

[0071] N5. Repeat steps N3 and N4 at the current temperature drop rate until the temperature drops to the termination temperature or the number of iterations is reached, then stop the calculation. At this point, the optimal loading path is obtained.

[0072] In this embodiment, the initial temperature T0 is defined as 2000℃ and the termination temperature T end =0.1℃, maximum number of iterations 1000, defined temperature decrease rate alpha = 0.98; when the temperature drops to 0.1℃ or the number of iterations is reached, the calculation is completed and the result is output.

[0073] In summary, this solution can identify the most suitable stacking points for loading based on weight, and also find the optimal loading path among these stacking points, thus significantly improving the efficiency of vehicle cargo handling.

[0074] Example 2:

[0075] like Figure 3 As shown, this embodiment of the invention provides a cargo scheduling system, including a marking module, a material storage information acquisition module, a first objective function construction module, a fitness function construction module, an optimal stacking point group acquisition module, a cargo scheduling scheme acquisition and judgment module, and a cargo scheduling scheme output module;

[0076] The marking module is used to mark the stockpiling points and label them with numbers 1, 2, 3, 4, 5...n;

[0077] The material storage information acquisition module is used to obtain the storage weight of each stockpile point;

[0078] The first objective construction module is used to construct the objective function F = Q - ∑ i∈M q i ;

[0079] The fitness function module is used to construct the fitness function, which is the objective function itself or the derivative of the objective function. In this embodiment, the fitness function is the objective function itself. The fitness function is used to select the stacking point group with higher fitness.

[0080] The optimal stockpiling point group acquisition module obtains the optimal stockpiling point group based on a genetic algorithm, and updates the storage weight of each stockpiling point in a loop according to the obtained optimal stockpiling point group to obtain all optimal stockpiling point groups in a loop.

[0081] Specifically, the optimal stockpiling point group acquisition module first randomly generates a set of stockpiling point groups based on constraints (i.e., generates an initial population). Then, it performs selection and mutation operations on the stockpiling point groups in the set according to the fitness function until the optimal stockpiling point group is found. Next, it updates the storage weight of the stockpiling points and re-acquires the optimal stockpiling point group, until all optimal stockpiling point groups are found. The constraints include the number of stockpiling points and the vehicle loading capacity constraint. In this embodiment, the constraint on the number of stockpiling points is i≤4; the constraint on the vehicle loading capacity is 0.9Q≤∑ i∈M q i ≤Q.

[0082] The cargo scheduling scheme acquisition and judgment module is used to summarize all the best stacking point groups to form a cargo scheduling scheme, and to determine whether there are any stacking points where the cargo has not been fully loaded based on the formed cargo scheduling scheme. If not, the formed cargo scheduling scheme is sent to the cargo scheduling scheme output module. If so, the constraints are adjusted and the cargo scheduling scheme is formed and judged again until a cargo scheduling scheme that meets the requirements is obtained.

[0083] Genetic algorithms, due to the control of the fitness function, can provide optimal results for large amounts of data in a short time, and the results are accurate and effective. Therefore, a genetic algorithm is used to find the optimal single-transport loading scheme in this invention.

[0084] like Figure 4 As shown, the cargo scheduling system also includes a location information acquisition module, a second target construction module, and an optimal loading path solution module;

[0085] The location information acquisition module is used to acquire the location of the stacking points in each stacking point group in the cargo scheduling plan;

[0086] The second objective construction module is used to construct the objective function E = ∑ for each stockpile group. x∈g ∑ y∈g E xy Z xy Where g represents the union of the vehicle's starting point and the set of material stacking points along the desired path, and x and y represent any two material stacking points in the set. xy Z represents the time taken to travel from stockpile point x to stockpile point y. xy This indicates whether the path from x to y is selected during transportation; if the path from x to y is selected, then Z... xy The value of Z is 1; if the path from x to y is not selected, then Z... xy The value is 0;

[0087] The optimal loading path solution module obtains the optimal loading path for each stockpile group based on the annealing algorithm.

[0088] In this embodiment, the annealing algorithm is used to find the optimal loading path for each stacking point group in the cargo scheduling scheme, thereby improving the transportation efficiency of the vehicles. The annealing algorithm is a global optimization algorithm used to find the optimal or near-optimal solution in the search space, and it is more likely to find the global optimal or near-optimal solution; therefore, the annealing algorithm is used to find the optimal loading path (with the least time) mentioned in this invention.

[0089] It should be noted that the cargo scheduling method mentioned in Example 1 and the cargo scheduling system mentioned in Example 2 are not specifically designed for cargo transportation at the cold-rolled steel production area's stacking points. The cargo scheduling method and system proposed in this application are still applicable in other production or transportation fields. For example, in the express delivery industry, transit warehouses use multiple assembly lines (by region) to classify express packages. However, the volume and weight of the packages vary, and when different packages are stacked at the end of the assembly line, their total volume is unpredictable. The solution proposed in this application is applicable to the above situation.

[0090] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A cargo dispatching method characterized by, The method comprises the following steps: S1, obtaining storage information of each stacking point; S2, generating a single transport loading plan model, a target function of the single transport loading plan model being F = Q -∑ i∈ M q i , wherein Q is a maximum loading capacity of a vehicle, q i is a loading capacity of an i-th stockpile point in the single transport loading plan, and M is a stockpile point distribution of the single transport loading plan; the single transport loading plan model has constraint conditions including a stockpile point number constraint and a vehicle loading capacity constraint; the stockpile point distribution is a group of stockpile points passed through by the vehicle in a single transport. S3, obtaining an optimal stacking point group based on genetic algorithm optimization solution of the single transport loading scheme model; S4, updating the storage information of the stacking point, and repeatedly obtaining the optimal stacking point group by repeatedly solving the single transport loading scheme model until a preset termination condition is reached; based on all the obtained optimal stacking point groups, a cargo scheduling scheme is obtained; S5, determining whether the cargo scheduling scheme meets the preset requirement; if yes, the scheduling scheme is the final confirmed scheme; if no, adjusting the constraint condition of the single transport loading scheme model, and then re-optimizing the single transport loading scheme model until a cargo scheduling scheme meeting the requirement is obtained.

2. The method of claim 1, wherein, The storage information is the weight of the cargo.

3. The method of claim 1, wherein, The stacking point number constraint condition is: v≤4, v is the number of stacking points.

4. The method of claim 1 or 2 or 3, wherein, The vehicle loading capacity constraint is: 0.9Q≤∑ i∈M q i ≤Q, wherein ∑ i∈M q i is the loading weight after the vehicle passes all the stockpile points in the group of stockpile points in a single transport.

5. The method of claim 1, wherein, A path model with the least transport time as the target is constructed for each stacking point group in the cargo scheduling scheme; and the path model is optimized and solved based on the annealing algorithm to obtain the best loading path of each stacking point group in the cargo scheduling scheme.

6. The method of claim 5, wherein, The objective function of the path model is: E =∑ x∈g ∑ y∈g E xy Z xy Wherein, g represents the union of the starting point of the vehicle and the to-be-solved path stockpiling point group, x and y represent any two stockpiling points in the stockpiling point group, E xy represents the time for the stockpiling point x to the stockpiling point y, Z xy represents whether the path from x to y is selected during transportation, if the path from x to y is selected, the value of Z xy is 1; if the path from x to y is not selected, the value of Z xy is 0.

7. The method of claim 6, wherein, The method for optimizing and solving the path model based on the annealing algorithm comprises the following steps: N1, define initial temperature T0, end temperature T end , maximum number of iterations N, define temperature drop rate alpha; N2, randomly generating an initial solution S, i.e. randomly generating a loading path, and obtaining the target function E of the time taken by the loading path; N3, change the order of the stacking points of the random loading path to generate a new solution S ′ and calculate the increment ΔE; N4, if the increment ΔE<0, accepting the new solution as the current solution, and if the increment ΔE>0, determining whether to accept the new solution according to a certain probability P; N5, repeating steps N3 and N4 at the current temperature reduction rate until the temperature is reduced to the termination temperature or the number of iterations is reached, and stopping the operation, at which time the best loading path is obtained.

8. A cargo dispatching system characterized by comprising: The method comprises a marking module, a storage information acquisition module, a first target function construction module, a fitness function construction module, an optimal stacking point group acquisition module, a cargo scheduling scheme acquisition and judgment module, and a cargo scheduling scheme output module. The marking module is used for marking the stacking points. The storage information acquisition module is used for obtaining the storage weight of each stacking point. The first target configuration module is configured to build a target function F = Q -∑ i∈M q i ; The fitness function module is used for constructing the fitness function. The optimal stacking point group acquisition module obtains the optimal stacking point group based on the genetic algorithm, and repeatedly updates the storage weight of each stacking point based on the obtained optimal stacking point group to repeatedly obtain all the optimal stacking point groups. The cargo scheduling scheme acquisition and judgment module is used for summarizing all the optimal stacking point groups to form a cargo scheduling scheme, and judging whether there is a stacking point with cargo not being loaded; if no, the formed cargo scheduling scheme is sent to the cargo scheduling scheme output module; if yes, the constraint condition is adjusted and the formation and judgment of the cargo scheduling scheme are re-performed until a cargo scheduling scheme meeting the requirement is obtained.

9. A goods dispatch system according to claim 8, wherein, The method comprises a position information acquisition module, a second target construction module, and an optimal loading path solution module. The position information acquisition module is used for obtaining the position of the stacking point in each stacking point group in the cargo scheduling scheme. The second target configuration module is used for constructing the target function E =∑ x∈g ∑ y∈g E xy Z xy ; The optimal loading path solution module obtains the optimal loading path of each stacking point group based on the annealing algorithm.

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