Tobacco shred elevated warehouse batch warehouse-in and warehouse-out dynamic storage location distribution and task scheduling integrated optimization method

Abstract

The invention discloses a dynamic storage location allocation and task scheduling integrated optimization method for batch warehouse-in and warehouse-out of a tobacco shred elevated warehouse. The method comprises the following steps: S1, carrying out coding design; s2, determining a warehouse-in and warehouse-out location optimization strategy; s3, distribution and scheduling of warehouse-in and warehouse-out storage locations; and S4, setting a penalty function. According to the method, the tobacco shred elevated warehouse is used as an important cache storage space in a production intermediate link, the same warehouse location is repeatedly used in the batch task completion process, the warehouse location utilization rate can be increased, and the situation that empty warehouse locations are not enough is reduced. The number of goods allocations of the high-rack warehouse is large, under the background of batch operation, the moving distance of the stacking machine can be reduced through the reasonable warehouse-in and warehouse-out operation sequence, and therefore the efficiency of the stacking machine is improved, and the operation time is shortened.

Description

Technical Field

[0001] The invention belongs to the technical field of tobacco processing and storage management, and specifically is a method for integrated optimization of dynamic storage location allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses. Background Art

[0002] Automated Storage / Retrieval System (AS / RS) is widely used in various manufacturing fields due to its high space utilization and fast goods in and out speed. In order to meet the production and storage needs, tobacco companies upgraded traditional warehouses to automated warehouses (called tobacco high-bay warehouses). The tobacco high-bay warehouse is the main place for storing semi-finished tobacco, blending materials, finished tobacco, etc. It is an important transit buffer in the tobacco production process, and undertakes the functions of warehousing, preservation, aging and providing tobacco according to the production plan. As an important link in tobacco production, the in and out efficiency of the tobacco high-bay warehouse directly affects the production efficiency of the enterprise, so it is necessary to comprehensively consider the shortest operation time and the least number of stacker operation times. Therefore, the in and out task scheduling and cargo allocation strategy of the automated warehouse are considered to be the key factors affecting the in and out efficiency.

[0003] For the problem of scheduling inbound and outbound tasks, some people take minimizing the task scheduling cost and scheduling time as the optimization goal, and propose a method that combines dynamic programming with geometric technology to solve the two-level batch size problem that does not allow backlogs in outbound transportation from TPW to DC, and prove that the use of dynamic programming algorithms is better than static scheduling methods; some people propose that in the context of just-in-time production, the sorting problem of fixed storage strategies should be converted into a single stacker batch scheduling problem, with the goal of minimizing the maximum delay time of the task, which significantly improves the delay time of the task; some people propose an optimization algorithm based on genetic algorithms, which converts multiple objectives into single-objective optimization problems by assigning weights, thereby minimizing the inbound and outbound time and the lightness of goods on the top and the heaviness of goods on the bottom, thereby reducing the inbound and outbound time.

[0004] In order to solve the problem of cargo space allocation, some people used the allocation of goods in and out of the warehouse as a scheduling model for the two-terminal automated warehouse, and used the vortex search algorithm to obtain the optimal solution; some people applied the multi-population genetic algorithm to the normalized objective function, and the results showed that the algorithm was better than the simple weighted genetic algorithm.

[0005] Since the scheduling of inbound and outbound tasks and the allocation of storage locations affect each other, the overall optimal goal cannot be achieved by optimizing only from a single aspect. Some people take the optimization of the shortest travel time of the stacker as the goal, and propose a step-by-step optimization method for the problems of storage location allocation and task sorting, which effectively shortens the travel time of the stacker; some people design a two-stage taboo search algorithm, with the shortest single instruction cycle time as the goal, to optimize the mutual influence of storage location allocation and inbound and outbound task sorting, and optimize single batch operations; some people group the inbound tasks according to the arrival order, propose a two-layer genetic algorithm, and optimize the storage location allocation and task scheduling at the same time; some people design a two-stage wolf pack algorithm, which integrates the optimization of storage location allocation and task scheduling, and effectively reduces the operation time.

[0006] In summary, most of the current studies on task scheduling and cargo space allocation in automated warehouses only consider the storage location of incoming warehousing tasks and adopt a first-come, first-served (FCFS) strategy, without considering minimizing the moving distance of the stacker crane through sorting; in addition, most studies assume that the number of empty shelf spaces is much larger than the number of incoming warehousing tasks, without considering the situation of high shelf utilization and insufficient empty spaces in actual production. Summary of the invention

[0007] In order to solve the above-mentioned technical problems existing in the existing task scheduling and cargo space allocation of automated stereoscopic warehouses, the present invention provides a method for integrated optimization of dynamic storage space allocation and task scheduling for batch entry and exit of tobacco elevated warehouses, which improves the entry and exit efficiency of tobacco elevated warehouses by solving the problems of entry and exit storage space allocation and entry and exit operation scheduling.

[0008] The technical solution adopted by the present invention is:

[0009] A method for integrated optimization of dynamic storage location allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses, characterized in that the method comprises the following steps:

[0010] S1. Perform coding design:

[0011] S11, converting the outbound task storage locations into numbers in sequence, and encoding the chromosomes into an integer sequence consisting of the storage location numbers;

[0012] S12, the algorithm uses a random method to generate the initial population to ensure the diversity of the population;

[0013] S13, using the inverse of the objective function as the fitness function;

[0014] S14, adopting a selection strategy combining best individual retention and roulette wheel selection;

[0015] S15, design the crossover operator as a single-point crossover, and achieve solution diversity by rearranging storage locations and replacing duplicate storage locations;

[0016] S16, in the initial stage, multi-point mutation is used to increase diversity and avoid premature phenomenon, and single-point mutation is used to improve accuracy in the final stage of evolution;

[0017] S2. Determine the optimal strategy for inbound and outbound storage locations:

[0018] S21. Determine the probability of the selected storage location for outbound delivery based on the relative distance;

[0019] S22. Propose a dynamic cargo location allocation strategy to optimize the selection of warehouse locations for incoming batch orders;

[0020] S3. Allocation and scheduling of inbound and outbound storage locations:

[0021] S31, a composite operation is adopted for the in-and-out storage task, and the smaller value between the number of storage tasks and the number of in-and-out storage tasks is taken as the composite operation number;

[0022] S32, calculating the operation time of the composite operation;

[0023] S4. Set the penalty function:

[0024] Add time penalties to locations that violate constraints.

[0025] Furthermore, in step SS11, the tobacco high-bay warehouse system receives warehousing and out-of-warehouse tasks from the MES system and completes the operations in batches; the warehouse locations of the out-of-warehouse tasks are converted into numbers according to the order of the shelves, and the chromosomes are encoded into an integer sequence consisting of the warehouse location numbers, and the sequence order represents the order of task execution.

[0026] Furthermore, in step S13, the objective function of this paper is to minimize the completion time of the batch in and out warehouse tasks, so the inverse of the objective function is used as the fitness function, which is defined as shown in the formula:

[0027]

[0028] Among them, SumT is the batch in and out completion time, and Fit(SumT) is the fitness function.

[0029] Furthermore, in step S14, a selection strategy combining the best individual retention and roulette wheel selection is adopted to select individuals from the population through roulette wheel for crossover and mutation operations. The probability of individual selection is prob T Determined by formula (2), the details are as follows:

[0030]

[0031] Among them, SumT is the completion time of batch in and out of the warehouse, Fit(SumT) is the fitness function, and pop is the population size.

[0032] Furthermore, in step S15, the crossover operator uses a single-point crossover to generate two offspring generations by crossing two initial solutions; when the storage location numbers of the two parents are the same but the order is different, the storage locations before the intersection are retained, and the remaining storage locations are rearranged to replace the original storage locations; when the storage location numbers of the parent generations are different, the repeated storage locations are replaced after crossing and replaced with the first different storage location number that appears.

[0033] Furthermore, in step S16, multi-point mutation is used in the initial stage of genetic evolution, and single-point mutation is used in the final stage of evolution.

[0034] Furthermore, in step S21, in the operation process of the tobacco high-bay warehouse, semi-finished tobacco needs to be aged in the high-bay warehouse before being used in production; the quantity of alcoholized tobacco in the warehouse and the quantity of alcoholized tobacco out of the warehouse is controlled by the production order, and it is possible that the quantity of alcoholized tobacco in the warehouse exceeds the production demand. Therefore, it is necessary to optimize the warehouse location of the batch outbound task to maximize the efficiency of warehouse entry and exit; in order to shorten the total time of warehouse entry and exit, the probability of the warehouse location being selected is determined by the relative distance, and the location closer to the warehouse entry and exit is more likely to be selected;

[0035] Set the inverse of the distance between location i and the entrance and exit to d i , then the probability that the storage location is selected is prob i Determined by formula (3), the details are as follows:

[0036]

[0037] Furthermore, in step S22, when a batch order is put into storage, the storage location is selected in combination with the location to be shipped out and the empty storage location; the numbers of all empty storage locations are recorded in T empty In the table, the outbound storage location number is T out In the table, according to the number of warehousing tasks, empty and T out Randomly select and arrange numbers to form an initial solution. The sequence order represents the order in which the warehousing tasks are completed.

[0038] Furthermore, in step S31, for a batch of in-and-out warehouse tasks, in order to improve the efficiency of in-and-out warehouse, a composite operation is used for operation; the number of out-and-out warehouse tasks is set to m, the number of in-and-out warehouse tasks is set to n, and in the same batch of in-and-out warehouse tasks, the i-th in-warehouse location is The i-th outbound storage location is

[0039] When all operations are composite operations, the smaller value of the number of outbound tasks and the number of inbound tasks is taken as the number of composite operations, and the decision variables for completing m inbound and outbound batch tasks can be expressed as:

[0040]

[0041] Furthermore, in step S32, the composite operation time can be expressed as:

[0042] T(i,j)=t(o,i)+t(i,j)+t(j,o) (5)

[0043] Among them: T(i,j) represents the operation time of first entering and exiting the i-th warehouse, then running to the j-th warehouse for exiting, and finally returning to the entrance of the warehouse; t(o,i) represents the operation time from the entrance to the i-th warehouse; t(i,j) represents the operation time from the i-th warehouse to the j-th warehouse; t(j,o) represents the operation time from the j-th warehouse to the entrance of the warehouse;

[0044] The optimal batch operation time target for completing m tasks is:

[0045]

[0046] The traditional dynamic selection method updates the shelf information according to the change of the cargo location after each group of tasks is completed, and selects the cargo location for the next group of tasks. This method requires re-selection after each operation, and the initial solution is randomly generated, which may cause the warehouse location to be selected before the warehouse is out, resulting in an illegal solution. The present invention avoids illegal solutions by setting a penalty function. The specific description is as follows:

[0047] Count the coordinates of repeated positions in the inbound task sequence and the outbound task sequence, and compare the order of these positions in the task sequence. If the inbound order is earlier than the outbound order, it is considered a constraint violation and a time penalty is added. Compare each repeated position and impose a penalty on each position that violates the constraint. The penalty time is 10 times the time it takes for the stacker to move from the origin to the farthest end of the shelf. The final solution is sorted by time. The more constraints are violated, the lower the priority of the solution. The solution that does not violate the constraint has the highest priority, and the priority decreases in order.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] (1) The present invention uses the tobacco elevated warehouse as an important buffer storage space in the intermediate link of production, and reuses the same warehouse position in the process of completing batch tasks, which can improve the warehouse utilization rate and reduce the situation of insufficient empty warehouse positions.

[0050] (2) The high-bay warehouse of the present invention has a large number of cargo spaces. Under the background of batch operations, a reasonable order of inbound and outbound operations can reduce the moving distance of the stacker, thereby improving the efficiency of the stacker and reducing the operation time. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of the present invention.

[0052] Figure 2The invention discloses a dynamic allocation and scheduling of storage locations for inbound and outbound storage.

[0053] Figure 3 It is a combined diagram of the storage location allocation and storage in and out task scheduling of the present invention.

[0054] Figure 4 It is the different parent crossover operation of the present invention.

[0055] Figure 5 It is a variation operation of the present invention. DETAILED DESCRIPTION

[0056] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0057] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0058] The present invention will be described in detail below in conjunction with exemplary embodiments with reference to the accompanying drawings.

[0059] The present invention provides a method for integrated optimization of dynamic storage location allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses, the method comprising the following steps:

[0060] S1. Perform coding design:

[0061] S11, converting the outbound task storage locations into numbers in sequence, and encoding the chromosomes into an integer sequence consisting of the storage location numbers;

[0062] S12, the algorithm uses a random method to generate the initial population to ensure the diversity of the population;

[0063] S13, using the inverse of the objective function as the fitness function;

[0064] S14, adopting a selection strategy combining best individual retention and roulette wheel selection;

[0065] S15, design the crossover operator as a single-point crossover, and achieve solution diversity by rearranging storage locations and replacing duplicate storage locations;

[0066] S16, in the initial stage, multi-point mutation is used to increase diversity and avoid premature phenomenon, and single-point mutation is used to improve accuracy in the final stage of evolution;

[0067] S2. Determine the optimal strategy for inbound and outbound storage locations:

[0068] S21. Determine the probability of the selected storage location for outbound delivery based on the relative distance;

[0069] S22. Propose a dynamic cargo location allocation strategy to optimize the selection of warehouse locations for incoming batch orders;

[0070] S3. Allocation and scheduling of inbound and outbound storage locations:

[0071] S31, a composite operation is adopted for the in-and-out storage task, and the smaller value between the number of storage tasks and the number of in-and-out storage tasks is taken as the composite operation number;

[0072] S32, calculating the operation time of the composite operation;

[0073] S4. Set the penalty function:

[0074] Add time penalties to locations that violate constraints.

[0075] Specifically, in step SS11, the tobacco high-bay warehouse system receives warehousing and outbound tasks from the MES system and completes the operations in batches; the storage locations of the outbound tasks are converted into numbers according to the order of the shelves, and the chromosomes are encoded into an integer sequence consisting of the storage location numbers, and the sequence order represents the order of task execution.

[0076] Specifically, in step S13, the objective function of this paper is to minimize the completion time of the batch in and out warehouse tasks, so the inverse of the objective function is used as the fitness function, which is defined as shown in the formula:

[0077]

[0078] Among them, SumT is the batch in and out completion time, and Fit(SumT) is the fitness function.

[0079] Specifically, in step S14, a selection strategy combining the best individual retention and roulette wheel selection is adopted, and individuals are selected from the population through roulette wheel for crossover and mutation operations. The probability prob of individual selection is T Determined by formula (2), the details are as follows:

[0080]

[0081] Among them, SumT is the completion time of batch in and out of the warehouse, Fit(SumT) is the fitness function, and pop is the population size.

[0082] Specifically, in step S15, the crossover operator uses a single-point crossover to generate two offspring by crossing two initial solutions; when the storage location numbers of the two parents are the same but in different orders, the storage locations before the intersection are retained, and the remaining storage locations are rearranged to replace the original storage locations; when the storage location numbers of the parent generations are different, the repeated storage locations are replaced after crossing and replaced with the first different storage location number that appears.

[0083] Specifically, in step S16, multi-point mutation is used in the initial stage of genetic evolution, and single-point mutation is used in the final stage of evolution.

[0084] Specifically, in step S21, in the operation process of the tobacco high-bay warehouse, semi-finished tobacco needs to be aged in the high-bay warehouse before being used in production; the quantity of alcoholized tobacco in the warehouse and the quantity of alcoholized tobacco out of the warehouse is controlled by the production order, and it is possible that the quantity of alcoholized tobacco in the warehouse exceeds the production demand. Therefore, it is necessary to optimize the warehouse location of the batch outbound task to maximize the efficiency of warehouse entry and exit; in order to shorten the total time of warehouse entry and exit, the probability of the warehouse location being selected is determined by the relative distance, and the location closer to the warehouse entry and exit is more likely to be selected;

[0085] Set the inverse of the distance between location i and the entrance and exit to d i , then the probability that the storage location is selected is prob i Determined by formula (3), the details are as follows:

[0086]

[0087] Specifically, in step S22, when a batch order is put into storage, the storage location is selected in combination with the location to be shipped out and the empty storage location; the numbers of all empty storage locations are recorded in T empty In the table, the outbound storage location number is T out In the table, according to the number of warehousing tasks, empty and T out Randomly select and arrange numbers to form an initial solution. The sequence order represents the order in which the warehousing tasks are completed.

[0088] Specifically, in step S31, for a batch of inbound and outbound tasks, in order to improve the efficiency of inbound and outbound, a composite operation is used to perform the operation; the number of outbound tasks is set to m, the number of inbound tasks is n, and in the same batch of inbound and outbound tasks, the i-th inbound storage location is The i-th outbound storage location is

[0089] When all operations are composite operations, the smaller value of the number of outbound tasks and the number of inbound tasks is taken as the number of composite operations, and the decision variables for completing m inbound and outbound batch tasks can be expressed as:

[0090]

[0091] Specifically, in step S32, the composite operation time can be expressed as:

[0092] T(i,j)=t(o,i)+t(i,j)+t(j,o) (5)

[0093] Among them: T(i,j) represents the operation time of first entering and exiting the i-th warehouse, then running to the j-th warehouse for exiting, and finally returning to the entrance of the warehouse; t(o,i) represents the operation time from the entrance to the i-th warehouse; t(i,j) represents the operation time from the i-th warehouse to the j-th warehouse; t(j,o) represents the operation time from the j-th warehouse to the entrance of the warehouse;

[0094] The optimal batch operation time target for completing m tasks is:

[0095]

[0096] The traditional dynamic selection method updates the shelf information according to the change of the cargo location after each group of tasks is completed, and selects the cargo location for the next group of tasks. This method requires re-selection after each operation, and the initial solution is randomly generated, which may cause the warehouse location to be selected before the warehouse is out, resulting in an illegal solution. The present invention avoids illegal solutions by setting a penalty function. The specific description is as follows:

[0097] Count the coordinates of repeated positions in the inbound task sequence and the outbound task sequence, and compare the order of these positions in the task sequence. If the inbound order is earlier than the outbound order, it is considered a constraint violation and a time penalty is added. Compare each repeated position and impose a penalty on each position that violates the constraint. The penalty time is 10 times the time it takes for the stacker to move from the origin to the farthest end of the shelf. The final solution is sorted by time. The more constraints are violated, the lower the priority of the solution. The solution that does not violate the constraint has the highest priority, and the priority decreases in order.

[0098] Figure 1 The main process of solving the genetic algorithm for the present invention is: receiving inbound and outbound tasks, allocating and scheduling inbound and outbound storage locations, converting them into storage location numbers in sequence, initializing the population, performing fitness evaluation, retaining the best individual operation and roulette operation, crossover operation, mutation operation, judging whether it is the best operator, and recording the best operator.

[0099] like Figure 2 As shown in the figure, for a batch of inbound and outbound tasks, in order to improve the efficiency of inbound and outbound, a composite operation is used to operate. The number of outbound tasks is set to m, and the number of inbound tasks is n. In the same batch of inbound and outbound tasks, the i-th inbound storage location is The i-th outbound storage location is

[0100] When all operations are composite operations, the smaller value of the number of outbound tasks and the number of inbound tasks is taken as the number of composite operations, and the decision variables for completing m inbound and outbound batch tasks can be expressed as:

[0101]

[0102] like Figure 3As shown in the figure, in the cigarette production process, it is necessary to batch out the aged tobacco from the high-bay warehouse to the cigarette making process. The outbound task follows the first-in-first-out principle, and optimizes the inbound and outbound task scheduling and inbound storage location allocation. The study is based on a genetic algorithm, setting 20 inbound tasks and 15 outbound tasks. IN is used to represent inbound tasks, and OUT is used to represent outbound tasks. The task number in the figure is used to represent the order of operations.

[0103] It can be seen from the figure that when the outbound storage location is specified, there are two situations for selecting the inbound storage location.

[0104] In the first case, the combined task gives priority to selecting the empty spaces around the outbound storage location as the inbound storage location, and the individual task gives priority to selecting the empty spaces close to the I / O point as the inbound storage location.

[0105] The second situation is when there is no empty space around the outbound task, the combined task selects the outbound storage location closest to the outbound task as the inbound storage location, and the individual task selects the outbound storage location close to the I / O point as the inbound storage location.

[0106] In the first case, combined tasks 1, 3, and 7 give priority to selecting empty spaces around the outbound storage location as the inbound storage location, and individual tasks 16, 17, 18, and 19 give priority to selecting empty spaces close to the I / O point as the inbound storage location. In the second case, when there are no empty spaces around outbound tasks 2 and 13, inbound tasks 2 and 13 give priority to selecting the nearest outbound storage location as the inbound storage location.

[0107] The allocation of inbound storage locations takes into account the distance from the outbound task and the I / O point. The combined task gives priority to the location close to the outbound task; when the distance between the two inbound tasks and the outbound task is the same, the location far from the I / O point is selected as the combined task.

[0108] For example, the coordinates of outbound task 15 are (3, 2), the coordinates of inbound task 15 are (7, 3), and the coordinates of inbound task 16 are (6, 6). The two inbound tasks are at the same distance from the outbound task, but inbound task 16 is closer to the I / O point, so as a separate task, it can achieve the shortest total task time. We verify the time of different combinations separately. For example, the completion time of the combination of inbound task 15 and outbound task 15 is 26, and the completion time of the combination of inbound task 16 and outbound task 15 is 27. This verifies our method.

[0109] like Figure 4As shown in the figure, the crossover operator is designed as a single-point crossover, and two offspring are obtained by crossing two initial solutions. When the two parent generations have the same location numbers but different orders, a single-point crossover is used to retain the locations before the intersection, and the remaining locations are rearranged to replace the original locations. When the two parents are not exactly the same, that is, the location numbers are different, the repeated locations are replaced after the crossover operation, and replaced with the first different location number that appears. The crossover operation achieves the diversity of solutions and speeds up the search for the solution space.

[0110] For example, for two parent stock locations numbered (2, 11, 9, 8, 32, 45, 96, 42, 58, 22) and (2, 7, 96, 11, 8, 31, 42, 32, 22, 58), when the corresponding fragment (32, 45, 96, 42, 58, 22) is swapped with the fragment (8, 31, 42, 32, 22, 58), it is a crossover operation, and two new stock location numbers (2, 11, 9, 8, 8, 31, 42, 32, 22, 58) and (2, 7, 96, 11, 32, 45, 96, 42, 58, 22) will appear. At this time, the new stock location numbers have duplicate stock locations 8 and 96, which need to be replaced with another different stock location number that appears first, such as using the second numbered stock location 7 to replace the duplicate stock location 8, and using the first numbered stock location 9 to replace the stock location 96. By doing so, we can achieve diversity in solutions and speed up the search for the optimal solution.

[0111] like Figure 5 As shown in the figure, in terms of mutation operator design, this paper uses multi-point mutation in the initial stage of genetic evolution, so that the new individuals generated by mutation can be evenly distributed in the entire solution space, thereby making the distribution of individuals diverse, improving the diversity of the population gene pool, and avoiding the occurrence of premature phenomenon. At the end of population evolution, single-point mutation is used to improve the accuracy of the optimal individual.

[0112] For example, if there are empty positions numbered (3, 11, 7, 21, 31, 96, 42, 32, 45, 58) and the initial storage positions (3, 11, 21, 32, 42, 58, 2), the remaining positions are numbered (7, 31, 96, 45). After the multi-point mutation operation, the storage positions 3 and 32 are mutated into the remaining positions 45 and 31, thereby improving the diversity of the population and avoiding the premature end of the search for the optimal solution. At the end of the solution, a single-point mutation operation is performed, and the storage position 32 is mutated into the remaining position 31, so as to achieve the purpose of improving the accuracy of the optimal individual.

[0113] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for integrated optimization of dynamic storage space allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses, characterized in that: The method comprises the following steps: S1. Perform coding design: S11, converting the outbound task storage locations into numbers in sequence, and encoding the chromosomes into an integer sequence consisting of the storage location numbers; S12, the algorithm uses a random method to generate the initial population to ensure the diversity of the population; S13, using the inverse of the objective function as the fitness function; S14, adopting a selection strategy combining best individual retention and roulette wheel selection; S15, design the crossover operator as a single-point crossover, and achieve solution diversity by rearranging storage locations and replacing duplicate storage locations; S16, in the initial stage, multi-point mutation is used to increase diversity and avoid premature phenomenon, and single-point mutation is used to improve accuracy in the final stage of evolution; S2. Determine the optimal strategy for inbound and outbound storage locations: S21. Determine the probability of the selected storage location for outbound delivery based on the relative distance; S22. Propose a dynamic cargo location allocation strategy to optimize the selection of warehouse locations for incoming batch orders; S3. Allocation and scheduling of inbound and outbound storage locations: S31, a composite operation is adopted for the in-and-out storage task, and the smaller value between the number of storage tasks and the number of in-and-out storage tasks is taken as the composite operation number; S32, calculating the operation time of the composite operation; S4. Set the penalty function: Add time penalties to locations that violate constraints.

2. A method for integrated optimization of dynamic storage space allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses as claimed in claim 1, characterized in that: In step SS11, the tobacco high-bay warehouse system receives warehousing and outbound tasks from the MES system and completes the operations in batches; the warehouse locations of the outbound tasks are converted into numbers according to the order of the shelves, and the chromosomes are encoded into an integer sequence consisting of the warehouse location numbers, and the sequence order represents the order of task execution.

3. A method for integrated optimization of dynamic storage space allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses as claimed in claim 1, characterized in that: In step S13, the objective function of this paper is to minimize the completion time of the batch in and out warehouse tasks, so the inverse of the objective function is used as the fitness function, which is defined as shown in the formula: Among them, SumT is the batch in and out completion time, and Fit(SumT) is the fitness function.

4. A method for integrated optimization of dynamic storage space allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses as claimed in claim 1, characterized in that: In step S14, a selection strategy combining the best individual retention and roulette wheel selection is adopted to select individuals from the population for crossover and mutation operations through roulette wheel. The probability of individual selection is prob T Determined by formula (2), the details are as follows: Among them, SumT is the completion time of batch in and out of the warehouse, Fit(SumT) is the fitness function, and pop is the population size.

5. A method for integrated optimization of dynamic storage space allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses as claimed in claim 1, characterized in that: In step S15, the crossover operator uses a single-point crossover to generate two offspring by crossing two initial solutions; when the storage location numbers of the two parents are the same but in different orders, the storage locations before the intersection are retained, and the remaining storage locations are rearranged to replace the original storage locations; when the storage location numbers of the parent generations are different, the repeated storage locations are replaced after crossing and replaced with the first different storage location number that appears.

6. A method for integrated optimization of dynamic storage space allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses as claimed in claim 1, characterized in that: In step S16, multi-point mutation is used in the initial stage of genetic evolution, and single-point mutation is used in the final stage of evolution.

7. A method for integrated optimization of dynamic storage space allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses as claimed in claim 1, characterized in that: In step S21, in the operation process of the tobacco high-bay warehouse, semi-finished tobacco needs to be aged in the high-bay warehouse before being used in production; the quantity of alcoholized tobacco in the warehouse and the quantity of alcoholized tobacco out of the warehouse is controlled by the production order, and it is possible that the quantity of alcoholized tobacco in the warehouse exceeds the production demand. Therefore, it is necessary to optimize the warehouse location of the batch outbound task to maximize the efficiency of warehouse entry and exit; in order to shorten the total time of warehouse entry and exit, the probability of the warehouse location being selected is determined by the relative distance, and the location closer to the warehouse entry and exit is more likely to be selected; Set the inverse of the distance between location i and the entrance and exit to d i , then the probability that the storage location is selected is prob i Determined by formula (3), the specific details are as follows: 。 8. A method for integrated optimization of dynamic storage space allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses as claimed in claim 1, characterized in that: In step S22, when a batch order is put into storage, the storage location is selected in combination with the location to be shipped out and the empty storage location; the numbers of all empty storage locations are recorded in T empty In the table, the outbound storage location number is T out In the table, according to the number of warehousing tasks, empty and T out Randomly select and arrange numbers to form an initial solution. The sequence order represents the order in which the warehousing tasks are completed.

9. A method for integrated optimization of dynamic storage space allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses as claimed in claim 1, characterized in that: In step S31, for a batch of inbound and outbound tasks, in order to improve the efficiency of inbound and outbound, a composite operation is used to perform the operation; the number of outbound tasks is set to m, the number of inbound tasks is n, and in the same batch of inbound and outbound tasks, the i-th inbound storage location is The i-th outbound storage location is When all operations are composite operations, the smaller value of the number of outbound tasks and the number of inbound tasks is taken as the number of composite operations, and the decision variables for completing m inbound and outbound batch tasks can be expressed as: 。 10. A method for integrated optimization of dynamic storage space allocation and task scheduling for batch entry and exit of tobacco high-bay warehouses as claimed in claim 1, characterized in that: In step S32, the composite operation time can be expressed as: T(i,j)=t(o,i)+t(i,j)+t(j,o) (5) Among them: T(i,j) represents the operation time of first entering and exiting the i-th warehouse, then running to the j-th warehouse for exiting, and finally returning to the entrance of the warehouse; t(o,i) represents the operation time from the entrance to the i-th warehouse; t(i,j) represents the operation time from the i-th warehouse to the j-th warehouse; t(j,o) represents the operation time from the j-th warehouse to the entrance of the warehouse; The optimal batch operation time target for completing m tasks is: 。

Images