A task scheduling method for a four-way shuttle vehicle type dense warehouse system

Abstract

The application provides a four-way shuttle vehicle type dense warehouse system task scheduling method, which comprises the following steps: establishing a mathematical model of hoist task execution time, a mathematical model of warehouse in-out task execution time, and solving a multi-hoist and shuttle vehicle operation sequencing problem by using a multi-strategy fusion whale optimization algorithm. The scheduling time, operation time and waiting time of the hoist and the shuttle vehicle are calculated, and the total time of the operation is optimized through the objective function. A multi-strategy fusion whale optimization algorithm is introduced, which combines multiple intelligent optimization strategies to obtain the optimal operation time of all warehouse in-out tasks. The operation efficiency and resource utilization of the warehouse system can be significantly improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of task scheduling algorithm in the four-way shuttle car type dense storage, and particularly relates to a four-way shuttle car type dense storage system task scheduling method. BACKGROUND

[0002] In recent years, the logistics system gradually develops in the direction of automation, and automatic storage systems such as stacking type vertical warehouse, mother and child shuttle car type storage system and the like appear. These automatic warehouses have the outstanding advantages of large capacity, low labor cost, strong turnover and strong environmental adaptability, and have been widely applied. The four-way shuttle car type dense storage system is a new emerging efficient automatic storage system. The system can effectively improve the storage space utilization rate of the warehouse by using dense storage, greatly reduce the storage cost, and fully utilize the automatic storage equipment such as the elevator, the shuttle car and the like, greatly reduce the personnel investment, and reduce the labor cost and the management cost. At the same time, the dense storage system can complete the warehouse-in and warehouse-out tasks of materials through automatic picking equipment, and can realize various automatic storage functions through flexible storage configuration, and promote the process of intelligent change of automatic storage.

[0003] The storage equipment scheduling strategy usually takes the shortest total operation time, the minimum energy consumption and the balanced equipment operation amount as the target, considers the operation process, the number of equipment and the task priority and the like constraints, adopts intelligent optimization algorithm to calculate the mathematical model of multi-objective and multi-constraint, and solves to obtain an efficient scheduling scheme. At present, the dynamic scheduling method is the main scheduling form of the automatic warehouse, and the main research methods include the traditional analysis method, the simulation and modeling, the intelligent optimization method and the hybrid optimization method. Although the mathematical solving method can accurately calculate the optimal task scheduling result, the time consumption is too long and the calculation amount is too large, and it is not applicable in the actual operation scheduling of the storage system. At present, the intelligent search algorithm is more used to solve the NP problem, such as genetic algorithm, simulated annealing, ant colony algorithm and the like. Although the dynamic scheduling strategy can solve the disturbance problem, the model has good robustness, and even can complete the real-time scheduling problem, but cannot coordinate the cooperation problem between the elevators and the shuttle cars, and will waste the flexibility advantage of the dense storage of multiple devices. SUMMARY

[0004] The present application aims to provide a four-way shuttle car dense storage system task scheduling method, which solves the problem of cooperative scheduling of multiple elevators and shuttle cars in dense storage, and improves the operation efficiency of the storage.

[0005] The technical solution for achieving the present application is as follows:

[0006] A four-way shuttle car type dense storage system task scheduling method, comprising:

[0007] S1, establish the mathematical model of the task execution time of the elevator: the mathematical model of the task execution time of the elevator is established, that is, the elevator first transports goods to the layer of the shuttle vehicle, then transports the goods and the shuttle vehicle to the target layer of the goods, and the mathematical model of the time of the elevator first carrying the shuttle vehicle working together to obtain the goods transported to the target layer of the goods, and the mathematical model of the time of the cooperative outbound operation when the nearest idle shuttle vehicle is transported to the target layer of the goods by the elevator, and then the shuttle vehicle is transported to the target layer of the goods to take out the goods, and the task execution time mathematical model of the elevator is obtained;

[0008] S2, establish the mathematical model of the target function expression of the inbound and outbound task execution time: according to the mathematical model of the task execution time of the shuttle vehicle and the mathematical model of the task execution time of the elevator obtained in S1, the total time of the task execution of the shuttle vehicle and the elevator in different modes is obtained, and then the mathematical model of the inbound and outbound task execution time is obtained;

[0009] S3, according to the target of the shortest overall operation time of all tasks in the order, the multi-strategy fusion whale optimization algorithm is used to solve the multi-elevator and shuttle vehicle operation scheduling problem.

[0010] Compared with the prior art, the present application has the following advantages:

[0011] (1) By calculating and optimizing the scheduling time and operation time of the elevator and the shuttle vehicle, efficient cooperation of equipment operation can be realized: the present application establishes the mathematical model of the task execution time of the elevator and the shuttle vehicle, considers the scheduling time and waiting time in multiple working modes, and optimizes the time efficiency of cooperative operation. Compared with the traditional technology, the present application can significantly reduce the waiting and idle time between devices, and improve the efficiency and speed of the overall operation.

[0012] (2) By introducing the multi-strategy fusion whale optimization algorithm, intelligent and efficient task scheduling can be realized: the whale optimization algorithm used in the present application combines multiple optimization strategies, including nonlinear convergence factor strategy, adaptive weight strategy and random difference mutation strategy, etc., which provides an effective solution for solving complex multi-objective and multi-constraint optimization problems. This algorithm can effectively improve the solving speed and accuracy, and realize intelligent and efficient task scheduling.

[0013] (3) By dynamically adjusting the operation sequence of the elevator and the shuttle vehicle and selecting the shortest operation time of the cooperative vehicle, the scheduling efficiency can be significantly improved: the present application uses the whale optimization algorithm to dynamically select the optimal combination of the elevator and the shuttle vehicle for each inbound and outbound task, and adjusts the operation sequence, which not only reduces the mutual interference between tasks, but also optimizes the working route of the equipment. By reducing the moving distance and waiting time of the equipment, the operation efficiency is directly improved, so that the overall operation efficiency of the warehouse system is improved without increasing additional resource investment. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a multi-strategy fusion whale optimization algorithm flow chart;

[0015] Figure 2 is the gantry chart of the task scheduling of the hoist and the shuttle vehicle. DETAILED DESCRIPTION

[0016] The application will be further described below in combination with the drawings and specific embodiments.

[0017] In combination Figure 1 , the task scheduling method of the four-way shuttle vehicle type dense warehouse system comprises the following steps:

[0018] S1, a mathematical model t1, t2, t3 of hoist task execution time is established;

[0019] In the warehouse system, for the jth hoist L j , the node at the current time is P j , the coordinates are (P jx , P jy , P jz ), P lj represents the warehouse entrance and exit position (x j , y j , z j ), P ji represents the node of the warehouse entrance of the i-th task target executed by the jth hoist, the coordinates are (P jix , P jiy , P jiz ), S ji represents the starting time of the jth hoist executing the i-th task. The total number of shuttle vehicles is N, C k represents the kth shuttle vehicle, P k represents the entrance and exit position of the jth hoist of the current storage layer of the kth shuttle vehicle, the coordinates are (P kx , P ky, P kz ), P C(i-1) , E k(i-1) respectively represent the coordinates and time when the kth shuttle vehicle completes a task. For the i-th task executed by the jth hoist, a shuttle vehicle needs to be selected to execute the task and make the cooperative task time shortest.

[0020] When the hoist and the shuttle vehicle cooperatively complete the warehousing task, there are the following two working modes, after calculating the scheduling time of the shuttle vehicle, when selecting the cooperative working shuttle vehicle, the shuttle vehicle with the minimum working time in the two working modes needs to be considered, so that it cooperates with the hoist to complete the warehouse entrance and exit task.

[0021] (1) The lift first transports the goods to the layer where the shuttle is located, and then transports the goods and the shuttle to the target layer. In this mode, the minimum call time t1 required for the jth lift to perform the ith task is expressed as

[0022] In the above formula, V L represents the running speed of the lift, D L (P j , P lj ) represents the walking distance from the current position of the jth lift to the storage position, D L (P lj , P k ) represents the walking distance from the storage position of the jth lift to the layer where the kth shuttle is currently located, and D L (P k , P ji ) represents the walking distance from the layer where the kth shuttle is currently located to the layer where the jth lift performs the ith task.

[0023] (2) The lift first carries the shuttle for cooperative work, and then goes to the storage and retrieval layer to get the goods and transport them to the target layer. In this mode, the minimum call time t2 required for the jth lift to perform the ith task is expressed as

[0024] In the above formula, V L represents the running speed of the lift, D L (P j , P k ) represents the walking distance from the current position of the jth lift to the layer where the kth shuttle is currently located, D L (P k , P lj ) represents the walking distance from the layer where the kth shuttle is currently located to the storage position of the jth lift, and D L (P lj , P ji ) represents the walking distance from the layer where the jth lift performs the ith task to the storage position of the lift.

[0025] In the above two modes, the waiting time of the lift at the P k position after reaching the layer where the kth shuttle is located is respectively:

[0026]

[0027] In the above two formulas, E k(i-1) represents the time when the kth shuttle completes the previous task, D C (PC(i-1) k (P L (P j (P k (P L (P j (P ji (P ji (P C (P L (P

[0028] When the outbound operation is coordinated, the idle shuttle closest to the target layer is transported to the target layer by the lift, and then the shuttle takes the goods from the target layer and completes the outbound operation by the lift. Let t3 represent the minimum calling time required for the jth lift to call the shuttle for the ith task:

[0029]

[0030] In the above formula, D L (P C(i-1) (P k (P L (P k (P ji (P C (P C(i-1) (P k (P C (P L (P

[0031] After the lift reaches the layer where the kth shuttle is located, the waiting time at the P k position is:

[0032]

[0033] In the above formula, E k(i-1) represents the time when the kth shuttle completes the previous task, and D​C (P C(i-1) ,P k ) represents the walking distance of the kth shuttle from the position after the completion of the previous task to the jth position of the shuttle on the layer of the kth shuttle, D L (P j ,P k ) represents the walking distance of the jth shuttle from the current position to the current layer of the kth shuttle, S ji V C represents the running speed of the four-way shuttle, V L represents the running speed of the shuttle.

[0034] S2, establishing an entry and exit warehouse task execution time target function expression mathematical model;

[0035] (1) The shuttle executes the entry task. The time of the shuttle to execute the task i includes the time from the current position to the current layer of the entry, the time of following the shuttle to change the layer, and the time from receiving the goods at the entry to the target position. If the kth shuttle is selected to complete the entry task, the operation time of the jth shuttle and the kth shuttle to complete the i th entry task can be represented as:

[0036] t in L ji = min (t1, t2)

[0037]

[0038] In the above formula, D L (P k ,P ji ) represents the walking distance of the jth shuttle from the current layer of the kth shuttle to the entry position of the shuttle, D C (P C(i-1) ,P k ) represents the walking distance of the kth shuttle from the position after the completion of the previous task to the jth position of the shuttle on the layer of the kth shuttle, D C (P k ,P ji ) represents the walking distance of the kth shuttle from the current position of the jth shuttle to the target node of the i th task executed, D L (P k ,P lj ) represents the walking distance of the kth shuttle from the current layer of the kth shuttle to the jth entry position of the shuttle, D L (P lj ,P ji) represents the walking distance of the jth elevator to the warehouse entry position of the i th task, V C represents the running speed of the four-way shuttle, V L represents the running speed of the elevator.

[0039] (2) The elevator executes the outbound task. When executing the outbound task, if there is no idle shuttle available at the outbound storage layer, the elevator needs to transport the shuttle to the outbound storage layer. The jth elevator selects the operation time of the kth shuttle to complete the i th outbound task:

[0040]

[0041] The kth shuttle completes the i th task operation time:

[0042]

[0043] In the above formula, D L (P k ,P ji ) represents the walking distance of the jth elevator from the current storage layer of the kth shuttle to the outbound position of the i th task of the elevator, D C (P j(i-1) ,P k ) represents the walking distance of the kth shuttle from the last task target node executed to the jth elevator exit position of the current storage layer, D C (P ji ,P k ) represents the walking distance of the kth shuttle from the jth elevator exit position of the current storage layer to the inbound port node of the i th task target of the jth elevator executed, V C represents the running speed of the four-way shuttle, V L represents the running speed of the elevator.

[0044] (3) The total sum of the task execution time of all elevators can be obtained by the above formula, and the value of .

[0045]

[0046] The total sum of the task execution time of all shuttles can be obtained by the above formula, and the value of .

[0047]

[0048] The multi-elevator and shuttle vehicle collaborative scheduling process is to first assign a task queue to the elevator, then sort the elevator tasks, and then select a collaborative shuttle vehicle to complete the task. The total optimization goal of collaborative scheduling is to make the total time of equipment completing the job shortest, so the objective function is defined as:

[0049]

[0050] The total time for all elevators and shuttle vehicles to execute tasks, respectively, is obtained by adding the time for elevators to execute warehouse-in and warehouse-out tasks and adding the time for shuttle vehicles to execute tasks.

[0051] S3, according to the goal of minimizing the overall job time of all tasks in the order, a multi-strategy fusion whale optimization algorithm is used to solve the multi-elevator and shuttle vehicle job scheduling problem, which includes the following steps:

[0052] (1) Coding:

[0053] To realize the sorting of multiple tasks in the order and select elevators and shuttle vehicles, a three-section coding structure based on natural number coding is designed. The first section is the main coding layer of task number, which is natural number coding of all tasks in the order, i.e. 1, 2, …, T, which is used to determine the order of all tasks in the order. The second section is the elevator coding layer, which uses natural numbers from 1 to M to represent the corresponding number of elevators, which is used to select the elevators for different tasks. The third section is the shuttle vehicle coding layer, which uses natural numbers from 1 to N to represent the corresponding number of shuttle vehicles, which is used to select the shuttle vehicles for different tasks.

[0054] (2) Initialization of population based on Tent chaotic mapping:

[0055] Tent mapping mathematical expression:

[0056] In the formula: x k' is a chaotic value, k' is a chaotic sequence number, is a control parameter, which is 0.5. The specific steps are as follows:

[0057] (a) Randomly generate m initial sequences with n dimensions, where the variable value of each dimension is (0, 1);

[0058] (b) Generate a chaotic sequence according to the Tent mapping;

[0059] (c) Map the chaotic sequence to the variable value range to finally obtain the initial population based on the Tent mapping.

[0060] (3) Coding conversion:

[0061] The solution space of whale optimization algorithm is continuous space, while the scheduling problem is discrete problem, so it is necessary to map the solution of continuous space to discrete solution space, and to convert the encoding of task ordering, elevator selection and shuttle selection. For task ordering, the conversion is carried out by adopting the ascending order rule (Ranked Order Value, ROV) rule, and for the solution space conversion of elevator and shuttle, the following method is adopted:

[0062]

[0063] In the formula, m(i) is the gene value of continuous encoding; z(i) is the number of elevators or shuttles; u(i) is the obtained discrete gene value; and δ is the upper limit of the number of elevators or shuttles.

[0064] (4) Decoding and fitness calculation:

[0065] The decoding is carried out for the discrete three-segment encoding, and the structure respectively represents the execution order of the tasks in the order, the selection of elevators and shuttles. The total time for completing the work of elevators and shuttles is taken as the fitness, and the whale optimization algorithm is used to calculate the fitness (total time). The whale position update adopts continuous genes, and the fitness calculation is carried out after conversion to discrete genes and decoding. The whale optimization algorithm (WOA) is divided into three stages of surrounding predation, spiral update and search for prey. In order to improve the convergence ability and optimization ability of the whale optimization algorithm, the nonlinear convergence factor strategy, the adaptive weight strategy and the random difference mutation strategy are introduced. The specific process is shown in Fig. 1, and the update strategy model of the three stages is as follows: Figure 1

[0066] Surrounding predation stage: X(t+1) = w·X p (t)-A·|C·X p (t)-X(t)|

[0067] In the formula, t is the current iteration number, X p (t) is the optimal solution of the current iteration, X(t) is the position of the current whale iteration, X(t+1) is the position of the next iteration of the whale, A and C are coefficient vectors, and w is the adaptive weight strategy value introduced, and:

[0068]

[0069] In the formula, max_iter is the maximum iteration number, r1 and r2 are random numbers generated in [0, 1], a is a variable that changes with the iteration number, a nonlinear convergence factor strategy is introduced, and the specific relationship is as follows:

[0070] ​​

[0071] wherein t is the current iteration number, and max iter is the maximum iteration number.

[0072] Spiral update phase: X(t+1) = |C X p (t) - X(t) | e bl cos(2πl) + w X p (t)

[0073] wherein b is a constant, and l is a random number between [-1, 1].

[0074] In order to determine whether to perform the spiral update phase or the surrounding predation phase, a random probability p is introduced, the value range of p is [0, 1], and the mathematical model is:

[0075]

[0076] Prey searching phase: X(t+1) = X rand (t) - A |C X rand (t) - X(t) |

[0077] wherein X rand is a randomly selected position vector.

[0078] Further in order to improve the search ability of the whale optimization, a random differential mutation strategy is introduced:

[0079] X(t+1) = r1 x (X p (t) - X(t)) + r2 x (X'(t) - X(t))

[0080] wherein r1 and r2 are random numbers in [0, 1], and X'(t) is a randomly selected individual in the population.

[0081] (5) Determine the iteration number and output the result:

[0082] Determine whether the final iteration number reaches the set value of the whale optimization algorithm, if the set maximum iteration number is reached, output the final task time minimum value, and draw a Gantt chart according to the starting time of each task shuttle car and elevator as Figure 2 shown; if the set maximum iteration number is not reached, continue iteration.

[0083] Table 1 is the in-out warehouse task list in the embodiment, wherein the warehouse system shelf specification is 9 rows 19 columns 4 layers, and the length, width and height of each storage location are all 1m. The system is configured with 3 elevators and 12 shuttle cars, wherein 3 shuttle cars are distributed in each layer of shelf. The average running speed of the elevator is 2m / s, and the average running speed of the shuttle car is 1m / s.

[0084] Table 1 warehouse in and out task list

[0085]

[0086] Based on the order in and out of the warehouse task location and the information of the elevator, the elevator task allocation is carried out. The position coordinates (x, y) of the three elevators are (0, 3), (0, 6) and (0, 9) respectively. The above scheduling case is used as the data basis for the test of the elevator task allocation, the elevator operation task is allocated, and the results of the elevator task allocation scheme are shown in Table 2.

[0087] Table 2 elevator task allocation scheme

[0088]

[0089] Based on the task set of each elevator after the in and out of the warehouse task allocation, the multi-strategy fusion whale optimization algorithm is called to carry out the cooperative scheduling test of the elevator and the shuttle vehicle. The task scheduling algorithm results give the order and starting time of each elevator and shuttle vehicle to execute the task, the scheduling scheme Gantt chart is obtained, and the total time to complete all tasks is 60.5 time units.

[0090] The above only further illustrates the technical content of the present application by way of examples, so that the reader can more easily understand, but does not represent that the embodiments of the present application are limited to this, any technical extension or re-creation made according to the present application is protected by the present application. The protection scope of the present application is subject to the claims.

Claims

1. A method for task scheduling of a four-way shuttle vehicle type dense warehouse system, characterized in that, include: S1. Establish a mathematical model for the execution time of the hoist task: Establish a mathematical model for the execution time of the hoist task, which first transports the goods to the storage level where the shuttle car is located, and then transports the goods and the shuttle car together to the target storage level; in the working mode where the hoist first picks up the shuttle car working in cooperation and then goes to the inbound / outbound level to retrieve the goods and transport them to the target storage level; and in the working mode where the hoist transports the nearest available shuttle car to the target storage level during collaborative outbound operations, and then the shuttle car retrieves the goods from the target storage level and completes the outbound task through the hoist. S2. Establish a mathematical model for the objective function expression of the inbound and outbound task execution time: Based on the mathematical model of the shuttle task execution time and the mathematical model of the elevator task execution time obtained in S1, the total execution time of the shuttle and elevator tasks under different modes is obtained, and then the mathematical model of the inbound and outbound task execution time is obtained. S3. Based on the goal of minimizing the overall inbound and outbound operation time of all tasks in the order, use the whale optimization algorithm with multi-strategy fusion to solve the multi-lift and shuttle operation sequencing problem. The three mathematical models established by S1 are as follows: wherein t1 is the minimum calling time of the jth elevator for executing the ith task in the mode of the first work of the elevator, V L represents the running speed of the elevator, D L (P j , P lj ) represents the walking distance from the current position of the jth elevator to the storage position, D L (P lj , P k ) represents the walking distance from the storage position of the jth elevator to the current storage layer of the kth shuttle vehicle, D L (P k , P ji ) represents the walking distance from the current storage layer of the kth shuttle vehicle to the layer level where the jth elevator executes the ith task; t2 is the minimum calling time of the jth elevator for executing the ith task in the mode of the second work of the elevator, V L represents the running speed of the elevator, D L (P j , P k ) represents the walking distance from the current position of the jth elevator to the current storage layer of the kth shuttle vehicle, D L (P k , P lj ) represents the walking distance from the current storage layer of the kth shuttle vehicle to the storage position of the jth elevator, D L (P lj , P ji ) represents the walking distance from the layer level where the jth elevator executes the ith task to the storage position of the elevator; t3 is the minimum calling time of the jth elevator for executing the ith task in the mode of the third work of the elevator, V.

2. The method of claim 1, wherein, in: t'1 and t'2 represent two modes: the hoist first transports goods to the storage level where the shuttle car is located, and then transports the goods and the shuttle car together to the target storage level for work; the hoist first picks up the shuttle car working in tandem, and then goes to the inbound / outbound level to retrieve the goods and transport them to the target storage level for work. After the hoist reaches the storage level where the k-th shuttle car is located, at P... k Location wait time, E k(i-1) D represents the time when the previous task of the k-th shuttle was completed. C (P C(i-1) ,P k D represents the distance traveled by the k-th shuttle from its position after completing the previous task to the position of the j-th hoist on the floor where the k-th shuttle is located. L (P j ,P k D represents the distance traveled from the current position of the j-th hoist to the current cargo level of the k-th shuttle car. L (P j ,P ji S represents the distance traveled from the current position of the j-th hoist to the floor where the i-th task objective is located. ji V represents the start time of the j-th hoist performing the i-th task. C V represents the operating speed of the four-way shuttle. L This indicates the operating speed of the hoist; t'3 represents the speed at which the hoist transports the nearest available shuttle to the target storage level during collaborative outbound operations, and then the shuttle retrieves the goods from the target storage location before the outbound task is completed via the hoist. In this mode, after the hoist reaches the storage level of the kth shuttle, at point P... k The location waiting time is E. k(i-1) D represents the time when the previous task of the k-th shuttle was completed. C (P C(i-1) ,P k D represents the distance traveled by the k-th shuttle from its position after completing the previous task to the position of the j-th hoist on the floor where the k-th shuttle is located. L (P j ,P k S represents the distance traveled by the j-th hoist from its current position to the current cargo level of the k-th shuttle car. ji V represents the start time of the j-th hoist performing the i-th task. C V represents the operating speed of the four-way shuttle. L This indicates the operating speed of the hoist.

3. The method of claim 1, wherein, The operation time for the hoist and shuttle car to perform the warehousing task established by S2 is as follows: t in L ji = min(t1, t2) wherein, t in L ji , t in C ki are the operation time of the jth elevator and the kth shuttle vehicle to complete the ith warehousing task, respectively, D L (P k , P ji ) represents the walking distance of the jth elevator from the current layer of the kth shuttle vehicle to the warehousing position of the elevator, D C (P C(i-1) , P k ) represents the walking distance of the kth shuttle vehicle from the position after completing the last task to the position of the jth elevator in the layer where the kth shuttle vehicle is located, D C (P k , P ji ) represents the walking distance of the kth shuttle vehicle from the jth elevator position in the current layer to the target node of the ith task to be executed, D L (P k , P lj ) represents the walking distance of the kth shuttle vehicle from the current layer to the warehousing position of the jth elevator, D L (P lj , P ji ) represents the walking distance of the jth elevator to the warehousing position from the layer where the ith task is executed, V C represents the running speed of the four-way shuttle vehicle, and V L represents the running speed of the elevator.

4. The method of claim 1, wherein, The operation time for the hoist and shuttle car to perform outbound tasks established by S2 is as follows: wherein, t out L ji , t out C ki are the operation times of the jth elevator and the kth shuttle vehicle to complete the ith outbound task, respectively, D L (P k , P ji ) represents the walking distance of the jth elevator from the current storage layer of the kth shuttle vehicle to the outbound position of the ith task of the elevator, D C (P j(i-1) , P k ) represents the walking distance of the kth shuttle vehicle from the last executed task target node to the outbound position of the jth elevator at the current storage layer, D C (P ji , P k ) represents the walking distance of the kth shuttle vehicle from the outbound position of the jth elevator at the current storage layer to the inbound node of the jth elevator at the storage layer where the ith task target is located, V C represents the running speed of the four-way shuttle vehicle, and V L represents the running speed of the elevator.

5. The task scheduling method for a four-way shuttle-type dense warehousing system according to claim 1, specifically includes the mathematical model of the objective function expression for the execution time of inbound and outbound tasks: According to the jth elevator task execution time, the sum of all elevator task execution times is obtained, and the value of is obtained. According to the kth shuttle task execution time, the sum of all shuttle task execution times is obtained, and the value of is obtained. t in L ji 、t in C ki respectively are the operation time of the jth elevator and the kth shuttle vehicle to complete the ith warehousing task; t out L ji 、t out C ki respectively are the operation time of the jth elevator and the kth shuttle vehicle to complete the ith warehousing task; the objective function is defined as: Ttotai is the total time for all lifts and shuttles to perform their tasks.

6. The method of claim 1, wherein, S3 specifically includes: (1) Coding: The first segment is the main coding layer for task number; the second segment is the coding layer for hoist; the third segment is the coding layer for shuttle car; (2) Initialize the population based on Tent chaotic mapping; (3) Encoding conversion: The encoding of task sorting, elevator selection and shuttle selection is converted to obtain discrete gene values; (4) Decoding and fitness calculation: Decoding is performed on the discrete three-segment code. The total time for the elevator and shuttle to complete the operation is taken as the fitness. The fitness is calculated using the whale optimization algorithm. (5) Determine the number of iterations and output the results: By determining whether the final number of iterations has reached the set value of the whale optimization algorithm, the task list of the elevator and the shuttle can be obtained.

7. The method of claim 6, wherein, Encoding conversion includes: The following method is used for the solution space transformation between the hoist and the shuttle: In the formula: m(i) is the continuously encoded gene value; z(i) is the number of elevators or shuttles; u(i) is the obtained discrete gene value; δ is the upper limit of the number of elevators or shuttles.

8. The method of claim 6, wherein, Determining the number of iterations and outputting the results includes: Determine whether the final number of iterations has reached the set value of the whale optimization algorithm. If it has reached the set maximum number of iterations, output the minimum final task time and draw a Gantt chart based on the start time of the shuttle and elevator for each task. If it has not reached the set maximum number of iterations, continue iterating.

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