Intelligent scheduling method for trays and automatic trolleys in multi-layer goods shelf automatic warehouse

Abstract

The invention discloses a method for intelligently scheduling trays and automatic trolleys in an automatic warehouse with multiple layers of shelves, and provides a mixed integer programming model and a three-stage heuristic algorithm, which comprises the following steps of: firstly, analyzing main factors involved in tray distribution transportation and vehicle scheduling processes in the automatic warehouse with the multiple layers of shelves and two storage positions; secondly, different types of trays are classified and matched with the corresponding transport vehicles, so that distribution of the trays is achieved; then, linking each different transport vehicle and carrying out cooperative scheduling on a plurality of different types of vehicles so as to realize continuous transportation of trays; and finally, different types of vehicles have specific unloading places, and automatic storage of the two storage warehouses is achieved according to different types of transport vehicles. According to the method, the mixed integer programming model and the three-stage heuristic algorithm are provided, so that the efficiency of tray transportation and vehicle scheduling in an automatic warehouse is improved, and the overall operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to a method for dispatching multiple pallets and multiple automatic vehicles, and in particular to a method for dispatching multiple pallets and multiple automatic vehicles (AGVs) in a multi-layer shelf automated warehouse, and belongs to the technical field of intelligent warehousing. Background Art

[0002] With the popularization of digitalization, the intelligent upgrade of traditional large warehouses is particularly important. A well-designed large-scale warehousing system can not only reduce the infrastructure cost of the warehouse, but also provide good feedback for the future operation of the warehouse. Currently, most traditional manufacturing companies have a great demand for intelligent warehousing systems.

[0003] Currently, most manufacturing companies have different storage needs, requiring a variety of transport vehicles and corresponding storage space to sort, transport and store different types of pallets. These systems usually include multiple types of vehicles, such as automated guided vehicles, lifts, shuttles and forklifts. Connecting these vehicles within the warehouse to transport pallets interactively requires an efficient and intelligent warehouse management system to handle complex scheduling.

[0004] Existing smart warehousing systems are not mature enough in terms of the intelligence of scheduling and data processing. Transport scheduling within the warehouse involves the processing of a large amount of real-time data, including planning of transportation routes, tracking of package locations, sorting of task priorities, etc. Most traditional warehouse systems rely on pre-set rules or fixed scheduling logic and lack the ability to respond to environmental changes in real time. In addition, the problem of data integration in warehousing systems cannot be ignored. Data intercommunication between different equipment, vehicles and systems often has inconsistent standards or incompatible interfaces, which makes system integration and expansion complicated and costly.

[0005] Existing technologies still have room for improvement in terms of energy consumption and sustainability. Although many automated warehousing equipment can improve efficiency, they consume a lot of energy during operation. This not only increases the operating costs of warehouses, but also affects the goals of contemporary enterprises for green operations. Therefore, while improving the level of warehouse intelligence, reducing energy consumption and achieving sustainable development are also issues that need to be urgently addressed in current intelligent warehousing technology. Summary of the invention

[0006] Purpose of the invention: In view of the problems and shortcomings in the prior art, the present invention provides a method for intelligent scheduling of pallets and automatic carts in a multi-layer shelf automated warehouse, which analyzes the demand for classified storage in the warehouse, classifies different pallets, and schedules multiple AGVs (automatic carts) to continuously transport pallets. The transportation and classified storage of pallets in the warehouse are realized by constructing a time model for pallet allocation and cart scheduling. The overall problem in the warehouse is divided into three sub-problems for processing, and the overall operating efficiency of the warehouse is optimized, thereby reducing the cost of warehouse operation and improving warehouse operation efficiency.

[0007] Technical solution: A method for intelligent scheduling of pallets and automatic carts in a multi-layer shelf automated warehouse, comprising the following steps: Step 1: Analyze the interaction status of multi-layer shelves, large and small storage locations, pallets, delivery vehicles and other resources contained in the automated warehouse, and construct the intelligent scheduling scenario, objective function and constraint function of pallets and AGVs in the multi-layer shelf automated warehouse to ensure the reasonable allocation of pallet storage locations, the optimal path for cargo delivery and the maximization of storage space utilization; the large and small storage locations are called large storage locations and small storage locations respectively; Step 2: Based on the above step 1, pallets of different sizes and types are matched with their corresponding transportation equipment, and the pallet transportation problem is further decomposed into three sub-problems: Sub-problem 1 is to determine the transportation sequence of pallets by automated guided vehicles, sub-problem 2 is to determine the transportation sequence of small pallets by lifts and shuttles, and sub-problem 3 is to determine the transportation sequence of large pallets by forklifts. By solving the three sub-problems, fully automated storage in the warehouse can be achieved; Step 3: For a pallet, select the vehicle sequence that transports the pallet, record the unloading and pickup locations of the pallet, coordinate the scheduling of multiple different types of pallets and vehicles to achieve continuous transportation of multiple pallets, and ensure that different delivery vehicles can automatically pick up the pallets that have been unloaded by the previous automated equipment when they arrive at the pickup point; Step 4: Set different unloading locations for different types of vehicles, and classify and store large and small pallets according to the type of transport vehicle. At the same time, calculate the optimal delivery path for the pallets to minimize the overall operation time of the warehouse.

[0008] Preferably, in step 2, the process of matching pallets of different sizes and types with their corresponding transportation equipment is as follows: the automated guided vehicle picks up the pallet from the platform and places it on the large pallet buffer or conveyor belt in the order of the pallet allocation; the large pallet is transported to the large pallet buffer by the automated guided vehicle, and then loaded from the large pallet buffer by a forklift and transported to the shelf for storing large-sized goods; the small pallet is transported to the conveyor belt by the automated guided vehicle, and the conveyor belt is transported to the elevator, the elevator rises to the level corresponding to the pallet, and then the pallet is unloaded to the small pallet buffer, and the shuttle car on this level transports the pallet to the shelf for storing small-sized goods.

[0009] In step 3, multiple different types of pallets and vehicles are coordinated and dispatched, and the specific implementation process is as follows: The pallets are classified by placing them at different unloading locations through automated guided vehicles, and then transport vehicles pick up and transport different types of pallets. Specifically, by recording the placement of the goods, calculating the transport carrier sequence corresponding to the pallets, and coordinating the pallets and vehicles, the transport vehicles can automatically pick up the pallets for transportation at the loading and unloading points.

[0010] In step 4, designated locations are set up to store pallets according to different types of transport vehicles, so that the same type of pallets can be stored on different types of shelves in the warehouse. At the same time, the path of the automatic transport vehicle is changed by modifying the transportation sequence of the vehicle for the pallet, and the optimal path is calculated to improve the operation efficiency of the warehouse.

[0011] Preferably, in the intelligent scheduling scenario, a mathematical model for intelligent scheduling of pallets and AGVs is established. In the process of establishing the model, the following issues are considered: Different sized pallets correspond to different storage locations and transport vehicles; Transportation sequencing of multiple pallets; The dispatch sequence for multiple vehicles to transport pallets.

[0012] Based on the above problems, the final objective function is to minimize the latest completion time of the transportation of two types of pallets in the warehouse. After analyzing the above problems, the solution process is as follows: assign pallets to automated guided vehicles, which will place pallets of different sizes on the conveyor belt or buffer zone, and set the order for each automated guided vehicle to transport pallets; assign large pallets to forklifts, and set the order for each forklift to transport large pallets; assign small pallets to lifts, and determine the order for each lift to transport small pallets; assign small pallets to shuttles, and set the order for shuttles to transport small pallets; determine the storage location of each pallet, and transport it to the storage location by shuttles or forklifts.

[0013] Preferably, the transportation of all pallets in the entire warehouse and the three sub-problems of scheduling various types of vehicles are as follows: Sub-problem 1 is to determine the transportation sequence of pallets by automated guided vehicles, Sub-problem 2 is to determine the transportation sequence of small pallets by lifts and shuttles, and Sub-problem 3 is to determine the transportation sequence of large pallets by forklifts. The specific descriptions are as follows: Subproblem 1 involves determining the order in which pallets are transported by each AGV, scheduling the order of the AGVs, and deciding whether to store the pallets on the conveyor or in the buffer according to the intended storage location of the pallets; Sub-problem 2 involves determining the transportation sequence of small pallets for the elevator and shuttle. When the elevator reaches the designated floor, it places the small pallet in the small pallet buffer, dispatches multiple shuttles on each floor, and lets the shuttles place the pallets in the designated storage locations for the small pallets. Sub-problem three requires determining the transportation order of large pallets for each forklift, scheduling the arrangement order of multiple forklifts, and placing the large pallets in the corresponding storage locations on the specified layer according to the storage location of each large pallet.

[0014] Preferably, the implementation steps of the mathematical model designed for the three sub-problems are as follows: Large pallets are transported to the large pallet buffer by automated guided vehicles, and then forklifts go to the large pallet buffer to pick up and transport them. The unloading time of the automated guided vehicle and the picking time of the forklift are connected to achieve continuous transportation of large pallets. Then the forklifts go to large storage locations to store the large pallets. The small pallet is transported to the conveyor belt by the automatic guided vehicle, and then the small pallet on the conveyor belt is transported to the elevator to complete the pallet pickup. The continuous transportation of the small pallet is achieved by connecting the unloading time of the automatic guided vehicle and the pickup time of the elevator. The elevator places the small pallet in the small pallet buffer, and the shuttle goes to the small pallet buffer to pick up the small pallet. By connecting the unloading time of the elevator and the picking time of the shuttle, the continuous transportation of the small pallet is realized, and then the shuttle goes to the small storage location to store the small pallet.

[0015] Preferably, a three-stage heuristic algorithm is proposed to solve the three sub-problems. The steps for solving sub-problem 1 are as follows: The taboo search algorithm is applied to sub-problem 1. The main idea of ​​this algorithm is to optimize the path of the automated guided vehicle to transport pallets and schedule the automated guided vehicle more efficiently. The algorithm steps are as follows: Initialize the number of alternative paths and the maximum number of iterations; Initialize the taboo table and the initial sequence of the pallets as the optimal solution; Exchange the delivery order of two random pallets multiple times within the maximum number of iterations to find the optimal path; The optimal solution is updated through multiple iterations to become the optimal path.

[0016] Preferably, the steps for solving sub-problem 2 of the three-stage heuristic algorithm are as follows: For sub-problem 2, a genetic algorithm is used to modify the shuttle's transportation path and the storage location of the small pallet to change the order in which the shuttle transports the pallets. This process is implemented by the crossover and mutation operations in the genetic algorithm. Since the storage location also affects the pallet transportation time, this operation allows the storage location to be changed by modifying the parameters, and the optimal solution is found through mutation. The algorithm steps are as follows: Initialize the maximum number of iterations and the maximum population size per generation; The optimal path of the contemporary population is initialized as the order in which the small pallets arrive at the conveyor belt; Set the global optimal path as the optimal path of the contemporary population; In each iteration, multiple selection and crossover operations are performed on the global optimal path, the storage location of each small pallet is modified, and the optimal path for this iteration is calculated based on the modified transportation sequence; The optimal path found in the current iteration is compared with the global optimal path, and the minimum path of the two is set as the new global optimal path.

[0017] Preferably, the steps for solving sub-problem three of the three-stage heuristic algorithm are as follows: For sub-problem 3, the simulated annealing algorithm is used to optimize the scheduling of multiple forklifts and find the optimal transportation path for each forklift. The key feature of the algorithm is to selectively accept new solutions after perturbing the neighborhood and adjusting the iteration conditions. The algorithm steps are as follows: Initialize loop iteration rules and loop end conditions; Assign the initial pallet sequence as the initial path to the optimal path; Randomly modify the order of two pallets to change the forklift's transportation path; The time consumption of the changed transportation path is calculated, compared with the time consumption of the optimal transportation path, and the path corresponding to the smaller time consumption is set as the new optimal transportation path.

[0018] The final running time of the two storage warehouses is obtained by combining the optimal transportation routes of the shuttle and forklift. The maximum value of the two final running times is found to obtain the final running time of the entire warehouse.

[0019] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for intelligent scheduling of pallets and automatic carts in a multi-layer shelf automated warehouse as described above is implemented.

[0020] A computer-readable storage medium stores a computer program for executing the method for intelligently scheduling pallets and automatic carts in a multi-layer shelf automated warehouse as described above.

[0021] Compared with traditional warehouses, the present invention has the following beneficial effects: First, it realizes the automatic classification storage of different pallets in the warehouse; second, it provides technical support for the scheduling of AGVs in the commercialization of automated warehouses; third, it helps warehouse operators provide convenient operation strategies and management systems. Fourth, it analyzes the demand for classified storage in the warehouse, classifies different pallets, and schedules multiple AGVs to continuously transport pallets. By constructing a time model for pallet allocation and trolley scheduling, it realizes the transportation and classified storage of pallets in the warehouse. The overall problem in the warehouse is divided into three sub-problems for processing, and the overall operation efficiency of the warehouse is optimized, thereby reducing the cost of warehouse operation and improving warehouse operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a dispatching warehouse for multiple pallets and multiple automatic carts in a multi-layer shelf automated warehouse according to an embodiment of the present invention; Figure 2 A schematic diagram of pallet transportation and vehicle scheduling in a small storage warehouse in an embodiment of the present invention; Figure 3 A schematic diagram of pallet transportation and vehicle scheduling in a large storage warehouse in an embodiment of the present invention; Figure 4 This is a flow chart of the transportation of pallets in a warehouse in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0024] The invention discloses a method for dispatching multiple pallets and multiple automatic trolleys in a multi-layer shelf automated warehouse, involving multiple pallet allocations, multiple automatic vehicle dispatching and two warehouse storage systems.

[0025] The method for dispatching multiple pallets and multiple automatic carts in a multi-layer shelf automated warehouse comprises the following steps: Step 1: Analyze the interaction status of resources such as multi-layer shelves, large and small storage locations, pallets, and delivery vehicles contained in the automated warehouse, and build intelligent scheduling scenarios, objective functions, and constraint functions for pallets and AGVs in multi-layer shelf automated warehouses to ensure reasonable allocation of pallet storage locations, optimal paths for cargo delivery, and maximum storage space utilization, such as Figure 1As shown, an embodiment of the present invention involves a warehouse including two types of pallets, large and small, automatic guided vehicles, forklifts, lifts and shuttles.

[0026] Step 2: In the above step 1, we analyze the main factors involved in the process of pallet allocation, transportation, storage and vehicle scheduling in an automated warehouse with multi-layer shelves and two storage locations. According to different pallets, we classify and match different types of pallets with their corresponding transport vehicles, and obtain sub-problems of pallet transportation by different vehicles. Sub-problem 1 involves determining the transportation sequence of pallets for each automated guided vehicle, sub-problem 2 involves determining the transportation sequence of small pallets for lifts and shuttles, and sub-problem 3 requires determining the transportation sequence of large pallets for each forklift. By solving the three sub-problems, fully automated storage in the warehouse can be achieved. The specific problem classification and problem solving content are as follows: (1) Scenario description of the implementation example in the warehouse The AGV picks up the pallet from the platform and places it on the large pallet buffer or conveyor belt according to the order of pallet allocation; the large pallet is transported to the large pallet buffer by the AGV, and then the forklift retrieves the large pallet from the large pallet buffer and transports it to a large warehouse (i.e., a large storage location); the small pallet is transported to the conveyor belt by the AGV, and then the conveyor belt is transported to the elevator, which rises to the level corresponding to the small pallet, and then the small pallet is unloaded to the small pallet buffer, and the shuttle vehicles on each level transport the small pallet to the small storage location; The pallets are sorted by placing them at different unloading locations through automated guided vehicles. Subsequently, automated vehicles pick and transport different types of pallets. The pick-up and unloading times of different types of vehicles are linked to ensure that the pallets are transported continuously by multiple vehicles. According to different transport vehicles, each transport vehicle has its designated location to store pallets, so as to realize the storage of the same pallets in different types of warehouses. At the same time, the transportation sequence of pallets and the paths of multiple automatic vehicles are optimized to improve the operation efficiency of the warehouse.

[0027] (1) Main issues in establishing mathematical models for implementation scenarios Different sized pallets correspond to different storage locations and transport vehicles; Transportation sequencing of multiple pallets; The scheduling sequence of multiple vehicles transporting pallets; Based on the above problems, the ultimate goal is to minimize the latest completion time of the transportation of two types of pallets in the warehouse. After analyzing the above problems, the solution process is as follows: assign pallets to automated guided vehicles, which will place pallets of different sizes on the conveyor belt or buffer zone, and set the order for each automated guided vehicle to transport pallets; assign large pallets to forklifts, and set the order for each forklift to transport large pallets; assign small pallets to lifts, and determine the order for each lift to transport small pallets; assign small pallets to shuttles, and set the order for shuttles to transport small pallets; determine the storage location of each pallet, and transport it to the storage location by shuttles or forklifts.

[0028] (1) Example The transportation of all pallets in the warehouse and the scheduling of various types of vehicles are divided into three sub-problems: Subproblem 1 involves determining the order in which pallets are transported by each AGV, scheduling the order of the AGVs, and deciding whether to store them on the conveyor or in the buffer according to the pallets’ intended storage locations; Subproblem 2 involves determining the transportation sequence of small pallets for the elevator and shuttles. When the elevator reaches the designated floor, it places the pallets in the small pallet buffer, dispatches multiple shuttles on each floor, and has the shuttles place the pallets in their designated storage locations. Sub-problem three requires determining the transportation order of large pallets for each forklift, scheduling the arrangement order of multiple forklifts, and placing the large pallets in the corresponding storage locations on the specified layer according to the storage location of each large pallet.

[0029] Step 3: For a pallet, select a series of transport vehicles to transport the pallet, record the unloading and pickup locations of the pallet, ensure that each vehicle automatically picks up the pallet for transportation at the unloading location of the previous automated equipment, and coordinately schedule multiple different types of pallets and vehicles to achieve continuous transportation of multiple pallets, that is, design a mathematical model and algorithm for pallet allocation and multiple vehicle scheduling in the warehouse. The specific contents are as follows: (1) Mathematical model for pallet allocation and multiple vehicle scheduling in the warehouse The continuous transportation of large pallets is achieved by the interaction between the automatic guided vehicle and the forklift, as shown in the following formula:

[0030] The continuous transportation of small pallets is achieved by the interaction between the automatic guided vehicle and the elevator and the interaction between the elevator and the shuttle, as shown in the following formula:

[0031]

[0032] The constraint for an automated guided vehicle to continuously transport the assigned large and small pallets is as follows:

[0033]

[0034] The constraint on a forklift to continuously transport the assigned large pallet is as follows:

[0035] The constraint of a lift on the continuous delivery of the assigned small pallets is as follows:

[0036] The constraint of a shuttle on the continuous delivery of the assigned small pallets is as follows:

[0037] parameter and Automated Guided Vehicle a Shipping large pallets i and j The start time of ρ iu and ρ ju Respectively forklift u The starting time of the delivery of the large pallet; parameters and Automated Guided Vehicle a Shipping small pallets i and j The start time of ω iv and ω jv Respectively represent elevators v Shipping small pallets i and j The starting time of is and Ω js Shuttle s Shipping small pallets i and j The start time of Indicates the time taken by the automated guided vehicle to transport when it is empty; parameter Indicates the time it takes for a pallet to be transported on an automated guided vehicle; parameter Indicates the time it takes for a pallet to be transported on a forklift; parameter Indicates the time it takes for the pallet to be transported on the elevator; parameter Indicates the time it takes for the pallet to be transported on the shuttle; parameter M Represents an infinite number; variable and Indicates that the automated guided vehicle and forklift transport the package continuously; variable and Indicates that the automatic guided vehicle and the elevator transport the package continuously; variable and Indicates that the automated guided vehicle transports two pallets in succession; the variable δ iju Indicates that the forklift transports two pallets in succession; the variable μ ijv Indicates that the elevator transports two pallets in succession; the variable ξ ijs Indicates that the shuttle transports two pallets in succession; the variable Indicates that the automated guided vehicle and forklift continuously transport the same pallet and store the pallet in a large type storage location; variable It indicates that the lift and shuttle continuously transport the same pallet and store the pallet in a small type storage location; A, U, V, S represent the collection of automatic guided vehicles, forklifts, lifts, and shuttles, respectively; P 1 , P 2 Respectively represent the collection of large and small pallets; N 1 、N 2 Respectively represent a collection of small type storage locations.

[0038] (2) Propose a three-stage heuristic algorithm for pallet allocation and multiple vehicle scheduling in a warehouse The steps for solving subproblem 1 of the three-stage heuristic algorithm are as follows: The taboo search algorithm is applied to sub-problem 1. The main idea of ​​this algorithm is to optimize the path of the automated guided vehicle to transport pallets and schedule the automated guided vehicle more efficiently. The algorithm steps are as follows: Initialize the number of alternative paths and the maximum number of iterations; Initialize the taboo table and initialize the current solution as the optimal solution; Exchange the delivery order of two random pallets multiple times within the maximum number of iterations to find the optimal path; The optimal solution is updated through multiple iterations to become the optimal path.

[0039] The steps to solve sub-problem 2 of the three-stage heuristic algorithm are as follows: The second sub-problem uses a genetic algorithm to modify the order in which the shuttle transports pallets. This process is implemented by the crossover and mutation operations in the genetic algorithm. Since the storage location also affects the pallet transportation time, this operation allows the storage location to be changed by modifying the parameters and finding the optimal solution through mutation. The algorithm steps are as follows: Initialize the maximum number of iterations and the maximum population size per generation; The optimal path of the contemporary population is initialized as the order in which the small pallets arrive at the conveyor belt; Set the global optimal path as the optimal path of the contemporary population; In each iteration, the global optimal path is selected and crossed multiple times, the storage location of each small pallet is modified, and the time consumption of the optimal path of this iteration is calculated according to the modified transportation sequence; The optimal path found in the current iteration is compared with the global optimal path in terms of time consumption, and the path with the smallest time consumption is set as the new global optimal path.

[0040] The steps for solving subproblem 3 of the three-stage heuristic algorithm are as follows: Subproblem 3 uses the simulated annealing algorithm to optimize the scheduling of multiple forklifts and find the optimal transportation path for each forklift. The key feature of this algorithm is to selectively accept new solutions after perturbing the neighborhood and adjusting the iteration conditions. The algorithm steps are as follows: Initialize loop iteration rules and loop end conditions; Assign the initial path to the optimal path; Randomly modify the order of two pallets to change the forklift's transportation path; The time consumption of the changed transportation path is calculated, and compared with the time consumption of the optimal transportation path, and the optimal transportation path is set to be the transportation path corresponding to the smaller time consumption of the two.

[0041] Step 4: Different types of vehicles have different unloading locations. The two types of pallets are stored according to different types of transport vehicles. At the same time, the optimal storage path is selected to store the pallets to minimize the overall operation time of the warehouse. The specific contents are as follows: The optimal transport routes of shuttles and forklifts are combined to obtain the final operating time of the two storage warehouses; The final runtime constraint for a large storage warehouse is as follows:

[0042] The final runtime constraint for the small storage warehouse is as follows:

[0043] Find the maximum of the two final running times, that is, the final running time of the entire warehouse; The final running time constraint of the entire warehouse is as follows:

[0044]

[0045] The overall warehouse operation objective function constraints are as follows: min

[0046] parameter , Respectively represent the final running time of large and small storage warehouses, Indicates the final transportation completion time of the two pallets, that is, the end time of the overall warehouse operation.

[0047] Aiming at the problem of scheduling multiple pallets and multiple automatic carts in a multi-layer shelf automated warehouse, the present invention constructs a mathematical model of multiple pallet allocation storage and AGV collaborative scheduling in the warehouse from the perspective of optimizing warehouse operation efficiency and saving warehouse operation time, and designs and develops a three-stage heuristic algorithm, which can save warehouse operation costs and improve the efficiency of warehouse managers.

[0048] Obviously, those skilled in the art should understand that the various steps of the method for dispatching multiple pallets and multiple automatic carts in the multi-layer shelf automated warehouse of the above-mentioned embodiment of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the embodiment of the present invention is not limited to any specific combination of hardware and software.

Claims

1. A method for intelligent dispatching of pallets and automatic trolleys in a multi-layer shelf automated warehouse, characterized in that: The following steps are involved: Step 1: Analyze the interaction status of resources in the automated warehouse, and construct the intelligent scheduling scenario, objective function and constraint function of pallets and AGVs in the multi-layer shelf automated warehouse; Step 2: Match pallets of different sizes and types with their corresponding transportation equipment, and decompose the pallet transportation problem into three sub-problems: Sub-problem 1 is to determine the transportation sequence of pallets for automated guided vehicles, sub-problem 2 is to determine the transportation sequence of small pallets for lifts and shuttles, and sub-problem 3 is to determine the transportation sequence of large pallets for forklifts. By solving the three sub-problems, fully automated storage in the warehouse can be achieved; Step 3: For a pallet, select the vehicle sequence that transports the pallet, record the unloading and pickup locations of the pallet, and coordinate the scheduling of multiple different types of pallets and vehicles to achieve continuous transportation of multiple pallets, so that different delivery vehicles can automatically pick up the pallets that have been unloaded by the previous automated equipment when they arrive at the pickup point; Step 4: Set different unloading locations for different types of vehicles, and classify and store large and small pallets according to the type of transport vehicle. At the same time, calculate the optimal delivery path for the pallets to minimize the overall operation time of the warehouse.

2. The method for intelligent dispatching of pallets and automatic trolleys in a multi-layer shelf automated warehouse according to claim 1 is characterized in that: In step 2, the process of matching pallets of different sizes and types with their corresponding transportation equipment is as follows: the automated guided vehicle picks up the pallet from the platform and places it on the large pallet buffer or conveyor belt in the order of pallet allocation; the large pallet is transported to the large pallet buffer by the automated guided vehicle, and then loaded from the large pallet buffer by a forklift and transported to the shelf for storing large-sized goods; the small pallet is transported to the conveyor belt by the automated guided vehicle, and the conveyor belt is transported to the elevator, the elevator rises to the level corresponding to the pallet, and then the pallet is unloaded to the small pallet buffer, and the shuttle car on this level transports the pallet to the shelf for storing small-sized goods.

3. The method for intelligent dispatching of pallets and automatic trolleys in a multi-layer shelf automated warehouse according to claim 1, characterized in that: In step 3, multiple different types of pallets and vehicles are coordinated and dispatched, and the specific implementation process is as follows: Pallets are classified by placing them at different unloading locations through automated guided vehicles, and then different types of pallets are picked and transported by subsequent delivery vehicles. The placement of the goods is recorded, the transport carrier sequence corresponding to the pallet is calculated, and pallets and vehicles are coordinated and scheduled, so that delivery vehicles can automatically pick up pallets for transportation at the loading and unloading points.

4. The method for intelligent dispatching of pallets and automatic trolleys in a multi-layer shelf automated warehouse according to claim 1, characterized in that: In step 4, designated locations are set up to store pallets according to different types of transport vehicles, so that the same type of pallets can be stored on different types of shelves in the warehouse. At the same time, the path of the automatic transport vehicle is changed by modifying the transportation sequence of the vehicle for the pallet, and the optimal path is calculated to improve the operation efficiency of the warehouse.

5. The method for intelligent dispatching of pallets and automatic trolleys in a multi-layer shelf automated warehouse according to claim 1, characterized in that: In the intelligent scheduling scenario, a mathematical model for intelligent scheduling of pallets and AGVs is established. In the process of establishing the model, the following issues are considered: Different sized pallets correspond to different storage locations and transport vehicles; Transportation sequencing of multiple pallets; The dispatching sequence of multiple vehicles transporting pallets; Based on the above problems, the ultimate objective function is to minimize the latest completion time of the transportation of two types of pallets in the warehouse; after analyzing the above problems, the solution process is as follows: assign pallets to automated guided vehicles, which will place pallets of different sizes on the conveyor belt or buffer zone, and set the order in which each automated guided vehicle transports pallets; assign large pallets to forklifts, and set the order in which each forklift transports large pallets; assign small pallets to lifts, and determine the order in which each lift transports small pallets; assign small pallets to shuttles, and set the order in which the shuttles transport small pallets; determine the storage location of each pallet, and transport it to the storage location by a shuttle or forklift.

6. The method for intelligent dispatching of pallets and automatic trolleys in a multi-layer shelf automated warehouse according to claim 1, characterized in that: The sub-problem 1 involves determining the order of transporting pallets for each automated guided vehicle, scheduling the order of the automated guided vehicles, and deciding whether to store the pallets on a conveyor belt or in a buffer according to the predetermined storage location of the pallets; The second sub-problem involves determining the transportation order of the small pallets of the elevator and the shuttle, placing the small pallets in the small pallet buffer after the elevator arrives at the designated floor, dispatching multiple shuttles on each floor, and letting the shuttles place the pallets in the designated storage locations of the small pallets; The sub-problem 3 requires determining the transportation order of the large pallets of each forklift, scheduling the arrangement order of multiple forklifts, and placing the large pallets at the corresponding storage position of the designated layer according to the storage position of each large pallet; The implementation steps of the mathematical model designed for the three sub-problems are as follows: Large pallets are transported to the large pallet buffer by automated guided vehicles, and then forklifts go to the large pallet buffer to pick up and transport them. The unloading time of the automated guided vehicle and the picking time of the forklift are connected to achieve continuous transportation of large pallets. Then the forklifts go to large storage locations to store the large pallets. The small pallets are transported to the conveyor belt by the automatic guided vehicle, and the small pallets on the conveyor belt are transported to the elevator to complete the pallet pickup. The continuous transportation of small pallets is achieved by connecting the unloading time of the automatic guided vehicle and the pickup time of the elevator; The elevator places the small pallet in the small pallet buffer, and the shuttle goes to the small pallet buffer to pick up the small pallet. By connecting the unloading time of the elevator and the picking time of the shuttle, the continuous transportation of the small pallet is realized, and then the shuttle goes to the small storage location to store the small pallet.

7. The method for intelligent dispatching of pallets and automatic trolleys in a multi-layer shelf automated warehouse according to claim 1, characterized in that: A three-stage heuristic algorithm is proposed to solve the three sub-problems. The steps for solving sub-problem 1 are as follows: The taboo search algorithm is applied to sub-problem 1. The main idea of ​​this algorithm is to optimize the path of the automated guided vehicle to transport pallets and schedule the automated guided vehicle more efficiently. The algorithm steps are as follows: Initialize the number of alternative paths and the maximum number of iterations; Initialize the taboo table and the initial sequence of the pallets as the optimal solution; Exchange the delivery order of two random pallets multiple times within the maximum number of iterations to find the optimal path; Update the optimal solution through multiple iterations to become the optimal path; The steps to solve sub-problem 2 are as follows: For sub-problem 2, a genetic algorithm is used to modify the shuttle's transportation path and the storage location of the small pallet to change the order in which the shuttle transports the pallets. This process is implemented by the crossover and mutation operations in the genetic algorithm. Since the storage location also affects the pallet transportation time, this operation allows the storage location to be changed by modifying the parameters, and the optimal solution is found through mutation. The algorithm steps are as follows: Initialize the maximum number of iterations and the maximum population size per generation; The optimal path of the contemporary population is initialized as the order in which the small pallets arrive at the conveyor belt; Set the global optimal path as the optimal path of the contemporary population; In each iteration, multiple selection and crossover operations are performed on the global optimal path, the storage location of each small pallet is modified, and the optimal path for this iteration is calculated based on the modified transportation sequence; Compare the optimal path found in the current iteration with the global optimal path, and set the minimum path of the two as the new global optimal path; The steps for solving sub-problem 3 are as follows: For sub-problem 3, the simulated annealing algorithm is used to optimize the scheduling of multiple forklifts and find the optimal transportation path for each forklift. The key feature of the algorithm is to selectively accept new solutions after perturbing the neighborhood and adjusting the iteration conditions. The algorithm steps are as follows: Initialize loop iteration rules and loop end conditions; Assign the initial pallet sequence as the initial path to the optimal path; Randomly modify the order of two pallets to change the forklift's transportation path; Calculate the time consumption of the changed transportation path, compare it with the time consumption of the optimal transportation path, and set the path corresponding to the smaller time consumption as the new optimal transportation path; The final running time of the two storage warehouses is obtained by combining the optimal transportation routes of the shuttle and forklift. The maximum value of the two final running times is found to obtain the final running time of the entire warehouse.

8. A computer device, characterized in that: The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for intelligent scheduling of pallets and automatic carts in a multi-layer shelf automated warehouse as described in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for executing the method for intelligent scheduling of pallets and automatic carts in a multi-layer shelf automated warehouse as described in any one of claims 1-7.

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