A scheduling optimization method of a four-way shuttle vehicle system

Abstract

The present application relates to the technical field of logistics warehousing, and aims to provide a scheduling optimization method for a four-way shuttle vehicle system, which takes the shortest warehouse-in time and the minimum total energy consumption of equipment as optimization objectives to construct a scheduling model for the four-way shuttle vehicle system, solves the scheduling model, and then obtains an optimal scheduling scheme. The method can simultaneously meet the time efficiency and energy consumption requirements of scheduling optimization of current logistics enterprises. The algorithm used for solving has a fast convergence speed, can significantly improve the calculation efficiency of solving the scheduling model, thereby accelerating the generation speed of the scheduling scheme, enabling the four-way shuttle vehicle system to make a quick response, and further improving the overall operation efficiency of the system.

Description

Technical Field

[0001] This invention relates to the field of logistics and warehousing technology, and more specifically to a scheduling optimization method for a four-way shuttle system in a cross-level operation mode. Background Technology

[0002] With the rapid growth of global trade and e-commerce, and the increasing demand from consumers for efficient logistics services, the logistics and warehousing industry is experiencing comprehensive development. Against this backdrop, automated storage and retrieval systems (AS / RS) have emerged to improve the efficiency and accuracy of warehousing operations.

[0003] Automated storage and retrieval systems (AS / RS) are warehouse systems that utilize a limited floor area to increase the storage density and handling efficiency of goods through a multi-layered structure. They also employ automation technologies and equipment to enhance operational efficiency. These systems are typically used in scenarios requiring large-scale storage and efficient handling of goods, such as e-commerce warehousing centers, logistics distribution centers, and cold chain logistics warehouses.

[0004] The four-way shuttle system, a new type of automated storage and retrieval system (AS / RS) currently on the market, mainly consists of four-way shuttles, elevators, tracks, and storage racks. In this system, the four-way shuttles are responsible for the horizontal transport of goods. After receiving a task instruction, the four-way shuttles can move in four directions (forward, backward, left, and right) across aisles on rack levels and can also be transported to other rack levels by connecting with the elevators. The elevators, on the other hand, are responsible for the vertical transport of goods. Once the elevators have transported goods to the corresponding receiving rack level, they hand over the task to the four-way shuttles, which then transport the goods to the corresponding storage location. Compared to traditional shuttle systems, the four-way shuttle system overcomes the limitation of shuttles operating only in a single aisle and on a single rack level, offering higher operational efficiency and flexibility.

[0005] In four-way shuttle systems, multiple shuttles typically perform inbound operations simultaneously, leading to scheduling problems such as inbound task allocation and path planning. Therefore, optimizing the scheduling of four-way shuttle systems is a crucial issue. Existing research on scheduling optimization for four-way shuttle systems mostly employs a single optimization objective, aiming to achieve a predetermined goal through optimization. For example, Chinese patent document CN105858044B discloses an optimized scheduling method for a warehouse system combining shuttles and elevators. This method uses minimizing scheduling travel time as its optimization objective and employs a genetic algorithm to optimize the inbound / outbound model, ultimately obtaining a scheduling scheme with the minimum travel time.

[0006] However, the above-mentioned technical solutions have certain technical drawbacks. First, in the context of rapid economic development, minimizing scheduling time is not the sole objective for logistics companies optimizing the four-way shuttle system. This singular optimization objective means that the resulting scheduling scheme cannot meet the actual operational needs of logistics companies. Second, this method of using genetic algorithms for optimization requires more iterations to reach convergence. This slower convergence directly increases the overall computation time for solving the inbound and outbound models, resulting in the inability to generate the optimal scheduling scheme in a timely manner. This deficiency prevents the four-way shuttle system from responding quickly to scheduling changes, thus affecting the overall operational efficiency of the system. Summary of the Invention

[0007] The purpose of this invention is to provide a scheduling optimization method for a four-way shuttle system. This method constructs a scheduling model for the four-way shuttle system with the shortest inbound time and the minimum total energy consumption of the equipment as optimization objectives, and solves this scheduling model to obtain the optimal scheduling scheme. This method can simultaneously meet the timeliness and energy consumption requirements of modern logistics enterprises for scheduling optimization. The algorithm used for solving the problem has a fast convergence speed, which can significantly improve the computational efficiency of solving the scheduling model, thereby accelerating the generation of scheduling schemes and enabling the four-way shuttle system to respond quickly, thus improving the overall operational efficiency of the system.

[0008] The present invention is achieved through the following technical solutions.

[0009] An optimization method for scheduling a four-way shuttle system includes the following steps:

[0010] S01, Steps for generating warehouse layout data points;

[0011] Warehouse layout data points are generated based on the basic information of the four-way shuttle system. The basic information includes inbound task information, four-way shuttle information, elevator information, and shelf information.

[0012] S02, Basic Model Construction Steps;

[0013] Based on the warehouse layout data points, a basic model is constructed; the basic model includes an inbound time model F(T) and an equipment energy consumption model F(W);

[0014] Wherein, the warehousing time model F(T) is used to obtain the warehousing time of the scheduling scheme, and the equipment energy consumption model F(W) is used to obtain the equipment energy consumption of the scheduling scheme;

[0015] S03, Steps for constructing the scheduling model;

[0016] A scheduling model is constructed by linearly weighting the inbound time model F(T) and the equipment energy consumption model F(W):

[0017] in, For weight values, Specifyed by the user;

[0018] S04, Scheduling scheme optimization steps;

[0019] With the shortest warehousing time and minimum equipment energy consumption as optimization objectives, the adaptive genetic annealing algorithm is used to solve the scheduling model, and the optimal solution obtained is the optimal scheduling scheme.

[0020] The adaptive genetic annealing algorithm uses the task number as the encoding sequence to generate an initial population. It iteratively optimizes the initial population and continuously generates a new population until the maximum number of iterations is reached. The new population is generated by individual judgment: if the new individual is better than the current individual, the new individual is accepted; otherwise, the new individual is accepted with probability P according to the Metropolis criterion.

[0021] As a preferred embodiment of the present invention, the solution of the adaptive genetic annealing algorithm is also subject to constraints, including: task allocation constraints, warehousing time constraints, and hoisting process constraints.

[0022] As a preferred embodiment of the present invention, the task allocation constraint stipulates that an inbound task can only be performed by one four-way shuttle, and the formula is as follows:

[0023]

[0024] Where i is the task number, j is the four-way shuttle number, r is the total number of four-way shuttles, and α ij As a decision variable, when the four-way shuttle j performs task i, α ij It is 1 if it is true, otherwise it is 0.

[0025] As a preferred embodiment of the present invention, the storage time constraint means that the storage time cannot be less than the continuous working time of the hoist, and the formula is:

[0026]

[0027] Where i is the task number, n is the total number of tasks, t2 is the time it takes for the cargo elevator to move from the first floor to the storage floor, and t5 is the time it takes for the four-way shuttle to pick up (place) goods.

[0028] As a preferred embodiment of the present invention, the elevator process constraint refers to the requirement that when performing cross-level warehousing, the time for requesting a change-level elevator should be earlier than the time for requesting a goods elevator, and the time interval between the two requests should not be less than the time required for the change-level operation. The formula is as follows:

[0029]

[0030] Where i represents the task number and j represents the four-way shuttle number; The time required to apply for a cargo lift for the four-way shuttle vehicle. t7 is the time it takes for the four-way shuttle to request a floor-changing elevator; t8 is the time it takes for the floor-changing elevator to move from the last floor-changing point to the floor where the four-way shuttle is located; t9 is the time it takes for the four-way shuttle to move from the floor where the four-way shuttle is located to the floor where the inbound goods are located; t1 is the time it takes for the four-way shuttle to move from the floor-changing elevator to the goods elevator. 11 The time required for the four-way shuttle car to be picked up (placed) by the floor-changing elevator.

[0031] As a preferred embodiment of the present invention, in steps S02 and S03, the formula for the warehousing time model F(T) is:

[0032]

[0033] Among them, T total Here, α represents the inbound time, i represents the task number, n represents the total number of tasks, and j represents the four-way shuttle number; ij and β ij As a decision variable, when the four-way shuttle j performs task i, α ij β is 1 if the task is to be performed by the four-way shuttle j and it needs to cross layers to perform task i. ij Kt is 1 if it is 1, otherwise 0; ij The time required for the four-way shuttle j to perform the cross-layer task i, Bt ij The time required for the four-way shuttle j to perform a non-layer-crossing task i.

[0034] As a preferred embodiment of the present invention, in steps S02 and S03, the formula for the equipment energy consumption model F(W) is:

[0035]

[0036] in,

[0037]

[0038]

[0039] Among them, W total Let α represent equipment energy consumption, i represent the task number, n represent the total number of tasks, j represent the number of the four-way shuttle, and r represent the total number of four-way shuttles; ij and β ij As a decision variable, when the four-way shuttle j performs task i, α ijβ is 1 if the task is to be performed by the four-way shuttle j and it needs to cross layers to perform task i. ij It is 1 if it is true, otherwise it is 0.

[0040] w j For the energy consumption of the four-way shuttle j, Kt ij The energy consumption required for the four-way shuttle j to perform cross-level task i, Bt ij Energy consumption required for the four-way shuttle j to perform a non-layer-crossing task i;

[0041] W e For the energy consumption of the cargo hoist, sw i The energy consumption of the cargo elevator moving the goods from the first floor to the receiving floor;

[0042] W h For the energy consumption of the floor-changing hoist, GW jk The energy consumption for moving from the last floor change location to the floor where the four-way shuttle is located, pw ij The energy consumption of the floor-changing elevator moving from the floor where the four-way shuttle is located to the storage floor.

[0043] As a preferred embodiment of the present invention, before the initial population is generated, a parameter initialization step is further included, wherein the parameters include: population size N, maximum number of iterations maxgen, initial temperature T0, and final temperature T. min And the cooling coefficient γ.

[0044] As a preferred embodiment of the present invention, the iteration includes selection, crossover, and mutation, and calculates the fitness value of the new individual.

[0045] As a preferred embodiment of the present invention, the probability P of the Metropolis criterion accepting the new individual is formulated as follows:

[0046]

[0047] Where T is the current temperature, and ΔE is the change of the new individual relative to the current individual.

[0048] In summary, the present invention has the following beneficial effects:

[0049] This invention constructs a scheduling model for a four-way shuttle system with the optimization objectives of minimizing warehousing time and total equipment energy consumption, and solves this model to obtain the optimal scheduling scheme. This method can simultaneously meet the timeliness and energy consumption requirements of modern logistics companies for scheduling optimization. The algorithm used for solving the model has a fast convergence speed, significantly improving the computational efficiency of solving the scheduling model, thereby accelerating the generation of scheduling schemes and enabling the four-way shuttle system to respond quickly, thus improving the overall operational efficiency of the system. Attached Figure Description

[0050] Figure 1 This is a flowchart of the adaptive genetic annealing algorithm;

[0051] Figure 2 This is a top view of the four-way shuttle system;

[0052] Figure 3 This is a side view of the four-way shuttle system;

[0053] Figure 4 This is a flowchart of the coordinated operation of the four-way shuttle and the hoist;

[0054] Figure 5 This is a flowchart of the four-way shuttle system's warehouse entry operation.

[0055] Figure 6 This is a table of equipment operating parameters;

[0056] Figure 7 It is the inbound task table;

[0057] Figure 8 This is a comparison table of algorithm results;

[0058] Figure 9 This is an optimized comparison diagram of the present invention. Detailed Implementation

[0059] The present invention will be further described in detail below with reference to the accompanying drawings.

[0060] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of the claims of the present invention.

[0061] Combination Figures 1 to 5 The technical solution of the present invention includes the following methods:

[0062] S01. Warehouse layout data point generation steps: Generate warehouse layout data points based on the basic information of the four-way shuttle system. The basic information includes inbound task information, four-way shuttle information, elevator information, and shelf information.

[0063] The warehouse data distribution points are constructed based on a layer-changing elevator, a cargo elevator, and several four-way shuttles operating in cross-layer mode to collaboratively complete a batch of inbound tasks. Four-way shuttles prioritize tasks on the same layer. If there is no four-way shuttle on the inbound layer, the task is executed by the nearest available four-way shuttle. If multiple four-way shuttles are at the same distance from the inbound layer, the task is executed by the shuttle with the smallest number.

[0064] The system includes several data points: Inbound task information (location, material type, and time); 4-way shuttle information (size, maximum speed, maximum acceleration, starting position, and operating range); elevator information (location, height, and load capacity of cargo elevators and level-change elevators); and racking information (location, type, height, and storage capacity). This information determines the warehouse layout design and the 4-way shuttle path planning. The warehouse data distribution points generated based on this information provide data support for the design and optimization of the subsequent scheduling model.

[0065] S02. Basic Model Construction Steps: Based on warehouse layout data points, construct a basic model; the basic model includes an inbound time model F(T) and an equipment energy consumption model F(W). The inbound time model F(T) is used to obtain the inbound time of the scheduling plan, and its formula is:

[0066]

[0067] Among them, T total Here, α represents the inbound time, i represents the task number, n represents the total number of tasks, and j represents the four-way shuttle number; ij and β ij As a decision variable, when the four-way shuttle j performs task i, α ij β is 1 if the task is to be performed by the four-way shuttle j and it needs to cross layers to perform task i. ij Kt is 1 if it is 1, otherwise 0; ij The time required for the four-way shuttle j to perform the cross-layer task i, Bt ij The time required for the four-way shuttle j to perform a non-layer-crossing task i.

[0068] The inbound time model F(T) refers to the total time taken for a four-way shuttle to complete a batch of inbound tasks, including the time for the shuttle to perform cross-level tasks and the time for performing non-cross-level tasks. Cross-level tasks occur when the inbound rack level and the shuttle's location are on different rack levels; in this case, the shuttle needs to request both a level-change elevator and a cargo elevator to complete the inbound task. Non-cross-level tasks occur when the inbound rack level and the shuttle's location are on the same rack level; in this case, the shuttle only needs to request a cargo elevator to complete the inbound task.

[0069] Since the four-way shuttle operates in parallel mode, meaning multiple four-way shuttles perform the storage operation simultaneously, the total storage time for the entire task is the time taken by the last four-way shuttle to complete its storage task. The coordinates of task i are (x... i y i , z i), where x represents the row number of the goods, y represents the column number of the goods, and z represents the shelf level of the goods. The shelf level of the four-way shuttle j is represented by z. j .

[0070] If the four-way shuttle j and the inbound task i are on different layers, i.e. z i ≠z j If the four-way shuttle needs to cross multiple levels to enter the warehouse, its operation time is:

[0071] Kt ij = t6+t7+t8+t9+t2+t3+t 10 +2t 11 +2t5.

[0072] Where t6 is the time it takes for the four-way shuttle to move from its current position to the level-changing elevator, t7 is the time it takes for the level-changing elevator to move from its last level-changing location to the level where the four-way shuttle is located, t8 is the time it takes for the level-changing elevator to move from the level where the four-way shuttle is located to the level where the inbound goods are located, and t9 is the time it takes for the four-way shuttle to move from the level-changing elevator to the goods elevator. 10 The time t is the waiting time for the four-way shuttle to perform its inbound task while waiting for the floor-changing elevator to respond. 11 t5 is the time for the four-way shuttle car to pick up (place) goods on the changing elevator.

[0073] If the four-way shuttle j and the inbound task i are on the same level, i.e. z i =z j Then the four-way shuttle car does not need to cross levels to enter the warehouse, and its operation time is: Bt ij = t1 + t2 + t3 + t4 + 2t5. Where, t1 is the time it takes for the four-way shuttle to move from its current position to the cargo elevator, t2 is the time it takes for the cargo elevator to move from the first floor to the storage floor, t3 is the time it takes for the four-way shuttle to move from the cargo elevator to the storage location, t4 is the time it takes for the four-way shuttle to perform the storage task while waiting for the cargo elevator to respond, and t5 is the time it takes for the four-way shuttle to pick up (place) goods.

[0074] The equipment energy consumption model F(W) is used to obtain the equipment energy consumption of the scheduling scheme, and its formula is:

[0075]

[0076] in,

[0077]

[0078]

[0079] Among them, W totalFor equipment energy consumption, i represents the task number, n represents the total number of tasks, j represents the number of the four-way shuttle, and r represents the total number of four-way shuttles; α ij and β ij As a decision variable, when the four-way shuttle j performs task i, α ij β is 1 if the task is to be performed by the four-way shuttle j and it needs to cross layers to perform task i. ij =1 otherwise =0; W j For the energy consumption of the four-way shuttle j, Kt ij The energy consumption required for the four-way shuttle j to perform cross-level task i, Bt ij Energy consumption required for the four-way shuttle j to perform a non-layer-crossing task i; W e For the energy consumption of cargo hoists, Sw i Energy consumption for the cargo elevator to move goods from the first floor to the receiving floor; W h For the energy consumption of the floor-changing hoist, GW jk The energy consumption for moving from the last floor change location to the floor where the four-way shuttle is located, pw ij The energy consumption of the floor-changing elevator moving from the floor where the four-way shuttle is located to the storage floor.

[0080] The equipment energy consumption model F(W) refers to the energy consumption required for the four-way shuttle system to complete a batch of warehousing tasks. Specifically, it includes the total energy consumption of one cargo elevator, one floor-changing elevator, and several four-way shuttles.

[0081] S03. Scheduling Model Construction Steps: By linearly weighting the inbound time model F(T) and the equipment energy consumption model F(W), a scheduling model is constructed: in, For weight values, As specified by the user. In this embodiment, the weight value φ = 0.4 is used.

[0082] The scheduling model is obtained by linearly weighting the inbound time model F(T) and the equipment energy consumption model F(W), and serves as the objective function for optimizing the scheduling scheme. Users, i.e., logistics companies, can choose appropriate models based on their priorities regarding inbound time and equipment energy consumption, i.e., their emphasis on efficiency and environmental protection. The value of is determined by the logistics company's priorities. A larger weight is chosen if efficiency is prioritized, and a smaller weight is chosen if efficiency is desired, allowing for some flexibility.

[0083] S04. Optimization steps of the scheduling scheme: Taking the shortest storage time and the minimum equipment energy consumption as the optimization objectives, based on the constraints, the adaptive genetic annealing algorithm is used to solve the scheduling model, and the optimal solution obtained is the optimal scheduling scheme.

[0084] The constraints are used to ensure that the optimization results of the scheduling model are limited to a feasible set, so as to guarantee that the final optimized scheduling scheme is feasible in actual operation. These include task allocation constraints, warehouse entry time constraints, and hoist operation constraints.

[0085] Task allocation constraints stipulate that an inbound task can only be executed by one four-way shuttle, avoiding conflicts caused by multiple four-way shuttles simultaneously executing the same task. The formula is as follows:

[0086]

[0087] Where i is the task number, j is the four-way shuttle number, r is the total number of four-way shuttles, and α ij As a decision variable, when the four-way shuttle j performs task i, α ij It is 1 if it is true, otherwise it is 0.

[0088] The inbound time constraint means that the inbound time cannot be less than the continuous working time of the hoist (including the continuous working time of the level-changing hoist and the cargo hoist), so that the inbound time is more in line with the actual scheduling operation. The formula is:

[0089]

[0090] Where i is the task number, n is the total number of tasks, t2 is the time it takes for the cargo elevator to move from the first floor to the storage floor, and t5 is the time it takes for the four-way shuttle to pick up (place) goods.

[0091] The elevator process constraint stipulates that when performing cross-level warehousing, the request for a level-change elevator should be made earlier than the request for a goods elevator, and the time interval between the two requests should not be less than the time required for the level change. This ensures the stability of the four-way shuttle in performing cross-level tasks and avoids unexpected situations caused by improper sequence. The formula is as follows:

[0092]

[0093] Where i represents the task number and j represents the four-way shuttle number; The time required to apply for a cargo lift for the four-way shuttle vehicle. t7 is the time it takes for the four-way shuttle to request a floor-changing elevator; t8 is the time it takes for the floor-changing elevator to move from the last floor-changing point to the floor where the four-way shuttle is located; t9 is the time it takes for the four-way shuttle to move from the floor where the four-way shuttle is located to the floor where the inbound goods are located; t1 is the time it takes for the four-way shuttle to move from the floor-changing elevator to the goods elevator. 11 The time required for the four-way shuttle car to be picked up (placed) by the floor-changing elevator.

[0094] The adaptive genetic annealing algorithm solves the target model through the following steps:

[0095] Step 1: Parameter initialization, including: population size N, maximum number of iterations maxgen, initial temperature T0, and final temperature T. min And the cooling coefficient γ.

[0096] Step 2: Using natural number encoding, the task number is used as the encoding sequence number. Based on the number of tasks and the length of the chromosome, an initial population is generated.

[0097] It should be noted that a population is a collection of individuals, and the initial population is the initial set of scheduling schemes.

[0098] Step 3: Calculate the fitness value of individuals in the population.

[0099] Step 4: Selection, crossover, and mutation are performed iteratively on the individuals in the population to generate new individuals, and the fitness value of the new individuals is calculated. Wherein:

[0100] Selection: Individuals are selected using a roulette wheel selection operation, which then generates the next generation of the population;

[0101] Crossover: Individuals undergo PMX crossover, and the crossover probability is adaptively adjusted;

[0102] Mutation: Individuals undergo random mutations and adaptively adjust the mutation probability.

[0103] Step 5: Individual Judgment: Based on the fitness value, compare the new individual generated after the iteration with the current individual before the iteration to determine the quality of the new individual.

[0104] If the new individual is superior to the current individual, then the new individual is a superior individual. The superior individual will be accepted, replacing the current individual. Otherwise, it is a inferior individual. Inferior individuals need to be judged according to the Metropolis criterion.

[0105] Step 5.1, Metropolis Criterion: Based on probability Accept inferior individuals. Accepted inferior individuals will replace the current individual, while unaccepted inferior individuals will not be able to replace the current individual, thus the current individual will be retained. Here, T is the current temperature, ΔE≥0 is the change in the new individual relative to the current individual.

[0106] After individual evaluation, some individuals will be retained, while others will be replaced by superior or inferior individuals, thus generating a new population. Since the Metropolis criterion allows for the acceptance of inferior individuals with probability P, the adaptive genetic annealing algorithm may escape local optima and search towards the global optimum, thereby accelerating its convergence speed and improving the optimization efficiency of the scheduling model.

[0107] Step 6, Cooling treatment: T′=γT, where 0≤γ≤1.

[0108] Where T is the current temperature, and T′ is the temperature after cooling. By continuously decreasing the temperature, it is possible to effectively avoid getting trapped in local optima.

[0109] Step 7: Output the optimal solution: After continuous iteration, a new population is generated. When the maximum number of iterations (maxgen) is reached, the individual with the highest fitness value in the new population is the optimal scheduling scheme.

[0110] To verify the effectiveness of the proposed scheduling model and solution method, this invention sets up a simulation scenario of a four-way shuttle system based on the performance parameters and storage specifications of a real-world four-way shuttle system. Specific equipment operating parameters are as follows: Figure 6 As shown.

[0111] Select appropriate initial parameters, where: population size N = 200, maximum number of iterations maxgen = 100, initial temperature T0 = 1000, and final temperature T... min =900, cooling coefficient γ=0.99.

[0112] Algorithm validity verification

[0113] like Figure 7 As shown, 10 data entry tasks are randomly generated.

[0114] The program randomly generates the following sequence for the data entry task: 3→7→1→8→2→10→4→6→5→9. The objective function value is 0.7136, the total task time is 986.7s, and the total task energy consumption is 2153.5KJ.

[0115] The effectiveness of the algorithm in solving this problem was verified by comparing the data entry task scheduling of the model using a genetic algorithm and an adaptive genetic annealing algorithm. The data entry task sequence obtained after optimization by the genetic algorithm is: 5→1→6→3→10→8→4→9→2→7, with an objective function value of 0.0526, a total task time of 905.8s, and a total task energy consumption of 1986.5KJ. The data entry task sequence obtained after optimization by the adaptive genetic annealing algorithm is: 8→2→5→1→4→6→9→7→3→10, with an objective function value of 0.0394, a total task time of 829.7s, and a total task energy consumption of 1874.7KJ. The algorithm results are as follows: Figure 8 As shown.

[0116] Depend on Figure 8 It can be seen that the adaptive genetic annealing algorithm outperforms the traditional genetic algorithm in terms of total operation time, total system energy consumption, and four-way vehicle waiting time. From Figure 9It can be seen that the traditional genetic algorithm has a slow convergence speed, while the adaptive genetic annealing algorithm has a faster convergence speed. Therefore, it can significantly improve the computational efficiency of solving the scheduling model, thereby speeding up the generation of scheduling schemes, enabling the four-way shuttle system to respond quickly, and thus improving the overall operating efficiency of the system.

Claims

1. An optimization method for scheduling a four-way shuttle system, characterized in that, Includes the following steps: S01, Steps for generating warehouse layout data points; Warehouse layout data points are generated based on the basic information of the four-way shuttle system. The basic information includes inbound task information, four-way shuttle information, elevator information, and shelf information. S02, Basic Model Construction Steps; Based on the warehouse layout data points, a basic model is constructed; the basic model includes an inbound time model. and equipment energy consumption model ; Among them, the warehousing time model The equipment energy consumption model is used to obtain the inbound time of the scheduling scheme. Energy consumption of equipment used to obtain scheduling plans; S03, Steps for constructing the scheduling model; By analyzing the inbound time model and the energy consumption model of the device Perform linear weighting to construct a scheduling model: ; in, For weight values, , Specifyed by the user; S04, Steps for optimizing the scheduling scheme; With the shortest warehousing time and minimum equipment energy consumption as optimization objectives, the adaptive genetic annealing algorithm is used to solve the scheduling model, and the optimal solution obtained is the optimal scheduling scheme. The adaptive genetic annealing algorithm uses the database task number as the encoding sequence to generate an initial population. It iteratively optimizes the initial population and continuously generates a new population until the maximum number of iterations is reached. The new population is generated by individual judgment: if the new individual is better than the current individual, the new individual is accepted; otherwise, the new individual is accepted with probability P according to the Metropolis criterion. In steps S02 and S03, the warehousing time model The formula is: ； in, The entry time is denoted by i, the task number is denoted by n, the total number of tasks is denoted by j, and the four-way shuttle number is denoted by j. and As the decision variable, when the four-way shuttle j performs task i, The value is 1 if the task is to cross layers, and 0 otherwise. It is 1 if it is true, otherwise it is 0. The time required for the four-way shuttle j to perform cross-level task i. The time required for the four-way shuttle j to perform a non-layer-crossing task i; The equipment energy consumption model The formula is: ； in, = ; =2 ； = ； in, The values ​​represent equipment energy consumption, i represents the task number, n represents the total number of tasks, j represents the four-way shuttle number, and r represents the total number of four-way shuttles. and As the decision variable, when the four-way shuttle j performs task i, The value is 1 if the task is to cross layers, and 0 otherwise. It is 1 if it is true, otherwise it is 0. The energy consumption of the four-way shuttle vehicle j The energy consumption required for the four-way shuttle j to perform the cross-level task i. Energy consumption required for the four-way shuttle j to perform a non-layer-crossing task i; Energy consumption of cargo elevators, Energy consumption for the cargo elevator to move goods from the first floor to the receiving floor; Energy consumption of the floor-changing hoist. The energy consumption for moving from the last floor change location to the floor where the four-way shuttle is located. The energy consumption for the elevator to move from the floor where the four-way shuttle is located to the storage floor.

2. The optimization method for scheduling a four-way shuttle system according to claim 1, characterized in that, The solution of the adaptive genetic annealing algorithm is also subject to constraints, including: task allocation constraints, warehousing time constraints, and hoisting process constraints.

3. The optimization method for scheduling a four-way shuttle system according to claim 2, characterized in that, The task allocation constraint stipulates that an inbound task can only be executed by one four-way shuttle, and the formula is as follows: =1； Where i is the task number, j is the four-way shuttle number, and r is the total number of four-way shuttles. As the decision variable, when the four-way shuttle j performs task i, It is 1 if it is true, otherwise it is 0.

4. The optimization method for scheduling a four-way shuttle system according to claim 3, characterized in that, The storage time constraint means that the storage time cannot be less than the continuous working time of the hoist, and the formula is as follows: 2 +2n ； Where i is the task number and n is the total number of tasks. The time it takes for the cargo elevator to move from the first floor to the receiving floor. The time for the four-way shuttle to pick up (place) goods.

5. The optimization method for scheduling a four-way shuttle system according to claim 4, characterized in that, The elevator process constraint refers to the requirement that when performing cross-level warehousing, the time for requesting a change-level elevator should be earlier than the time for requesting a goods elevator, and the time interval between the two requests should not be less than the time required for the change-level operation. The formula is as follows: + + +2 ； Where i represents the task number and j represents the four-way shuttle number; The time required to apply for a cargo lift for the four-way shuttle vehicle. Request the time for the four-way shuttle to apply for the floor-changing elevator; The time it takes for the floor-changing elevator to move from the last floor-changing point to the floor where the four-way shuttle is located. The time it takes for the floor-changing elevator to move from the floor where the four-way shuttle is located to the floor where the inbound goods are located. The time it takes for the four-way shuttle to move from the level-changing elevator to the cargo elevator. The time for the four-way shuttle car to be picked up (placed) by the floor-changing elevator.

6. The optimization method for scheduling a four-way shuttle system according to claim 1, characterized in that, Before the initial population is generated, a parameter initialization step is also included, wherein the parameters include: population size N, maximum number of iterations maxgen, and initial temperature. , final temperature and cooling coefficient .

7. The optimization method for scheduling a four-way shuttle system according to claim 1, characterized in that, The iteration includes selection, crossover, and mutation, and calculates the fitness value of the new individual.

8. The optimization method for scheduling a four-way shuttle system according to claim 7, characterized in that, The formula for the probability P of the Metropolis criterion accepting the new individual is: P= ； Where T is the current temperature. This represents the change in the new individual relative to the current individual.

Images