Storage location distribution optimization method based on crayfish optimization algorithm

Abstract

The invention discloses a storage location distribution optimization method based on a crayfish optimization algorithm. The method comprises the following steps: S1, obtaining three-dimensional warehouse information and cargo information; s2, establishing a multi-target storage location distribution mathematical model; respectively establishing a single-target storage location distribution mathematical model; the single-target storage location distribution mathematical model is converted into a multi-target storage location distribution mathematical model through a linear weighting method; s3, calculating an optimal storage location allocation result; and solving the multi-target storage location distribution mathematical model through a crayfish optimization algorithm to obtain an optimal storage location distribution result. According to the method, the relatively optimal storage location allocation scheme under the multi-target constraint can be more efficiently found.

Description

Technical Field

[0001] The present invention relates to the field of intelligent logistics technology, and in particular to a storage location allocation optimization method based on a crayfish optimization algorithm. Background Art

[0002] The stacker warehouse is an automated warehousing system that uses stackers to store, retrieve and manage goods. Hydraulic cylinders, as a heavy and usually large industrial component, are one of the items that need to be stored and managed in the stacker warehouse. In the application scenario of intelligent logistics, the stacker warehouse stores and manages large mechanical transmission components such as hydraulic cylinders through vertical storage and automated access. The reasonable allocation of storage locations in the stacker warehouse can not only improve system efficiency, but also reduce storage costs and shorten the storage and access time of goods. It is an important link in warehousing and logistics.

[0003] The existing patent with application number: 202210611361.4 discloses an automated stereoscopic warehouse location allocation optimization method based on a genetic algorithm. First, the frequency of goods entering and leaving the warehouse is obtained based on the historical order information of the parts, and a mathematical model is established to reduce the time of entering and leaving the warehouse; then the quality of the parts is counted, and a calculation formula for lowering the center of gravity of the shelf is established; on this basis, clustering is performed based on the degree of association, and a mathematical model is established with the goal of increasing the spatial aggregation of related products. The invention optimizes and solves the comprehensive mathematical model through an improved genetic algorithm, and the optimal solution obtained is the current optimal warehouse location configuration plan.

[0004] The optimization methods of storage location allocation mainly focus on the setting of optimization goals and the selection of heuristic algorithms. The optimization goals are usually focused on improving the efficiency of storage and retrieval of the three-dimensional warehouse and enhancing the stability of the shelves. The defect of the existing technology is that for the three-dimensional warehouse of special items such as hydraulic cylinders with large size and heavy weight, the optimization goals considered by the existing storage location allocation optimization methods are relatively single and incomplete. In terms of algorithm selection, although genetic algorithms are widely used, they still have room for improvement in search capabilities and computing efficiency, and cannot meet the efficient needs of goods storage and management in the three-dimensional warehouse. Summary of the invention

[0005] In view of the above-mentioned defects of the prior art, the present invention provides a storage location allocation optimization method based on crayfish optimization algorithm, which can more efficiently find the relatively optimal storage location allocation plan under multi-objective constraints.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A storage location allocation optimization method based on crayfish optimization algorithm comprises the following steps:

[0008] S1. Obtaining the information of the three-dimensional warehouse and the goods;

[0009] S2. Establish a multi-objective storage location allocation mathematical model;

[0010] Establishing a single-objective storage location allocation mathematical model respectively; converting the single-objective storage location allocation mathematical model into the multi-objective storage location allocation mathematical model by a linear weighted method;

[0011] S3, calculate the optimal storage location allocation result;

[0012] The multi-objective storage location allocation mathematical model is solved by the crayfish optimization algorithm to obtain the optimal storage location allocation result.

[0013] In step S1, collecting the three-dimensional warehouse information and cargo information is the basis for subsequent model building and algorithm application. The three-dimensional warehouse information includes the size, structure, warehouse layout, number of warehouses and other information, and the cargo information includes the size, weight, type, frequency of goods entering and leaving the warehouse and other information.

[0014] In step S2, there are multiple single-objective storage location allocation mathematical models, and all objectives that affect the storage location allocation results are included. If F1, F2, F3, and F4 are used to represent a single-objective storage location allocation mathematical model respectively, and multiple single-objective storage location allocation mathematical models are linearly weighted, the multi-objective storage location allocation mathematical model is expressed as:

[0015] minF=α1×F1+α2×F2+α3×F3+α4×F4

[0016] Among them, α1, α2, α3, and α4 represent the weights of single objectives F1, F2, F3, and F4 respectively. The choice of weights reflects the relative importance of different single objectives. The distribution of weights should be determined according to the importance of each objective in the actual problem. Multi-objective functions can consider different factors more comprehensively in optimization problems. The reason is that the problem of location allocation optimization often involves multiple objectives, and there may be conflicts or trade-offs between these objectives. Multi-objective functions can reflect this complexity and allow algorithms to consider multiple objectives at the same time. At the same time, in many cases, there may be conflicts between different objectives. For example, improving the efficiency of warehousing and retrieval may conflict with reducing the stability of the center of gravity of the shelf. Multi-objective functions allow algorithms to find a balance between these objectives.

[0017] In step S3, the crayfish optimization algorithm is a meta-heuristic optimization algorithm, which is inspired by the social behavior of crayfish in nature. Crayfish search for food resources within the perception range and move towards areas with higher food concentrations, similar to searching in the direction of a better objective function value in the algorithm. The crayfish optimization algorithm is an existing technology, but there is no technical solution for solving the storage and management of goods in a stereoscopic warehouse using the crayfish optimization algorithm.

[0018] Preferably, the single-objective storage location allocation mathematical model includes an inbound and outbound storage efficiency model F1.

[0019] The in-and-out efficiency model F1 is a mathematical model used to evaluate and optimize the efficiency of goods in-and-out operations in a three-dimensional warehouse. The purpose of this model is to reduce the storage and access time of goods in the three-dimensional warehouse and improve the efficiency of the three-dimensional warehouse operation. By optimizing this single objective function, we can find the best storage location for goods, thereby improving the efficiency of the in-and-out of the three-dimensional warehouse. The specific expression of the in-and-out efficiency model F1 is as follows:

[0020]

[0021] Where m, n, l are the number of storage locations on the X, Y, and Z axes of the three-dimensional warehouse, respectively. x 、v y 、v z are the speeds of the stacker on the X, Y, and Z axes, respectively. t is the length of the storage location, w t is the width of the storage location, i, j, k are the coordinates of the storage location on the X, Y, and Z axes respectively, and P ijk Indicates the frequency penalty for the goods entering and leaving the warehouse at this location. Indicates whether there is an incoming warehousing task at the storage location. The in-and-out efficiency model F1 is the sum of the product of the stacker operation time for all incoming warehousing tasks and the penalty coefficient of the goods in-and-out frequency.

[0022] Preferably, the single-objective storage location allocation mathematical model includes a low center of gravity model F2 of the entire shelf.

[0023] The overall low center of gravity model F2 of the shelf is used to optimize the stability of the shelf in the stereoscopic warehouse, especially the stereoscopic warehouse storing heavy or large goods. Its core goal is to lower the overall center of gravity of all goods on the shelf to enhance the stability and safety of the shelf. By lowering the center of gravity, the risk of the shelf tipping over due to uneven distribution of goods is reduced, ensuring that the shelf remains stable when subjected to external forces (such as earthquakes and impacts). The specific expression of the overall low center of gravity model F2 of the shelf is as follows:

[0024]

[0025] Among them, M ijk is the weight of the goods in this storage location, h t It is the height of the goods in the warehouse. Calculate the sum of the weights of all goods and their heights in the warehouse. By minimizing it, the center of gravity of the entire shelf can be lowered and the stability of the shelf can be improved.

[0026] Preferably, the single-objective storage location allocation mathematical model includes a single-side shelf anti-overturning model F3.

[0027] The single-sided shelf anti-overturning model F3 is used to ensure shelf stability. Its main goal is to reduce the risk of overturning due to uneven weight distribution by reasonably distributing the weight of the goods. The single-sided shelf anti-overturning model F3 balances the weight on both sides of the shelf to reduce the possibility of overturning due to uneven weight distribution. By reasonably distributing the goods, the storage space of the shelf is fully utilized while maintaining the stability of the shelf. The specific expression of the single-sided shelf anti-overturning model F3 is as follows:

[0028]

[0029] This function calculates the sum of the moments on the Y-axis relative to the center of each shelf generated by the goods on the shelves on both sides of the stereoscopic warehouse. By minimizing this function, overturning of high-rise shelves caused by excessive weight can be prevented.

[0030] Preferably, the single-target storage location allocation mathematical model includes a double-extension storage location priority same-type goods model F4.

[0031] The double-stretch storage location priority model F4 is used to optimize the allocation of storage locations in the three-dimensional warehouse. It aims to give priority to storing the same type of goods in double-stretch storage locations that can be accessed from both sides at the same time. By giving priority to storing the same type of goods in double-stretch storage locations, the efficiency of goods storage and retrieval can be improved because these goods can be accessed from both sides at the same time. It reduces the need to move goods due to improper storage locations, thereby reducing labor intensity and operating costs. This storage method can improve the efficiency of three-dimensional warehouse operations, reduce warehouse reversing operations, and reduce errors caused by confusion of goods. The specific expression of the double-stretch storage location priority model F4 is as follows:

[0032]

[0033] Among them, δ is an indicator function, which returns 1 when the condition in the brackets is met, otherwise it returns 0. D is the set of all double-stretch goods. This function compares the goods on all shelves and imposes penalties when the types of goods on two shelves in the same double-stretch are different. By reducing the value of this function, the number of reverse warehouse operations can be reduced.

[0034] Preferably, the step S2 includes adding constraints to the multi-objective storage location allocation mathematical model.

[0035] The multi-objective storage location allocation mathematical model is obtained by linearly weighting the above four single-objective storage location allocation mathematical model functions, and the formula is expressed as follows:

[0036]

[0037] minF=α1×F1+α2×F2+α3×F3+α4×F4

[0038] Among them, 0<α1<1, 0<α2<1, 0<α3<1, 0<α4<1, α1+α2+α3+α4=1; the constraints are:

[0039] A. Each cargo can only be assigned to one location:

[0040]

[0041] B. Only one item can be placed in each position:

[0042]

[0043] C. Restrictions on the conditions of the three-dimensional warehouse:

[0044]

[0045] Constraints ensure the feasibility and practical applicability of the solution in the optimization problem. Constraint A ensures that each item can only be assigned to one location, preventing items from being duplicated or missed. Constraint B ensures that only one item can be placed in each location, ensuring that the location is not occupied by multiple items and preventing the location from being vacant. Constraint C ensures compliance with the physical limitations of the stereoscopic warehouse. The stereoscopic warehouse constraints ensure that the allocation of goods complies with the physical size and structural limitations of the stereoscopic warehouse.

[0046] Preferably, in the summer-avoidance stage of the crayfish optimization algorithm, a logistic curve is defined as a scaling factor C2 of the crayfish optimization algorithm, and Levy flight is defined as a rand random number step length of the crayfish optimization algorithm.

[0047] In the crayfish optimization algorithm, when the ambient temperature is greater than 30 degrees, half of the individuals enter the heat-avoidance stage, during which they move toward caves to avoid the high temperature.

[0048] The Logistic curve is used as a scaling factor to allow individuals to explore the optimal point with larger steps in the early stages of algorithm iteration. The Logistic curve is an S-shaped curve that grows rapidly in the early stages and slowly in the later stages. This feature allows the algorithm to quickly explore the solution space in the early stages and to conduct detailed searches in the later stages to achieve a more detailed exploration of the optimal point. As a step replacement, Levy flight is characterized by occasional long-distance jumps, which help the algorithm to jump out of the local optimal solution and enhance the global search capability, thereby increasing the probability of finding the global optimal solution. Through improved scaling factors and step strategies, the algorithm can more effectively balance the conflicts between different objectives in multi-objective optimization problems and find a better trade-off solution. The long jump feature of Levy flight significantly improves the global search capability of the algorithm and reduces the risk of the algorithm falling into the local optimal solution. The flexible control of the Logistic curve parameters makes the convergence process of the algorithm smoother and more natural, which helps the algorithm to stably approach the optimal solution. The moving formula is:

[0049]

[0050] The scaling factor C2 formula is:

[0051]

[0052] Levy(n,m,β) is an n×m-dimensional Levy step-size matrix with parameter β, and its formula is:

[0053]

[0054] u n×m ~N(0,σ u )

[0055] v n×m ~N(0,1)

[0056] in:

[0057]

[0058] The improvement of scaling factor C2 enables individuals entering the summer retreat stage at the beginning of algorithm iteration to explore the optimal point with larger steps, which accelerates the convergence speed of the algorithm and the exploration ability of the solution space. In the later stage, the step size is gradually reduced to achieve a more detailed exploration of the optimal point. Compared with the linear scaling factor logistic curve parameter control of the basic crayfish optimization algorithm, the convergence process is smoother and more natural, and the occasional long jump characteristics of Levy flight significantly improve the global search ability of the algorithm and prevent it from falling into the local optimal solution.

[0059] Preferably, the crayfish optimization algorithm includes an elite individual probability update stage, and the positions of other individuals are selectively moved closer to the current optimal individual according to the update probability, and the update probability increases with the iterative process.

[0060] The specific formula for updating probability is:

[0061]

[0062] The ind probability formula is:

[0063]

[0064] By gradually increasing the update probability, the algorithm is more inclined to move closer to the optimal solution in later iterations, which helps the algorithm converge faster. In early iterations, lower update probabilities help maintain the diversity of the population and avoid premature convergence to local optimal solutions. The gradual increase in update probability allows the algorithm to strike a balance between exploration and exploitation.

[0065] Preferably, the crayfish optimization algorithm includes a boundary correction stage, and the individuals exceeding the upper and lower bounds are reallocated during the iteration process.

[0066] The crayfish optimization algorithm enters the improved boundary correction stage. Compared with the basic crayfish optimization algorithm, the strategy of directly setting the value of the out-of-bounds individual to the boundary strengthens the global search capability. The boundary correction stage ensures that all individuals do not exceed these constraints, thereby ensuring that they are legal solutions. By redistributing individuals that exceed the boundary to near the boundary, the boundary correction stage helps maintain the integrity of the search space and prevents individuals from being "lost" during the search process. When individuals exceed the boundary and are redistributed, they may be placed in different areas of the search space, which helps the algorithm explore new areas and enhance global search capabilities. If individuals are concentrated in a certain area too early in the search process, it may cause premature convergence of the algorithm. Boundary correction helps avoid this situation by redistributing individuals.

[0067] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0068] 1. The present invention provides an optimized allocation mathematical model for the storage, access and management of goods in a three-dimensional warehouse by establishing a multi-objective storage location allocation mathematical model. The model takes into account multiple optimization objectives related to storage location allocation, and converts multiple single-objective functions into a comprehensive multi-objective function through a linear weighted method, which can consider various factors more comprehensively, thereby obtaining a relatively optimal storage location allocation plan. At the same time, according to the actual application situation, the weight of each single objective can be flexibly adjusted to reflect the relative importance of different objectives, thereby enhancing the adaptability and flexibility of the storage location allocation method. Compared with the genetic algorithm of the prior art, the use of the crayfish optimization algorithm to solve the multi-objective storage location allocation mathematical model provides a more efficient search capability and calculation efficiency, and can more quickly find a storage location allocation plan that meets the multi-objective requirements, meet the efficient needs of the storage, access and management of goods in the three-dimensional warehouse, thereby reducing the cost of warehousing and logistics, and improving the safety and stability of the operation of the three-dimensional warehouse.

[0069] 2. The present invention improves the crayfish optimization algorithm in the summer stage, so that in the early stage of the algorithm, the Logistic curve allows individuals to explore with large steps, which helps to quickly cover the solution space and find potential optimal solution areas. As the iteration proceeds, the growth of the Logistic curve slows down, allowing individuals to conduct a more detailed search for the optimal solution area. The long jump characteristics of Levy flight help individuals jump out of the local optimal solution, enhance the global search ability of the algorithm, and increase the probability of finding the global optimal solution. This improved strategy allows the algorithm to strike a balance between exploring new areas and going deep into known areas, improving the efficiency and effectiveness of the search.

[0070] 3. The present invention improves the probability update stage of the elite individual through the crayfish optimization algorithm. By gradually increasing the probability of approaching the optimal individual, the algorithm is more inclined to refine the search in the later iterations, which speeds up the convergence speed. In the early iterations, the lower update probability helps to maintain the diversity of the population and avoid premature convergence to the local optimal solution. By gradually guiding the population to approach the optimal solution, the quality of the final solution can be improved, making the storage allocation more reasonable and efficient.

[0071] 4. The present invention ensures that all individuals do not exceed the boundaries of the search space through improvements in the boundary correction stage of the crayfish optimization algorithm, thereby ensuring that they are legal solutions. By reallocating individuals that exceed the boundaries to near the boundaries, it helps to maintain the integrity of the search space and prevent individuals from being "lost" during the search process. When individuals exceed the boundaries and are reallocated, they may be placed in different areas of the search space, which helps the algorithm explore new areas and enhance global search capabilities. Boundary correction helps avoid the algorithm from concentrating on a certain area too early by reallocating individuals, thereby avoiding premature convergence. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a flow chart of the storage location allocation optimization method of Example 1 of the present invention;

[0073] Figure 2 This is an algorithm flow chart of Embodiment 1 of the present invention;

[0074] Figure 3 A schematic diagram of a portion of existing storage location data randomly generated in Example 1 of the present invention;

[0075] Figure 4 This is a schematic diagram of the optimal storage location allocation result data of Example 1 of the present invention;

[0076] Figure 5 This is a schematic diagram of the storage location allocation result of the three-dimensional warehouse in Example 1 of the present invention;

[0077] Figure 6 It is the optimal fitness curve of the algorithm running result of the embodiment of the present invention. DETAILED DESCRIPTION

[0078] In order to make the technical means, creative features, objectives and effects of the invention easier to understand, the invention is further described with reference to specific diagrams. However, the invention is not limited to the following implementation cases.

[0079] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0080] Embodiment 1:

[0081] like Figure 1 The storage location allocation optimization method based on the crayfish optimization algorithm shown in the figure comprises the following steps:

[0082] S1. Obtaining the information of the three-dimensional warehouse and the goods;

[0083] S2. Establish a mathematical model for multi-objective storage location allocation;

[0084] Establishing single-objective storage location allocation mathematical models respectively; transforming the single-objective storage location allocation mathematical models into multi-objective storage location allocation mathematical models through linear weighting method;

[0085] S3, calculate the optimal storage location allocation result;

[0086] The multi-objective storage location allocation mathematical model is solved by the crayfish optimization algorithm to obtain the optimal storage location allocation result.

[0087] In step S1, collecting the three-dimensional warehouse information and cargo information is the basis for subsequent model building and algorithm application. The three-dimensional warehouse information includes the size, structure, warehouse layout, number of warehouses and other information, and the cargo information includes the size, weight, type, frequency of goods entering and leaving the warehouse and other information.

[0088] In step S2, there are multiple single-objective storage location allocation mathematical models, and all objectives that affect the storage location allocation results are included. If F1, F2, F3, and F4 are used to represent a single-objective storage location allocation mathematical model respectively, and multiple single-objective storage location allocation mathematical models are linearly weighted, the multi-objective storage location allocation mathematical model is expressed as:

[0089] minF=α1×F1+α2×F2+α3×F3+α4×F4

[0090] Among them, α1, α2, α3, and α4 represent the weights of single objectives F1, F2, F3, and F4 respectively. The choice of weights reflects the relative importance of different single objectives. The distribution of weights should be determined according to the importance of each objective in the actual problem. Multi-objective functions can consider different factors more comprehensively in optimization problems. The reason is that the problem of location allocation optimization often involves multiple objectives, and there may be conflicts or trade-offs between these objectives. Multi-objective functions can reflect this complexity and allow algorithms to consider multiple objectives at the same time. At the same time, in many cases, there may be conflicts between different objectives. For example, improving the efficiency of warehousing and retrieval may conflict with reducing the stability of the center of gravity of the shelf. Multi-objective functions allow algorithms to find a balance between these objectives.

[0091] In step S3, the crayfish optimization algorithm is a meta-heuristic optimization algorithm, which is inspired by the social behavior of crayfish in nature. Crayfish search for food resources within the perception range and move towards areas with higher food concentrations, similar to searching in the direction of a better objective function value in the algorithm. The crayfish optimization algorithm is an existing technology, but there is no technical solution for solving the storage and management of goods in a stereoscopic warehouse using the crayfish optimization algorithm.

[0092] The mathematical model of single-objective storage location allocation includes the storage efficiency model F1, the low center of gravity model of the shelf F2, the single-sided shelf anti-overturning model F3, and the double-extended storage location priority model F4.

[0093] The in-and-out efficiency model F1 is a mathematical model used to evaluate and optimize the efficiency of goods in-and-out operations in a three-dimensional warehouse. The purpose of this model is to reduce the storage and access time of goods in the three-dimensional warehouse and improve the efficiency of the three-dimensional warehouse operation. By optimizing this single objective function, we can find the best storage location for goods, thereby improving the efficiency of the in-and-out of the three-dimensional warehouse. The specific expression of the in-and-out efficiency model F1 is as follows:

[0094]

[0095] Where m, n, l are the number of storage locations on the X, Y, and Z axes of the three-dimensional warehouse, respectively. x 、v y 、v z are the speeds of the stacker on the X, Y, and Z axes, respectively. t is the length of the storage location, w t is the width of the storage location, i, j, k are the coordinates of the storage location on the X, Y, and Z axes respectively, and P ijk Indicates the frequency penalty for the goods entering and leaving the warehouse at this location. Indicates whether there is an incoming warehousing task at the storage location. The in-and-out efficiency model F1 is the sum of the product of the stacker operation time for all incoming warehousing tasks and the penalty coefficient of the goods in-and-out frequency.

[0096] The overall low center of gravity model F2 of the shelf is used to optimize the stability of the shelf in the stereoscopic warehouse, especially the stereoscopic warehouse storing heavy or large goods. Its core goal is to lower the overall center of gravity of all goods on the shelf to enhance the stability and safety of the shelf. By lowering the center of gravity, the risk of the shelf tipping over due to uneven distribution of goods is reduced, ensuring that the shelf remains stable when subjected to external forces (such as earthquakes and impacts). The specific expression of the overall low center of gravity model F2 of the shelf is as follows:

[0097]

[0098] Among them, M ijk is the weight of the goods in this storage location, h t It is the height of the goods in the warehouse. Calculate the sum of the weights of all goods and their heights in the warehouse. By minimizing it, the center of gravity of the entire shelf can be lowered and the stability of the shelf can be improved.

[0099] The single-sided shelf anti-overturning model F3 is used to ensure shelf stability. Its main goal is to reduce the risk of overturning due to uneven weight distribution by reasonably distributing the weight of the goods. The single-sided shelf anti-overturning model F3 balances the weight on both sides of the shelf to reduce the possibility of overturning due to uneven weight distribution. By reasonably distributing the goods, the storage space of the shelf is fully utilized while maintaining the stability of the shelf. The specific expression of the single-sided shelf anti-overturning model F3 is as follows:

[0100]

[0101] This function calculates the sum of the moments on the Y-axis relative to the center of each shelf generated by the goods on the shelves on both sides of the stereoscopic warehouse. By minimizing this function, overturning of high-rise shelves caused by excessive weight can be prevented.

[0102] The double-stretch storage location priority model F4 is used to optimize the allocation of storage locations in the three-dimensional warehouse. It aims to give priority to storing the same type of goods in double-stretch storage locations that can be accessed from both sides at the same time. By giving priority to storing the same type of goods in double-stretch storage locations, the efficiency of goods storage and retrieval can be improved because these goods can be accessed from both sides at the same time. It reduces the need to move goods due to improper storage locations, thereby reducing labor intensity and operating costs. This storage method can improve the efficiency of three-dimensional warehouse operations, reduce warehouse reversing operations, and reduce errors caused by confusion of goods. The specific expression of the double-stretch storage location priority model F4 is as follows:

[0103]

[0104] Among them, δ is an indicator function, which returns 1 when the condition in the brackets is met, otherwise it returns 0. D is the set of all double-stretch goods. This function compares the goods on all shelves and imposes penalties when the types of goods on two shelves in the same double-stretch are different. By reducing the value of this function, the number of reverse warehouse operations can be reduced.

[0105] The multi-objective storage location allocation mathematical model is obtained by linearly weighting the above four single-objective storage location allocation mathematical model functions, and the formula is expressed as follows:

[0106]

[0107] minF=α1×F1+α2×F2+α3×F3+α4×F4

[0108] Among them, 0<α1<1, 0<α2<1, 0<α3<1, 0<α4<1, α1+α2+α3+α4=1; constraints are added to the multi-objective storage location allocation mathematical model, and the constraints are:

[0109] A. Each cargo can only be assigned to one location:

[0110]

[0111] B. Only one item can be placed in each position:

[0112]

[0113] C. Restrictions on the conditions of the three-dimensional warehouse:

[0114]

[0115] Constraints ensure the feasibility and practical applicability of the solution in the optimization problem. Constraint A ensures that each item can only be assigned to one location, preventing items from being duplicated or missed. Constraint B ensures that only one item can be placed in each location, ensuring that the location is not occupied by multiple items and preventing the location from being vacant. Constraint C ensures compliance with the physical limitations of the stereoscopic warehouse. The stereoscopic warehouse constraints ensure that the allocation of goods complies with the physical size and structural limitations of the stereoscopic warehouse.

[0116] like Figure 2 As shown, the crayfish optimization algorithm solution in step S3 includes the following steps:

[0117] S3.1. Initialization parameters: set the number of algorithm iterations, population size, warehouse scale, storage location size, speed of the stacker in all directions, etc.

[0118] S3.2, randomly generate some existing goods to occupy the storage space to simulate the conditions of the actual warehousing task. Figure 3 shown.

[0119] S3.3, using real number coding to take the ratio of the cargo location index of the task to be stored to all cargo locations as the individual value, and the number of tasks to be stored as the individual dimension. After randomly generating the initial population, calculate the initial fitness value and record the optimal individual of the population and its fitness value, and assign its value to the global optimal individual and its fitness value at the same time.

[0120] S3.4, the algorithm starts to iterate. First, it calculates and according to the formula, takes their average as the location of the cave, and generates the ambient temperature temp to determine the subsequent individual behavior.

[0121] S3.5, when the ambient temperature is greater than 30 degrees, half of the individuals enter the summer heat stage. In this stage, the individuals will move towards the cave to avoid the high temperature. Here, improvements are proposed for the crayfish optimization algorithm. The main thing is to replace the scaling factor C2 with the logistic curve and the rand random number step with Levy flight. The movement formula is:

[0122]

[0123] The C2 formula is:

[0124]

[0125] Levy(n, m, β) is an n×m-dimensional Levy step-size matrix with parameter β, and its formula is:

[0126]

[0127] u n×m ~N(0,σ u )

[0128] v n×m ~N(0,1)

[0129] in:

[0130]

[0131] The improvement of scaling factor C2 enables individuals entering the summer retreat stage at the early stage of algorithm iteration to explore the optimal point with larger steps, accelerating the convergence speed of the algorithm and the ability to explore the solution space. In the later stage, the step size is gradually reduced to achieve a more detailed exploration of the optimal point. Compared with the linear scaling factor of the basic COA algorithm, the logistic curve parameter control is flexible, and the convergence process is smoother and more natural. The occasional long jump characteristics of Levy flight significantly improve the global search ability of the algorithm and prevent it from falling into the local optimal solution.

[0132] S3.6, when the temperature is higher than 30 degrees, the other half of the individuals enter the competition mode. In this stage, the difference between the current individual and another random individual is used as the disturbance and the location of the cave X shade The addition is used to simulate the competition of crayfish individuals for the same cave, which increases the variation of individuals and prevents them from falling into the local optimal solution. The formula is:

[0133]

[0134] S3.7. When the temperature is below 30 degrees, the individual enters the eating stage and first defines the food size according to formula 14.

[0135]

[0136] Food Q measures the size of the current individual fitness relative to the optimal individual fitness. When the food is too large (that is, the difference between the current individual and the optimal individual is too large), the crayfish individual decomposes the food according to formula 15:

[0137]

[0138] By reducing the attractiveness of food to individuals, the over-dependence of individuals on the current optimal individual is reduced, prompting individuals to explore other possible solutions. Then individuals will simulate crayfish to alternately use the second and third pairs of claws to eat. The formula is:

[0139]

[0140] When the food is the right size, crayfish will eat it directly. The specific formula is:

[0141]

[0142] Where p is the distribution probability related to temperature, which affects the speed of individual feeding (i.e. the step length of the search) through temperature. The specific formula is:

[0143]

[0144] S3.8, then the algorithm enters the improved elite individual probability update stage, and selectively moves the individual position closer to the current optimal individual through probability. This probability continues to increase with the iteration. The specific formula is:

[0145]

[0146] The ind probability formula is:

[0147]

[0148] S3.9, then the algorithm enters the improved boundary correction stage. During the iteration process, individuals that exceed the upper and lower bounds will be redistributed to the vicinity of the boundary with a certain probability. Compared with the basic COA strategy of directly setting the value of the out-of-bounds individual as the boundary, the global search capability is enhanced.

[0149] S3.10, then compare the current position of each individual with their respective best position and the group's historical best, dynamically update the optimal solution of fitness, record and iterate until the termination condition is met. The optimal warehouse location allocation for warehousing tasks under multiple objectives is obtained. The allocation of some warehouse locations is shown in the following figure. Figure 4 The occupancy of the allocated storage space is shown in Figure 5 As shown in the figure, the gray cargo space is the original cargo, and the green cargo space is the warehousing task. The fitness function decline curves of COA, IOC, GA, and PSO algorithms for solving the same mathematical model are shown in the figure. Figure 6 shown.

Claims

1. A storage location allocation optimization method based on crayfish optimization algorithm, characterized in that: Contains steps: S1. Obtaining the information of the three-dimensional warehouse and the goods; S2. Establish a multi-objective storage location allocation mathematical model; Establishing a single-objective storage location allocation mathematical model respectively; converting the single-objective storage location allocation mathematical model into the multi-objective storage location allocation mathematical model by a linear weighted method; S3, calculate the optimal storage location allocation result; The multi-objective storage location allocation mathematical model is solved by the crayfish optimization algorithm to obtain the optimal storage location allocation result.

2. The storage location allocation optimization method based on crayfish optimization algorithm according to claim 1 is characterized in that: The single-objective storage location allocation mathematical model includes a storage efficiency model F1.

3. The storage location allocation optimization method based on crayfish optimization algorithm according to claim 2 is characterized in that: The single-objective storage location allocation mathematical model includes a low center of gravity model F2 of the entire shelf.

4. The storage location allocation optimization method based on crayfish optimization algorithm according to claim 3 is characterized in that: The single-objective storage location allocation mathematical model includes a single-side shelf anti-overturning model F3.

5. The storage location allocation optimization method based on crayfish optimization algorithm according to claim 4 is characterized in that: The single-objective storage location allocation mathematical model includes a double-extension storage location priority same-type goods model F4.

6. The storage location allocation optimization method based on crayfish optimization algorithm according to claim 1 is characterized in that: The step S2 includes adding constraints to the multi-objective storage location allocation mathematical model.

7. The storage location allocation optimization method based on crayfish optimization algorithm according to claim 1 is characterized in that: In the summer vacation stage of the crayfish optimization algorithm, the logistic curve is defined as the scaling factor C2 of the crayfish optimization algorithm, and the Levy flight is defined as the rand random number step length of the crayfish optimization algorithm.

8. The storage location allocation optimization method based on crayfish optimization algorithm according to claim 1 is characterized in that: The crayfish optimization algorithm includes an elite individual probability update phase, in which other individuals are selectively positioned closer to the current optimal individual according to the update probability, and the update probability increases with the iteration process.

9. The storage location allocation optimization method based on crayfish optimization algorithm according to claim 1 is characterized in that: The crayfish optimization algorithm includes a boundary correction phase, and the individuals that exceed the upper and lower bounds are redistributed during the iteration process.

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