A multi-objective scheduling optimization method for intelligent warehousing and logistics systems

Abstract

The present invention provides a multi-objective scheduling optimization method for an intelligent warehousing and logistics system, belonging to the technical field of intelligent warehousing and logistics distribution. An intelligent decision-making strategy is introduced. First, according to the attribute relationship between intelligent warehousing and logistics entities, when there is an order, the order is counted, and the relevant information is counted into the database; a multi-objective optimization model for the stability of the storage environment shelves, the uniform distribution of the operating goods, the handling efficiency of inbound and outbound, and the shortest transportation operation path is established. The good point set is used to uniformly initialize the population, and a position perturbation factor is introduced to adjust the particle position update of the particle swarm algorithm for multi-objective optimization solution to determine the optimal decision on the storage position of goods under the current situation; an enhanced A* algorithm is proposed to plan the approximate path of the transport vehicle carrying the order from the storage place to under the shelf, and the output path scheme is recorded as the initial path planning solution of the improved particle swarm algorithm, and the improved particle swarm algorithm is used for further precise optimization to achieve the lowest cost.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent warehousing logistics distribution, and particularly relates to a multi-objective scheduling optimization method for an intelligent warehousing logistics system. Background Art

[0002] In recent years, the increasing cost pressure, the slowdown in the growth of warehousing demand, and the changing uncertainties in social consumption have posed multiple challenges to the warehousing industry. The traditional low-value-added service model relying on land and labor costs is no longer sustainable. Therefore, relying on digitalization and intelligentization can effectively improve the ability of resource allocation efficiency and operation quality. In order to achieve visual monitoring of warehousing entity resources and intelligent allocation decision-making of storage locations, intelligent scheduling has become an important implementation method for intelligent warehousing logistics.

[0003] To address these challenges, the intelligent warehousing logistics system, with functions such as automatic handling, intelligent monitoring, operation process management, and multi-device collaborative scheduling, has improved the degree of automation and reduced labor costs; its flexible warehousing layout, high space utilization rate, and high flexibility of four-way vehicles have also reduced land costs, which is an effective solution for the construction of intelligent warehousing logistics systems.

[0004] In the intelligent warehousing logistics system, by comprehensively considering factors such as the weight of materials, the scheduling frequency of equipment, the scheduling path, the degree of operation balance, and the types of materials, the optimization of storage location decision-making is achieved. This can dynamically allocate storage locations and improve the efficiency of warehousing operations to meet the growing order demands. Therefore, the optimization of storage location decision-making has become the core issue for improving the efficiency of the warehousing logistics system.

[0005] Traditional research mainly focuses on optimizing the stability of warehousing shelves and finding the shortest scheduling path, which has improved the logistics distribution efficiency to a certain extent. However, there are still some limitations:

[0006] 1. Traditional research often only considers the Manhattan path between the goods and the target location, ignoring the actual paths of obstacles, inventory, or other equipment, resulting in the real-time performance and quality of storage location allocation being unable to meet actual needs.

[0007] 2. Traditional research does not take into account energy consumption and operation distribution constraints, and it is necessary to redesign the path planning algorithm considering dynamic constraints such as equipment and inventory. Therefore, to better meet the actual storage location decision-making requirements, it is also necessary to consider the goals of low energy consumption and balanced operation distribution on each layer of the shelf, which increases the complexity of constraint conditions and scheduling resources and makes intelligent storage location allocation more difficult.

[0008] 3. Functions such as location decision-making, equipment monitoring, and equipment scheduling can be independent services and form a visualization page through integration with the Web. There are still some difficulties in the decoupling of each functional module in the warehousing system, the development of visualization applications, and their use in the actual application environment. Summary of the Invention

[0009] In view of the above problems, the present invention provides a multi-objective scheduling optimization method for an intelligent warehousing logistics system, including the following processes:

[0010] S1. Construct an entity information model in combination with the actual warehousing environment; determine the relationship model of each factor, generate an E-R diagram in the database table, that is, an entity association diagram including at least six entities: warehouse, order, operation, goods location, inventory, and pallet. When there is an order, count the order and statistic the relevant information into the database table;

[0011] Set three-dimensional coordinate information Mark the goods location and shelf passage information to provide data information for multi-objective goods scheduling optimization;

[0012] S2. Establish a model for the multi-objective optimization problem of shelf stability, uniform distribution of operating goods, inbound and outbound handling efficiency, and shortest transportation operation path in the warehousing environment, which conforms to the operating procedures of the actual warehousing logistics system;

[0013] S3. Design a meta-heuristic algorithm for solving the multi-objective optimization problem of goods location. Use the good point set to uniformly initialize the population, adjust the parameters in the velocity update formula by non-linear transformation, and introduce a position perturbation factor to adjust the particle position update of the particle swarm algorithm for multi-objective optimization solution to determine the optimal goods location storage decision in the current situation.

[0014] Preferably, model the road surface of the intelligent warehousing logistics environment, design an enhanced A* algorithm to plan the path when the goods are out of the warehouse to obtain a general path; use the general path result as the initial path plan, and use the improved particle swarm optimization algorithm to optimize the path planning problem to determine the final route trajectory.

[0015] Preferably, in S1, first, a complete warehousing management process is determined. When there are ordering and replenishment orders, information is uniformly entered through order management. Different operations are generated according to the occurrence time and type of the order. According to the operation type, the storage and retrieval locations of the goods are determined, and the location information transmission command is given to the warehousing transport vehicle for operation scheduling to realize the storage and retrieval operations of the warehousing goods. After determining the operation process of the warehousing logistics, an entity relationship model of the intelligent warehousing logistics system is established, a relational database is established, and its relationship mapping is carried out with various types in the warehousing logistics system to complete the storage and retrieval of information in the warehousing logistics system. Among them, the E-R diagram contains six entities: warehouse, order, operation, goods location, inventory, and pallet. The respective related association attributes are set as follows:

[0016] Warehouse: Warehouse number, warehouse name;

[0017] Order: Order number, order date, logistics information, customer number;

[0018] Operation: Operation number, operation details, operation item, operation item number;

[0019] Goods location: Goods location number, goods location, number of layers, layer name, layer number, layer specification;

[0020] Inventory: Inventory number, goods location number, pallet number;

[0021] Pallet: Pallet number, maximum load, pallet type, pallet specification.

[0022] Preferably, in S2, the shelf stability is to minimize the product of the weight of the goods and the number of layers where the goods are located, and the optimization objective function The model is:

[0023] (1)

[0024] In the formula, represents the weight of the stored and retrieved goods, represents the height of the goods location, respectively correspond to the total number of columns, rows, and layers of the shelf;

[0025] Regarding the uniform distribution of the operation goods, from the perspective of considering the uniform distribution of the operation goods, the existing goods placement and the to-be-completed operations are evenly distributed to form an operation uniform distribution objective function The model:

[0026] (2)

[0027] Among them, The smaller the value of, the more uniform and reasonable the operation distribution is; It means that when performing operation operations, only the cases where the number of layers of the shelf is more than two are considered; It represents the numerical value of the number of storage locations occupied by goods in the shelf at the z layer. It represents the average value of the storage locations occupied by goods on each layer.

[0028] The in-out handling efficiency takes into account the moving time of the operator in the row, column, and layer during goods inbound and the corresponding in-out frequencies, and constitutes the in-out handling efficiency objective function Model:

[0029] (3)

[0030] Among them, They respectively correspond to the length, width, and height information of the goods location. It represents the moving speed of the operator. It represents the in-out frequency of the goods.

[0031] The shortest transportation operation path is designed by taking the minimum value of the matching degree between the actual storage location and the scheduling decision as the shortest transportation operation path; specifically, determine the location of the goods at the storage platform The goods placement coordinates The goods elevator coordinates constitute the shortest transportation operation path objective function Model:

[0032] (4)

[0033] Among them, when the location of the storage platform and the goods placement location are on the same layer, the value is , if they are on different layers, the shortest path distance is ; It represents the layer difference between the storage platform location and the goods placement location. It represents the row-column difference between the elevator and the goods location.

[0034] The above optimization models of shelf stability, uniform distribution of operating goods, in-out handling efficiency, and shortest transportation operation path in the warehousing environment together constitute a multi-objective optimization design problem.

[0035] Preferably, the specific process of S3 is as follows:

[0036] S31, Use the good point set to evenly distribute the population in the search space, set the particle swarm population number, maximum iteration number, and related parameters:

[0037] (5)

[0038] Among them, It represents the position of the i th particle of the initialized population. They respectively correspond to the upper and lower limits of the search space. Indicates the optimal point taken; Satisfy ;

[0039] S32. The feasible region of the shelf location is updated in real time with the data transmitted through the intelligent warehousing and logistics system database, and the decision-making operation quantity is determined as the spatial dimension;

[0040] S33. Calculate the positions of each particle and determine , Indicates the global optimal position;

[0041] S34. Update the velocity and position information of the particles respectively according to the following formula (6), formula (7) with the added position perturbation operator, and formulas (8) to (11);

[0042] Velocity and position The update formulas are as follows:

[0043] (6)

[0044] (7)

[0045] where t represents the iteration number of the calculation execution; i represents the particle number , n represents the particle population size; Represents the inertia weight; Respectively correspond to random numbers between (0, 1); Represents the acceleration constant, Represents the individual optimal position of the particle, that is, after one iteration, the optimal cargo position value obtained by this particle; Represents the global optimal position, that is, after one iteration, the best record of the optimal cargo position value among all particles;

[0046] Perform a non-linear transformation on the inertia weight and the acceleration constant The three parameters, and the non-linear transformation is as follows:

[0047] (8)

[0048] (9)

[0049] (10)

[0050] where, The value range of Decreases regularly; the acceleration constant Changes between ; the acceleration constant vary between; i represents the current iteration number, and represents the maximum number of iterations of the algorithm;

[0051] The perturbation operator added in combination with formula (7) is as follows:

[0052] (11)

[0053] where is the -dimensional component in the D-dimensional search space; The value range of is between is a random number randomly generated in [0, 1], b is the perturbation degree factor, t represents the current iteration number, T represents the total number of iterations, and the random value of l is 0 or 1;

[0054] S35. Calculate the function value according to the optimization objectives of the shelf stability in the storage environment, the even distribution of the goods in operation, the handling efficiency of inbound and outbound, and the shortest transportation operation path;

[0055] S36. Update the and of the particle;

[0056] S37. Increment the iteration number by 1, and determine whether the process has reached the set maximum number of iterations. If not, return to S34; if so, output the optimal solution of the multi-objective goods location decision optimization.

[0057] Preferably, the pavement modeling of the intelligent warehousing and logistics environment is specifically as follows:

[0058] Convert the actual environment into a grid map to provide an environmental basis for path planning; the grid map decomposes the actual environment into multiple grid cells of the same size , and each cell is represented by the state variable 0-1; constituting a two-dimensional map information:

[0059] (12)

[0060] where l and w respectively represent the row and column where the current grid is located, and their respective maximum values correspond to L and W; when has a value of 1, it means there is an obstacle currently, and when it is 0, it means it is passable;

[0061] At the same time, design the path planning objective function; the path planning objective of the transport vehicle is to find a path from the starting point of the goods in operation to the end point under the shelfAnd the shortest path without touching obstacles; therefore, the objective function of the path planning problem is designed as follows:

[0062] (13)

[0063] Among them, represents the penalty operator, whose function is to exclude the paths passing through the obstacle range, represents the sum of the Euclidean distances of each point passed in the path trajectory, and its expression is as follows:

[0064] (14)

[0065] Set represents the variable for judging the obstacle level of the current planned path, and its initial value is set to 0:

[0066] (15)

[0067] (16)

[0068] Among them, represents the coordinate value of the s-th path point on the planned path; corresponds to the coordinate value of the k-th obstacle; n represents the total number of points on the planned path, represents the total number of obstacles, that is, other shelves and placed obstacles on the path in the warehousing and logistics environment; represents the obstacle radius of the abstract obstacle.

[0069] Preferably, in the intelligent warehousing and logistics, the position of the operation goods is the starting point of the transport vehicle, and the goods unloading position is the end point for the transport vehicle to perform path planning. The evaluation function for path planning from the starting point to the target end point by the A* algorithm is as follows:

[0070] (17)

[0071] Among them, n represents the current node position, represents the estimated distance from the current position to the end point, represents the actual driving distance from the starting point to the current position; The expression of

[0072] (18)

[0073] Among them, correspond to the coordinates of the current node and the target end point respectively;

[0074] (19)

[0075] Among them, is obtained by accumulating the shortest path distances of the previous nodes of the current node of the previous node is obtained by accumulation;

[0076] By calculating the position cost between the current node and the target end point, the requirements for the shortest path are gradually obtained;

[0077] To improve the calculation effect of path planning, a non-linear factor is added :

[0078] (20)

[0079] where length represents the length of the grid and width represents the width of the grid; the non-linear factor is added to the evaluation function:

[0080] (21)

[0081] If the position of the current node is far from the target end point, P takes a larger value to enhance the search efficiency. On the contrary, if the position of the current node is close to the target end point, P takes a smaller value to enhance the search accuracy of the algorithm.

[0082] Preferably, the improved particle swarm optimization algorithm is used to optimize the path planning problem to determine the final route trajectory. The approximate path obtained by the enhanced A* algorithm is recorded as the initial optimal solution record of the improved particle swarm algorithm;

[0083] According to the evaluation function represented by formula (13), the shortest distance from the starting point to the end point is determined, and the node at the next position is selected according to the estimation value as the solution of the fitness function of the improved particle swarm algorithm.

[0084] Compared with the prior art, the present invention has the following beneficial effects:

[0085] The method of the present invention introduces an intelligent decision-making strategy. First, according to the attribute relationships among the intelligent warehousing and logistics entities, an entity association graph of six entities including warehouses, orders, operations, cargo locations, inventories, and pallets is established. When there is an order, the order is counted and the relevant information is counted into the database; a multi-objective optimization model of warehouse environment shelf stability, uniform distribution of operating goods, inbound and outbound handling efficiency, and shortest transportation operation path is established. The good point set is used to uniformly initialize the population, and a position perturbation factor is introduced to adjust the particle position update of the particle swarm algorithm for multi-objective optimization solution to determine the optimal cargo position storage decision under the current situation; an enhanced A* algorithm is proposed to plan the approximate path of the transport vehicle carrying the order operation from the storage location to under the shelf, and the output path plan is recorded as the initial path planning solution of the improved particle swarm algorithm, and is further accurately optimized by the improved particle swarm algorithm to form a two-layer optimization, so as to achieve the lowest cost. Brief Description of the Drawings

[0086] Figure 1 This is a schematic diagram of the overall process of the multi-objective scheduling optimization method for the intelligent warehousing and logistics system of the present invention.

[0087] Figure 2 This is the operation flow chart of the multi-intelligent warehousing and logistics system of the present invention.

[0088] Figure 3 This is the E-R diagram of the order and operation of the present invention.

[0089] Figure 4 This is the flow chart of the multi-objective storage location optimization of the intelligent warehousing and logistics system of the present invention.

[0090] Figure 5 This is the flow chart of the optimal path optimization of the intelligent warehousing and logistics system of the present invention. Detailed Embodiment

[0091] The present invention will be further described below in conjunction with embodiments. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0092] A multi-objective scheduling optimization method for an intelligent warehousing and logistics system provided by the present invention has an overall process as Figure 1 shown below:

[0093] S1. Modeling of the entity information for the storage location decision of the intelligent warehousing and logistics system. An entity information model is constructed in combination with the actual warehousing environment, the relationship model of each factor is determined, and the E-R diagram in the database table is generated, including relevant factors such as warehouses, goods locations, inventory, orders, etc. The corresponding relationship between the designed database table and the object class is constructed to obtain the relevant database of warehousing logistics. Set the three-dimensional coordinate information to mark the goods location and the shelf passage information, providing data information for the multi-objective goods scheduling optimization.

[0094] S2. Modeling for the optimization design of the multi-objective storage location decision of the intelligent warehousing and logistics system. Consider the relevant mathematical modeling of the inbound operation in the warehousing logistics system. This includes establishing a multi-objective optimization model for the stability of the shelves in the warehousing environment, the uniform distribution of the operating goods, the handling efficiency of inbound and outbound operations, and the shortest transportation operation path, which conforms to the operating procedures of the actual warehousing logistics system.

[0095] S3. Design a meta-heuristic algorithm for solving the multi-objective optimization problem of cargo location. The improved particle swarm optimization algorithm is used for optimization. The particle swarm population represents the solution to the multi-objective optimization problem. The good point set is used to optimize the population generation of the original particle swarm algorithm, and a position perturbation factor is introduced to adjust the particle position update. Apply it to solve the multi-objective cargo location optimization problem to obtain the optimal cargo location information.

[0096] S4. Design a path planning algorithm for transporting the cargo of the transport vehicle under the shelf. After determining the decision on the storage location of the cargo for the current order, use the transport vehicle to transport it to the cargo location. An enhanced A* algorithm is proposed to plan the path when the cargo is out of the warehouse to obtain a rough path; use the rough path result as the initial path plan, and use the improved particle swarm optimization algorithm to perform double-layer optimization on the path planning problem to determine the final route trajectory.

[0097] 1. Information Modeling of the Cargo Location Decision Entity in the Intelligent Warehouse Logistics System

[0098] (1) Design and analysis of the warehousing logistics operation process. This part consists of three parts: order management, cargo location decision, and operation execution.

[0099] (2) First, determine the complete warehousing management process. When there are ordering and replenishment orders, the information is uniformly entered through order management, and different operations are generated according to the occurrence time and type of the order. According to the operation type, determine the storage location of the cargo, and transmit the location information command to the warehousing transport vehicle for operation scheduling to realize the storage and retrieval operations of the warehousing cargo. The program block diagram of the intelligent warehouse logistics system operation process is as follows Figure 2 as shown.

[0100] (3) Multi-attribute relational database mapping modeling. After determining the warehousing logistics operation process, establish the entity relationship model of the intelligent warehouse logistics system, establish a relational database, map its relationships with various types in the warehousing logistics system, and complete the storage and retrieval of information in the warehousing logistics system.

[0101] The present invention models by constructing a relational database, that is, designs an entity relationship model diagram (E-R diagram) of the warehousing logistics to describe the types of information stored in the database.

[0102] The E-R diagram includes six entities: warehouse, order, operation, cargo location, inventory, and pallet. Their respective related connection attributes are set as follows:

[0103] (1) Warehouse: Warehouse number, warehouse name;

[0104] (2) Order: Order number, order date, logistics information, customer number;

[0105] (3) Operation: Operation number, operation details, operation item, operation item number;

[0106] (4) Goods location: bin number, bin location, number of layers, layer name, layer number, layer specification;

[0107] (5) Inventory: inventory number, bin number, pallet number;

[0108] (6) Pallet: pallet number, maximum load, pallet type, pallet specification.

[0109] There are related connections between different entities. Figure 3 Give the associated entity relationship diagram of orders and operations.

[0110] According to the attributes of each entity in the designed warehousing and logistics system, construct an E-R diagram to complete the design and modeling of the database.

[0111] (4) Set three-dimensional coordinate information Mark the goods location and shelf passage information. Simulate the actual placement form of goods on the warehouse shelves, where x represents the number of columns of the shelf, y represents the number of rows of the shelf, and z represents the number of operating layers of the goods elevator. According to the storage situation of the corresponding shelf based on the set three-dimensional coordinate information, obtain the three-dimensional environment model.

[0112] Information mapping of the intelligent warehousing and logistics system. When a new order is generated in the intelligent warehousing and logistics system, set the goods information based on the current goods situation and the attributes determined by the E-R diagram, and add it to the warehousing and logistics system database to complete order management.

[0113] 2. Multi-objective bin location decision optimization design and modeling

[0114] After the goods information of the order is completed for order management through the database, it is necessary to perform multi-objective optimization access decisions according to the current storage and logistics storage situation, generate operation instructions, send them to the transport vehicle for operation and transportation, and complete the goods access.

[0115] The multi-objective bin location decision optimization design meets the optimization objectives, transforms the actual situation into a mathematical expression, including establishing a multi-objective optimization model design for the stability of the storage environment shelf, uniform distribution of operating goods, efficiency of inbound and outbound handling, and shortest transportation operation path.

[0116] (1) Shelf stability. The above objective is considered for the safety of intelligent warehousing and logistics scheduling. A reasonable placement of goods is to make the overall center of gravity of the goods close to the bottom area of the shelf, reducing the impact of raising the center of gravity due to placing goods at a high position. That is, the product of the goods weight and the number of layers where the goods are located is the smallest. Therefore, the present invention designs an optimization objective function for shelf stability considering safety Model:

[0117] (1)

[0118] In the formula, represents the weight of the goods to be accessed, represents the height of the goods position, respectively corresponding to the total number of columns, rows and layers of the shelves.

[0119] (2) The distribution of the goods to be processed is uniform. The above goal is based on the existing goods storage situation in the current warehousing and logistics warehouse. If the goods to be stored are too dense, it will cause congestion in the operation work and reduce the operation efficiency. Therefore, from the perspective of considering the uniform distribution of the goods to be processed, the present invention evenly distributes the existing goods placement and the pending operations to form a target function for the uniform distribution of operations Model:

[0120] (2)

[0121] Wherein, The smaller the value of, the more uniform and reasonable the operation distribution is; It means that when performing operation, only the cases where the number of shelves is two or more are considered. represents the number value of the goods occupying the storage locations in the shelves at the z-th layer, represents the average value of the storage locations occupied by the goods on each layer.

[0122] (3) Inbound and outbound handling efficiency. The above goal is based on the economic requirements of warehousing and logistics. When performing inbound and outbound operations, the principle of placing the goods as close as possible is primarily considered, and the handling efficiency is determined based on the inbound and outbound frequencies of the goods in combination with the access positions of the goods. The present invention considers the moving time of the operator in the columns, rows and layers during the inbound process of the goods and the corresponding inbound and outbound frequencies to form a target function for the inbound and outbound handling efficiency Model:

[0123] (3)

[0124] Wherein, respectively correspond to the length, width and height information of the goods position; represents the moving speed of the operator, represents the inbound and outbound frequencies of the goods.

[0125] (4) The shortest transportation operation path. The above goal is based on considering the distance from the goods to the target position. In the operation, the storage position of the goods should be kept as close as possible to the principle. Therefore, the present invention designs to use the minimum value of the matching degree between the actual storage location and the scheduling decision as the shortest transportation operation path. Specifically, determine the position of the goods on the platform , the placement coordinates of the goods , the coordinates of the goods elevator to form a target function for the shortest transportation operation path Model:

[0126] (4)

[0127] Among them, when the position of the storage platform is on the same layer as the position where the goods are placed, the value is . If they are on different layers, the shortest path distance is . represents the floor difference between the position of the storage platform and the position where the goods are placed, represents the row and column difference between the elevator and the position of the goods.

[0128] The above optimization models for the stability of the storage environment shelves, the even distribution of the operating goods, the handling efficiency of inbound and outbound, and the shortest transportation operation path together constitute a multi-objective optimization design problem.

[0129] 3. Solving the multi-objective decision optimization of the goods position in the intelligent warehousing and logistics system by an improved particle swarm algorithm

[0130] The actual environment of the intelligent warehousing and logistics system has a high degree of complexity. According to the designed optimization objectives for the position of intelligent warehousing goods, a problem with multiple objectives to be optimized is designed. The present invention uses an improved particle swarm algorithm to solve this multi-objective optimization problem, as Figure 4 shown, and designs a path planning algorithm for transporting goods of the transport vehicle to the position below the shelf by the improved particle swarm algorithm, and performs double-layer optimization to determine the route trajectory, saving computing resources.

[0131] The multi-objective particle swarm algorithm is developed based on the original particle swarm algorithm for solving single-objective optimization problems. In each iteration process of the algorithm, a set of Pareto optimal solutions within the constraint conditions is generated, and optimization is performed through the mutual learning among particles.

[0132] (1) The original particle swarm algorithm. The particle swarm algorithm is a group of particle populations randomly generated in the D-dimensional space. Each particle corresponds to a set of solutions to the multi-objective optimization problem, that is, a feasible solution for the decision-making of the intelligent warehousing and logistics goods location. With the iterative update of the calculation, the particles adjust their own positions and speeds, and continuously update towards the optimal solution position, that is, the optimal goods position. The velocity and position update formulas are as follows:

[0133] (5)

[0134] (6)

[0135] Among them, t represents the iteration number of the calculation execution; i represents the particle number , n represents the particle population size; represents the inertia weight; respectively correspond to random numbers between (0, 1); represents the acceleration constant, represents the individual optimal position of the particle, that is, after one iteration, the optimal cargo position value obtained by the particle; represents the global optimal position, that is, after one iteration, the best record of the optimal cargo position value among all particles.

[0136] (2) Improved particle swarm algorithm.

[0137] Good point set uniformly distributed population. The population initialization of the original particle swarm algorithm is randomly generated within the D-dimensional search space, making the population distribution uneven. Therefore, to improve the global search ability of the particle swarm algorithm, a good point set is used to uniformly distribute the population in the search space:

[0138] (7)

[0139] Among them, represents the position of the i-th particle in the initialized population; respectively correspond to the upper and lower limits of the search space; represents the selected good point; satisfies .

[0140] (3) Particle velocity parameter transformation. It can be seen from formula (5) that in the particle velocity update formula, the inertia weight and the acceleration constant have an important impact on the algorithm performance.

[0141] As described above a larger value of

[0142] As described above , when takes a small value and takes a large value, the algorithm has good local search ability. Conversely, the global search ability of the algorithm is better.

[0143] To enhance the influence of the inertia weight and acceleration constant on the algorithm performance in the algorithm, the present invention proposes to perform a non-linear transformation on the three parameters. The non-linear transformation is as follows:

[0144] (8)

[0145] (9)

[0146] (10)

[0147] Among them, the value range of decreases regularly; the acceleration constant is within transform between; acceleration constant at transform between; i represents the current iteration number, represents the maximum iteration number of the algorithm.

[0148] (4) Perturbation operator for particle position. It can be understood that the update of the particle position in each iteration changes in a fixed direction, making the algorithm prone to falling into a local optimal solution. Adding a perturbation operator can enhance the local search ability of the algorithm. The added perturbation operator combined with formula (6) is as follows:

[0149] (11)

[0150] where, is the dimensional component in the D-dimensional search space; The value range of is between is a random number randomly generated in [0, 1], b is the perturbation degree factor, t represents the current iteration number, T represents the total iteration number, and l randomly takes a value of 0 or 1.

[0151] By adding the position perturbation operator and combining it into formula (6), the local search ability of the algorithm is enhanced.

[0152] (5) Solving the multi-objective goods location decision of the intelligent warehousing and logistics system by the improved particle swarm algorithm. The process of using the above improved particle swarm algorithm to solve the optimization of the intelligent warehousing goods location decision is as follows:

[0153] S1. Set the particle swarm population number, the maximum iteration number and related parameters according to formula (7);

[0154] S2. Through the data transmitted by the intelligent warehousing and logistics system database, update the feasible region of the shelf storage location in real time, and determine the number of decision-making operations as the spatial dimension;

[0155] S3. Calculate the position of each particle and determine ;

[0156] S4. Update the velocity and position information of the particles respectively according to formula (5), formula (6) with the added position perturbation operator, and formulas (8) to (11);

[0157] S5. Calculate the function value according to the optimization objectives of shelf stability, uniform distribution of operating goods, inbound and outbound handling efficiency, and shortest transportation operation path in the warehousing environment;

[0158] S6. Update the and of the particle;

[0159] In S7, increment the iteration count by 1, and determine whether the process has reached the set maximum iteration count. If not, return to S4; if so, output the optimal solution for the multi-objective cargo location decision optimization.

[0160] Through the above step process, the best cargo storage and retrieval location for the real-time order generation operation is obtained.

[0161] 4. Double-layer optimization to determine the path planning route trajectory for transporting the cargo of the transport vehicle under the shelf

[0162] The overall process is as Figure 5 shown. First, complete the road surface modeling of the intelligent warehousing and logistics environment. Convert the actual environment into a grid map to provide an environmental basis for path planning. The grid map decomposes the actual environment into multiple grid cells of the same size , and each cell is represented by the state variable 0 - 1. It constitutes a two-dimensional map information.

[0163] (12)

[0164] Among them, l and w respectively represent the row and column where the current grid is located, and their respective maximum values correspond to L and W. When the value is 1, it means there is an obstacle currently, and when it is 0, it means it is passable.

[0165] Secondly, design the path planning objective function. The path planning objective of the transport vehicle is to find the shortest path from the starting point of the operation cargo to the end point under the shelf without touching obstacles. Therefore, the objective function of the path planning problem is designed as follows:

[0166] (13)

[0167] Among them, represents the penalty operator, and its role is to exclude the paths passing through the obstacle range. represents the sum of the Euclidean distances of each point passed through in the path trajectory. It is expressed as follows:

[0168] (14)

[0169] Furthermore, represents the variable for determining the obstacle level of the current planned path, and its initial value is set to 0.

[0170] (15)

[0171] (16)

[0172] Among them, represents the coordinate value of the sth path point on the planned path; corresponding to the coordinate value of the kth obstacle; n represents the total number of points on the planned path, represents the total number of obstacles, that is, other shelves and placed obstacles on the path in the warehousing and logistics environment; represents the obstacle radius of the abstract obstacle.

[0173] Finally, design the A* algorithm. It can be understood that the A* algorithm determines the shortest distance from the starting point to the end point according to the evaluation function and selects the next position node according to the estimation value. In intelligent warehousing and logistics, the position of the operating goods is the starting point of the transport vehicle, and the goods unloading position is the end point for the transport vehicle to perform path planning. Then, the evaluation function for path planning from the starting point to the target end point using the A* algorithm is as follows:

[0174] (17)

[0175] where n represents the current node position, represents the estimated distance from the current position to the end point, represents the actual driving distance from the starting point to the current position. The representation of is as follows:

[0176] (18)

[0177] where, correspond to the coordinates of the current node and the target end point respectively.

[0178] (19)

[0179] where, is accumulated according to the shortest path distance of the previous node of the current node to obtain.

[0180] By calculating the position cost between the current node and the target end point, the requirements for the shortest path are gradually obtained.

[0181] Furthermore, design an enhanced A* algorithm. According to the above analysis, it can be known that the evaluation function of the A* algorithm is composed of the actual driving distance from the goods starting point to the current node and the estimated distance from the current position to the end point

[0182] (20)

[0183] Among them, length represents the length of the grid, and width represents the width of the grid. A non-linear factor is added to the evaluation function:

[0184] (21)

[0185] Therefore, when the position of the current node is far from the target end point, the value of P is larger, enhancing the search efficiency. On the contrary, when the position of the current node is close to the target end point, the value of P is smaller, which can enhance the search accuracy of the algorithm.

[0186] Through the above design, the enhanced A* algorithm is applied to solve the intelligent warehousing and logistics path planning problem. Taking the initial position of the operation goods as the starting point of the path planning, the transport vehicle conducts the optimal path planning. After reaching the optimal path trajectory, it unloads the goods at the target end point. A general path planning scheme is obtained .

[0187] Taking the general path result scheme obtained by the enhanced A* algorithm as the initial path record, using the initial optimal solution of the above improved particle swarm algorithm for the particle population, and further optimizing and calculating with the improved particle swarm algorithm to obtain a more accurate optimal trajectory path, realizing double-layer optimization.

[0188] Through the enhanced A* algorithm, a general path planning scheme for the transport vehicle to transport the operation goods is obtained. Since the A* algorithm needs to judge the Euclidean distance of the surrounding grids, with the increase of grid points, the search result is inaccurate and the optimal path length is increased.

[0189] Therefore, the present invention further optimizes and solves by using the above improved particle swarm algorithm to realize double-layer optimization calculation. Recording the general path obtained by the enhanced A* algorithm as the initial optimal solution record of the improved particle swarm algorithm.

[0190] Furthermore, the shortest distance from the starting point to the end point is determined according to the evaluation function represented by formula (13), and the node at the next position is selected according to the estimation value as the fitness function solution of the improved particle swarm algorithm.

[0191] Through the above design, taking the general planned path obtained by the enhanced A* algorithm as the initial solution record of the improved particle swarm algorithm, the improved particle swarm further performs refined calculation to obtain a more accurate path planning scheme.

[0192] Taking the initial position of the operation goods as the starting point of the path planning, the transport vehicle conducts the optimal path planning. After reaching the optimal path trajectory, it unloads the goods at the target end point and places them under the shelf. After the goods are unloaded, they are stored in the corresponding storage location through the elevator. Realizing the double-layer optimization of the intelligent warehousing and logistics system, and accurately and timely placing the goods under the shelf.

[0193] The above are only the preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0194] Although the specific implementation manners of the present invention are described above, it is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solutions of the present invention, various modifications or deformations that can be made without creative efforts by those skilled in the art are still within the protection scope of the present invention.

Claims

1. A multi-objective scheduling optimization method for a smart warehousing logistics system, characterized in that: The process includes: S1, build an entity information model based on the actual warehousing environment; determine the relationship model of each factor, generate the ER diagram in the database table, that is, an entity association diagram including at least six entities: warehouse, order, operation, cargo location, inventory, and pallet. When there is an order, count the order and store the relevant information in the database table; Set 3D coordinate information Mark the cargo location and shelf channel information to provide data information for multi-objective cargo scheduling optimization; S2, establish a model for multi-objective optimization problems such as shelf stability in the storage environment, uniform distribution of cargo, efficiency of inbound and outbound handling, and shortest transportation operation path, which conforms to the operating procedures of the actual storage logistics system; S3, design a meta-heuristic algorithm to solve the multi-objective optimization problem of cargo location, use the best point set to uniformly initialize the population, use nonlinear transformation to adjust the parameters in the speed update formula, and introduce the position disturbance factor to adjust the particle position update of the particle swarm algorithm, perform multi-objective optimization and solve, and determine the optimal cargo location storage decision under the current situation; The specific process of S3 is as follows: S31, use the best point set to evenly distribute the population in the search space, set the particle swarm population, the maximum number of iterations and related parameters: （5） in, Represents the initial population i The position of a particle; They correspond to the upper and lower limits of the search space respectively; Indicates the best point taken; satisfy ; S32, through the data transmitted by the smart warehouse logistics system database, the feasible domain of shelf locations is updated in real time, and the number of decision-making operations is determined as the spatial dimension; S33, calculate the position of each particle and determine , represents the global optimal position; S34, updating the velocity and position information of the particle according to the following formula (6), formula (7) with the position disturbance operator added, and formulas (8) to (11); speed and location The update formula is as follows: (6) (7) Where t represents the number of iterations of the calculation; i represents the number of particles , n represents the particle population size; represents the inertia weight; They correspond to random numbers between (0,1) respectively; represents the acceleration constant, It represents the individual optimal position of the particle, that is, the optimal cargo position value obtained by the particle after one iteration; represents the global optimal position, that is, the record with the best optimal cargo position value among all particles after one iteration; Weight for inertia and the acceleration constant The three parameters are transformed nonlinearly, and the nonlinear transformation is as follows: (8) (9) (10) in, The value range is Decrease according to the law; acceleration constant exist Conversion between; acceleration constant exist between; i represents the current iteration number, Indicates the maximum number of iterations of the algorithm; The perturbation operator added in combination with formula (7) is as follows: (11) in, is the first Dimensional component; The value range is between; is a random number randomly generated between [0,1], b is the perturbation factor, t is the current iteration number, T is the total iteration number, and the random value of l is 0 or 1; S35, calculating the function value according to the optimization objectives of shelf stability in the storage environment, uniform distribution of the cargo, efficiency of inbound and outbound handling, and shortest transportation operation path; S36, Update particle and ; S37, the number of iterations is increased by 1, and it is determined whether the process has reached the set maximum number of iterations. If not, it returns to S34; if it has, it outputs the optimal solution for the multi-objective cargo location decision optimization.

2. A multi-objective scheduling optimization method for a smart warehousing logistics system as claimed in claim 1, characterized in that: The road surface of the smart warehousing and logistics environment is modeled, and an enhanced A* algorithm is designed to plan the path for goods out of the warehouse to obtain a rough path; the rough path result is used as the initial path plan, and the improved particle swarm optimization algorithm is used to optimize the path planning problem to determine the final route trajectory.

3. The multi-objective scheduling optimization method for a smart warehousing logistics system according to claim 1, characterized in that: In S1, the complete warehouse management process is first determined. When there are orders for ordering and replenishment, the information is uniformly entered through the order management, and different operations are generated according to the time and type of the order. According to the operation type, the storage and access location of the goods is determined, and the location information is transmitted to the warehouse transport vehicle for operation scheduling to realize the storage and access operations of the warehouse goods. After determining the operation process of warehousing and logistics, an entity relationship model of the smart warehousing and logistics system is established, and a relational database is established. It is mapped with various types in the warehousing and logistics system to complete the storage and access of information in the warehousing and logistics system. Among them, the ER diagram contains six entities: warehouse, order, operation, cargo location, inventory, and pallet; their respective related connection attributes are set as follows: Warehouse: warehouse number, warehouse name; Order: order number, order date, logistics information, customer number; Job: job number, job details, job item, job item number; Cargo location: cargo location number, cargo location, number of layers, layer name, layer number, layer specification; Inventory: inventory number, shelf number, pallet number; Pallet: Pallet number, maximum load, pallet type, pallet specifications.

4. The multi-objective scheduling optimization method for a smart warehousing logistics system according to claim 1, characterized in that: The shelf stability in S2 is to minimize the product of the weight of the goods and the number of layers where the goods are located, and optimize the objective function The model is: (1) In the formula, Indicates the weight of the stored and retrieved goods. Indicates the height of the cargo position. The total number of corresponding rack columns, rows and layers respectively; The uniform distribution of the operation cargo considers the uniform distribution of the operation cargo, and evenly distributes the existing cargo placement and the operations to be completed to form the objective function of uniform distribution of the operation Model: (2) in, The smaller the value, the more even and reasonable the job distribution is; Indicates that when operating, only the case where the number of shelves is more than two is considered; Indicates the number of cargo spaces occupied by goods on the shelf at layer z. Indicates the average number of cargo spaces occupied by cargo on each floor; The in-and-out handling efficiency takes into account the operator's movement time in the rows, rows, and layers of the goods entering the warehouse and the corresponding in-and-out frequency, forming an in-and-out handling efficiency objective function Model: (3) in, The length, width and height information corresponding to the cargo location respectively; Indicates the movement speed of the operator. Indicates the frequency of goods entering and leaving the warehouse; The transportation operation path is the shortest. The design takes the minimum value of the matching degree between the actual cargo position and the scheduling decision as the shortest transportation operation path; specifically, determine the location of the goods on the warehouse platform , cargo placement coordinates , cargo elevator coordinates Construct the shortest objective function of the transportation operation path Model: (4) Among them, when the location of the warehouse and the location of the goods are on the same floor, the value , if they are on different layers, the shortest path distance is ; Indicates the level difference between the warehouse platform and the cargo placement location. Indicates the column difference between the elevator and the cargo position; The above-mentioned storage environment shelf stability, uniform distribution of operating goods, in-and-out warehouse handling efficiency, and shortest transportation operation path optimization model together constitute a multi-objective optimization design problem.

5. The multi-objective scheduling optimization method for a smart warehousing logistics system according to claim 2, characterized in that: The road modeling of the smart warehousing logistics environment is specifically as follows: The actual environment is converted into a grid map to provide an environmental basis for path planning; the grid map decomposes the actual environment into multiple grid units of the same size. , each unit Represented by state variables 0-1; forming a two-dimensional map information: (12) Among them, l and w represent the row and column where the current grid is located, and their respective maximum values ​​correspond to L and W; when When the value is 1, it means there is an obstacle, and when it is 0, it means it is passable; At the same time, the path planning objective function is designed; the path planning goal of the transport vehicle is to find a path from the starting point of the cargo Transport to the end point under the shelf The shortest path without touching obstacles; therefore, the objective function of the path planning problem is designed as follows: (13) in, represents the penalty operator, which is used to exclude paths that pass through the obstacle range. It represents the sum of the Euclidean distances of all points in the path trajectory, which is expressed as follows: (14) set up Represents the variable that determines the obstacle level of the current planned path, and its initial value is set to 0: (15) (16) in, Represents the coordinate value of the sth path point on the planned path; The coordinate value corresponding to the kth obstacle; n represents the total number of points on the planned path, It represents the total number of obstacles, i.e. other racks and placed obstacles on the path in the warehouse logistics environment; Represents the obstacle radius of an abstract obstacle.

6. A multi-objective scheduling optimization method for a smart warehousing logistics system as claimed in claim 5, characterized in that: In the A* algorithm in smart warehousing logistics, the location of the cargo is the starting point of the transport vehicle, and the cargo unloading location is the end point of the transport vehicle's path planning. The evaluation function of the A* algorithm for path planning from the starting point to the target end point is as follows: (17) Among them, n represents the current node position, Indicates the estimated distance from the current position to the end point. Indicates the actual driving distance from the starting point to the current location; The representation is as follows: (18) in, The coordinates corresponding to the current node and the target endpoint respectively; (19) in, Based on the current node Previous node The shortest path distance is accumulated; By calculating the location cost between the current node and the target end point, the shortest path requirement can be gradually met; To improve the calculation effect of path planning, add nonlinear factors : (20) Among them, length represents the length of the grid, and width represents the width of the grid; the nonlinear factor is added to the evaluation function: (21) If the current node is far from the target end point, the P value is larger to enhance the search efficiency. On the contrary, if the current node is close to the target end point, the P value is smaller to enhance the search accuracy of the algorithm.

7. A multi-objective scheduling optimization method for a smart warehousing logistics system as claimed in claim 6, characterized in that: The improved particle swarm optimization algorithm is used to optimize the path planning problem to determine the final route trajectory, which is to record the approximate path obtained by the enhanced A* algorithm. As the initial optimal solution record of the improved particle swarm algorithm; The shortest distance from the starting point to the end point is determined according to the evaluation function expressed in formula (13), and the node at the next position is selected according to the estimated value as the fitness function of the improved particle swarm algorithm.

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