Warehouse Goods Management Method

Abstract

The present invention discloses a method for managing warehouse goods, including a general control module of a logistics center; the general control module of the logistics center is configured to: determine the replenishment points of each city warehouse and calculate the total replenishment points; calculate the current inventory levels of each city warehouse and calculate the total inventory level; compare the replenishment points of each city warehouse with the current inventory level of that city warehouse respectively; when it cannot be satisfied that the replenishment points of all city warehouses are greater than their respective inventory levels, compare the total replenishment points with the total inventory level, and when the total replenishment points are greater than the total inventory level, transfer goods between city warehouses, otherwise, the logistics center replenishes goods for all city warehouses. In the method for managing warehouse goods of the present invention, only when the total replenishment points are greater than the total inventory level, goods are transferred between city warehouses without the need for the logistics center to replenish goods. Since the distance between city warehouses is relatively close to the logistics center, it can greatly save logistics costs and at the same time meet the supply demands between each city warehouse.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics information processing, and more specifically, relates to a method for managing warehouse goods. Background Art

[0002] Logistics is an important link between the supply side and consumers. The development of logistics has changed the traditional lifestyle and consumption patterns. With the rapid development of modern logistics, more and more goods are gradually transported by logistics. However, insufficient goods in the warehouse will undoubtedly seriously affect the normal operation of logistics. Therefore, various replenishment strategies have been gradually proposed.

[0003] In the existing replenishment strategies, the sales volume model trained by historical sales data is usually used to predict the current replenishment volume. Once replenishment is needed, the warehouse is replenished through the logistics center. Although it can solve the problem of out-of-stock in the warehouse, directly replenishing the warehouse through the logistics center will cause the technical problem of high replenishment cost due to the long distance.

[0004] The above information disclosed in this background art is only used to increase the understanding of the background art of the present application. Therefore, it may include prior art that is not known to those of ordinary skill in the art. Summary of the Invention

[0005] In view of the technical problem of high replenishment cost in the existing technology, the present invention provides a method for managing warehouse goods, which can solve the above problems.

[0006] To achieve the above-mentioned invention object, the present invention adopts the following technical solutions:

[0007] A method for managing warehouse goods, including a general control module of the logistics center;

[0008] The general control module of the logistics center is configured to:

[0009] Predict the expected demand quantity E of customers within the distribution area of all city warehouses;

[0010] Determine the replenishment points of each city warehouse according to the expected demand quantity E, and calculate the total replenishment point B;

[0011] Calculate the current inventory quantity of each city warehouse, and calculate the total inventory quantity M;

[0012] Compare the replenishment point of each city warehouse with the current inventory quantity of that city warehouse respectively. When the replenishment points of all city warehouses are greater than their respective inventory quantities, neither replenishment nor inventory adjustment is required for each city warehouse;

[0013] When it cannot be satisfied that the replenishment points of all urban warehouses are greater than their respective inventory levels, the total replenishment point B is compared with the total inventory level M. When the total replenishment point B is greater than the total inventory level M, goods are transferred between urban warehouses; otherwise, the logistics center replenishes all urban warehouses.

[0014] In some embodiments of the present invention, the calculation method for the replenishment point of each urban warehouse in the distribution area is as follows:

[0015] Obtain the expected delivery volume of urban warehouse i during the planning period as ∑ j∈J e j M ij , then the replenishment point b i of urban warehouse i is:

[0016]

[0017] where e j is the expected demand of customer j during the planning period; M ij takes the value: 1 when the expected demand of customer j is allocated to urban warehouse i, and 0 when the expected demand of customer j is not allocated to urban warehouse i. is the standard deviation of the expected demand of customer j during the order lead time, L is the number of days of the order lead time, and T is the number of days of the planning period;

[0018] Total replenishment point B:

[0019] B = ∑ i∈I b i , and I is the set of urban warehouses i.

[0020] In some embodiments of the present invention, when the replenishment point b i of urban warehouse i is not greater than the current inventory level m i of this urban warehouse, and the total replenishment point B is greater than the total inventory level M, goods are transferred for urban warehouse i;

[0021] M = ∑ i∈I m i .

[0022] In some embodiments of the present invention, the method for transferring goods for urban warehouse i is as follows:

[0023] Determine the ordering cost F 1 generated by the transfer:

[0024]

[0025] Determine the transportation cost F 2 generated by the transfer:

[0026] F 2 = γ∑ i，k∈I Y k Xik d ik

[0027] Establish a mathematical model for the total cost F generated during the transfer process:

[0028]

[0029] Among them, Y k takes the value: when the city warehouse k initiates a transfer, it takes the value of 1, otherwise it takes the value of 0, d ik is the distance between the city warehouse i and the city warehouse k, γ is the transportation cost coefficient, is the single-order cost coefficient, X ik is the transfer quantity from the city warehouse k to the city warehouse i;

[0030] Solve for the value of X when the total cost F is minimized ik .

[0031] In some embodiments of the present invention, the constraint conditions of the mathematical model of the total cost F are:

[0032]

[0033]

[0034]

[0035] X ik ≥0, represents the largest integer not exceeding , D represents the average demand per unit day of all customers in the distribution area, and β j represents the demand per unit day of customer j.

[0036] In some embodiments of the present invention, an adaptive improved particle swarm optimization algorithm is used to solve the mathematical model of the total cost F, including:

[0037] Initialize the velocity and position of each particle in the population. If the search space is L-dimensional, each particle will contain L variables. Set the currently best position P best of each particle as the initial position, and take the globally best position of the particle as G best ;

[0038] Calculate the objective function value of each particle, that is, the fitness value, and save the best position and fitness value of each particle. In the population, if the fitness value of a certain particle is the best, then select it and use it as the position of the population;

[0039] Adjust the velocity and position of the particles;

[0040] After each position update, the fitness value of each particle is calculated again, and then according to the optimal position P found by each particle in the historical optimization best , and the corresponding fitness value, which is the optimal fitness value. Compare the fitness value of the particle with the corresponding optimal fitness value in the particle's historical optimization. If the fitness value of a particle is better than the corresponding optimal fitness value in the historical optimization, P best is updated to the current position of the particle;

[0041] Compare the fitness value of each particle with the fitness value corresponding to the optimal position G of all particles best . If the fitness value of a particle is better than the fitness value corresponding to the optimal position G of all particles best , update G best to the current position of the particle;

[0042] Generate a random particle, calculate the fitness value of the random particle. If the fitness value of the random particle is better than the fitness value corresponding to G best , update G best to the current position of the random particle; otherwise, keep G best unchanged;

[0043] Check the particle search termination condition. When the particle search termination condition is met, terminate the search.

[0044] In some embodiments of the present invention, in the self-adaptive improved particle swarm algorithm, the method for adjusting the speed and position of the particle is:

[0045] Adjust the inertia parameter:

[0046]

[0047]

[0048] Adjust the learning factor:

[0049]

[0050] Adjust the speed of the particle:

[0051]

[0052] Adjust the position of the particle:

[0053]

[0054] Where: ω is the inertia weight; k is the current iteration number; V is the speed of the particle; c 1 , c 2 are the learning factors, pb and gb are the individual optimal value and the group optimal value respectively, r1 , r 2 is a random number ω in the range (0, 1) max = 0.85, ω min = 0.5, c 1_max = 2, c 1_min = 1, c 2_max = 2, c 2_min = 1; k max is the maximum number of iterations.

[0055] In some embodiments of the present invention, in the self-adaptive improved particle swarm algorithm, the random particle selection method is traversal selection;

[0056] The selection rule of random particles is:

[0057]

[0058] where: I max = 9.30, I min = 3; d is the number of random times, d max is the maximum number of random times.

[0059] In some embodiments of the present invention, the method for the logistics center to replenish the city warehouses in the distribution area is as follows:

[0060] Calculate the expected demand E of all customers in the distribution area:

[0061] E = ∑ j∈J e j ;

[0062] Calculate the total cost: QH / 2 + P∑ j∈J e j / Q;

[0063] Take the derivative of the total cost with respect to Q to obtain the optimal total replenishment quantity Q * :

[0064]

[0065] Among them, QH / 2 is the average inventory cost of all city warehouses, P∑ j∈J e j / Q is the ordering cost, and J is the set of all customers.

[0066] In some embodiments of the present invention, it further includes calculating the replenishment quantity q * of each city warehouse respectively according to the demand ratio of each city warehouse and the optimal total replenishment quantity Q i .

[0067] Compared with the prior art, the advantages and positive effects of the present invention are:

[0068] The warehouse goods management method of the present invention obtains the replenishment points of each city warehouse, the current inventory levels of each city warehouse, the total replenishment point B, and the total inventory M. Only when the total replenishment point B is greater than the total inventory M, goods are transferred between city warehouses. Otherwise, the logistics center replenishes all city warehouses. That is, when certain conditions are met, goods can be transferred between city warehouses, and there is no need for the logistics center to replenish. Since the distance between city warehouses is relatively close to the logistics center, it can greatly save logistics costs and at the same time meet the supply and demand between each city warehouse.

[0069] After reading the specific embodiments of the present invention in conjunction with the accompanying drawings, other features and advantages of the present invention will become clearer. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for use in the embodiments. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0071] Figure 1 It is a processing flowchart of an embodiment of the warehouse goods management method proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0073] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the accompanying drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation of the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0074] In the present invention, unless otherwise clearly defined and limited, terms such as "installation", "connection", "linkage", "fixation" shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral one; it may be a mechanical connection or an electrical connection; it may be a direct connection or an indirect connection through an intermediate medium, and it may be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0075] Embodiment 1

[0076] In a logistics supply chain, multiple city warehouses are arranged in a city or a region. These multiple city warehouses have their respective distribution areas, and there is territorial spatial adjacency between the distribution areas. A logistics center is equipped between multiple city warehouses in the same region or multiple city warehouses in multiple regions. The logistics center is the coordination and control center of the entire system, manages the replenishment problem of the city warehouses, actively replenishes the city warehouses, and the city warehouses are responsible for the online order demands within their respective regions. Due to the lack of cooperation and independent operation among the city warehouses, the phenomenon of local shortage and overall overstocking of the city warehouse inventory often occurs.

[0077] Based on this, the warehouse goods management method of the present invention needs to conduct inventory management and control. Under the original management mode, a distributed inventory strategy is adopted, a replenishment and transfer model is established, and a virtual coordination center is used to manage the replenishment and transfer of each city warehouse. When the total current inventory of each city warehouse is lower than the total replenishment point, the logistics center replenishes each city warehouse according to the instructions of the total control module. When the current inventory of a city warehouse is lower than its delivery point while the total inventory of all city warehouses is not lower than the total delivery point, the total control module calculates the transfer volume between each city warehouse to solve the technical problem of high replenishment cost in the prior art.

[0078] The following will be described with a specific embodiment.

[0079] This embodiment proposes a warehouse goods management method, including a total control module of the logistics center, and the logistics center is used to manage the replenishment and transfer of multiple city warehouses.

[0080] As Figure 1 shown, the total control module of the logistics center is configured to:

[0081] Predict the total expected demand E of customers within the distribution areas of all city warehouses.

[0082] Determine the replenishment points of each city warehouse according to the total expected demand E, and calculate the total replenishment point B, where the total replenishment point B is also the sum of the replenishment points of each city warehouse.

[0083] Calculate the current inventory levels of each city warehouse and calculate the total inventory M, which is also the sum of the current inventory levels of each city warehouse.

[0084] Compare the replenishment points of each city warehouse with the current inventory level of that city warehouse respectively. When the replenishment points of all city warehouses are greater than their respective inventory levels, it means that the inventory levels of each city warehouse can meet the current demand, and each city warehouse neither needs replenishment nor needs to transfer goods.

[0085] When it cannot be satisfied that the replenishment points of all city warehouses are greater than their respective inventory levels, that is, when not all the replenishment points of city warehouses are greater than their respective inventory levels, compare the total replenishment point B with the total inventory M. When the total replenishment point B is greater than the total inventory M, it means that the total inventory in this area can meet the current demand, and goods are transferred between city warehouses. Otherwise, the logistics center replenishes all city warehouses.

[0086] This solution determines the total inventory of each city warehouse and the total demand in the same area through comparison and judgment. When it can be satisfied, goods can be transferred between city warehouses, and there is no need for the logistics center to replenish. Since the distance between city warehouses is relatively close to the logistics center, it can greatly save logistics costs, meet the supply and demand between city warehouses at the same time, and prevent out-of-stock situations.

[0087] Since the unified transfer of goods between city warehouses is a support and cooperation relationship, the total system cost will be reduced, and the possibility of inventory backlog or out-of-stock occurring simultaneously in each city warehouse is relatively small. Therefore, adopting this distributed inventory management can effectively reduce the safety inventory, replenishment point, and delivery volume of the system, and the total inventory cost of the entire system will also be reduced.

[0088] During the planning period, the safety inventory of city warehouse i is:

[0089]

[0090] Based on the (Q, R) inventory strategy, the expected delivery volume of city warehouse i during the planning period is ∑ j∈J B j M ij , and the expected delivery volume of city warehouse i during the lead time L is obtained as Then the replenishment point of city warehouse i. α i Represents the safety factor of the inventory level of warehouse i.

[0091] In some embodiments of the present invention, the calculation method of the replenishment points of each city warehouse in the distribution area is:

[0092] Obtain the expected delivery volume of city warehouse i during the planning period as ∑ j∈J e j M ij , then the replenishment point b of city warehouse ii is:

[0093]

[0094] where e j is the expected demand of customer j during the planning period; m ij takes values as follows: when the expected demand of customer j is allocated to urban warehouse i, it takes the value of 1, and when the expected demand of customer j is not allocated to urban warehouse i, it takes the value of 0. is the standard deviation of the expected demand of customer j during the order lead time, L is the number of days of the order lead time, and T is the number of days of the planning period.

[0095] The replenishment point is the sum of the safety inventory of the urban warehouse and the expected delivery volume during the lead time L. If the inventory level is lower than the replenishment point, it is considered that the urban warehouse needs to be replenished.

[0096] e j can be obtained based on historical data or other existing means.

[0097] As can be seen from the above formula, the replenishment point b i of urban warehouse i is positively correlated with the expected demand of the customer. The greater the expected demand of the customer, the greater the value of the replenishment point.

[0098] Total replenishment point B:

[0099] B = ∑ i∈I b i , where I is the set of urban warehouses i.

[0100] In some embodiments of the present invention, the size between the replenishment point of each urban warehouse and its current inventory level is judged respectively. When the replenishment point b i of urban warehouse i is not greater than the current inventory level m i of this urban warehouse, and the total replenishment point B is greater than the total inventory M, then goods are transferred for urban warehouse i.

[0101] where M = ∑ i∈I m i .

[0102] The current inventory level of each urban warehouse is obtained from the inventory counting system or the inbound and outbound record system of this urban warehouse.

[0103] When the current inventory level m i of urban warehouse i is lower than its delivery point b i , and the total inventory M of each urban warehouse is higher than the total replenishment point B, no replenishment is carried out. The virtual coordination center makes a decision, and the logistics center implements the transfer of goods to meet the requests of the out-of-stock warehouses in a timely manner. The goal is to find X ik, that is, to calculate the quantity of goods transferred between warehouses to meet the sales demand of each warehouse in a timely manner. To minimize the transfer cost of the entire system, a transfer model is determined.

[0104] In some embodiments of the present invention, the method for transferring goods to urban warehouse i is as follows:

[0105] Determine the ordering cost F generated by the transfer 1 :

[0106]

[0107] Determine the transportation cost F generated by the transfer 2 :

[0108] F 2 = γ∑ i,k∈I Y k X ik d ik

[0109] Establish a mathematical model for the total cost F generated during the transfer process:

[0110]

[0111] Among them, the value of Y k is: when the transfer is initiated by urban warehouse k, the value is 1, otherwise the value is 0, d ik is the distance between urban warehouse i and urban warehouse K, γ is the transportation cost coefficient, is the single-ordering cost coefficient, and X ik is the quantity of goods transferred from urban warehouse k to urban warehouse i.

[0112] Solve for the value of X ik when the total cost F is minimized.

[0113] In some embodiments of the present invention, the constraint conditions for the mathematical model of the total cost F are:

[0114]

[0115]

[0116]

[0117] X ik ≥0, represents the largest integer not exceeding , D represents the average daily demand of all customers in the distribution area, and β j represents the daily demand of customer j.

[0118] min F is the objective function, including minimizing the ordering cost and transportation cost generated by allocation. d represents the number of days until the next replenishment, which is the number of days that the inventory products in all current city warehouses can still be used. ∑ i∈I X ik ≥d∑ j∈J β j M kj , represents the limit on the quantity of products that can be allocated from the warehouse. X ik ≥0 represents the value constraint of the variable.

[0119] When the inventory level m i in a certain city warehouse i drops to the replenishment point b i , and when the total inventory in the city warehouse M is higher than the total replenishment point B, according to the allocation model, an adaptive particle swarm optimization algorithm (APSO) is used to calculate the allocation strategy with the lowest total system cost.

[0120] In some embodiments of the present invention, an improved adaptive particle swarm optimization algorithm is used to solve the mathematical model of the total cost F, including:

[0121] Initialize the velocity and position of each particle in the population. If the search space is L-dimensional, each particle will contain L variables. Set the currently best position P best found by each particle as the initial position, and take the best position G best globally found by the particle.

[0122] Calculate the objective function value of each particle, that is, the fitness value, and save the best position and fitness value of each particle. In the population, if the fitness value of a certain particle is the best, select it and use it as the position of the population.

[0123] Adjust the velocity and position of the particle.

[0124] After each position update, calculate the fitness value of each particle again, and then according to the best position P best found by each particle in the historical optimization and its corresponding fitness value, which is the optimal fitness value, compare the fitness value of the particle with the optimal fitness value corresponding to it in the historical optimization of the particle. If the fitness value of a particle is better than the optimal fitness value corresponding to it in the historical optimization, P best is updated to the current position of the particle.

[0125] Compare the fitness value of each particle with the fitness value corresponding to the best position G best of all particles respectively. If the fitness value of a particle is better than the fitness value corresponding to the best position G best of all particles, update G best to the current position of the particle.

[0126] Generate random particles, calculate the fitness value of the random particles. If the fitness value of the random particles is better than the fitness value corresponding to G best Update G to the current position of the random particle; otherwise, keep G best unchanged. best

[0127] Check the particle search termination condition. When the particle search termination condition is met, terminate the search.

[0128] In the step of checking the particle search termination condition, one of the conditions is reaching the maximum number of iterations: G max ; Another condition is the deviation between two adjacent generations within a specified range. If the termination condition is not met, return to the step of adjusting the velocity and position of the particles and continue to update the velocity and position of the particles.

[0129] In the ordinary particle swarm optimization algorithm, due to fixed parameters, it is sensitive to the initial conditions, the population is prone to premature convergence, and it falls into the local optimal solution. To improve the efficiency of the entire search, the adaptive particle swarm optimization algorithm proposed in this embodiment dynamically changes the parameters with the number of iterations on the basis of the ordinary particle swarm optimization algorithm, enabling it to converge quickly, and at the same time adding random particles for traversal optimization.

[0130] In some embodiments of the present invention, in the adaptive improved particle swarm optimization algorithm, the method of adjusting the velocity and position of the particles is as follows:

[0131] Adjust the inertia parameter:

[0132]

[0133]

[0134] Adjust the learning factor:

[0135]

[0136] Adjust the velocity of the particles:

[0137]

[0138] Adjust the position of the particles:

[0139]

[0140] Where: ω is the inertia weight; k is the current number of iterations; V is the velocity of the particle; c 1 、c 2 are the learning factors, pb and gb are the individual optimal value and the global optimal value respectively, r 1 、r 2 ​A random number ω in the range (0, 1) max = 0.85, ω min = 0.5, c 1_max = 2, c 1_min = 1, c 2_max = 2, c 2_min = 1; k max is the maximum number of iterations.

[0141] To prevent the overall algorithm from falling into the optimal solution, in addition to parameter adjustment, random particles are added. The random particles are selected by traversal, which can uniformly search this search area to ensure that the particle swarm jumps out of the local optimal solution.

[0142] In some embodiments of the present invention, in the adaptively improved particle swarm algorithm, the random particle selection method is traversal selection;

[0143] The selection rule of random particles is as follows:

[0144]

[0145] where: I max = 9.30, I min = 3; d is the number of random times, d max is the maximum number of random times.

[0146] In some embodiments of the present invention, the method for the logistics center to replenish the city warehouses in the distribution area is as follows:

[0147] Calculate the expected demand E of all customers in the distribution area:

[0148] E = ∑ j∈J e j ;

[0149] Calculate the total cost: QH / 2 + P∑ j∈J e j / Q.

[0150] The inventory cost consists of two parts, namely the ordering cost and the inventory holding cost. During the entire planning period, the expected demand of all customers is ∑ j∈J B j , the average inventory cost of all city warehouses is QH / 2, and the ordering cost is P∑ j∈J B j / Q. Therefore, the sum of the total ordering cost and the inventory holding cost is Taking the derivative of this formula with respect to Q, the optimal total replenishment quantity Q * can be obtained:

[0151]

[0152] Among them, J is the set of all customers, P is the replenishment cost of the urban warehouse during the planning period, H is the inventory holding cost per unit product during the planning period, and Q is the total replenishment volume of the urban warehouse.

[0153] Obtain the optimal total replenishment volume Q * , and then according to the demand ratio of each urban warehouse, it is easy to obtain the replenishment volume q of each urban warehouse i . For example, for urban warehouse 1 and urban warehouse 2, the customer demands within the coverage of these two warehouses are w 1 , w 2 , respectively, then

[0154] In some embodiments of the present invention, it further includes calculating the replenishment volume q of each urban warehouse according to the demand ratio of each urban warehouse and the optimal total replenishment volume Q * . i

[0155] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, for those of ordinary skill in the art, it is still possible to modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions required to be protected by the present invention.

Claims

1. A warehouse goods management method, characterized in that, it includes a logistics center general control module; The logistics center general control module is configured to: Predict the expected demand quantity E of customers within the distribution areas of all city warehouses; Determine the replenishment points of each city warehouse according to the expected demand quantity E, and calculate the total replenishment point B; Calculate the current inventory levels of each city warehouse, and calculate the total inventory quantity M; Compare the replenishment point of each city warehouse with the current inventory level of that city warehouse respectively. When the replenishment points of all city warehouses are greater than their respective inventory levels, neither replenishment nor inventory transfer is required for each city warehouse; When it cannot be satisfied that the replenishment points of all city warehouses are greater than their respective inventory levels, compare the total replenishment point B with the total inventory quantity M. When the total replenishment point B is greater than the total inventory quantity M, inventory transfer is carried out among city warehouses. Otherwise, replenishment is carried out for all city warehouses; The calculation method for the replenishment points of each city warehouse within the distribution area is: The expected delivery volume of urban warehouse i within the planning period is ∑ j∈J e j M ij , then the replenishment point b i of urban warehouse i is as follows: Among them, e j is the expected demand of customer j during the planning period; M ij takes the value: 1 when the expected demand of customer j is allocated to urban warehouse i, and 0 when the expected demand of customer j is not allocated to urban warehouse i. is the standard deviation of the expected demand of customer j during the order lead time, L is the number of days of the order lead time, T is the number of days of the planning period, and α i represents the safety factor of the inventory level of urban warehouse i. Total replenishment point B: B = ∑ i∈I b i , where I is the set of urban warehouses i; Replenishment point b of urban warehouse i i Not greater than the current inventory m of this urban warehouse i When the total replenishment point B is greater than the total inventory M, transfer goods to urban warehouse i; N = ∑ i∈I m i ; The method for carrying out inventory transfer for city warehouse i is: Determine the order cost F generated by transfer of goods 1 : Determine the transportation cost F generated by inventory transfer 2 : F 2 = γ ∑ i,k∈I Y k X ik d ik Establish a mathematical model of the total cost F generated during the inventory transfer process: Among them, Y k takes the value: when the city warehouse k initiates inventory transfer, it takes the value of 1, otherwise it takes the value of 0, d ik is the distance between the city warehouse i and the city warehouse k, γ is the transportation cost coefficient, is the single order cost, X ik is the inventory transfer quantity from the city warehouse k to the city warehouse i; Solve for X when the total cost F is minimized ik value.

2. The warehouse goods management method according to claim 1, characterized in that, The constraint conditions of the mathematical model of the total cost F are: X ik ≥ 0, represents the largest integer not exceeding , D represents the average demand per customer per day in the delivery area, and β j represents the demand of customer j per day.

3. The warehouse goods management method according to claim 1, characterized in that, An adaptive improved particle swarm optimization algorithm is used to solve the mathematical model of the total cost F, including: Initialize the velocity and position of each particle in the population. If the search space is L-dimensional, each particle will contain L variables. Set the currently best position P best found by each particle as the initial position, and take the globally best position found by the particles as G best ; Calculate the objective function value of each particle, that is, the fitness value, save the best position and fitness value of each particle. In the population, if the fitness value of a certain particle is the best, then select it and use it as the position of the population; Adjust the velocity and position of the particles; After each position update, recalculate the fitness value of each particle, and then, based on the optimal position P found by each particle in historical optimization best , and its corresponding fitness value, which is the optimal fitness value, compare the fitness value of the particle with the optimal fitness value corresponding to the particle in historical optimization. If the fitness value of a particle is better than the optimal fitness value corresponding to it in historical optimization, P best is updated to the current position of the particle; Compare the fitness value of each particle with the fitness value corresponding to the optimal position G of all particles best If the fitness value of a particle is better than the fitness value corresponding to the optimal position G of all particles best Update G best to the current position of this particle; Generate random particles, calculate the fitness values of the random particles, and if the fitness value of a random particle is better than that of G best corresponding fitness value, update G best to the current position of the random particle; otherwise, keep G best unchanged; Check the particle search termination condition. When the particle search termination condition is satisfied, terminate the search.

4. The warehouse goods management method according to claim 3, characterized in that, In the adaptive improved particle swarm optimization algorithm, the method for adjusting the velocity and position of the particles is: Adjust the inertia parameter: Adjust the learning factor: Adjust the velocity of the particles: Adjust the position of the particles: Where: ω is the inertia weight; k is the current iteration number; V is the velocity of the particle; c 1 and c 2 are learning factors, pb and gb are the individual optimal value and the global optimal value respectively, r 1 and r 2 are random numbers in the range of (0, 1), ω max = 0.85, ω min = 0.5, c 1_max = 2, c 1_min = 1, c 2_max = 2, c 2_min = 1; k max is the maximum number of iterations.

5. The warehouse goods management method according to claim 3, characterized in that, In the adaptive improved particle swarm optimization algorithm, the random particle selection method is traversal selection; The selection rule for random particles is: Where: I max = 9.30, I min = 3; d is the number of random times, d max is the maximum number of random times.

6. The warehouse goods management method according to any one of claims 2-5, characterized in that, The method for the logistics center to replenish each city warehouse in this distribution area is: Calculate the expected demand quantity E of all customers within the distribution area: E = ∑ j∈J e j ; Calculate the total cost: QH / 2 + P∑ j∈J e j / Q; Derive the total cost with respect to Q to obtain the optimal total replenishment quantity Q * : Among them, QH / 2 is the average inventory cost of all city warehouses, and P∑ j∈J e j / Q is the ordering cost, and J is the set of all customers.

7. The warehouse goods management method according to claim 6, characterized in that, It also includes calculating the replenishment quantity q of each city warehouse respectively according to the demand ratio of each city warehouse and the optimal total replenishment quantity Q * i .​

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