CN114841634A - Warehouse goods management method - Google Patents

Abstract

The invention discloses a warehouse cargo management method, which comprises a logistics center general control module; the logistics center master control module is configured as follows: determining replenishment points for each city bin, and calculating total replenishment points

(ii) a Calculating the current stock of each city warehouse and calculating the total stock

(ii) a Respectively comparing the replenishment points of the urban warehouses with the current inventory of the urban warehouses; when the replenishment points of all the urban warehouses cannot be met and are larger than respective inventory, the total replenishment points are added

And total inventory

Comparing, when the total replenishment point

Greater than total inventory

And when the goods are transferred among the urban warehouses, otherwise, the logistics center replenishes all the urban warehouses. The warehouse goods management method of the invention only needs to be at the current total replenishment point

Greater than total inventory

During the time, transfer goods between the city storehouse, need not the logistics center and mend goods, because the distance is nearer for the logistics center between the city storehouse, can greatly practice thrift the logistics cost, satisfy the supply demand between each city storehouse simultaneously.

Description

warehouse cargo management

technical field

The invention belongs to the technical field of logistics information processing, and in particular relates to a warehouse cargo management method.

Background technique

Logistics is an important link between the supply side and consumers. The development of logistics has changed the traditional way of life and consumption. With the rapid development of modern logistics, more and more items are gradually transported by logistics, but the shortage of goods in the warehouse will undoubtedly seriously affect the normal operation of logistics, so various replenishment strategies are gradually proposed.

The replenishment strategy in the prior art usually uses a sales model trained by historical sales data to predict the current replenishment volume. Once replenishment is required and the warehouse is replenished through the logistics center, it can solve the problem of shortage of goods in the warehouse. However, directly replenishing the warehouse through the logistics center will bring technical problems of high replenishment cost due to the long distance.

The above information disclosed in this Background is only for enhancement of understanding of the background of the application and therefore it may contain that it does not form the prior art that is already known to a person of ordinary skill in the art.

SUMMARY OF THE INVENTION

Aiming at the technical problem of high replenishment cost in the prior art, the present invention proposes a warehouse goods management method, which can solve the above problem.

In order to realize the above-mentioned purpose of the invention, the present invention adopts the following technical scheme to realize:

A warehouse cargo management method, comprising a logistics center master control module;

The general control module of the logistics center is configured as:

Predict the expected demand E of customers in the distribution area of all city warehouses;

Determine the replenishment point of each city warehouse according to the expected demand E, and calculate the total replenishment point B;

Calculate the current inventory of each city warehouse, and calculate the total inventory M;

Compare the replenishment point of each city warehouse with the current inventory of the city warehouse. When the replenishment points of all city warehouses are greater than their respective inventory, each city warehouse needs neither replenishment nor adjustment;

When the replenishment points that cannot satisfy all the city warehouses are greater than their respective inventory, the total replenishment point B is compared with the total inventory M. When the total replenishment point B is greater than the total inventory M, the adjustment between the city warehouses is made. Otherwise, the logistics center will replenish all city warehouses.

In some embodiments of the present invention, the calculation method of the replenishment point of each city warehouse in the distribution area is:

Obtaining the expected delivery volume of city warehouse i during the planning period is ∑ _j∈J e _j M _ij , then the replenishment point b _i of city warehouse i is:

为订货提前期内客户j的期望需求的标准差，L为订货提前期的天数，T为计划期的天数；Among them, e _j is the expected demand of customer j during the planning period; the value of M _ij is 1 when the expected demand of customer j is allocated to city warehouse i, and when the expected demand of customer j is not allocated to city warehouse i Take the value 0,

is the standard deviation of the expected demand of customer j in the order lead time, L is the number of days in the order lead time, and T is the number of days in the planning period;

Total replenishment point B:

B=∑ _i∈I b _i , where I is the set of city bins i.

In some embodiments of the present invention, when the replenishment point b _i of the city warehouse i is not greater than the current stock quantity m _i of the city warehouse, and the total replenishment point B is greater than the total stock quantity M, the city warehouse i is adjusted;

M=∑ _{i∈Im i} .

In some embodiments of the present invention, the method for adjusting goods for city warehouse i is:

Determine the order cost F ₁ generated by the transfer:

Determine the transportation cost F ₂ caused by the transfer:

F ₂ =γ∑ _{i, k∈I} Y _k X _ik d _ik

Establish a mathematical model of the total cost F generated by the transfer process:

为单次订货成本系数，X_ik为城市仓k到城市仓i的调货量；Among them, the value of Y _k is 1 when the city warehouse k initiates the dispatch of goods, otherwise it is 0, d _ik is the distance between city warehouse i and city warehouse k, γ is the transportation cost coefficient,

is the single order cost coefficient, X _ik is the quantity of goods transferred from city warehouse k to city warehouse i;

Solve for the value of X _ik that minimizes the total cost F.

In some embodiments of the present invention, the constraints of the mathematical model of the total cost F are:

表示不超过

的最大整数，D表示配送区域内所有客户的单位天数的平均需求量，β_j表示客户j的单位天数的需求量。X _ik ≥ 0,

means no more than

The largest integer of , D represents the average demand per unit day of all customers in the delivery area, and β _j represents the demand per unit day of customer j.

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

Initialize the speed and position of each particle in the population. If the search space is L-dimensional, each particle will contain L variables, set the optimal position P _best currently searched by each particle as the initial position, and take the particle The optimal position searched globally is G _best ;

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 particle is the best, it is selected. come out and serve as the position of the population;

Adjust the speed and position of particles;

After each position update, calculate the fitness value of each particle again, and then according to the optimal position P _best found by each particle in the historical optimization, and its corresponding fitness value, it is the optimal fitness value. The fitness value of the particle is compared with the corresponding optimal fitness value in the historical optimization of the particle. If the fitness value of a particle is better than the optimal fitness value in the historical optimization, P _best is updated to the particle current location;

Compare the fitness value of each particle with the fitness value corresponding to the optimal position G _best of all particles. If the fitness value of any particle is better than the fitness value corresponding to the optimal position G _best of all particles, set G _best is updated to the current position of the particle;

Generate random particles, 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;

Check the particle search termination conditions, and terminate the search when the particle search termination conditions are met.

In some embodiments of the present invention, in the adaptive improved particle swarm algorithm, the method for adjusting the speed and position of particles is:

Adjust inertia parameters:

Adjust the learning factor:

Adjust the speed of the particles:

Adjust the position of the particles:

In the formula: ω is the inertia weight; k is the current number of iterations; V is the speed of the particle; c ₁ , c ₂ are learning factors, pb and gb are the individual optimal value and the group optimal value, respectively, r ₁ , r ₂ are The random numbers 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; km _max is the maximum number of iterations.

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

The selection rule for random particles is:

In the formula: I _max =9.30, I _min =3; d is the random number, and d _max is the maximum random number.

In some embodiments of the present invention, the method that the logistics center carries out replenishment for each city warehouse of this distribution area is:

Calculate the expected demand E for all customers in the delivery area:

E=∑ _j∈J e _j ;

Calculate the total cost: QH/2+P∑ _j∈J e _j /Q;

Differentiate the total cost with respect to Q to get the optimal total replenishment quantity Q ^* :

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.

In some embodiments of the present invention, the method further includes calculating the replenishment quantity qi of each city warehouse according to the demand ratio of each city warehouse and the optimal total replenishment quantity _Q ^* .

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

The warehouse goods management method of the present invention obtains the replenishment point of each city warehouse, the current inventory 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 When the goods are transferred between the city warehouses, otherwise, the logistics center will replenish the goods for all the city warehouses, that is, when certain conditions are met, the goods can be transferred between the city warehouses. The distance is relatively close to the logistics center, which can greatly save logistics costs and meet the supply demand between warehouses in various cities.

Other features and advantages of the present invention will become more apparent after reading the detailed description of the present invention in conjunction with the accompanying drawings.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

FIG. 1 is a process flow chart of an embodiment of the warehouse goods management method proposed by the present invention.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

It should be noted that, in the description of the present invention, the terms "up", "down", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate directions or positions The terminology of the relationship is based on the direction or positional relationship shown in the drawings, which is only for the 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 should not be construed as Limitations of the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed to indicate or imply relative importance. In the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

In the present invention, unless otherwise expressly specified and limited, the terms "installed", "connected", "connected", "fixed" and other terms should be understood in a broad sense, for example, it may be a fixed connection or a detachable connection , or integrated; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of the two elements or the interaction relationship between the two elements. 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 situations.

Example 1

In a logistics supply chain, multiple city warehouses are arranged in a city or a region, and the multiple city warehouses have their own distribution areas, and the distribution areas have geographic spatial adjacency. Multiple urban warehouses in the same area or multiple urban warehouses in multiple areas will be equipped with a logistics center. The logistics center is the coordination and control center of the entire system. It manages the replenishment of urban warehouses and proactively replenishes urban warehouses. goods, and the city warehouse is responsible for the online order demand in the region. Because the city warehouses do not cooperate with each other and operate independently, the phenomenon of partial shortage of urban warehouse inventory and total volume pressure often occurs.

Based on this, the warehouse goods management method of the present invention requires inventory management and control. Under the original management mode, a distributed inventory strategy is adopted to establish a replenishment and adjustment model, and a virtual coordination center is used to manage the replenishment and adjustment of warehouses in each city. When the current total 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 master control module. When the current inventory of the city warehouse is lower than its delivery point and the total inventory of all city warehouses is not lower than the total delivery point, the master control module calculates the amount of goods transferred between the city warehouses to solve the problem of replenishment in the existing technology. Costly technical issues.

A specific embodiment will be described below.

This embodiment proposes a warehouse cargo management method, which includes a logistics center master control module, and the logistics center is used to manage multiple city warehouses for replenishment and delivery.

As shown in Figure 1, the general control module of the logistics center is configured as:

Predict the total expected demand E of customers in the distribution area of all city warehouses.

According to the total expected demand E, the replenishment point of each city warehouse is determined, and the total replenishment point B is calculated. The total replenishment point B is the sum of the replenishment points of each city warehouse.

Calculate the current inventory in each city warehouse, and calculate the total inventory M, which is the sum of the current inventory in each city warehouse.

Compare the replenishment points of each city warehouse with the current inventory of the city warehouse. When the replenishment points of all city warehouses are greater than their respective inventory, it means that the inventory of each city warehouse can meet the current demand. Each city warehouse needs neither replenishment nor adjustment.

When it cannot be satisfied that the replenishment points of all city warehouses are greater than their respective inventory, that is, not all urban warehouses have replenishment points greater than their respective inventory, the total replenishment point B and the total inventory M are compared. By comparison, when the total replenishment point B is greater than the total inventory M, it means that the total inventory in the area can meet the current demand, and goods are transferred between city warehouses, otherwise, the logistics center replenishes all city warehouses.

This scheme judges the total inventory of each city warehouse in the same area and the total demand of the area by comparison. When it can be satisfied, the goods can be adjusted between the city warehouses, and the logistics center does not need to replenish the goods. Because the distance between urban warehouses is relatively close to the logistics center, it can greatly save logistics costs, and at the same time meet the supply demand between urban warehouses and prevent out-of-stock situations.

Since the unified transfer of goods between various city warehouses is a support and cooperative relationship, the total cost of the system will be reduced, and the possibility of inventory backlog or out of stock in each city warehouse at the same time is relatively small, so the use of this distributed inventory management can effectively By reducing the safety stock, replenishment points, and delivery volumes of the system, the total cost of inventory for the entire system is also reduced.

During the planning period, the city warehouse i safety stock is:

则城市仓i的补货点。α_i表示仓库i的库存水平的安全系数。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 within the lead time L is:

Then the replenishment point of city warehouse i. α _i represents the safety factor of the inventory level of warehouse i.

In some embodiments of the present invention, the calculation method of the replenishment point of each city warehouse in the distribution area is:

Obtaining the expected delivery volume of city warehouse i during the planning period is ∑ _j∈J e _j M _ij , then the replenishment point b _i of city warehouse i is:

为订货提前期内客户j的期望需求的标准差，L为订货提前期的天数，T为计划期的天数。Among them, e _j is the expected demand of customer j during the planning period; the value of m _ij is 1 when the expected demand of customer j is allocated to city warehouse i, and when the expected demand of customer j is not allocated to city warehouse i Take the value 0,

is the standard deviation of the expected demand of customer j in the order lead time, L is the number of days in the order lead time, and T is the number of days in the planning period.

The replenishment point is the sum of the safety stock of the city warehouse and the expected delivery volume within the lead time L. If the inventory is lower than the replenishment point, it is considered that the city warehouse needs replenishment.

e _j can be obtained based on historical data or other existing means.

It can be seen from the above formula that the replenishment point b _i of the city warehouse i is positively correlated according to the customer's expected demand. The greater the customer's expected demand, the higher the value of the replenishment point.

Total replenishment point B:

B=∑ _i∈I b _i , where I is the set of city bins i.

In some embodiments of the present invention, the size between the replenishment point of each city warehouse and its current inventory is determined respectively. When the replenishment point b _i of the city warehouse i is not greater than the current stock quantity m _i of the city warehouse, and the total replenishment point B is greater than the total stock quantity M, then the city warehouse i will be adjusted.

Wherein, M=∑ _i∈I m _i .

The current inventory of each city warehouse is obtained from the inventory system or the warehouse entry and exit recording system of the city warehouse.

When the current inventory m _i of city warehouse i is lower than its delivery point b _i , and the total inventory M of each city warehouse is higher than the total replenishment point B, replenishment is not performed. The virtual coordination center makes a decision, and the logistics center implements the adjustment of goods to meet the request of the out-of-stock warehouse in time. The goal is to find X _ik , that is, to find the amount of goods transferred between each warehouse, and to meet the sales demand of each warehouse in time. In order to minimize the transfer cost of the whole system, the transfer model is determined.

In some embodiments of the present invention, the method for adjusting goods for city warehouse i is:

Determine the order cost F ₁ generated by the transfer:

Determine the transportation cost F ₂ caused by the transfer:

F ₂ =γ∑ _i,k∈I Y _k X _ik d _ik

Establish a mathematical model of the total cost F generated by the transfer process:

为单次订货成本系数，X_ik为城市仓k到城市仓i的调货量。Among them, the value of Y _k is 1 when city warehouse k initiates the dispatch of goods, otherwise it is 0, d _ik is the distance between city warehouse i and city warehouse K, γ is the transportation cost coefficient,

is the single order cost coefficient, and X _ik is the quantity of goods transferred from city warehouse k to city warehouse i.

Solve for the value of X _ik that minimizes the total cost F.

In some embodiments of the present invention, the constraints of the mathematical model of the total cost F are:

表示不超过

的最大整数，D表示配送区域内所有客户的单位天数的平均需求量，β_j表示客户j的单位天数的需求量。X _ik ≥ 0,

means no more than

The largest integer of , D represents the average demand per unit day of all customers in the delivery area, and β _j represents the demand per unit day of customer j.

表示仓库可允许调拨的产品数量限制，X_ik≥0表示变量的取值约束。min F is the objective function, including the minimum order cost and transportation cost caused by allocation, d represents the number of days until the next replenishment, and the current inventory products in all city warehouses can be used, ∑ _i∈I X _ik ≥d∑ _j∈J β _j M _kj ,

Indicates the limit of the quantity of products that the warehouse can allocate, and Xi _ik ≥ 0 indicates the value constraint of the variable.

When the inventory m _i of a city warehouse i drops to the replenishment point b _i , and the total inventory M of the city warehouse is higher than the total replenishment point B, according to the allocation model, an adaptive particle swarm algorithm (APSO) is used. Calculate the allocation strategy with the lowest total system cost.

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

Initialize the speed and position of each particle in the population. If the search space is L-dimensional, each particle will contain L variables, set the optimal position P _best currently searched by each particle as the initial position, and take the particle The optimal position searched globally is G _best .

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 particle is the best, it is selected. come out and serve as the location of the population.

Adjust the speed and position of the particles.

After each position update, calculate the fitness value of each particle again, and then according to the optimal position P _best found by each particle in the historical optimization, and its corresponding fitness value, it is the optimal fitness value. The fitness value of the particle is compared with the corresponding optimal fitness value in the historical optimization of the particle. If the fitness value of a particle is better than the optimal fitness value in the historical optimization, P _best is updated to the particle current location.

Compare the fitness value of each particle with the fitness value corresponding to the optimal position G _best of all particles. If the fitness value of any particle is better than the fitness value corresponding to the optimal position G _best of all particles, set G _best is updated to the current position of the particle.

Generate random particles, 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.

Check the particle search termination conditions, and terminate the search when the particle search termination conditions are met.

In the step of checking the termination conditions of particle search, one of the conditions is that the maximum number of iterations is reached: G _max ; the other condition is the deviation between the two adjacent generations within the specified range. If the termination conditions are not met, return to adjusting the particles The velocity and position steps continue to update the velocity and position of the particles.

Ordinary particle swarm optimization is sensitive to initial conditions due to fixed parameters, and the population tends to mature prematurely and fall into a local optimal solution. In order to improve the efficiency of the whole search, the adaptive particle swarm algorithm proposed in this embodiment dynamically changes the parameters with the number of iterations on the basis of the ordinary particle swarm algorithm, so that it can quickly converge, and at the same time adds random particles for traversal optimization .

In some embodiments of the present invention, in the adaptive improved particle swarm algorithm, the method for adjusting the speed and position of particles is:

Adjust inertia parameters:

Adjust the learning factor:

Adjust the speed of the particles:

Adjust the position of the particles:

In the formula: ω is the inertia weight; k is the current number of iterations; V is the speed of the particle; c ₁ , c ₂ are learning factors, pb and gb are the individual optimal value and the group optimal value, respectively, r ₁ , r ₂ are The random numbers 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; km _max is the maximum number of iterations.

In order to prevent the overall algorithm from falling into the optimal solution, in addition to parameter adjustment, random particles are also added. The random particles are selected as traversal selection, and this search area can be searched uniformly to ensure that the particle swarm jumps out of the local optimal solution.

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

The selection rule for random particles is:

In the formula: I _max =9.30, I _min =3; d is the random number, and d _max is the maximum random number.

In some embodiments of the present invention, the method that the logistics center carries out replenishment for each city warehouse of this distribution area is:

Calculate the expected demand E for all customers in the delivery area:

E=∑ _j∈J e _j ;

Calculate the total cost: QH/2+P∑ _j∈J e _j /Q.

该式对Q求导，可以得到最优总补货量Q^*：Inventory cost consists of two parts, ordering cost and inventory holding cost. In the whole 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. So the sum of the total ordering cost and inventory holding cost is

By taking the derivative of this formula with respect to Q, the optimal total replenishment quantity Q ^* can be obtained:

Among them, J is the set of all customers, P is the replenishment cost of the city 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 city warehouse.

The optimal total replenishment quantity Q ^* is obtained, and then according to the demand ratio of each city warehouse, the replenishment quantity _qi of each city warehouse can be easily obtained. For example, city warehouse 1 and city warehouse 2, the customer needs within the coverage of these two warehouses are w ₁ and w ₂ respectively, then

In some embodiments of the present invention, the method further includes calculating the replenishment quantity qi of each city warehouse according to the demand ratio of each city warehouse and the optimal total replenishment quantity _Q ^* .

The above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art can still The recorded technical solutions are modified, or some technical features thereof are equivalently replaced; 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 claimed in the present invention.

Claims (10)

1. A warehouse cargo management method is characterized by comprising a logistics center general control module;

the logistics center general control module is configured as follows:

predicting expected demand E of customers in distribution areas of all city warehouses;

determining the replenishment points of all the urban warehouses according to the expected demand E, and calculating a total replenishment point B;

calculating the current stock of each city bin, and calculating the total stock M;

comparing the replenishment points of all the city warehouses with the current inventory of the city warehouse respectively, wherein when the replenishment points of all the city warehouses are larger than the respective inventory, the city warehouses do not need replenishment or adjustment;

And when the total replenishment points B are larger than the total stock M, the stocks are transferred between the urban warehouses, otherwise, the stocks are replenished for all the urban warehouses.

2. The warehouse cargo management method according to claim 1, wherein the calculation method of the replenishment points of the urban warehouses in the distribution area comprises the following steps:

obtaining the expected delivery amount of the city bin i in the planning period as sigma _j∈J e _j M _ij Then the replenishment point b of the city bin i _i Comprises the following steps:

wherein e is _j The expected demand for client j during the planning period; m _ij The values of (A) are as follows: the value is 1 when the expected demand of the client j is distributed to the city bin i, the value is 0 when the expected demand of the client j is not distributed to the city bin i,

the standard deviation of expected demands of a client j in the order lead period is shown, L is the number of days of the order lead period, and T is the number of days of the planning period;

total replenishment point B:

B＝∑ _i∈I b _i and I is the set of city bins I.

3. The warehouse cargo management method of claim 2,

cityReplenishment point b of bin i _i Not more than the current stock m of the city warehouse _i When the total replenishment point B is larger than the total stock quantity M, adjusting the stock for the urban warehouse i;

M＝∑ _i∈I m _i 。

4. The warehouse cargo management method according to claim 3, wherein the method for adjusting the cargo for the city warehouse i comprises:

determining order cost F for a shipment ₁ ：

Determining shipping costs F resulting from a shipment ₂ ：

F ₂ ＝γ∑ _i,k∈I Y _k X _ik d _ik

Establishing a mathematical model of the total cost F generated by the dispatching process:

wherein, Y _k The values of (A) are as follows: when the city bin k starts to transfer goods, the value is 1, otherwise, the value is 0, and d _ik Is the distance between the city bin i and the city bin k, gamma is the transportation cost coefficient,

for a cost factor of one order, X _ik The quantity of the cargos is from a city bin k to a city bin i;

solving for X when the total cost F is minimized _ik The value of (c).

5. The warehouse cargo management method of claim 4 wherein the constraint on the mathematical model of the total cost F is:

X _ik ≥0，

means not exceeding

D represents the average demand per day of all customers in the delivery area, β _j Representing the demand per day for customer j.

6. The warehouse cargo management method of claim 4, wherein solving the mathematical model of the total cost F using an adaptively modified particle swarm algorithm comprises:

initializing the speed and position of each particle in the population, if the search space is L-dimensional, each particle comprises L variables, and the optimal position P searched by each particle at present _best Setting the optimal position as G _best ；

Calculating an objective function value, namely a fitness value, of each particle, storing the optimal position and the fitness value of each particle, and selecting a particle from the population as the position of the population if the fitness value of the particle is the best;

adjusting the speed and position of the particles;

after each position updating, the fitness value of each particle is calculated again, and then the fitness value is found in historical optimization according to each particleIs located at the optimum position P _best And the corresponding fitness value, as the optimal fitness value, comparing the fitness value of the particle with the corresponding optimal fitness value in the historical optimization of the particle, if the fitness value of the particle is better than the corresponding optimal fitness value in the historical optimization, P _best Updating the current position of the particle;

respectively connecting the fitness value of each particle with the optimal positions G of all the particles _best The corresponding fitness values are compared, if the fitness value of any particle is better than the optimal position G of all particles _best Corresponding fitness value, G _best Updating the current position of the particle;

generating random particles, calculating the fitness value of the random particles, if the fitness value of the random particles is better than G _best Corresponding fitness value, G _best Updating the current position of the random particle; otherwise, keep G _best Is unchanged;

and checking a particle search termination condition, and terminating the search when the particle search termination condition is met.

7. The warehouse cargo management method of claim 6, wherein in the adaptive improved particle swarm optimization, the method for adjusting the speed and the position of the particles comprises:

adjusting inertial parameters:

adjusting a learning factor:

adjusting the speed of the particles:

adjusting the position of the particles:

in the formula: omega is the inertial weight; k is the current iteration number; v is the velocity of the particle; c. C ₁ 、c ₂ For learning factors, pb, gb are the individual and population optima, r, respectively ₁ 、r ₂ A random number ω of (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.

8. The warehouse cargo management method of claim 6, wherein in the adaptive improved particle swarm optimization, the random particle selection mode is traversal selection;

the selection rule of the random particles is as follows:

in the formula: i is _max ＝9.30，I _min 3; d is a random number of times, d _max Is the maximum random number.

9. The warehouse cargo management method according to any one of claims 2 to 8, wherein the method for replenishing the urban warehouses of the distribution area by the logistics center comprises the following steps:

Calculating the expected demand E of all customers in the distribution area:

E＝∑ _j∈J e _j ；

calculating the total cost：QH/2+P∑ _j∈J e _j /Q；

The total cost is derived from Q to obtain the optimal total replenishment quantity Q ^* ：

Wherein QH/2 is the average inventory cost of all city bins, P sigma _j∈J e _j the/Q is the order cost, and J is the set of all customers.

10. The warehouse cargo management method of claim 9, further comprising determining the proportion of each urban warehouse required and the optimal total replenishment quantity Q ^* Respectively calculating the replenishment quantity q of each city bin _i 。

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