CN114841634A - Warehouse goods management method - Google Patents
Warehouse goods management method Download PDFInfo
- Publication number
- CN114841634A CN114841634A CN202210344308.2A CN202210344308A CN114841634A CN 114841634 A CN114841634 A CN 114841634A CN 202210344308 A CN202210344308 A CN 202210344308A CN 114841634 A CN114841634 A CN 114841634A
- Authority
- CN
- China
- Prior art keywords
- city
- particle
- total
- replenishment
- warehouses
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Strategic Management (AREA)
- Quality & Reliability (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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
Technical Field
The invention belongs to the technical field of logistics information processing, and particularly relates to a warehouse cargo management method.
Background
Logistics is an important link linking the supply end and the consumer, and the development of logistics has changed the traditional way of living and consumption. With the rapid development of modern logistics, more and more articles are transported in a logistics mode, but the normal operation of logistics is seriously affected by insufficient articles in a warehouse, so various replenishment strategies are gradually proposed.
According to the replenishment strategy in the prior art, the current replenishment quantity is generally predicted by adopting a sales model obtained by training historical sales data, and once replenishment is needed and the warehouse is replenished through a logistics center, although the problem of shortage in the warehouse can be solved, the warehouse is directly replenished through the logistics center, so that the technical problem of high replenishment cost is brought due to long distance.
The above information disclosed in this background section is only for enhancement of understanding of the background of the application and therefore it may comprise prior art that does not constitute known to a person of ordinary skill in the art.
Disclosure of Invention
The invention provides a warehouse goods management method aiming at the technical problem of high replenishment cost in the prior art, and can solve the problem.
In order to realize the purpose of the invention, the invention is realized by adopting the following technical scheme:
A warehouse cargo management method comprises 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;
when the total replenishment points B are larger than the total stock M, the goods are transferred between the urban warehouses, otherwise, the logistics center replenishes all the urban warehouses.
In some embodiments of the present invention, the method for calculating the replenishment points of each city bay in the distribution area comprises:
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.
In some embodiments of the invention, the replenishment point b of the city 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 。
in some embodiments of the present invention, the method for dispatching goods for the city bin 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 adjusting 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).
In some embodiments of the invention, the constraint of 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.
In some embodiments of the present invention, solving a mathematical model of the total cost F using an adaptively improved 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 position as an initial position, and taking the optimal position globally searched by the particles 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 optimal position P found in the history optimization of each particle is used as the basis best And its corresponding fitness value, as an optimal fitness value, comparing the fitness value of the particle with the corresponding optimal fitness value in the historical optimization of the particle, if there is a particle fitness value superior to 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.
In some embodiments of the present invention, in the adaptively improved particle swarm algorithm, the method for adjusting the speed and the position of the particle is:
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.
In some embodiments of the invention, in the self-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.
In some embodiments of the present invention, the method for replenishing goods for each city bin of the distribution area by the logistics center comprises:
calculating the expected demand E of all customers in the distribution area:
E＝∑ j∈J e j ；
calculating the total cost: QH/2+ P ∑ S 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.
Some embodiments of the invention further comprise the steps of proportioning the demands of the urban bins and optimizing the total replenishment quantity Q * Respectively calculating the replenishment quantity q of each city bin i 。
Compared with the prior art, the invention has the advantages and positive effects that:
according to the warehouse goods management method, the goods supplementing points of all the city warehouses, the current stock of all the city warehouses, the total goods supplementing point B and the total stock M are obtained, only when the total goods supplementing point B is larger than the total stock M, the goods are adjusted among the city warehouses, otherwise, the logistics center supplements the goods for all the city warehouses, namely, the goods are adjusted among the city warehouses when a certain condition is met, the goods are not needed to be supplemented by the logistics center, the distance among the city warehouses is close to the logistics center, the logistics cost can be greatly saved, and meanwhile, the supply requirements among the city warehouses are met.
Other features and advantages of the present invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a process flow diagram of an embodiment of a warehouse goods management method according to the present invention.
Detailed Description
In order to make the objects, 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 drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Example one
In a logistics supply chain, a city or a region is provided with a plurality of city bins, the city bins are provided with respective distribution regions, and the distribution regions are adjacent to each other in a territorial space. A plurality of city warehouses in the same area or a plurality of city warehouses in a plurality of areas are provided with a logistics center, the logistics center is a coordination and control center of the whole system, manages the replenishment problem of the city warehouses and actively replenishes the city warehouses, and the city warehouses are responsible for the online order demands in the areas. Because the urban warehouses are not cooperated with each other and operated independently, the phenomenon that the urban warehouses are partially in short supply and are totally overstocked frequently 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, a replenishment and transfer model is established, and replenishment and transfer management is carried out on each city bin by using a virtual coordination center. And when the total inventory of each city bin is lower than the total replenishment point, the logistics center replenishes the city bins according to the indication of the total control module. When the current stock of the urban warehouse is lower than the delivery point and the total stock of all the urban warehouses is not lower than the total delivery point, the total control module calculates the dispatching quantity among the urban warehouses, and the technical problem of high replenishment cost in the prior art is solved.
The following description will be given with reference to a specific embodiment.
The embodiment provides a warehouse cargo management method, which comprises a logistics center general control module, wherein the logistics center is used for replenishing and transferring cargos to a plurality of urban warehouses.
As shown in fig. 1, the logistics center general control module is configured as:
and predicting the total expected demand E of customers in the distribution area of all city warehouses.
And determining the replenishment points of all the city bins according to the total expected demand E, and calculating a total replenishment point B, wherein the total replenishment point B is the sum of the replenishment points of all the city bins.
And calculating the current inventory of each city warehouse, and calculating the total inventory M, wherein the total inventory M is the sum of the current inventory of each city warehouse.
Comparing the replenishment points of the urban warehouses with the current inventory of the urban warehouses respectively, and when the replenishment points of all the urban warehouses are larger than the respective inventory, showing that the inventory of each urban warehouse can meet the current requirement, and each urban warehouse does not need replenishment or adjustment.
When the replenishment points of all the urban warehouses cannot meet the requirement that the replenishment points of all the urban warehouses are larger than the respective inventory amount, namely when the replenishment points of all the urban warehouses are not larger than the respective inventory amount, comparing the total replenishment points B with the total inventory amount M, when the total replenishment points B are larger than the total inventory amount M, showing that the total inventory amount in the area can meet the current requirement, and adjusting the cargos among the urban warehouses, otherwise, carrying out replenishment on all the urban warehouses by the logistics center.
The total inventory of all the urban warehouses and the total demand of the area in the same area are judged through comparison, and when the total inventory can be met, the urban warehouses can be adjusted, and the logistics center is not needed for supplementing goods. Because the distance between the city storehouse is nearer for the commodity circulation center, can greatly practice thrift the logistics cost, satisfy the supply demand between each city storehouse simultaneously, prevent the disconnected goods condition.
Because the unified dispatching among all the urban warehouses is a supporting and cooperative relationship, the total cost of the system can be reduced, the possibility that the urban warehouses simultaneously have stock backlog or lack of goods is lower, the safety stock, the replenishment point and the delivery quantity of the system can be effectively reduced by adopting the distributed stock management, and the total stock cost of the whole system can also be reduced.
In the planning period, the safety stock of the city bin i is as follows:
based on the (Q, R) inventory strategy, the expected delivery volume for the municipality i during the planning period is ∑ j∈J B j M ij And obtaining the expected delivery quantity of the city bin i in the lead period L as
The replenishment point for city bin i. Alpha (alpha) ("alpha") i A safety factor representing the stock level of warehouse i.
In some embodiments of the present invention, the method for calculating the replenishment points of each city bay in the distribution area comprises:
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 is 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 the expected demand of the customer j in the order lead period, L is the number of days of the order lead period, and T is the number of days of the planning period.
The replenishment point is the sum of the safe stock of the urban warehouse and the expected delivery amount in the lead period L, and if the stock is lower than the replenishment point, the urban warehouse is considered to need replenishment.
e j May be obtained from historical data or other existing means.
According to the formula, the replenishment point b of the city bin i i The method is positively correlated according to the expected demand of the customer, and the larger the expected demand of the customer is, the larger the value of the replenishment point is correspondingly increased.
Total replenishment point B:
B＝∑ i∈I b i and I is the set of city bins I.
In some embodiments of the present invention, the size between the replenishment point of each city bin and its current inventory is determined separately. When the replenishment point b of the city bin i i Not more than the current stock m of the city warehouse i And when the total replenishment point B is larger than the total stock quantity M, adjusting the stock for the urban warehouse i.
Wherein M ═ Σ i∈I m i 。
The current inventory of each city warehouse is obtained by an inventory system or an in-out warehouse recording system of the city warehouse.
When the current stock m of the city bin i i Below its delivery point b i And when the total stock M of each city warehouse is higher than the total replenishment point B, the replenishment is not carried out. The virtual coordination center makes a decision, and the logistics center transfers goods to meet the request of the backorder warehouse in time. The goal is to find X ik The goods transferring quantity among all the warehouses is solved, and the sales requirements of all the warehouses are met in time. To minimize the diversion cost of the entire system, a diversion model is determined.
In some embodiments of the invention, the method for adjusting the goods for the city bin i comprises the following steps:
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 transfer quantity from the city bin k to the city bin i.
Solving for X when the total cost F is minimized ik The value of (c).
In some embodiments of the invention, the constraint of 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 distribution area, β j Representing the demand per day for customer j.
min F is an objective function and comprises the minimum order cost and the minimum transportation cost generated by allocation, d represents the number of days, sigma, which can be used by stock products of all the current urban bins from the next replenishment i∈I X ik ≥d∑ j∈J β j M kj ，
Product quantity limit, X, indicating allowable allocation of warehouse ik And the value of the variable is restricted by being more than or equal to 0.
When the stock m of a certain city warehouse i i Down to the replenishment point b i And when the total quantity M of the urban warehouse inventory is higher than the total replenishment point B, calculating a transfer strategy with the lowest total system cost by adopting an Adaptive Particle Swarm Optimization (APSO) according to the transfer model.
In some embodiments of the present invention, solving a mathematical model of the total cost F using an improved adaptive 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 。
The objective function value, i.e. fitness value, of each particle is calculated, the best position of each particle and the fitness value are saved, and if the fitness value of a certain particle is the best in the population, it is selected and used as the position of the population.
The velocity and position of the particles are adjusted.
After each position updating, the fitness value of each particle is calculated again, and then the optimal position P found in the history optimization of each particle is used as the basis 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 And 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 And 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.
In the step of checking the particle search termination condition, one of the conditions is that the maximum iteration number is reached: g max (ii) a Another condition is a deviation between two adjacent generations within a specified range, and if the end condition is not met, returning is madeReturning to the step of adjusting the speed and the position of the particle, and continuously updating the speed and the position of the particle.
The common particle swarm algorithm is sensitive to initial conditions due to fixed parameters, so that the population is easy to precocious and falls into a local optimal solution. In order to improve the efficiency of the whole search, the adaptive particle swarm algorithm provided in this embodiment dynamically changes parameters along with the number of iterations on the basis of a general particle swarm algorithm, so that the parameters can be rapidly converged, and meanwhile, random particles are added to perform traversal optimization.
In some embodiments of the present invention, in the adaptively improved particle swarm algorithm, the method for adjusting the speed and the position of the particle is:
adjusting inertia 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.
In order that the overall algorithm does not fall into the optimal solution, random particles are added besides parameter adjustment, the random particles are selected to be ergodic selection, the search area can be uniformly searched, and the particle swarm is guaranteed to jump out of the local optimal solution.
In some embodiments of the invention, in the self-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.
In some embodiments of the present invention, the method for replenishing goods for each city bin of the distribution area by the logistics center comprises:
calculating the expected demand E of all customers in the distribution area:
E＝∑ j∈J e j ；
calculating the total cost: QH/2+ P ∑ S j∈J e j /Q。
Inventory costs are made up of two parts, the order cost and the inventory holding cost, respectively. The desired demand of all customers is Σ during the entire planning period j∈J B j The average stock cost of all the urban warehouses is QH/2, and the ordering cost is P sigma j∈J B j and/Q. So that the sum of the total order cost and inventory holding cost is
The formulaThe Q is derived to obtain the optimal total replenishment quantity Q * ：
Wherein J is the set of all customers, P is the replenishment cost of the urban warehouse in the planning period, H is the inventory holding cost of the unit product in the planning period, and Q is the total replenishment quantity of the urban warehouse.
Obtaining the optimal total replenishment quantity Q * Then according to the demand proportion of each city bin, the replenishment quantity q of each city bin can be easily obtained i . For example, city bin 1 and city bin 2, where the customer demands in the coverage areas of the two bins are w 1 ,w 2 Then, then
Some embodiments of the invention further comprise the steps of proportioning the demands of the urban bins and optimizing the total replenishment quantity Q * Respectively calculating the replenishment quantity q of each city bin i 。
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions.
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 。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210344308.2A CN114841634A (en) | 2022-03-31 | 2022-03-31 | Warehouse goods management method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210344308.2A CN114841634A (en) | 2022-03-31 | 2022-03-31 | Warehouse goods management method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114841634A true CN114841634A (en) | 2022-08-02 |
Family
ID=82564577
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210344308.2A Pending CN114841634A (en) | 2022-03-31 | 2022-03-31 | Warehouse goods management method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114841634A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116882905A (en) * | 2023-09-08 | 2023-10-13 | 深圳市元美供应链管理有限公司 | Big data-based supply chain intelligent inventory management system and method |
-
2022
- 2022-03-31 CN CN202210344308.2A patent/CN114841634A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116882905A (en) * | 2023-09-08 | 2023-10-13 | 深圳市元美供应链管理有限公司 | Big data-based supply chain intelligent inventory management system and method |
| CN116882905B (en) * | 2023-09-08 | 2023-12-22 | 深圳市元美供应链管理有限公司 | Big data-based supply chain intelligent inventory management system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Park et al. | Waiting strategy for the vehicle routing problem with simultaneous pickup and delivery using genetic algorithm | |
| CN109993484B (en) | Data processing method and system, medium and computing device | |
| US11615373B2 (en) | Inventory management | |
| CN109255481B (en) | Distributed inventory scheduling system and improvement method | |
| CN112884404A (en) | Intelligent supply chain inventory transfer optimization and transaction early warning system | |
| Jolai et al. | A fuzzy goal programming and meta heuristic algorithms for solving integrated production: distribution planning problem | |
| CN110097203A (en) | Inventory's dispatching method, inventory's dispatching device and computer readable storage medium | |
| Jamrus et al. | Extended priority-based hybrid genetic algorithm for the less-than-container loading problem | |
| CN106960301A (en) | Medical electric business mirror image picking method and system | |
| CN113762885B (en) | Method, apparatus, device, storage medium and program product for determining replenishment quantity | |
| CN114841642B (en) | Auxiliary material warehouse entry cargo space distribution method based on eagle perch optimization | |
| CN114819831B (en) | Method, equipment and storage medium for replenishment and dispatch based on Amazon FBA warehouse | |
| CN112884393A (en) | Inventory replenishment method and device and electronic equipment | |
| CN114841634A (en) | Warehouse goods management method | |
| Won et al. | Workload-based yard-planning system in container terminals | |
| KR20220006728A (en) | Device and method for providing optimal cargo delivery route based on artificial intelligence | |
| Hasani Goodarzi et al. | A multi-objective imperialist competitive algorithm for vehicle routing problem in cross-docking networks with time windows | |
| Singh et al. | Carton Set Optimization in E‐commerce Warehouses: A Case Study | |
| CN108154332A (en) | A kind of warehouse goods yard distribution method and system based on genetic algorithm | |
| CN116739840B (en) | Travel package recommendation method, device and storage medium based on multi-objective group optimization | |
| Pirayesh et al. | GPSO-LS algorithm for a multi-item EPQ model with production capacity restriction | |
| CN114169557A (en) | Inventory replenishment method, computer-readable storage medium and terminal device | |
| Yan et al. | Study on optimization of a multi-location inventory model with lateral transshipment considering priority demand | |
| CN115034945A (en) | Method and device for integrated scheduling of batch production and vehicle delivery of assembly line workshop | |
| CN113762848A (en) | Replenishment method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |