CN112884305B - Warehouse logistics distribution method based on big data platform - Google Patents
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- CN112884305B CN112884305B CN202110148839.XA CN202110148839A CN112884305B CN 112884305 B CN112884305 B CN 112884305B CN 202110148839 A CN202110148839 A CN 202110148839A CN 112884305 B CN112884305 B CN 112884305B
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- FGUUSXIOTUKUDN-IBGZPJMESA-N C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 Chemical compound C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 FGUUSXIOTUKUDN-IBGZPJMESA-N 0.000 claims description 10
- 230000008901 benefit Effects 0.000 abstract description 4
- 238000011161 development Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 1
- 229910052782 aluminium Inorganic materials 0.000 description 1
- 238000013461 design Methods 0.000 description 1
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- 238000007670 refining Methods 0.000 description 1
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- 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
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
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- 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
- G06Q10/083—Shipping
Abstract
The invention belongs to the technical field of warehouse logistics application, and particularly relates to a warehouse logistics distribution method based on a big data platform. Firstly, determining the quantity and the size of warehouses of different categories in a park; dividing the warehouse into a plurality of storage bins according to the length and the width of the existing semitrailer carriage; obtaining the spare capacity of different kinds of bins in the existing park; taking the near three days as a storage scheduling period, and acquiring the unloading amount in the near three days in the park, wherein the unloading amount comprises the goods and the occupied area; according to the empty allowance and the unloading amount of the existing different kinds of warehouses, determining the combination of the maximum filling of the empty allowance of the warehouse, and determining the combination as CG; through carrying out combination calculation on the loading capacity, the storage capacity and the free quantity, the storage method which is most in line with the park is effectively analyzed, the benefit maximization of the logistics park is ensured, the enterprise efficiency of the park is improved, and meanwhile, the method is simple, convenient to operate and remarkable in implementation effect, and is suitable for large-scale popularization and use.
Description
Technical Field
The invention belongs to the technical field of warehouse logistics application, and particularly relates to a warehouse logistics distribution method based on a big data platform.
Background
The logistics is taken as an arterial system of national economy, and is connected with all parts of the social industry to form an organic whole, the development degree of the logistics is one of important marks for measuring the modernization degree and comprehensive national force of a country, and the logistics industry of China in recent years has stepped into a high-speed development stage, but has lower information level than foreign developed countries. In the whole industrial or logistics enterprise operation, the high logistics cost has become a big bottleneck for restricting the enterprise development. The method is mainly characterized in that in the whole logistics process, informatization management and technical means are lagged, the information acquisition capacity and the analysis and utilization capacity are low. In the whole logistics process, most of the processes are decided and operated through manual experience, so that the logistics process is low in efficiency, high in error rate and high in cost.
The E-commerce logistics is mainly based on the field of electronic commerce and is based on individual logistics service, and is characterized by various storage varieties, small transportation quantity and short storage time. The supply chain warehouse logistics is mainly characterized by being provided by some traditional logistics companies for warehouse and transportation service, or by being self-used in large manufacturers for warehouse transfer and the like, and is characterized by being less in storage varieties, larger in transportation quantity and longer in warehouse time. In recent years, with the development of the mobile interconnection and the electronic commerce platform driving the electronic commerce logistics, the whole electronic commerce logistics is mature in informatization, automation and networking roads. Compared with the E-commerce logistics, the supply chain storage logistics are seriously privately owned, various logistics companies are uneven in level, the manual intervention process is too many, the refining process is not standard enough, and the like, so that standardization, systemization and efficiency are not improved well at present. For this reason, how to effectively improve the reasonable distribution of the warehouse logistics of the supply chain is the direction of the current important research of the logistics of the supply chain.
Disclosure of Invention
Aiming at the technical problems of the supply chain logistics, the invention provides the distribution method of the warehouse logistics based on the big data platform, which has reasonable design and simple method and can reasonably and effectively distribute the warehouse in the park.
In order to achieve the above purpose, the invention adopts the technical scheme that the invention provides a warehouse logistics distribution method based on a big data platform, which comprises the following effective steps:
a. firstly, determining the quantity and the size of warehouses of different categories in a park;
b. dividing the warehouse into a plurality of storage bins according to the length and the width of the existing semitrailer carriage;
c. obtaining the spare capacity of different kinds of bins in the existing park;
d. taking the near three days as a storage scheduling period, and acquiring the unloading amount in the near three days in the park, wherein the unloading amount comprises the goods and the occupied area;
e. according to the empty allowance and the unloading amount of the existing different kinds of warehouses, determining the combination of the maximum filling of the empty allowance of the warehouse, and determining the combination as CG;
f. determining the client types of cargoes in the combination according to the formed combination, assigning values to different client types, assigning values to a large client of 1, assigning values to a small client of 0.5 and assigning values to a scattered client of 0.2, and calculating the combination, wherein the maximum result is the optimal storage scheme;
g. acquiring the loading capacity of the loaded goods in the near day, wherein the loading capacity is the type and the occupied area of the loaded goods;
h. determining the empty quantity of the warehouse spaces of different products according to the loading quantity of the near day, determining the combination of filling up the empty quantity of the warehouse to the maximum according to the residual unloading quantity of the near three days, and repeating the step f;
i. acquiring the loading capacity of the loaded goods in the second day, wherein the loading capacity is the type and the occupied area of the loaded goods;
j. determining the empty quantity of the warehouse spaces of different products according to the loading quantity on the second day, determining the combination of maximally filling the empty quantity of the warehouse according to the residual unloading quantity on the near three days, and repeating the step f;
k. acquiring the loading capacity of the loaded goods in the third day, wherein the loading capacity is the type and the occupied area of the loaded goods;
and l, determining the empty quantity of the warehouse spaces of different products according to the loading quantity on the third day, determining the combination of maximally filling up the empty quantity of the warehouse according to the residual unloading quantity on the near three days, and repeating the step f.
Preferably, in the step f, the storage time of the loose clients is assigned, and the shorter the storage time is, the lower the assignment is.
Preferably, in the step f, the assignment of the loose customer is equal to the identity of the customer itself multiplied by the time of the goods storage.
Compared with the prior art, the invention has the advantages and positive effects that,
1. the invention provides a storage logistics distribution method based on a big data platform, which effectively analyzes the storage method which is most in line with a park by carrying out combined calculation on the loading capacity, the storage capacity and the free quantity, ensures the maximization of the benefit of the logistics park and improves the enterprise efficiency of the park.
Detailed Description
In order that the above objects, features and advantages of the invention may be more clearly understood, a further description of the invention will be provided with reference to the following examples. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as described herein, and therefore the present invention is not limited to the specific embodiments of the disclosure that follow.
In the embodiment, the method for distributing warehouse logistics based on the big data platform firstly determines the number and the size of warehouses of different types in a park, and the main purpose of the step is to know the overall warehouse in the park and know the inventory of different types to a certain extent. In addition, according to the existing transportation rules of cargoes, such as steel coils and aluminum strips, the weight of the cargoes reaches the standard, but the occupied volume of the cargoes is small, and the cargoes can be stored independently when placed in a warehouse, so that the warehouse is required to be classified according to the kinds of cargoes.
Then, divide a plurality of storage space with warehouse according to the length and the width of present semitrailer carriage, because the commodity circulation of supply chain all is shipment according to the semitrailer, and shipment volume is full dress for whole car generally, for this, with the better convenient corresponding goods of length and the width of semitrailer carriage, the length of present conventional semitrailer is wei 14m, and the vehicle width is 2.3m, and this kind of setting also can avoid the emergence of string goods problem.
And then, obtaining the empty quantity of different product bins in the existing park, wherein the shipment of goods takes a long time, and therefore, the empty quantity of different product bins in the existing park is generally obtained when the work is started in the morning of one day.
Then, taking the near three days as a storage scheduling period, of course, the period can also be one week, the specific period can be matched according to the goods condition of the park, and then, the unloading amount in the near three days of the park is obtained, wherein the unloading amount comprises the goods class and the occupied area of the goods. The floor space referred to in this embodiment refers to the total number of semi-trailer cars occupied by the batch of cargo, so as to facilitate the matching of the bins.
And then, combining according to the empty allowance and the unloading amount of the existing different kinds of warehouses, determining the combination of filling up the empty allowance of the warehouse to the maximum extent, and determining the combination as CG. The goods of different kinds are required to be put into the similar warehouses, the stock quantity of the existing warehouses is 1, and the combination which is close to 1 and is the combination which fills up the empty allowance of the warehouse at maximum is the result of adding the occupied areas of different goods by the empty allowance of the warehouse.
Because the significance of the big client, the small client and the scattered client to the park is different, the storage of the big client is required to be preferentially met, the client types of goods in the combination are determined according to the formed multiple groups of combinations, the different client types are assigned, the big client is assigned to be 1, the small client is assigned to be 0.5, the scattered client is assigned to be 0.2, and then the combination is calculated, so that the maximum result is taken as the optimal storage scheme. The calculation is an additive calculation, for example, in a combination, there are 5 large clients, 10 small clients and 40 loose clients, then the combination is 5+10×0.5+40×0.2=18, in another combination, there are 4 large clients, 10 small clients and 50 loose clients, then the combination is 4+10×0.5+50×0.2=19, and the score is greater than that of the first combination, therefore, the combination is preferably selected, and of course, in the case of equal division, the large clients are preferably selected. At this point, the existing inventory situation has been matched, but there must be a discharge of inventory in the campus to meet the near three day deposit. For this purpose, firstly, the loading capacity of the goods loaded in the next day is obtained, wherein the loading capacity is the goods loaded and the occupied area, and the loading capacity of the day is calculated in the next day, so that the empty space of the bin on the earliest date can be obtained, then, the empty space of the bin of different goods is determined according to the loading capacity of the next day, the combination of the maximum filling up of the empty space of the bin is determined according to the residual unloading capacity of the next three days, and the calculation and assignment are repeated to obtain the optimal storage combination of the batch. And then, calculating the combination of the second day and the third day, and calculating the goods storage in the whole dispatching period, wherein the whole calculation takes the storage of a large customer as priority, so that the storage of the large customer can be ensured, and as for the loose customers which cannot be stored, the distribution in the next dispatching period can be adjusted to give priority, and the loose customers are divided into the previous batch of loose customers and the batch of loose customers, wherein the previous batch of loose customers are assigned 1.1, and the present batch of loose customers are assigned 1, and the optimal storage scheme is obtained by utilizing a multiplication mode.
Considering that the storage time is also a main way of profit for the park, in the assignment calculation, the storage time of the loose clients is assigned, and the shorter the storage time is, the lower the assignment is. In this way, the customer's assignment is equal to the customer's own identity times the time of the goods deposit times the lot's assignment, thus ensuring maximum resource utilization at the campus.
The present invention is not limited to the above-mentioned embodiments, and any equivalent embodiments which can be changed or modified by the technical content disclosed above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above-mentioned embodiments according to the technical substance of the present invention without departing from the technical content of the present invention still belong to the protection scope of the technical solution of the present invention.
Claims (3)
1. The warehouse logistics distribution method based on the big data platform is characterized by comprising the following effective steps:
a. firstly, determining the quantity and the size of warehouses of different categories in a park;
b. dividing a warehouse into a plurality of storage bins according to the length and the width of the existing semitrailer carriage;
c. obtaining the spare capacity of different kinds of bins in the existing park;
d. taking the near three days as a storage scheduling period, and acquiring the unloading amount in the near three days in the park, wherein the unloading amount comprises the goods and the occupied area;
e. according to the empty allowance and the unloading amount of the existing different kinds of warehouses, determining the combination of the maximum filling of the empty allowance of the warehouse, and determining the combination as CG;
f. determining the client types of cargoes in the combination according to the formed combination, assigning values to different client types, assigning values to a large client of 1, assigning values to a small client of 0.5 and assigning values to a scattered client of 0.2, and calculating the combination, wherein the maximum result is the optimal storage scheme;
g. acquiring the loading capacity of the loaded goods in the near day, wherein the loading capacity is the type and the occupied area of the loaded goods;
h. determining the empty quantity of the warehouse spaces of different products according to the loading quantity of the near day, determining the combination of filling up the empty quantity of the warehouse to the maximum according to the residual unloading quantity of the near three days, and repeating the step f;
i. acquiring the loading capacity of the loaded goods in the second day, wherein the loading capacity is the type and the occupied area of the loaded goods;
j. determining the empty quantity of the warehouse spaces of different products according to the loading quantity on the second day, determining the combination of maximally filling the empty quantity of the warehouse according to the residual unloading quantity on the near three days, and repeating the step f;
k. acquiring the loading capacity of the loaded goods in the third day, wherein the loading capacity is the type and the occupied area of the loaded goods;
and l, determining the empty quantity of the warehouse spaces of different products according to the loading quantity on the third day, determining the combination of maximally filling up the empty quantity of the warehouse according to the residual unloading quantity on the near three days, and repeating the step f.
2. The method for distributing warehouse logistics based on a big data platform as set forth in claim 1, wherein the step f further assigns a value to the storage time of the loose clients, and the shorter the storage time, the lower the assignment.
3. The method of claim 2, wherein in step f, the assigned value of the loose customer is equal to the identity of the customer multiplied by the time the goods are stored.
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