CN114462946A - FBA (file system based) order purchase, sales, head stock and freight management system and method - Google Patents
FBA (file system based) order purchase, sales, head stock and freight management system and method Download PDFInfo
- Publication number
- CN114462946A CN114462946A CN202210126214.8A CN202210126214A CN114462946A CN 114462946 A CN114462946 A CN 114462946A CN 202210126214 A CN202210126214 A CN 202210126214A CN 114462946 A CN114462946 A CN 114462946A
- Authority
- CN
- China
- Prior art keywords
- decision
- head
- fba
- module
- order
- 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.)
- Withdrawn
Links
- 238000000034 method Methods 0.000 title claims abstract description 14
- 238000012544 monitoring process Methods 0.000 claims abstract description 34
- 235000012907 honey Nutrition 0.000 claims description 117
- 238000007726 management method Methods 0.000 claims description 94
- 241000257303 Hymenoptera Species 0.000 claims description 48
- 238000004458 analytical method Methods 0.000 claims description 42
- 241000256844 Apis mellifera Species 0.000 claims description 41
- 230000003044 adaptive effect Effects 0.000 claims description 33
- 238000010276 construction Methods 0.000 claims description 26
- 238000004088 simulation Methods 0.000 claims description 25
- 230000000630 rising effect Effects 0.000 claims description 12
- 238000007405 data analysis Methods 0.000 claims description 9
- 241000256837 Apidae Species 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 8
- 230000001419 dependent effect Effects 0.000 claims description 7
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 4
- 238000007418 data mining Methods 0.000 claims description 4
- 230000003111 delayed effect Effects 0.000 claims description 4
- 238000011156 evaluation Methods 0.000 claims description 4
- 238000012417 linear regression Methods 0.000 claims description 4
- 238000004806 packaging method and process Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000005303 weighing Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000032683 aging Effects 0.000 description 2
- 239000000969 carrier Substances 0.000 description 2
- 230000001550 time effect Effects 0.000 description 2
- 206010012186 Delayed delivery Diseases 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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
- G06Q10/083—Shipping
- G06Q10/0834—Choice of carriers
- G06Q10/08345—Pricing
-
- 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
- G06Q10/0838—Historical data
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
- G06Q30/0284—Time or distance, e.g. usage of parking meters or taximeters
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a system and a method for managing purchase, sale, storage and head distance freight based on FBA orders, and belongs to the technical field of head distance freight management. The system comprises an FBA order purchase, sale and storage data module, an order management module, a head distance transportation decision module, a freight management and monitoring module and a time fitting and prediction module; the output end of the FBA order purchase, sales and inventory data module is connected with the input end of the order management module; the output end of the order management module is connected with the input end of the headway transportation decision-making module; the output end of the head distance transportation decision module is connected with the input end of the freight management monitoring module; and the output end of the freight management monitoring module is connected with the input end of the time fitting prediction module. The invention can help the merchant select the transportation logistics management of FBA orders by an intelligent means, improve the selection efficiency of the merchant, analyze the time and reduce the occurrence of goods returning and changing situations.
Description
Technical Field
The invention relates to the technical field of head distance freight management, in particular to a system and a method for managing head distance freight by purchase, sale and storage based on FBA orders.
Background
The FBA head-end freight generally refers to the cost of goods delivered to an FBA warehouse from China, and comprises freight charges, tax charges, customs declaration charges, service charges and the like, and the cost of different transportation modes is different. There are generally three modes of FBA head-end transport: air transportation, sea transportation and international express delivery; the air transportation is a head-range transportation mode of directly flying by a cargo plane or a cargo plane, is more suitable for the transportation of products with lower weight, has higher cost and quicker time effect, and has higher cost performance for small products; the sea transportation has large carrying capacity and low cost, and is suitable for the transportation of large goods; but the shipping time is slow, requiring the seller to schedule the shipment in advance. The method is divided into two modes of whole cabinet transportation and box-splicing transportation, and the selection is flexible; the cost of international courier is the most expensive, but the transportation time is the fastest, like some small products, which can reach the United states in the fastest 3 days. If UPS transportation is selected, the UPS can be directly put into the FBA warehouse without reservation.
And each country area has different warehouses, each warehouse has multiple logistics carriers, each logistics carrier's price and requirement are different, and in fluctuation, the operation rule of each warehouse is different, each logistics company's the size of carrying, weight, can reach each difference the same, the same purpose delivery address has multiple warehouse delivery, multiple logistics can reach, it is difficult to select the optimum logistics according to the efficiency freight, face such a plurality of objects that need to be considered simultaneously, artifical excel statistics operating efficiency is low, artifical error rate is great, once making mistakes will increase the cost, reduce the profit, no repayment!
Disclosure of Invention
The present invention provides a system and a method for managing purchase, sale, inventory and shipping charges based on FBA orders to solve the above problems.
In order to solve the technical problems, the invention provides the following technical scheme:
a FBA-based order purchase, sales and head-stock freight management system comprises an FBA order purchase, sales and stock data module, an order management module, a head-stock transportation decision module, a freight management and monitoring module and a time fitting prediction module;
the FBA order purchase, sale and inventory data module is used for acquiring FBA order purchase, sale and inventory data; the order management module is used for constructing an order management model based on the idea of an artificial bee colony algorithm according to FBA order purchase, sales and inventory data; the head journey transportation decision module is used for outputting an optional head journey transportation decision according to the order management model output by the order management module for the user to select; the freight management monitoring module is used for constructing a time period according to a head-trip transportation decision selected by a user and acquiring historical freight data of the logistics company under the head-trip transportation decision in the time period; the time fitting prediction module is used for constructing a time fitting analysis model, predicting the arrival time of the goods under the head journey transportation decision selected by the user, calculating the head journey transportation cost at the same time, and outputting the head journey transportation cost to a user port;
the output end of the FBA order purchase, sales and inventory data module is connected with the input end of the order management module; the output end of the order management module is connected with the input end of the headway transportation decision-making module; the output end of the head distance transportation decision module is connected with the input end of the freight management monitoring module; and the output end of the freight management monitoring module is connected with the input end of the time fitting prediction module.
According to the technical scheme, the FBA order purchase-sale-inventory data module comprises an FBA order purchase submodule, an FBA order sale submodule and an FBA order inventory submodule;
the FBA order purchasing submodule is used for recording purchasing data of the FBA order; the FBA order sale sub-module is used for recording the sale data of the FBA order; the FBA order inventory submodule is used for recording inventory data of FBA orders;
the output ends of the FBA order purchasing submodule, the FBA order selling submodule and the FBA order inventory submodule are respectively connected with the input end of the order management module.
According to the technical scheme, the order management module comprises a data analysis sub-module and a model construction sub-module;
the data analysis submodule is used for analyzing the data information provided by the FBA order purchase, sales and inventory data module; the model construction submodule constructs an order management model based on the idea of an artificial bee colony algorithm;
the output end of the data analysis submodule is connected with the input end of the model construction submodule; and the output end of the model construction submodule is connected with the input end of the head journey transportation decision-making module.
According to the technical scheme, the head course transportation decision module comprises a head course transportation decision output submodule and a user selection submodule;
the head journey transportation decision output sub-module is used for outputting an optional head journey transportation decision according to the model output by the order management module; the user selection submodule is used for providing a head course transportation decision for a user for the user to select;
the output end of the headway transportation decision output submodule is connected with the input end of the user selection submodule; and the output end of the user selection submodule is connected with the input end of the freight management monitoring module.
According to the technical scheme, the freight management monitoring module comprises a time period construction submodule and a freight management monitoring submodule;
the time period construction submodule is used for constructing a time period according to a head journey transportation decision selected by a user; the freight management monitoring submodule is used for acquiring freight historical data of the logistics company under the first-distance transportation decision in a time period;
the output end of the time period construction submodule is connected with the input end of the freight management monitoring submodule; and the output end of the freight management monitoring submodule is connected with the input end of the time fitting prediction module.
According to the technical scheme, the time fitting prediction module comprises a time fitting analysis model construction sub-module and an output unit;
the time fitting analysis model construction submodule is used for constructing a time fitting analysis model, predicting the arrival time of the goods under the head journey transportation decision selected by the user, and calculating the head journey transportation cost; the output unit is used for outputting the forecast time information of the arrival of the goods under the head-trip transportation decision selected by the user and the head-trip transportation cost to the user port;
and the output end of the time fitting analysis model building submodule is connected with the input end of the output unit.
An FBA order-based purchase-sale-stock-head-distance freight management method comprises the following steps:
s1, acquiring FBA order purchase, sales and inventory data, and constructing an order management model based on the idea of an artificial bee colony algorithm according to the FBA order purchase, sales and inventory data;
s2, outputting alternative headway transportation decisions according to the constructed order management model for the user to select;
s3, constructing a time period T based on the head journey transportation decision selected by the user, and acquiring the transportation charge historical data of the logistics company under the head journey transportation decision in the time period T;
and S4, constructing a time fitting analysis model, predicting the arrival time of the goods under the head journey transportation decision selected by the user, calculating the head journey transportation cost, and outputting the head journey transportation cost to the user port.
According to the technical scheme, the step of constructing the order management model based on the idea of the artificial bee colony algorithm comprises the following steps:
respectively constructing a honey source and the nectar amount of the honey source, collecting bees, observing bees and detecting bees;
setting a bee collecting device for giving a decision according to the self characteristics of the order and setting an observation bee for selecting according to the given decision; setting up a new decision for the scout bees under the condition that the provided decision cannot meet the preset condition; setting up a honey source equal to the decision; setting the nectar amount of the nectar source as an adaptive value of the decision;
the self characteristics of the order comprise order timeliness, order weight, order scale, order bearable cost and the like;
the decision comprises an air transportation decision, a sea transportation decision, an international express transportation decision and the like;
for example, when the order has short aging, that is, the order has a transportation time requirement and must arrive before a certain day, a corresponding time threshold can be set for distinguishing, for example, the threshold is set to a, if the duration is less than a, the decision is selected as an international express transportation decision which mainly comprises DHL, UPS, TNT, FedEx and EMS, and the transportation aging is fast, but the cost is high; when the order timeliness requirement is general, that is, the order has a transportation time requirement but does not necessarily require to arrive at a certain day, a corresponding time threshold can be set for distinguishing, for example, the threshold is set as B, and if the duration is less than B and greater than A, the decision is selected as an air transportation decision; when the order has no time-effect requirement, a special marine line can be selected;
for example, in order weight and order size, if the order is small and light, the air transportation decision can be selected, namely, the product is transported to a designated country or region by a logistics company and then a cargo plane or a cargo plane; in order weight and order scale, if the order weight and order scale are larger, a marine transportation decision can be adopted, the bulk cargo is transported in a cargo ship mode, two modes of bulk cargo and whole cabinet transportation are adopted, the bearing capacity is large, the cost is lowest, and a corresponding threshold value can be set for limiting;
different decisions come from different self-characteristics of the orders, and the honey bee firstly establishes an initial decision according to the self-characteristics of the orders;
constructing a search dimensionality maximum value D, wherein the number of constructed bee-collecting bees and observation bees is K, the number of the constructed bee-collecting bees or the number of the observation bees is equal to the number of honey sources, and one bee-collecting bee corresponds to one honey source;
assigning a random value within a value range to all dimensions of each honey source so as to randomly generate an initial honey source, and recording the initial honey source of the ith bee in the jth dimension as xi，j，i∈{1，2，…，K}，j∈{1，2，…，D}；
The ith honey bee searches for a new honey source according to the following formula:
wherein x iskRepresenting a neighborhood honey source, wherein K belongs to {1, 2, …, K }, and K is not equal to i;
to take on the value of [ -1, 1]A random number over the interval; v. ofiRepresents a new source of honey; wherein j represents a dimension;
the observation bees perform probability selection on the new honey source and the initial honey source according to the nectar amount of the honey source:
wherein, fit (x)i) The adaptive value of the ith solution corresponds to the rich degree of the honey source, and the richer the honey source is, the greater the probability of being selected is; piIs the probability of being selected;
the abundance degree of the honey sources refers to contracted logistics companies under decision, and the more the number of the logistics companies under decision is, the richer the honey sources are represented;
for example, when selecting an air transportation decision, although the decisions with many results are all air transportation decisions, each air transportation decision is attached with a different logistics company, for example, a certain dimension of air transportation decision may be a1、a2、a3Three logistics companies, and in another dimension of air transportation decision may be a3、a4Two logistics companies; therefore, the more logistics companies under a decision belong to, the richer the nectar quantity in the honey source is represented;
selecting new honey source and P in initial honey sourceiThe larger one;
outputting the decision;
and (3) constructing a simulation analysis model to simulate all output decisions so as to obtain adaptive values of the decisions:
the simulation analysis model is a Monte Carlo simulation analysis model;
constructing an initial input: logistics evaluation, logistics transportation scale, logistics service attitude, logistics punctuality condition, logistics safety condition and logistics transportation cost;
utilizing SPSS software to carry out data mining, taking the adaptive value of the decision as a dependent variable and taking initial input as an independent variable, and establishing a linear regression function, which is marked as F (v);
constructing simulation parameters: the confidence level is recorded as E, and the running times are recorded as R;
under the simulation parameters, obtaining a simulation result as an adaptive value of a decision;
establishing a threshold value of the decision-making adaptive value, discarding honey sources which do not meet the threshold value, and converting honey bees corresponding to the discarded honey sources into reconnaissance bees;
the reconnaissance bee starts new search to obtain a new honey source:
xs，j＝xmin，j+rand[0，1](xmax，j-xmin，j)
wherein; x is the number ofmax，j、xmin，jRespectively representing an upper bound and a lower bound of a j dimension; rand [0, 1 ]]Refers to a random number in the interval of 0 to 1; x is the number ofs，jIs a new honey source;
discarding honey sources which do not meet the threshold, converting honey bees corresponding to the discarded honey sources into detection bees, starting new search by the detection bees, and acquiring new honey sources as one iteration;
carrying out probability selection and decision adaptive value simulation again on a new honey source and an initial honey source obtained by the scout bees, reserving a decision which meets the threshold value of the decision adaptive value, continuously abandoning the decision which is not met, and entering the next iteration;
and establishing the maximum iteration times, recording as H, terminating the decision selection when the iteration times reach H, and outputting the final reserved decision to a manual port for the user to select.
According to the above technical solution, the time fitting analysis model includes:
acquiring data of a delayed condition in logistics historical data of a logistics company under a head-end transportation decision;
correspondingly acquiring weather condition data, freight rising data, special festival data and assembly manual data of the data with the delay condition;
with delay time y0As dependent variables, the weather condition data, freight rising data, special festival data and assembly artificial data are used as independent variables and are respectively marked as c1、c2、c3、c4Establishing a time fitting analysis model:
wherein u is0、u1、u2、u3、u4To fit the regression coefficients of the analytical model in time,
the error factor is used for expressing the influence condition on the delay time length under special conditions;
constructing a time period T;
acquiring historical freight data of a logistics company under a head-trip transportation decision in a time period T;
calculating to obtain freight rising data;
acquiring weather condition data, special festival data and assembly manual data in a time period T;
substituting the time fitting analysis model, fitting a curve and predicting the delay time y0And predicting the arrival time of the goods under the head journey transportation decision selected by the user and outputting the arrival time to the user port.
In the time fitting analysis model, the influence of freight charge rising data is the largest, because weather, holidays, manpower and the like belong to special factors, in most cases, the weather, holidays and manpower belong to normal states, and the weather, holidays and manpower can be known by merchants, for example, in stormy weather, the merchants have certain sensing capability to know the problem of goods delay, and the freight charge rising data is unknown data of the merchants due to the fact that the freight charge rising data relates to storage and arrangement, so that in the time fitting analysis model, simulation prediction is mainly performed on the part;
in the logistics transportation process, the merchant cannot know the transportation information of other merchants, namely cannot immediately know whether the logistics of other merchants is delayed, and further cannot judge the transportation condition of the logistics in the latest time, but the transportation cost of the transportation is known, and under the condition that the basic transportation rate is not changed, the increase of the transportation cost comes from the storage cost and the placement cost, namely, the accumulation state of FBA storage is shown, and the condition of delayed delivery exists, so that the delay time of the delivery is considered when the merchant adopts the logistics.
The head shipping cost includes:
constructing a formula to calculate head distance freight:
E＝m1+m2+m3+m4+m5
wherein E is head distance freight, m1For order processing fee, m2For sorting packaging charges, m3For weighing the treatment fee, m4For storage charge, m5Placing service fees for the warehousing inventory;
the warehousing fee is the number of goods per unit quantity of goods per cubic meter of monthly storage fee;
the warehousing inventory placement service fee is the number of goods per unit volume of goods per cubic meter per month of service fee.
Due to the particularity of the FBA, the FBA is an overseas order, so that the delivery time of a merchant is inaccurate in the shopping process of a user, once a large amount of time delay occurs, the user can return goods in a large area, the reputation of the store is seriously influenced, the time prediction is significant, and meanwhile, the merchant can stand out from a plurality of merchants due to the accurate delivery time, so that more customers are attracted.
Compared with the prior art, the invention has the following beneficial effects: the invention obtains the data of FBA order purchase, sale and inventory according to the FBA order purchase, sale and inventory data module; constructing an order management model based on the idea of an artificial bee colony algorithm by using an order management module; outputting an optional head course transportation decision by using a head course transportation decision module for a user to select; constructing a time period by using a freight management monitoring module, and acquiring freight historical data of a logistics company under a head-trip transportation decision in the time period; a time fitting analysis model is built by using a time fitting prediction module, the arrival time of the goods under the head journey transportation decision selected by the user is predicted, and meanwhile, head journey transportation cost is calculated and output to a user port; the invention can help the user to select logistics decision by an intelligent means, avoid the loss caused by price fluctuation and requirements of different logistics carriers under different warehouses of different country regions, select the optimal logistics according to the timeliness freight and the like, and reduce the problems of low efficiency and large error rate caused by the current manual operation when facing a plurality of objects needing to be considered; meanwhile, the invention also provides a time prediction mode, which can detect the backlog state of the warehouse according to the change of the freight, output the predicted arrival time based on the fitting model and effectively reduce the problem of goods return and exchange of customers.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow chart of a purchase-sale-stock-head-distance freight management system and method based on FBA order according to the present invention;
fig. 2 is a schematic diagram illustrating the steps of the purchase-sale-stock-head-distance freight management method based on FBA orders according to the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1-2, the present invention provides a technical solution:
a FBA-based order purchase, sales and head-stock freight management system comprises an FBA order purchase, sales and stock data module, an order management module, a head-stock transportation decision module, a freight management and monitoring module and a time fitting prediction module;
the FBA order purchase, sale and inventory data module is used for acquiring FBA order purchase, sale and inventory data; the order management module is used for constructing an order management model based on the idea of an artificial bee colony algorithm according to FBA order purchase, sales and inventory data; the head journey transportation decision module is used for outputting an optional head journey transportation decision according to the order management model output by the order management module for the user to select; the freight management monitoring module is used for constructing a time period according to a head-trip transportation decision selected by a user and acquiring historical freight data of the logistics company under the head-trip transportation decision in the time period; the time fitting prediction module is used for constructing a time fitting analysis model, predicting the arrival time of the goods under the head journey transportation decision selected by the user, calculating head journey transportation cost at the same time, and outputting the head journey transportation cost to a user port;
the output end of the FBA order purchase, sales and inventory data module is connected with the input end of the order management module; the output end of the order management module is connected with the input end of the headway transportation decision-making module; the output end of the head distance transportation decision module is connected with the input end of the freight management monitoring module; and the output end of the freight management monitoring module is connected with the input end of the time fitting prediction module.
The FBA order purchase-sale-inventory data module comprises an FBA order purchase submodule, an FBA order sale submodule and an FBA order inventory submodule;
the FBA order purchasing submodule is used for recording purchasing data of the FBA order; the FBA order sale sub-module is used for recording the sale data of the FBA order; the FBA order inventory submodule is used for recording inventory data of FBA orders;
the output ends of the FBA order purchasing submodule, the FBA order selling submodule and the FBA order inventory submodule are respectively connected with the input end of the order management module.
The order management module comprises a data analysis sub-module and a model construction sub-module;
the data analysis submodule is used for analyzing the data information provided by the FBA order purchase, sales and inventory data module; the model construction submodule constructs an order management model based on the idea of an artificial bee colony algorithm;
the output end of the data analysis submodule is connected with the input end of the model construction submodule; and the output end of the model construction submodule is connected with the input end of the head journey transportation decision-making module.
The head course transportation decision module comprises a head course transportation decision output sub-module and a user selection sub-module;
the head journey transportation decision output sub-module is used for outputting an optional head journey transportation decision according to the model output by the order management module; the user selection submodule is used for providing a head course transportation decision for a user for the user to select;
the output end of the headway transportation decision output submodule is connected with the input end of the user selection submodule; and the output end of the user selection submodule is connected with the input end of the freight management monitoring module.
The freight management monitoring module comprises a time period construction submodule and a freight management monitoring submodule;
the time period construction submodule is used for constructing a time period according to a head journey transportation decision selected by a user; the freight management monitoring submodule is used for acquiring freight historical data of the logistics company under the first-distance transportation decision in a time period;
the output end of the time period construction submodule is connected with the input end of the freight management monitoring submodule; and the output end of the freight management monitoring submodule is connected with the input end of the time fitting prediction module.
The time fitting prediction module comprises a time fitting analysis model construction submodule and an output unit;
the time fitting analysis model construction submodule is used for constructing a time fitting analysis model, predicting the arrival time of the goods under the head journey transportation decision selected by the user, and calculating the head journey transportation cost; the output unit is used for outputting the forecast time information of the arrival of the goods under the head-trip transportation decision selected by the user and the head-trip transportation cost to the user port;
and the output end of the time fitting analysis model building submodule is connected with the input end of the output unit.
An FBA order-based purchase-sale-stock-head-distance freight management method comprises the following steps:
s1, acquiring FBA order purchase, sales and inventory data, and constructing an order management model based on the idea of an artificial bee colony algorithm according to the FBA order purchase, sales and inventory data;
s2, outputting alternative headway transportation decisions according to the constructed order management model for the user to select;
s3, constructing a time period T based on the head journey transportation decision selected by the user, and acquiring the transportation charge historical data of the logistics company under the head journey transportation decision in the time period T;
and S4, constructing a time fitting analysis model, predicting the arrival time of the goods under the head journey transportation decision selected by the user, calculating the head journey transportation cost, and outputting the head journey transportation cost to the user port.
The step of constructing the order management model based on the idea of the artificial bee colony algorithm comprises the following steps:
respectively constructing a honey source and the nectar amount of the honey source, collecting bees, observing bees and detecting bees;
setting a bee collecting device for giving a decision according to the self characteristics of the order and setting an observation bee for selecting according to the given decision; setting up a new decision for the scout bees under the condition that the provided decision cannot meet the preset condition; setting up a honey source equal to the decision; setting the nectar amount of the nectar source as an adaptive value of the decision;
different decisions come from different self-characteristics of the orders, and the honey bee firstly establishes an initial decision according to the self-characteristics of the orders;
constructing a search dimensionality maximum value D, wherein the number of constructed bee-collecting bees and observation bees is K, the number of the constructed bee-collecting bees or the number of the observation bees is equal to the number of honey sources, and one bee-collecting bee corresponds to one honey source;
assigning a random value within a value range to all dimensions of each honey source so as to randomly generate an initial honey source, and recording the initial honey source of the ith bee in the jth dimension as xi，j，i∈{1，2，…，K}，j∈{1，2，…，D}；
The ith honey bee searches for a new honey source according to the following formula:
wherein x iskRepresenting a neighborhood honey source, wherein K belongs to {1, 2, …, K }, and K is not equal to i;
to take on the value of [ -1, 1]A random number over the interval; v. ofiRepresents a new source of honey; wherein j represents a dimension;
the observation bees perform probability selection on the new honey source and the initial honey source according to the nectar amount of the honey source:
wherein, fit (x)i) The adaptive value of the ith solution corresponds to the rich degree of the honey source, and the richer the honey source is, the greater the probability of being selected is; piIs the probability of being selected;
the abundance degree of the honey sources refers to a contracted logistics company under decision, and the more the logistics companies under decision are, the more the honey sources are represented;
selecting new honey source and P in initial honey sourceiThe larger one;
outputting the decision;
and (3) constructing a simulation analysis model to simulate all output decisions so as to obtain adaptive values of the decisions:
the simulation analysis model is a Monte Carlo simulation analysis model;
constructing an initial input: logistics evaluation, logistics transportation scale, logistics service attitude, logistics punctuality condition, logistics safety condition and logistics freight;
utilizing SPSS software to carry out data mining, taking the adaptive value of the decision as a dependent variable and taking initial input as an independent variable, and establishing a linear regression function, which is marked as F (v);
constructing simulation parameters: the confidence level is recorded as E, and the running times are recorded as R;
under the simulation parameters, obtaining a simulation result as an adaptive value of a decision;
establishing a threshold value of the decision-making adaptive value, discarding honey sources which do not meet the threshold value, and converting honey bees corresponding to the discarded honey sources into reconnaissance bees;
the reconnaissance bee starts new search to obtain a new honey source:
xs，j＝xmin，j+rand[0，1](xmax，j-xmin，j)
wherein; x is the number ofmax，j、xmin，jRespectively representing an upper bound and a lower bound of a j dimension; rand [0, 1 ]]Refers to a random number in the interval of 0 to 1; x is the number ofs，jIs a new honey source;
discarding honey sources which do not meet the threshold, converting honey bees corresponding to the discarded honey sources into detection bees, starting new search by the detection bees, and acquiring new honey sources as one iteration;
carrying out probability selection and decision adaptive value simulation again on a new honey source and an initial honey source obtained by the scout bees, reserving a decision which meets the threshold value of the decision adaptive value, continuously abandoning the decision which is not met, and entering the next iteration;
and establishing the maximum iteration times, recording as H, terminating the decision selection when the iteration times reach H, and outputting the final reserved decision to a manual port for the user to select.
The time-fitting analysis model includes:
acquiring data of a delayed condition in logistics historical data of a logistics company under a head-end transportation decision;
correspondingly acquiring weather condition data, freight rising data, special festival data and assembly manual data of the data with the delay condition;
with delay time y0As dependent variables, the weather condition data, freight rising data, special festival data and assembly artificial data are used as independent variables and are respectively marked as c1、c2、c3、c4Establishing a time fitting analysis model:
wherein u is0、u1、u2、u3、u4To fit the regression coefficients of the analytical model in time,
the error factor is used for expressing the influence condition on the delay time length under special conditions;
constructing a time period T;
acquiring historical freight data of a logistics company under a head-trip transportation decision in a time period T;
calculating to obtain freight rising data;
acquiring weather condition data, special festival data and assembly manual data in a time period T;
substituting the time fitting analysis model, fitting a curve and predicting the delay time y0And predicting the arrival time of the goods under the head journey transportation decision selected by the user and outputting the arrival time to the user port.
The head shipping cost includes:
constructing a formula to calculate head distance freight:
E＝m1+m2+m3+m4+m5
wherein E is head distance freight, m1For order processing fee, m2For sorting packaging charges, m3For weighing the treatment fee, m4For storage charge, m5Placing service fees for the warehousing inventory;
the warehousing fee is the number of goods per unit quantity of goods per cubic meter of monthly storage fee;
the warehousing inventory placement service fee is the number of goods per unit volume of goods per cubic meter per month of service fee.
In this embodiment:
firstly, constructing an order management model based on the idea of an artificial bee colony algorithm:
respectively constructing a honey source and the nectar amount of the honey source, collecting bees, observing bees and detecting bees;
setting a bee collecting device for giving a decision according to the self characteristics of the order and setting an observation bee for selecting according to the given decision; setting up a new decision for the scout bees under the condition that the provided decision cannot meet the preset condition; setting up a honey source equal to the decision; setting the nectar amount of the nectar source as an adaptive value of the decision;
different decisions come from different self-characteristics of the orders, and the honey bee firstly establishes an initial decision according to the self-characteristics of the orders;
the self characteristics of the order comprise order timeliness, order weight, order scale and order bearable cost;
the decision comprises an air transportation decision, a sea transportation decision and an international express transportation decision;
constructing a search dimensionality maximum value D, wherein the number of constructed bee-collecting devices and observation devices is K to 3, the number of the constructed bee-collecting devices or the number of the observation devices is equal to the number of honey sources, and one bee-collecting device corresponds to one honey source;
assigning a random value within a value range to all dimensions of each honey source so as to randomly generate an initial honey source, and recording the initial honey source of the ith bee in the jth dimension as xi，j，i∈{1，2，…，K}，j∈{1，2，…，D}；
The ith honey bee searches for a new honey source according to the following formula:
wherein x iskRepresenting a neighborhood honey source, wherein K belongs to {1, 2, …, K }, and K is not equal to i;
to take on the value of [ -1, 1]A random number over the interval; v. ofiRepresenting a new honey source; wherein j represents a dimension;
the observation bees perform probability selection on the new honey source and the initial honey source according to the nectar amount of the honey source:
wherein, fit (x)i) The adaptive value of the ith solution corresponds to the rich degree of the honey source, and the richer the honey source is, the greater the probability of being selected is; piIs the probability of being selected;
the abundance degree of the honey sources refers to a contracted logistics company under decision, and the more the logistics companies under decision are, the more the honey sources are represented;
selecting a decision according to the self characteristics of the order, namely selecting one of an air transportation decision, a marine transportation decision and an international express transportation decision according to the self characteristics of the order, and selecting P in the new honey source and the initial honey sourceiThe larger one is taken as the standard;
outputting the decision;
and (3) constructing a simulation analysis model to simulate all output decisions so as to obtain adaptive values of the decisions:
different logistics companies correspond to each decision, the more logistics companies correspond to, the richer the types are, and the adaptive value of the decision is larger;
the simulation analysis model is a Monte Carlo simulation analysis model;
constructing an initial input: logistics evaluation, logistics transportation scale, logistics service attitude, logistics punctuality condition, logistics safety condition and logistics freight;
utilizing SPSS software to carry out data mining, taking the adaptive value of the decision as a dependent variable and taking initial input as an independent variable, and establishing a linear regression function, which is marked as F (v);
constructing simulation parameters: the confidence level is recorded as E, and the running times are recorded as R;
under the simulation parameters, obtaining a simulation result as an adaptive value of a decision;
establishing a threshold value of the decision-making adaptive value, discarding honey sources which do not meet the threshold value, and converting honey bees corresponding to the discarded honey sources into reconnaissance bees;
the reconnaissance bee starts new search to obtain a new honey source:
xs，j＝xmin，j+rand[0，1](xmax，j-xmin，j)
wherein; x is the number ofmax，j、xmin，jRespectively representing an upper bound and a lower bound of a j dimension; rand [0, 1 ]]Refers to a random number in the interval of 0 to 1; x is the number ofs，jIs a new honey source;
discarding honey sources which do not meet the threshold, converting honey bees corresponding to the discarded honey sources into detection bees, starting new search by the detection bees, and acquiring new honey sources as one iteration;
carrying out probability selection and decision adaptive value simulation again on a new honey source and an initial honey source obtained by the scout bees, reserving a decision which meets the threshold value of the decision adaptive value, continuously abandoning the decision which is not met, and entering the next iteration;
and establishing the maximum iteration times, recording the maximum iteration times as H, terminating the decision selection when the iteration times reach H, and outputting the final reserved decision to a manual port for the user to select.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: 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 changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A purchase-sale-stock-head-distance freight management system based on FBA orders is characterized in that: the system comprises an FBA order purchase, sale and storage data module, an order management module, a head distance transportation decision module, a freight management and monitoring module and a time fitting and prediction module;
the FBA order purchase, sale and inventory data module is used for acquiring FBA order purchase, sale and inventory data; the order management module is used for constructing an order management model based on the idea of an artificial bee colony algorithm according to FBA order purchase, sales and inventory data; the head journey transportation decision module is used for outputting an optional head journey transportation decision according to the order management model output by the order management module for the user to select; the freight management monitoring module is used for constructing a time period according to a head-trip transportation decision selected by a user and acquiring historical freight data of the logistics company under the head-trip transportation decision in the time period; the time fitting prediction module is used for constructing a time fitting analysis model, predicting the arrival time of the goods under the head journey transportation decision selected by the user, calculating the head journey transportation cost at the same time, and outputting the head journey transportation cost to a user port;
the output end of the FBA order purchase-sale-stock data module is connected with the input end of the order management module; the output end of the order management module is connected with the input end of the headway transportation decision-making module; the output end of the head distance transportation decision module is connected with the input end of the freight management monitoring module; and the output end of the freight management monitoring module is connected with the input end of the time fitting prediction module.
2. The FBA-based order purchase-sale-stock-head-distance freight management system according to claim 1, wherein: the FBA order purchase-sale-inventory data module comprises an FBA order purchase submodule, an FBA order sale submodule and an FBA order inventory submodule;
the FBA order purchasing submodule is used for recording purchasing data of the FBA order; the FBA order sale sub-module is used for recording the sale data of the FBA order; the FBA order inventory submodule is used for recording inventory data of FBA orders;
the output ends of the FBA order purchasing submodule, the FBA order selling submodule and the FBA order inventory submodule are respectively connected with the input end of the order management module.
3. The FBA-based order purchase-sale-stock-head-distance freight management system according to claim 1, wherein: the order management module comprises a data analysis sub-module and a model construction sub-module;
the data analysis submodule is used for analyzing the data information provided by the FBA order purchase, sales and inventory data module; the model construction submodule constructs an order management model based on the idea of an artificial bee colony algorithm;
the output end of the data analysis submodule is connected with the input end of the model construction submodule; and the output end of the model construction submodule is connected with the input end of the head journey transportation decision-making module.
4. The FBA-based order purchase-sale-stock-head-distance freight management system according to claim 1, wherein: the head course transportation decision module comprises a head course transportation decision output sub-module and a user selection sub-module;
the head journey transportation decision output sub-module is used for outputting an optional head journey transportation decision according to the model output by the order management module; the user selection submodule is used for providing a head course transportation decision for a user for the user to select;
the output end of the headway transportation decision output submodule is connected with the input end of the user selection submodule; and the output end of the user selection submodule is connected with the input end of the freight management monitoring module.
5. The FBA-based order purchase-sale-stock-head-distance freight management system according to claim 1, wherein: the freight management monitoring module comprises a time period construction submodule and a freight management monitoring submodule;
the time period construction submodule is used for constructing a time period according to a head journey transportation decision selected by a user; the freight management monitoring submodule is used for acquiring freight historical data of the logistics company under the first-distance transportation decision in a time period;
the output end of the time period construction submodule is connected with the input end of the freight management monitoring submodule; and the output end of the freight management monitoring submodule is connected with the input end of the time fitting prediction module.
6. The system for purchase-sale-stock-head-travel-fee management based on FBA orders according to claim 1, wherein: the time fitting prediction module comprises a time fitting analysis model construction submodule and an output unit;
the time fitting analysis model building submodule is used for building a time fitting analysis model, predicting the arrival time of the goods under the head journey transportation decision selected by the user and calculating the head journey transportation cost; the output unit is used for outputting the forecast time information of the arrival of the goods under the head-trip transportation decision selected by the user and the head-trip transportation cost to the user port;
and the output end of the time fitting analysis model building submodule is connected with the input end of the output unit.
7. A purchase-sale-stock-head-distance freight management method based on FBA orders is characterized by comprising the following steps: the method comprises the following steps:
s1, acquiring FBA order purchase, sales and inventory data, and constructing an order management model based on the idea of an artificial bee colony algorithm according to the FBA order purchase, sales and inventory data;
s2, outputting alternative headway transportation decisions according to the constructed order management model for the user to select;
s3, constructing a time period T based on the head journey transportation decision selected by the user, and acquiring the transportation charge historical data of the logistics company under the head journey transportation decision in the time period T;
and S4, constructing a time fitting analysis model, predicting the arrival time of the goods under the head journey transportation decision selected by the user, calculating the head journey transportation cost, and outputting the head journey transportation cost to the user port.
8. The FBA-based order purchase-sale-stock-head-haul management method as claimed in claim 7, wherein: the step of constructing the order management model based on the idea of the artificial bee colony algorithm comprises the following steps:
respectively constructing a honey source and the nectar amount of the honey source, collecting bees, observing bees and detecting bees;
setting a bee collecting device for giving a decision according to the self characteristics of the order and setting an observation bee for selecting according to the given decision; setting up a new decision for the scout bees under the condition that the provided decision cannot meet the preset condition; setting up a honey source equal to the decision; setting the nectar amount of the nectar source as an adaptive value of the decision;
different decisions come from different self-characteristics of the orders, and the honey bee firstly establishes an initial decision according to the self-characteristics of the orders;
constructing a search dimensionality maximum value D, wherein the number of constructed bee-collecting bees and observation bees is K, the number of the constructed bee-collecting bees or the number of the observation bees is equal to the number of honey sources, and one bee-collecting bee corresponds to one honey source;
assigning a random value within a value range to all dimensions of each honey source so as to randomly generate an initial honey source, and recording the initial honey source of the ith bee in the jth dimension as xi，j，i∈{1，2，…，K}，j∈{1，2，…，D}；
The ith honey bee searches for a new honey source according to the following formula:
wherein x iskRepresenting a neighborhood honey source, wherein K belongs to {1, 2, …, K }, and K is not equal to i;
to take on the value of [ -1, 1]A random number over the interval; v. ofiRepresents a new source of honey; wherein j represents a dimension;
the observation bees perform probability selection on the new honey source and the initial honey source according to the nectar amount of the honey source:
wherein, fit (x)i) The adaptive value of the ith solution corresponds to the rich degree of the honey source, and the richer the honey source is, the greater the probability of being selected is; piIs the probability of being selected;
the abundance degree of the honey sources refers to a contracted logistics company under decision, and the more the logistics companies under decision are, the more the honey sources are represented;
selecting new honey source and P in initial honey sourceiThe larger one;
outputting the decision;
and (3) constructing a simulation analysis model to simulate all output decisions so as to obtain adaptive values of the decisions:
the simulation analysis model is a Monte Carlo simulation analysis model;
constructing an initial input: logistics evaluation, logistics transportation scale, logistics service attitude, logistics punctuality condition, logistics safety condition and logistics freight;
utilizing SPSS software to carry out data mining, taking the adaptive value of the decision as a dependent variable and taking initial input as an independent variable, and establishing a linear regression function, which is marked as F (v);
constructing simulation parameters: the confidence level is recorded as E, and the running times are recorded as R;
under the simulation parameters, obtaining a simulation result as an adaptive value of a decision;
establishing a threshold value of the decision-making adaptive value, discarding honey sources which do not meet the threshold value, and converting honey bees corresponding to the discarded honey sources into reconnaissance bees;
the reconnaissance bee starts new search to obtain a new honey source:
xs，j＝xmin，j+rand[0，1](xmax，j-xmin，j)
wherein; x is the number ofmax，j、xmin，jRespectively representing an upper bound and a lower bound of a j dimension; rand [0, 1 ]]Refers to a random number in the interval of 0 to 1; x is the number ofs，jIs a new honey source;
discarding honey sources which do not meet the threshold, converting honey bees corresponding to the discarded honey sources into detection bees, starting new search by the detection bees, and acquiring new honey sources as one iteration;
carrying out probability selection and decision adaptive value simulation again on a new honey source and an initial honey source obtained by the scout bees, reserving a decision which meets the threshold value of the decision adaptive value, continuously abandoning the decision which is not met, and entering the next iteration;
and establishing the maximum iteration times, recording as H, terminating the decision selection when the iteration times reach H, and outputting the final reserved decision to a manual port for the user to select.
9. The FBA-based order purchase-sale-stock-head-distance freight management method according to claim 8, wherein: the time-fitting analysis model includes:
acquiring data of a delayed condition in logistics historical data of a logistics company under a head-end transportation decision;
correspondingly acquiring weather condition data, freight rising data, special festival data and assembly manual data of the data with the delay condition;
with delay time y0As dependent variables, the weather condition data, freight rising data, special festival data and assembly artificial data are used as independent variables and are respectively marked as c1、c2、c3、c4Establishing a time fitting analysis model:
wherein u is0、u1、u2、u3、u4To fit the regression coefficients of the analytical model in time,
the error factor is used for expressing the influence condition on the delay time length under special conditions;
constructing a time period T;
acquiring historical freight data of a logistics company under a head-trip transportation decision in a time period T;
calculating to obtain freight rising data;
acquiring weather condition data, special festival data and assembly manual data in a time period T;
substituting the time fitting analysis model, fitting a curve and predicting the delay time y0And predicting the arrival time of the goods under the head journey transportation decision selected by the user and outputting the arrival time to the user port.
10. The FBA-based order purchase-sale-stock-head-distance freight management method according to claim 9, wherein: the head shipping cost includes:
constructing a formula to calculate head distance freight:
E＝m1+m2+m3+m4+m5
wherein E is head distance freight, m1For order processing fee, m2For sorting packaging charges, m3For weighing the treatment fee, m4For storage charge, m5Placing service fees for the warehousing inventory;
the warehousing fee is the number of goods per unit quantity of goods per cubic meter of monthly storage fee;
the warehousing inventory placement service fee is the number of goods per unit volume of goods per cubic meter per month of service fee.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210126214.8A CN114462946A (en) | 2022-02-10 | 2022-02-10 | FBA (file system based) order purchase, sales, head stock and freight management system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210126214.8A CN114462946A (en) | 2022-02-10 | 2022-02-10 | FBA (file system based) order purchase, sales, head stock and freight management system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114462946A true CN114462946A (en) | 2022-05-10 |
Family
ID=81412916
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210126214.8A Withdrawn CN114462946A (en) | 2022-02-10 | 2022-02-10 | FBA (file system based) order purchase, sales, head stock and freight management system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114462946A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115459916A (en) * | 2022-11-09 | 2022-12-09 | 江苏翔晟信息技术股份有限公司 | Electronic signature management system based on quantum encryption technology |
| CN116342041A (en) * | 2023-04-17 | 2023-06-27 | 深圳市感恩网络科技有限公司 | International trade data storage management system and method based on blockchain |
-
2022
- 2022-02-10 CN CN202210126214.8A patent/CN114462946A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115459916A (en) * | 2022-11-09 | 2022-12-09 | 江苏翔晟信息技术股份有限公司 | Electronic signature management system based on quantum encryption technology |
| CN116342041A (en) * | 2023-04-17 | 2023-06-27 | 深圳市感恩网络科技有限公司 | International trade data storage management system and method based on blockchain |
| CN116342041B (en) * | 2023-04-17 | 2023-11-07 | 深圳市感恩网络科技有限公司 | International trade data storage management system and method based on blockchain |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11126965B2 (en) | Systems and methods for optimization of a product inventory by an intelligent adjustment of inbound purchase orders | |
| CN110598940B (en) | Logistics order analysis and prediction system based on Internet of things trade | |
| CN114462946A (en) | FBA (file system based) order purchase, sales, head stock and freight management system and method | |
| TWI810488B (en) | Computer-implemented system and computer-implemented method for intelligent generation of purchase orders | |
| KR102343759B1 (en) | Systems and methods for computerized balanced delivery route assignment and incentive structure | |
| TWI759025B (en) | Computer-implemented system for generating automated outbound profile and computer-readable medium having instructions | |
| CN112418536A (en) | Logistics arrival time real-time monitoring and pre-estimating system based on big data | |
| US20220036307A1 (en) | Systems and methods for inventory reshuffling and rebalancing | |
| CN110874670A (en) | Storage position configuration system | |
| CN116205560A (en) | Inventory automation management system and method thereof | |
| CN115081961B (en) | Logistics transport capacity intelligent dispatching method based on big data | |
| CN112101645A (en) | Multi-place multi-bin selection method, multi-place multi-bin selection system and multi-bin selection device | |
| CN115311005B (en) | Fresh food supply and demand management method and device, electronic equipment and storage medium | |
| CN115796732A (en) | Regional logistics delivery and home distribution management method based on E-commerce platform commodities | |
| CN114926062A (en) | Method and device for determining distribution time length and computer readable storage medium | |
| CN114819805A (en) | Vehicle transportation management system | |
| Darley-Waddilove | Allocating commodity volumes in the citrus export cold chain: A case for the Port of Durban | |
| JP2001243401A (en) | Order receipt prediction system | |
| Tan et al. | Optimal stochastic inventory control with deterioration and partial backlogging/service-level constraints | |
| Zeng et al. | Truck Departure Optimization from Distribution Center to Parcel Locker with Stochastic Demand Arrival | |
| KR102291038B1 (en) | Systems and methods for optimizing cost of goods sold | |
| KR20240029384A (en) | Method and apparatus for predicting volume of orders | |
| Lee | Improving Delivery Performance Through Predicted Transit Times | |
| Liang et al. | Economic optimization of fresh logistics pick-up routing problems with time windows based on gray prediction | |
| CN116205568A (en) | Method, device, equipment and storage medium for managing parts order |
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 | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220510 |
|
| WW01 | Invention patent application withdrawn after publication |