CN113592153B - Goods distribution method, device, medium and computer equipment - Google Patents

Goods distribution method, device, medium and computer equipment Download PDF

Info

Publication number
CN113592153B
CN113592153B CN202110770062.0A CN202110770062A CN113592153B CN 113592153 B CN113592153 B CN 113592153B CN 202110770062 A CN202110770062 A CN 202110770062A CN 113592153 B CN113592153 B CN 113592153B
Authority
CN
China
Prior art keywords
target
replenishment
store
goods
target store
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.)
Active
Application number
CN202110770062.0A
Other languages
Chinese (zh)
Other versions
CN113592153A (en
Inventor
高季尧
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Shanshu Network Technology Co ltd
Shanshu Science And Technology Suzhou Co ltd
Shanshu Science And Technology Beijing Co ltd
Shenzhen Shanzhi Technology Co Ltd
Original Assignee
Shanghai Shanshu Network Technology Co ltd
Shanshu Science And Technology Suzhou Co ltd
Shanshu Science And Technology Beijing Co ltd
Shenzhen Shanzhi Technology Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Shanghai Shanshu Network Technology Co ltd, Shanshu Science And Technology Suzhou Co ltd, Shanshu Science And Technology Beijing Co ltd, Shenzhen Shanzhi Technology Co Ltd filed Critical Shanghai Shanshu Network Technology Co ltd
Priority to CN202110770062.0A priority Critical patent/CN113592153B/en
Publication of CN113592153A publication Critical patent/CN113592153A/en
Application granted granted Critical
Publication of CN113592153B publication Critical patent/CN113592153B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • 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

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • Development Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Game Theory and Decision Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides a goods distribution method, a device, a medium and computer equipment, wherein the method comprises the following steps: determining a goods distribution decision variable; creating a goods distribution target function and a constraint condition based on the goods distribution decision variable; outputting a goods distribution strategy based on the constraint conditions and the objective function; the sorting objective function comprises:
Figure DDA0003151250460000011
therefore, by determining the goods distribution decision variable, the goods distribution decision variable comprehensively considers the global factors influencing the goods distribution, so that the goods distribution precision of the goods distribution objective function can be improved; and the random optimization modeling algorithm is adopted to carry out discretization processing on the sales forecast distribution of the stores to generate corresponding target sales level scenes, each sales level scene corresponds to one occurrence probability, so that even if the actual sales and the forecast sales have different degree deviations, the stability of the whole distribution can be improved based on the probabilities, the precision of a distribution objective function is improved, and the replenishment quantities of different stores can be accurately considered under the condition that the sales of the stores are uncertain.

Description

Goods distribution method, device, medium and computer equipment
Technical Field
The invention belongs to the technical field of intelligent goods distribution, and particularly relates to a goods distribution method, a device, a medium and computer equipment.
Background
In a common retail supply chain scenario, products produced by a factory are not directly sent to end stores, but are first transported to a regional warehouse through a logistics network, and then distributed and shipped by the regional warehouse to a corresponding sales store.
Due to the fact that the replenishment of the commodities of the stores has a certain replenishment period range, and the arrival of the stores needs a certain time lead, the replenishment quantity of the stores at each time needs to meet the sales requirement in a future period. Meanwhile, due to the fact that sales volume levels of different commodities in different stores at different periods are different, the required goods distribution volume of each goods distribution store is different. If the replenishment is insufficient, the sales volume is directly affected, and if the replenishment is excessive, the overstock of the goods is caused, and the extra cost of the inventory is increased.
The traditional manual goods distribution method is generally started from local parts, the future replenishment needs are estimated according to manual experience or simple fitting of historical replenishment data, and then the replenishment needs are met one by one according to the established priority sequence of stores. However, the requirements of all stores cannot be considered globally, and only the requirements are met locally but not optimized globally, so that the situation that the stores with low priority are not distributed completely and the stores with high priority are supplemented excessively under the condition that the total goods distribution amount is insufficient is easily caused, the goods distribution precision is seriously insufficient, and the situation that the goods supplementation of different stores cannot be considered under the condition that the sales volume of each store is uncertain is not met.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a goods distribution method, a device, a medium and computer equipment, which are used for solving the technical problems that in the prior art, when goods are distributed to each store, the goods distribution precision is insufficient, and further, accurate goods replenishment of different stores cannot be considered globally.
In a first aspect of the present invention, there is provided a method of sorting goods, the method comprising:
determining a goods distribution decision variable;
creating a goods distribution objective function and a constraint condition based on the goods distribution decision variable;
outputting a goods distribution strategy based on the constraint condition and the objective function; the goods distribution strategy is the optimal value of each goods distribution decision variable; wherein,
i is a target doorShop, said j is the target big warehouse, said whcosti,jLogistics cost of replenishing goods from the target large warehouse j for the target store i; said Xi,jThe quantity of replenishment from the target large warehouse j for the target store i; the emergentiA replenishment urgency weighting factor for the target store i; the dependencyslackA penalty factor for store shortage; said Xsi,sThe gap replenishment quantity of the target store i after replenishment under the target sales level scene s is obtained; said probsThe occurrence probability corresponding to the target sales level scene s; the Yoi,jA replenishment identification variable for the target store i when replenishing goods from the big bin j; the dependencyoutofstockIs a penalty factor for the backorder store; said Yki,sAn identification variable for the target store i to be out of stock after replenishment under the target sales level scene s; the dutyorder_minThe penalty factor is a punishment factor for a small replenishment order, wherein the small replenishment order is a replenishment order smaller than a replenishment quantity threshold value; the Ym isi,jAn identification variable which is used for the target store i and has the replenishment quantity from the target large warehouse j lower than the replenishment quantity threshold value; the Yoi,jA replenishment identification variable is set for a target store i when replenishment is carried out from a target large warehouse j; the dependencyorder_storeAs a penalty factor for the number of replenishment stores, the YsiA variable is identified for replenishment when the target store i replenishes from any large warehouse; the dutyinv_slackA penalty factor for exceeding the store inventory limit after replenishment in the store, XkliA first slack variable for which the total in-store inventory after replenishment at the target store i is less than the minimum in-store inventory; the XkuiA second slack variable for the total in-store inventory after replenishment at the target store i above the maximum in-store inventory.
Optionally, when the constraint condition is that the total amount of orders from each warehouse of the target store i is not less than the predicted replenishment amount of the target store i in the target sales level scene s, the constraint condition includes:
Figure BDA0003151250440000021
when the constraint condition is that the supply quantity of the large warehouse to different stores is not more than the total supply quantity of the large warehouse, the constraint condition comprises the following steps:
Figure BDA0003151250440000031
wherein, s is any sales level scene, and Xi,jThe quantity of goods replenishment from the large warehouse j for the target store i, the Xsi,sThe gap replenishment quantity of the target store i after replenishment under the target sales level scene s is obtained; said demandi,sPredicting the replenishment quantity of the target store i under a sales level scene s; the i is a target store, the XcjThe capacity is the remaining goods distribution allowance of the target large warehouse j after the goods replenishmentjIs the total stock of the target big bin j.
Optionally, when the constraint condition is to count whether the target store i is restocked from the target large warehouse j, the constraint condition includes:
Xi,j≤M·Yoi,j，Xi,j≥Yoi,j；
when the constraint condition is that whether the replenishment quantity of the target store i from the target large warehouse j is lower than a non-replenishment quantity threshold value or not is counted, the constraint condition comprises the following steps:
Xi,j≤mi·Ymi,j+M·(1-Ymi,j)，Xi,j≥mi·(1-Ymi,j)；
when the constraint condition is that whether the target store i is out of stock after replenishment under the target sales level scene is counted, the constraint condition comprises the following steps:
Xsi,s≤β·demandi,s+M·Yki,s(ii) a Wherein,
wherein said Xi,jNumber of restocks from big warehouse j for target store i, where M is bigM parameter, and Yoi,jA replenishment identification variable for a target store i when replenishing goods from a target large warehouse j, miA preset replenishment quantity threshold value of a target store i; the Ymi,jAn identification variable that the replenishment quantity of the target store i from the target large warehouse j is lower than the replenishment quantity threshold value is set, if the target store i from the target large warehouseWhen the replenishment quantity of j is lower than the replenishment quantity threshold value, the Ymi,jIs 1; if the replenishment quantity of the target store i from the target large warehouse j is not lower than the replenishment quantity threshold value, Ymi,jIs 0; said Xsi,sThe gap replenishment quantity of the target store i after replenishment under the target sales level scene s is obtained; the beta is a threshold factor of the target store i which is considered to be out of stock; said demandi,sPredicting the replenishment quantity of the target store i under a sales level scene s; when the target store i is restocked from the target warehouse j, the Yoi,jIs 1; when the target store i is not restocked from the target warehouse j, the Yoi,jIs 0; said Yki,sAn identification variable for the target store i that is out of stock after replenishment in the target sales level scenario s.
Optionally, when the constraint condition is to count the number of large warehouses that each store can order simultaneously, the constraint condition includes:
Figure BDA0003151250440000041
when the constraint condition is the number of statistical order stores, the constraint condition comprises:
Figure BDA0003151250440000042
when the constraint condition is that whether the statistics meet the replenishment specification requirements of the stores, the constraint condition comprises the following steps:
mi·Xoi,j＝Xi,j(ii) a Wherein,
the Yoi,jIdentifying variables for replenishment when a target store i replenishes from a target large warehouse J, wherein J is a large warehouse set, and orders is the number of large warehouses which can be simultaneously dispatched by the target store i; said Ys isiA variable is identified for replenishment when the target store i replenishes from any large warehouse; m isiA preset replenishment quantity threshold value of a target store i; the Xoi,jA preset replenishment quantity threshold multiple for replenishing goods from a target large warehouse j for a target store i, wherein X isi,jIs a target doorStore i replenishes the quantity from the target warehouse j.
Optionally, when the constraint condition is that the store in-transit amount + the inventory amount + the replenishment amount cannot be lower than the minimum inventory amount of the store and cannot be higher than the maximum inventory amount of the store, the constraint condition includes:
Figure BDA0003151250440000043
Figure BDA0003151250440000044
when the constraint condition is that if the target large warehouse corresponding to the target store i has a replenishment allowance, the remaining large warehouse with the priority lower than the target large warehouse j does not need to replenish the target store i, and the constraint condition includes:
Figure BDA0003151250440000045
Xcj-mi+0.1≤M·Yci,j(ii) a Wherein,
said X isi,jThe onhand for the quantity of the target store i restocked from the target large warehouse jiThe order is the inventory of the target store iiThe inv _ level _ max is the quantity of the target store i in transitiXku being the maximum inventory of the target store iiA second relaxation variable for the total quantity of the goods in the store after the replenishment of the target store i is higher than the maximum quantity of the goods in the store, the inv _ level _ miniXkl being the minimum inventory of the target store iiA first slack variable for a target store i after replenishment with a total in-store inventory that is less than a minimum in-store inventory, Xi,kThe quantity of goods replenishment from a large warehouse K for a target store i, wherein K is a set corresponding to the rest large warehouse with the priority lower than the target large warehouse j, M is a bigM parameter, and Yci,jThe residual goods quantity of the target large warehouse j is not less than the preset replenishment quantity threshold value m of the target store iiThe identity variable of, said XcjM is the remaining goods distribution allowance of the target large bin j after replenishment, andia preset replenishment quantity threshold value for the target store i.
Optionally, the method further includes:
according to the formula emergenti＝max(storevlti-lastdaysi+1，1)2·priorityiDetermining a replenishment urgency weight factor emergent of the target store ii(ii) a Wherein the storevltiLead period of replenishment for the target store i, the lastdaysiThe priority is the number of days that the goods quantity in transit and the goods quantity in storage of the target store i can support the turnover of the target store iiThe replenishment priority of the target store i.
Optionally, the method further includes:
according to the formula
Figure BDA0003151250440000051
Determining an average replenishment interval of a target store i;
according to the formula demandi,s＝min(repl_interval_updateiInt (delta max turnover days) determines the predicted replenishment quantity demand of the target store i under the sales level scene si,s(ii) a Wherein, repl _ interval _ updateiAverage replenishment interval for target store i; c is mentionedjIs the total quantity of the target big warehouse j, the pi,jThe preference coefficient of the target large warehouse j to the target store i is px,jPreference coefficient of target big bin j to store x, siIs the average daily sales volume of the target store i, sxIs the average daily sales of store x, x being any of all stores, the onhandiThe order is the inventory of the target store iiThe method comprises the steps that the goods-in-transit amount of a target store i is obtained, max _ turnover _ days is the maximum turnover days of the store, delta is the reliability of the maximum turnover days, and the value range of the delta is 0-1.
In a second aspect of the present invention, there is also provided a dispensing device, the device comprising:
the determining unit is used for determining a goods distribution decision variable;
the creating unit is used for creating a goods distribution objective function and a constraint condition based on the goods distribution decision variable;
the output unit is used for outputting a goods distribution strategy based on the constraint condition and the objective function and outputting the goods distribution strategy; the goods distribution strategy is an optimal value of each goods distribution decision variable;
wherein,
the sorting objective function comprises:
Figure BDA0003151250440000061
wherein,
i is any store, j is any large warehouse, whcosti,jLogistics cost of replenishing goods from the large warehouse j for the target store i; said Xi,jThe number of restocks for the target store i from the large warehouse j; the emergentiA replenishment urgency weight factor for the target store i; the dutyslackA penalty factor for store shortage; said Xsi,sThe gap replenishment quantity of the target store i after replenishment under the target sales level scene s is obtained; said probsThe occurrence probability corresponding to the target sales level scene s; the Yoi,jA replenishment identification variable is set for the target store i when replenishing goods from the big bin j; the dutyoutofstockIs a penalty factor for the backorder store; said Yki,sAn identification variable for the target store i to be out of stock after replenishment under the target sales level scene s; the dutyorder_minThe penalty factor is a punishment factor for a small replenishment order, wherein the small replenishment order is a replenishment order smaller than a replenishment quantity threshold value; the Ym isi,jAn identification variable for which the replenishment quantity of the target store i from the big bin j is lower than the replenishment quantity threshold value; the Yoi,jA replenishment identification variable is set for the target store i when replenishing goods from the target large warehouse j; the dutyorder_storeAs a penalty factor for the number of replenishment stores, the YsiA variable is identified for replenishment when the target store i replenishes from any large warehouse; the dutyinv_slackA penalty factor for exceeding the store inventory limit after replenishment in the store, XkliA first slack variable for which the total in-store inventory after replenishment at the target store i is less than the minimum in-store inventory; the XkuiA second slack variable for the total in-store inventory after replenishment at the target store i above the maximum in-store inventory.
In a third aspect of the invention, a storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the method of any one of the first aspects.
In a fourth aspect of the invention, there is provided a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of the first aspect when executing the program.
The invention provides a goods distribution method, a device, a medium and computer equipment, which are used for determining goods distribution decision variables; creating a goods distribution objective function and a constraint condition based on the goods distribution decision variable; outputting a goods distribution strategy based on the constraint condition and the objective function; the goods distribution strategy is an optimal value of each goods distribution decision variable; the sorting objective function comprises:
Figure BDA0003151250440000071
therefore, the optimal replenishment quantity of each store is determined by determining a distribution decision variable, creating a corresponding distribution constraint condition and a distribution objective function according to the distribution decision variable, and solving the distribution objective function under the limitation of the constraint condition; the reason distribution decision variables comprise the distribution decision variables comprising: the method comprises the steps of supplementing the goods from a large warehouse by a store, supplementing the goods at a gap after the goods are supplemented by the store under a target sales level scene, supplementing the goods from the large warehouse by a preset supplementing quantity threshold multiple of the goods at the store, and remaining goods distribution allowance of the large warehouse after the goods are supplemented, wherein the total goods quantity in the store after the goods are supplemented by the store is lower than a first loose variable of the minimum stock quantity in the store, the total goods quantity in the store after the goods are supplemented by the store is higher than a second loose variable of the maximum stock quantity in the store, and the goods supplementing mark when the store is supplemented from the target large warehouseThe method comprises the following steps of identifying variables, namely an identification variable of replenishment when a store replenishes goods from any large warehouse, an identification variable of goods shortage after the store replenishes goods under a target sales level scene, an identification variable of which the replenishment quantity from the large warehouse is lower than a replenishment quantity threshold value and an identification variable of which the residual quantity of the goods from the large warehouse is not less than a preset replenishment quantity threshold value of the store; the goods distribution decision variable comprehensively considers the global factors influencing the goods distribution, so that the goods distribution precision of the goods distribution objective function can be improved; in addition, the method adopts a random optimization modeling algorithm to carry out discretization processing on sales forecast distribution of stores to generate corresponding target sales level scenes s (scenario), each sales level scene corresponds to an occurrence probability, so that even if actual sales and forecast sales have different degree deviations, the stability of the whole distribution can be improved based on the probabilities, the precision of a distribution target function is further improved, and the replenishment quantities of different stores can be accurately considered under the condition that the sales of the stores are uncertain.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a schematic flow chart of a cargo distribution method according to an embodiment of the present invention;
fig. 2 is a schematic structural view of a cargo distribution device according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a computer storage medium for sorting according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device for sorting goods according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment provides a goods distribution method, as shown in fig. 1, the method includes:
s110, determining a goods distribution decision variable;
the embodiment comprehensively considers global factors influencing the goods distribution to determine the goods distribution decision variable. The shipment decision variables include: the number of stores restocking from the warehouse; the gap replenishment quantity after replenishment is carried out by a store under a target sales level scene; the threshold multiple of the preset replenishment quantity of the large-warehouse replenishment of the store; the remaining goods distribution allowance of the large warehouse after the goods replenishment; a first slack variable for the total in-store inventory after the store replenishment being less than the minimum in-store inventory; a second relaxation variable for the total quantity of the stock in the store after the replenishment of the store is higher than the maximum stock quantity in the store; a replenishment identification variable when the store replenishes from a target large warehouse and a replenishment identification variable when the store replenishes from any large warehouse; an identification variable for the store to be out of stock after replenishment in a target sales level scene; and the identification variable that the replenishment quantity of the store from the large warehouse is lower than the replenishment quantity threshold value and the identification variable that the residual quantity of the large warehouse is not less than the preset replenishment quantity threshold value of the store.
The target sales level scene can be understood as a scene corresponding to different sales volumes. Such as: the daily sales volume is 60 pieces and is a sales level scene, the daily sales volume is 80 pieces and is a sales level scene, and the daily sales volume is 100 pieces and is a sales level scene, etc.
S111, creating a goods distribution objective function and a constraint condition based on the goods distribution decision variable;
after the goods distribution decision variable is determined, a goods distribution objective function and a constraint condition are created based on the goods distribution decision variable, so that an optimal solution of the goods distribution objective function can be determined under the constraint of the constraint condition, and the optimal solution is a final goods distribution strategy.
Specifically, the sorting objective function includes:
Figure BDA0003151250440000091
wherein,
i is a target store, j is a target large warehouse, whcosti,jLogistics cost of replenishing goods from the target large warehouse j for the target store i; xi,jThe quantity of replenishment from the target large warehouse j for the target store i; emergentiA replenishment urgency weight factor for the target store i; dependencyslackA penalty factor for the quantity of the stores in shortage; xsi,sThe gap replenishment quantity of the target store i after replenishment under the target sales level scene s is obtained; probsThe occurrence probability corresponding to the target sales level scene s; yoi,jA replenishment identification variable is set for a target store i when replenishment is carried out from a target large warehouse j; dependencyoutofstockIs a penalty factor for the backorder store; yki,sAn identification variable for the target store i to be out of stock after replenishment under the target sales level scene s; dependencyorder_minThe small replenishment order is a penalty factor for the small replenishment order, and the small replenishment order is a replenishment order smaller than a replenishment quantity threshold value; ym isi,jAn identification variable for which the replenishment quantity of the target store i from the target large warehouse j is lower than the replenishment quantity threshold value; yoi,jA replenishment identification variable is set for the target store i when replenishing goods from the target large warehouse j; penaltyorder_storeAs a penalty factor for the number of replenishment stores, YsiA replenishment identification variable is set for a target store i when replenishment is carried out from any large warehouse; dependencyinv_slackPenalty factors for exceeding store limit amount after replenishment in stores, XkliA first slack variable for which the total in-store quantity after replenishment at the target store i is lower than the minimum in-store inventory quantity; xkuiA second slack variable for the total in-store inventory of the target store i after replenishment above a maximum in-store inventory.
According to the method, a random optimization modeling algorithm is adopted to carry out discretization processing on sales forecast distribution of stores, corresponding target sales level scenes s (scenario) are generated, each sales level scene corresponds to one occurrence probability, and therefore even if actual sales and forecast sales have different degree deviations, the stability of integral distribution can be improved based on the probabilities.
It is noted that if the target store i has historical logistics data (logistics cost) for restocking from the target large store j, wh can be determined directly according to the historical logistics datacosti,j(ii) a If the target store i does not have the historical logistics data for replenishing goods from the target warehouse j, an equivalent parameter (which can be understood as a tendency weight factor for replenishing goods from the target warehouse j) can be deduced by using the historical replenishment data of the target store i instead of whcosti,j。
Further, when the constraint condition is that the total order amount of the target store i from each large warehouse is not less than the predicted replenishment amount of the target store i under the target sales level scene s, the constraint condition comprises:
Figure BDA0003151250440000101
when the constraint condition is that the supply quantity of the large warehouse to different stores is not more than the total supply quantity of the large warehouse, the constraint condition comprises the following steps:
Figure BDA0003151250440000102
when the constraint condition is to count whether the target store i is restocked from the target large warehouse j, the constraint condition comprises:
Xi,j≤M·Yoi,j，Xi,j≥Yoi,j；
when the constraint condition is that whether the replenishment quantity of the target store i from the target large warehouse j is lower than the replenishment quantity threshold value is counted, the constraint condition comprises the following steps:
Xi,j≤mi·Ymi,j+M·(1-Ymi,j)，Xi,j≥mi·(1-Ymi,j)；
when the constraint condition is that whether the target store i is out of stock after replenishment under the target sales level scene is counted, the constraint condition comprises the following steps:
Xsi,s≤β·demandi,s+M·Yki,s；
when the constraint condition is to count the number of large bins which can be ordered simultaneously by each store, the constraint condition comprises the following steps:
Figure BDA0003151250440000103
when the constraint is the number of statistical order stores, the constraint includes:
Figure BDA0003151250440000111
when the constraint condition is that whether the statistics meets the replenishment specification requirements of the store or not, the constraint condition comprises the following steps:
mi·Xoi,j＝Xi,j；
when the constraint conditions are that the in-transit amount of the store + the in-stock amount + the replenishment amount cannot be lower than the minimum inventory amount of the store and cannot be higher than the maximum inventory amount of the store, the constraint conditions include:
Figure BDA0003151250440000112
Figure BDA0003151250440000113
when the constraint condition is that if the target large warehouse corresponding to the target store i has the replenishment allowance, the remaining large warehouse with the priority lower than the target large warehouse does not need to replenish the target store i, the constraint condition comprises:
Figure BDA0003151250440000114
Xcj-mi+0.1≤M·Yci,j；
wherein, Xi,jNumber of replenishment of target store i from target warehouse j, Xsi,sThe gap replenishment quantity of the target store i after replenishment under the target sales level scene s is obtained; β is a threshold factor for the target store i to be considered out of stock; demandi,sForecast replenishment for target store i under sales level scenario sAn amount; m is a bigM parameter (which may be preset), and the determination method of the M value may be a conventional determination method in the large M method, which is not described herein again.
Yoi,jA replenishment identification variable is set for the target store i when replenishing goods from the target large warehouse j; yo when the target store i is restocked from the target warehouse ji,jIs 1; yo when the target store i is not restocked from the target warehouse ji,jIs 0; yki,sIdentification variable, m, for the target store i to be out of stock after replenishment in the target sales level scenario siA preset replenishment quantity threshold value of a target store i; ym isi,jAn identification variable that the replenishment quantity of the target store i from the target large warehouse j is lower than a replenishment quantity threshold value is set as the target store i, and if the replenishment quantity of the target store i from the target large warehouse j is lower than the replenishment quantity threshold value, Ymi,jIs 1; if the replenishment quantity of the target store i from the target large warehouse j is not lower than the replenishment quantity threshold value, Ymi,jIs 0.
J is a large warehouse set, orders is the number of large warehouses of the target store i capable of simultaneously adjusting goods, YsiA variable is identified for replenishment when the target store i replenishes from any large warehouse; m isiA preset replenishment quantity threshold value of a target store i; xoi,jA preset replenishment quantity threshold multiple, X, for replenishment of a target store i from a target warehouse ji,jThe quantity of goods is replenished for the target store i from the target large warehouse j.
For example, the preset replenishment quantity threshold of the target store i is 5, if the target store i replenishes 10 from the target warehouse j, then Xoi,jThe value of (2).
onhandiIs the inventory quantity, order, of the target store iiThe quantity of in-transit of the target store i, inv _ level _ maxiThe maximum inventory of the target store i, XkuiA second slack variable inv _ level _ min for which the total in-store inventory after replenishment of the target store i is higher than the maximum in-store inventoryiMinimum inventory for target store i, XkliFirst slack variable, X, for a target store i after replenishment with total in-store inventory below minimum in-store inventoryi,kThe quantity of goods replenishment from a large warehouse K for a target store i is different from K and J, K is a set corresponding to the residual large warehouse with the priority lower than the target large warehouse J, K belongs to one of subsets of the large warehouse set J, Y isci,jAn identification variable which is used for enabling the residual cargo quantity of the large warehouse j to be not less than the preset replenishment quantity threshold value of the target store i, and Yc is used for judging whether the residual cargo quantity of the target large warehouse j is more than or equal to the preset replenishment quantity threshold value of the target store ii,jIs 1, otherwise Yci,jIs 0, XcjThe residual goods distribution allowance of the target large bin j after the goods replenishment is obtained.
Thus, the goods distribution objective function and each constraint condition are finally determined.
Further, in this embodiment, the method further includes:
according to the formula emergenti＝max(store_vlti-last_daysi+1，1)2·priorityiDetermining replenishment urgency weight factor emergent of target store ii(ii) a Wherein, store _ vltiLead period of replenishment for target store i, last _ daysiThe number of days, priority, of the turnover of the target store i can be supported for the amount of goods in transit and the amount of goods in the store of the target store iiThe replenishment priority of the target store i.
The lead time of replenishment of the target store i can be based on the probability distribution of replenishment of the stores, and the corresponding days of which the cumulative probability is not lower than a given probability threshold are determined as the lead time of replenishment of the store.
Further, the method further comprises:
according to the formula
Figure BDA0003151250440000121
Determining an average replenishment interval of a target store i;
according to a formula demandi,s＝min(repl_interval_updateiInt (delta. max _ turnover _ days)) determines the predicted replenishment quantity demand of the target store i under the sales level scene si,s；
Wherein, repl _ interval _ updateiAverage replenishment interval for target store i; c. CjIs the total amount of the goods in the target large bin j, pi,jPreference coefficient, p, for target store j to target store ix,jPreference coefficient, s, for target large bin j to store xiAverage daily sales volume, s, for target store ixAverage day for store xSales volume, x being any of all stores, i belonging to x; onhandiIs the inventory of the target store i, onerderiThe quantity of goods in transit of the target store i, and max _ turnover _ days is the maximum turnover number of days of the store; the delta is the reliability of the maximum turnover days, and the value range of the delta is 0-1.
It should be noted that the values of the goods-distribution objective function and other parameters in the constraint condition may be preset according to the actual situation of each goods-distribution scenario, and are not limited herein.
Other parameters include: a threshold factor for which the target store i is considered out-of-stock, which may be determined based on actual business data; the tendency weight factor of the target store i for replenishing goods from the target large warehouse j; a penalty factor for store shortage; a penalty factor for backorder stores; the penalty factor for a small replenishment order, the penalty factor for the quantity of replenishment stores, the penalty factor exceeding the store storage limit quantity after the stores are replenished, the preset replenishment quantity threshold value and the M value of the target store i, and the number of large warehouses for which the target store i can simultaneously transfer goods; the quantity of goods in transit of the target store i, the quantity of goods in stock of the target store i, and the maximum inventory quantity and the minimum inventory quantity of the target store i.
S112, outputting a goods distribution strategy based on the constraint conditions and the goods distribution objective function; and the goods distribution strategy is an optimal value of each goods distribution decision variable.
Then outputting a goods distribution strategy based on the constraint conditions and a goods distribution objective function; the goods distribution strategy is the optimal value of each goods distribution decision variable.
Specifically, the branch objective function is solved based on the constraint conditions, and the optimal solution of each branch decision variable is determined. Therefore, the optimal quantity of the replenishment of the target store i from the target large warehouse j can be determined, and the replenishment requirement of each store can be considered.
According to the embodiment, the optimal replenishment quantity of each store is determined by determining the goods distribution decision variable, creating the corresponding goods distribution constraint condition and the goods distribution objective function according to the goods distribution decision variable, and solving the goods distribution objective function under the limitation of the constraint condition; the reason distribution decision variables comprise the distribution decision variables comprising: the method comprises the following steps that the number of the door stores for replenishment from a large warehouse, the gap replenishment amount of the door stores after replenishment under a target sales level scene, the preset replenishment amount threshold multiple of the door stores for replenishment from the large warehouse, and the remaining stock distribution allowance of the door stores after replenishment, the total stock amount in the door stores after replenishment is lower than a first loose variable of the minimum stock amount in the door stores, the second loose variable of the total stock amount in the door stores after replenishment is higher than the maximum stock amount in the store, the replenishment identification variable when the door stores replenish from the target large warehouse, the replenishment identification variable when the door stores replenish from any large warehouse, the identification variable of the door stores which are still in short of stocks after replenishment under the target sales level scene, the identification variable of the door stores which replenish from the large warehouse is lower than the replenishment amount threshold and the identification variable of the large warehouse remaining stock amount which is not less than the preset replenishment amount threshold of the door stores; the goods distribution decision variable comprehensively considers the global factors influencing the goods distribution, so that the goods distribution precision of the goods distribution objective function can be improved; and the random optimization modeling algorithm is adopted, discretization is carried out on the sales volume prediction distribution of the stores, corresponding target sales level scenes s (scenario) are generated, each sales level scene corresponds to one occurrence probability, so that even if the actual sales volume and the predicted sales volume have different degree deviations, the stability of the whole goods distribution can be improved based on the probabilities, the precision of a goods distribution objective function is further improved, and the replenishment volumes of different stores can be accurately considered under the condition that the sales volumes of the stores are uncertain.
Based on the same inventive concept, an embodiment of the present invention further provides a cargo distribution device, as shown in fig. 2, the device includes:
a determining unit 21 for determining a distribution decision variable;
a creating unit 22, configured to create a distribution objective function and a constraint condition based on the distribution decision variable;
the output unit 23 is configured to output a distribution strategy based on the constraint condition and the objective function, and output the distribution strategy; the goods distribution strategy is the optimal value of each goods distribution decision variable;
the sorting objective function comprises:
Figure BDA0003151250440000141
wherein,
i is a target store, j is a target large warehouse, and whcosti,jLogistics cost of replenishing goods from the target large warehouse j for the target store i; said X isi,jThe quantity of replenishment from the target large warehouse j for the target store i; the emergentiA replenishment urgency weighting factor for the target store i; the dutyslackA penalty factor for store shortage; said Xsi,sThe gap replenishment quantity of the target store i after replenishment under the target sales level scene s is obtained; said probsThe occurrence probability corresponding to the target sales level scene s; the Yoi,jA replenishment identification variable is set for the target store i when replenishing goods from the big bin j; the dutyoutofstockIs a penalty factor for the backorder store; said Yki,sAn identification variable for the target store i to be out of stock after replenishment under the target sales level scene s; the dutyorder_minThe penalty factor is a punishment factor for a small replenishment order, wherein the small replenishment order is a replenishment order smaller than a replenishment quantity threshold value; the Ym isi,jAn identification variable for which the replenishment quantity of the target store i from the target large warehouse j is lower than the replenishment quantity threshold value; the Yoi,jA replenishment identification variable is set for the target store i when replenishing goods from the target large warehouse j; the dutyorder_storeAs a penalty factor for the number of replenishment stores, the YsiA variable is identified for replenishment when the target store i replenishes from any large warehouse; the dutyinv_slackA penalty factor for exceeding the store inventory limit after replenishment in the store, XkliA first relaxation variable in which the total in-store quantity after replenishment of the target store i is lower than the minimum in-store inventory quantity; the XkuiA second slack variable for the total in-store inventory after replenishment at the target store i above the maximum in-store inventory.
The device may be a computer, a server, or other equipment having a computing or storage function. The device may be a stand-alone server, and is not limited herein.
Since the apparatus described in the embodiment of the present invention is an apparatus used for implementing the method in the embodiment of the present invention, a person skilled in the art can understand the specific structure and the deformation of the apparatus based on the method described in the embodiment of the present invention, and thus the detailed description is omitted here. All devices adopted by the method of the embodiment of the invention belong to the protection scope of the invention.
Based on the same inventive concept, the present embodiment provides a computer apparatus 300, as shown in fig. 3, including a memory 310, a processor 320, and a computer program 311 stored on the memory 310 and operable on the processor 320, wherein the processor 320 executes the computer program 311 to implement the following steps:
determining a goods distribution decision variable;
creating a goods distribution objective function and a constraint condition based on the goods distribution decision variable;
outputting a goods distribution strategy based on the constraint condition and the objective function; the goods distribution strategy is the optimal value of each goods distribution decision variable; wherein,
the sorting objective function comprises:
Figure BDA0003151250440000161
wherein,
i is a target store, j is a target large warehouse, and whcosti,jLogistics cost of replenishing goods from the target large warehouse j for the target store i; said Xi,jThe quantity of replenishment from the target large warehouse j for the target store i; the emergentiA replenishment urgency weight factor for the target store i; the dependencyslackA penalty factor for store shortage; said Xsi,sThe gap replenishment quantity of the target store i after replenishment under the target sales level scene s is obtained; said probsThe occurrence probability corresponding to the target sales level scene s; the Yoi,jA replenishment identification variable is set for the target store i when replenishing goods from the target large warehouse j; the dutyoutofstockA penalty factor for backorder stores; said Yki,sAn identification variable for the target store i to be out of stock after replenishment under the target sales level scene s; the dutyorder_minOrder for replenishment of small quantitiesThe small replenishment orders are replenishment orders smaller than a replenishment quantity threshold value; the Ym isi,jAn identification variable which is used for the target store i and has the replenishment quantity from the target large warehouse j lower than the replenishment quantity threshold value; the Yoi,jA replenishment identification variable is set for the target store i when replenishing goods from the target large warehouse j; the dutyorder_storeAs a penalty factor for the number of replenishment stores, the YsiA variable is identified for replenishment when the target store i replenishes from any large warehouse; the dutyinv_slackA penalty factor for exceeding the store inventory limit after replenishment in the store, XkliA first slack variable for which the total in-store inventory after replenishment at the target store i is less than the minimum in-store inventory; the XkuiA second slack variable for the total in-store inventory after replenishment at the target store i above the maximum in-store inventory.
In particular embodiments, any of the foregoing embodiments may be implemented when processor 320 executes computer program 3411.
Since the computer device described in this embodiment is a device used for implementing a distribution method according to this embodiment, a specific implementation manner of the computer device of this embodiment and various variations thereof can be understood by those skilled in the art based on the methods described in the foregoing embodiments of this application, and therefore, a detailed description of how to implement the method in this embodiment by the server is not provided here. The equipment used by those skilled in the art to implement the methods in the embodiments of the present application is within the scope of the present application.
Based on the same inventive concept, the present embodiment provides a computer-readable storage medium 400, as shown in fig. 4, on which a computer program 411 is stored, the computer program 411 implementing the following steps when being executed by a processor:
determining a goods distribution decision variable;
creating a goods distribution objective function and a constraint condition based on the goods distribution decision variable;
outputting a goods distribution strategy based on the constraint condition and the objective function; the goods distribution strategy is the optimal value of each goods distribution decision variable; wherein,
the sorting objective function comprises:
Figure BDA0003151250440000171
wherein,
i is a target store, j is a target large warehouse, and whcosti,jLogistics cost of replenishing goods from the target large warehouse j for the target store i; said Xi,jThe quantity of replenishment from the target large warehouse j for the target store i; the emergentiA replenishment urgency weight factor for the target store i; the dutyslackA penalty factor for store shortage; said Xsi,sThe gap replenishment quantity of the target store i after replenishment under the target sales level scene s is obtained; said probsThe occurrence probability corresponding to the target sales level scene s; the Yoi,jA replenishment identification variable is set for the target store i when replenishing goods from the target large warehouse j; the dutyoutofstockIs a penalty factor for the backorder store; said Yki,sAn identification variable for the target store i to be out of stock after replenishment under the target sales level scene s; the dutyorder_minThe penalty factor is a punishment factor for a small replenishment order, wherein the small replenishment order is a replenishment order smaller than a replenishment quantity threshold value; the Ym isi,jAn identification variable for which the replenishment quantity of the target store i from the target large warehouse j is lower than the replenishment quantity threshold value; the Yoi,jA replenishment identification variable is set for the target store i when replenishing goods from the target large warehouse j; the dutyorder_storeAs a penalty factor for the number of replenishment stores, the YsiA variable is identified for replenishment when the target store i replenishes from any large warehouse; the dutyinv_slackA penalty factor for exceeding the store inventory limit after replenishment in the store, XkliA first slack variable for which the total in-store inventory after replenishment at the target store i is less than the minimum in-store inventory; the XkuiA second slack variable for the total in-store inventory after replenishment at the target store i above the maximum in-store inventory.
In a specific implementation, the computer program 411 may implement any of the foregoing embodiments when executed by a processor.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the present application.
The above description is only exemplary of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents, improvements, etc. that are within the spirit and principle of the present invention should be included in the present invention.

Claims (10)

1. A method of sorting, the method comprising:
determining a goods distribution decision variable;
creating a goods distribution objective function and a constraint condition based on the goods distribution decision variable;
outputting a goods distribution strategy based on the constraint condition and the objective function; the goods distribution strategy is an optimal value of each goods distribution decision variable; wherein,
the sorting objective function includes finding a set of
Figure DEST_PATH_IMAGE001
Satisfies the following conditions:
Figure 278008DEST_PATH_IMAGE002
(ii) a Wherein,
when the constraint condition is that the supply quantity of the large warehouse to different stores is not more than the total supply quantity of the large warehouse, the constraint condition comprises the following steps:
Figure DEST_PATH_IMAGE003
；
the above-mentionediIs a target store, thejIs a target big cabin, the
Figure 24248DEST_PATH_IMAGE004
Is a target storeiFrom the target big storehousejLogistics cost of replenishment; the above-mentioned
Figure DEST_PATH_IMAGE005
Is a target storeiFrom the target big storehousejThe number of supplies; the above-mentioned
Figure 457634DEST_PATH_IMAGE006
Is the target storeiThe replenishment urgency weighting factor; the above-mentioned
Figure DEST_PATH_IMAGE007
A penalty factor for store shortage; the above-mentioned
Figure 219791DEST_PATH_IMAGE008
Is the target storeiIn a target sales level scenariosThe gap replenishment quantity after the replenishment is carried out; the above-mentioned
Figure DEST_PATH_IMAGE009
For a target sales level scenariosCorresponding occurrence probability, wherein the target sales level scene is a scene corresponding to different sales volumes; the above-mentioned
Figure 930390DEST_PATH_IMAGE010
Is the target storeiFrom the target big storehousejA replenishment identification variable during replenishment; the above-mentioned
Figure DEST_PATH_IMAGE011
A penalty factor for backorder stores; the above-mentioned
Figure 221431DEST_PATH_IMAGE012
Is a target storeiIn a target sales level scenariosAn identification variable for the lack of goods after the replenishment; the above-mentioned
Figure DEST_PATH_IMAGE013
The penalty factor is a punishment factor for a small replenishment order, wherein the small replenishment order is a replenishment order smaller than a replenishment quantity threshold value; the above-mentioned
Figure 407693DEST_PATH_IMAGE014
Is a target storeiFrom the target big storehousejAn identification variable for which the replenishment quantity is below a replenishment quantity threshold; the above-mentioned
Figure DEST_PATH_IMAGE015
As a penalty factor for the number of replenishment stores, the
Figure 442383DEST_PATH_IMAGE016
Is a target storeiA replenishment identification variable when replenishing from any large bin; the above-mentioned
Figure DEST_PATH_IMAGE017
A penalty factor exceeding a store inventory limit amount after replenishment of the store, said
Figure 256755DEST_PATH_IMAGE018
The target storeiA first slack variable for a total in-store inventory after replenishment that is less than a minimum in-store inventory; the above-mentioned
Figure DEST_PATH_IMAGE019
The target storeiA second slack variable for the total in-store inventory after replenishment being greater than the maximum in-store inventory; the described
Figure 485742DEST_PATH_IMAGE020
For the purpose of large warehouse after replenishmentjRemaining stock remaining, said
Figure DEST_PATH_IMAGE021
Is a target large warehousejTotal inventory of (c).
2. As claimed inThe method of claim 1, wherein the constraint condition is a target storeiThe total amount of the orders from each large warehouse is not less than the target storeiIn a target sales level scenariosThe constraint condition comprises the following predicted replenishment quantity:
Figure 398116DEST_PATH_IMAGE022
；
wherein,sfor any sales level scenario; the described
Figure DEST_PATH_IMAGE023
Is a target storeiIn a sales level scenariosPredicting the replenishment quantity; the above-mentionediIs a target store.
3. The method of claim 1, wherein when the constraint is a statistical target storeiWhether to go from the target large warehousejWhen replenishing goods, the constraint conditions comprise:
Figure 737961DEST_PATH_IMAGE024
；
when the constraint condition is a statistical target storeiFrom the target big storehousejIf the replenishment quantity is lower than the replenishment quantity threshold value, the constraint condition includes:
Figure DEST_PATH_IMAGE025
；
when the constraint condition is a statistical target storeiWhen the goods are still out of stock after replenishment under the target sales level scene, the constraint conditions comprise:
Figure 921687DEST_PATH_IMAGE026
(ii) a Wherein,
the above-mentionedMIs a bigM parameter, said
Figure DEST_PATH_IMAGE027
Is a target storeiThe preset replenishment quantity threshold value; if the target storeiFrom the target big storehousejWhen the replenishment quantity is lower than the replenishment quantity threshold value, the
Figure 649471DEST_PATH_IMAGE028
Is 1; if the target storeiFrom the target big storehousejWhen the replenishment quantity is not less than the replenishment quantity threshold value, the method is adopted
Figure 607063DEST_PATH_IMAGE028
Is 0; the described
Figure DEST_PATH_IMAGE029
For the target storeiA threshold factor considered out-of-stock; the above-mentioned
Figure 249135DEST_PATH_IMAGE030
Is a target storeiIn a sales level scenariosPredicting the replenishment quantity; when the target store isiFrom the target big storehousejWhen replenishing goods, the
Figure DEST_PATH_IMAGE031
Is 1; when the target store isiBig warehouse without subordinate targetjWhen replenishing goods, the
Figure 54411DEST_PATH_IMAGE031
Is 0.
4. The method of claim 1, wherein when the constraint is counting the number of warehouses that each store can order simultaneously, the constraint comprises:
Figure 812151DEST_PATH_IMAGE032
；
when the constraint condition is the number of statistical order stores, the constraint condition comprises:
Figure DEST_PATH_IMAGE033
；
when the constraint condition is that whether the statistics meet the replenishment specification requirements of the stores, the constraint condition comprises the following steps:
Figure 693257DEST_PATH_IMAGE034
(ii) a Wherein,
the above-mentionedJIs a large collection of bins, theordersIs a target storeiThe number of large bins capable of adjusting goods simultaneously; the above-mentioned
Figure DEST_PATH_IMAGE035
Is a target storeiA replenishment identification variable when replenishing from any large bin; the described
Figure 515851DEST_PATH_IMAGE036
Target storeiThe preset replenishment quantity threshold value; the above-mentioned
Figure DEST_PATH_IMAGE037
Is a target storeiFrom the target big storehousejAnd the preset replenishment quantity threshold multiple of replenishment.
5. The method according to claim 1, wherein when the constraint condition is that the store in-transit amount + inventory amount + present replenishment amount cannot be lower than the minimum inventory amount of the store and cannot be higher than the maximum inventory amount of the store, the constraint condition includes:
Figure 81960DEST_PATH_IMAGE038
，
Figure DEST_PATH_IMAGE039
；
when the constraint condition is the target storeiWhen the corresponding target large warehouse has the replenishment allowance, the priority is lower than that of the target large warehousejThe residual large warehouse does not need to be aimed at a target storeiWhen replenishment is carried out, the constraint conditions comprise:
Figure 682706DEST_PATH_IMAGE040
，
Figure DEST_PATH_IMAGE041
(ii) a Wherein,
the described
Figure 818152DEST_PATH_IMAGE042
Is a target storeiThe amount of goods in stock, said
Figure DEST_PATH_IMAGE043
Is a target storeiThe quantity of goods in transit of, said
Figure 677392DEST_PATH_IMAGE044
Is a target storeiMaximum inventory of, said
Figure DEST_PATH_IMAGE045
Is a target storeiA minimum amount of inventory of, said
Figure 581894DEST_PATH_IMAGE046
Is a target storeiSlave large warehousekAmount of replenishment, saidKIs lower than the target large binjOf the remaining large bins, saidMIs a bigM parameter, said
Figure DEST_PATH_IMAGE047
Is a large target binjThe residual goods quantity is not less than the target storeiPresetting replenishment quantity threshold value
Figure 619120DEST_PATH_IMAGE048
Is identified by a variable, said
Figure DEST_PATH_IMAGE049
For the purpose of large warehouse after replenishmentjRemaining stock separation allowance of
Figure 474819DEST_PATH_IMAGE050
Target storeiIs determined by the pre-set replenishment quantity threshold.
6. The method of claim 1, wherein the method further comprises:
according to the formula
Figure DEST_PATH_IMAGE051
Determining the target storeiWeight factor of the urgency of replenishment
Figure 763849DEST_PATH_IMAGE052
(ii) a Wherein, the
Figure DEST_PATH_IMAGE053
Is the target storeiThe lead period of replenishment, said
Figure 896759DEST_PATH_IMAGE054
Is the target storeiThe goods quantity in transit and the goods quantity in storage can support the target storeiNumber of days in turnover, the
Figure DEST_PATH_IMAGE055
Is the target storeiReplenishment priority.
7. The method of claim 2, wherein the method further comprises:
according to the formula
Figure 245832DEST_PATH_IMAGE056
Determining a target storeiAverage replenishment interval of (1);
according to the formula
Figure DEST_PATH_IMAGE057
Determining the target storeiIn a sales level scenariosPredicted replenishment quantity of
Figure 214925DEST_PATH_IMAGE058
(ii) a Wherein,
Figure DEST_PATH_IMAGE059
is a target storeiAverage replenishment interval of (1); the above-mentioned
Figure 546461DEST_PATH_IMAGE060
Is a target large warehousejTotal amount of partial shipment of
Figure DEST_PATH_IMAGE061
Is the target large binjTo target storeiOf said preference coefficient, said
Figure 300921DEST_PATH_IMAGE062
Target large warehousejTo storexOf said preference coefficient, said
Figure DEST_PATH_IMAGE063
Is the target storeiAverage daily sales of, said
Figure 53851DEST_PATH_IMAGE064
As a storexThe average daily sales volume of (a),xfor any of all stores, said
Figure DEST_PATH_IMAGE065
Is a target storeiThe amount of goods in stock, said
Figure 510241DEST_PATH_IMAGE066
Is a target storeiThe amount of cargo in transit of, said
Figure DEST_PATH_IMAGE067
The maximum number of turnaround days for a store, thedeltaFor the confidence of the maximum number of turnaround days,deltathe value range of (1) is 0-1.
8. A dispensing device, the device comprising:
the determining unit is used for determining a goods distribution decision variable;
the creating unit is used for creating a goods distribution objective function and a constraint condition based on the goods distribution decision variable;
the output unit is used for outputting a goods distribution strategy based on the constraint condition and the target function and outputting the goods distribution strategy; the goods distribution strategy is the optimal value of each goods distribution decision variable;
wherein,
the sorting objective function includes finding a set of
Figure 141073DEST_PATH_IMAGE068
Satisfies the following conditions:
Figure DEST_PATH_IMAGE069
(ii) a Wherein,
when the constraint condition is that the supply quantity of the large warehouse to different stores is not more than the total supply quantity of the large warehouse, the constraint condition comprises the following steps:
Figure 248575DEST_PATH_IMAGE070
；
the above-mentionediIs any store, thejIs any large bin, the
Figure DEST_PATH_IMAGE071
Is a target doorShopiSlave large warehousejLogistics cost of replenishment; the above-mentioned
Figure 939451DEST_PATH_IMAGE072
Is a target storeiSlave large warehousejThe number of supplies; the above-mentioned
Figure DEST_PATH_IMAGE073
Is the target storeiThe replenishment urgency weighting factor; the above-mentioned
Figure DEST_PATH_IMAGE075
A penalty factor for store shortage; the above-mentioned
Figure DEST_PATH_IMAGE077
Is the target storeiIn a target sales level scenariosThe gap replenishment quantity after the replenishment is carried out; the above-mentioned
Figure DEST_PATH_IMAGE079
For a target sales level scenariosCorresponding occurrence probability, wherein the target sales level scene is a scene corresponding to different sales volumes; the above-mentioned
Figure DEST_PATH_IMAGE081
Is the target storeiFrom the target big storehousejA replenishment identification variable during replenishment; the above-mentioned
Figure DEST_PATH_IMAGE083
Is a penalty factor for the backorder store; the above-mentioned
Figure DEST_PATH_IMAGE085
Is a target storeiIn a target sales level scenariosAn identification variable for the lack of goods after the replenishment; the above-mentioned
Figure DEST_PATH_IMAGE087
Is a penalty factor for small replenishment orders, smaller than replenishmentReplenishment orders for quantity thresholds; the above-mentioned
Figure DEST_PATH_IMAGE089
Is a target storeiSlave large warehousejAn identification variable for which the replenishment quantity is below a replenishment quantity threshold; the above-mentioned
Figure DEST_PATH_IMAGE091
As a penalty factor for the number of replenishment stores, the
Figure DEST_PATH_IMAGE093
Is a target storeiA replenishment identification variable when replenishing from any large bin; the above-mentioned
Figure DEST_PATH_IMAGE095
A penalty factor for exceeding a store inventory limit amount after replenishment for an store, said
Figure DEST_PATH_IMAGE097
The target storeiA first slack variable for a total in-store inventory after replenishment that is less than a minimum in-store inventory; the above-mentioned
Figure DEST_PATH_IMAGE099
The target storeiA second slack variable for the total in-store inventory after replenishment being greater than the maximum in-store inventory; the above-mentioned
Figure DEST_PATH_IMAGE101
For the large warehouse of the target after replenishmentjRemaining stock remaining, said
Figure DEST_PATH_IMAGE103
Is a target large warehousejTotal inventory of (c).
9. A storage medium on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 7 when executing the program.
CN202110770062.0A 2021-07-07 2021-07-07 Goods distribution method, device, medium and computer equipment Active CN113592153B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110770062.0A CN113592153B (en) 2021-07-07 2021-07-07 Goods distribution method, device, medium and computer equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110770062.0A CN113592153B (en) 2021-07-07 2021-07-07 Goods distribution method, device, medium and computer equipment

Publications (2)

Publication Number Publication Date
CN113592153A CN113592153A (en) 2021-11-02
CN113592153B true CN113592153B (en) 2022-06-17

Family

ID=78246354

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110770062.0A Active CN113592153B (en) 2021-07-07 2021-07-07 Goods distribution method, device, medium and computer equipment

Country Status (1)

Country Link
CN (1) CN113592153B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115630899B (en) * 2022-11-01 2023-08-11 中国外运股份有限公司 Multi-platform inventory optimization method and system

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110363454A (en) * 2018-04-09 2019-10-22 杉数科技(北京)有限公司 For determining the method and device of commodity replenishment quantity
CN110689157A (en) * 2018-07-04 2020-01-14 北京京东尚科信息技术有限公司 Method and device for determining call relation
CN111325490A (en) * 2018-12-14 2020-06-23 顺丰科技有限公司 Replenishment method and device
CN111815198A (en) * 2020-07-27 2020-10-23 名创优品（横琴）企业管理有限公司 Method, device and equipment for replenishing goods in store

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8843404B2 (en) * 2012-04-04 2014-09-23 International Business Machines Corporation Joint pricing and replenishment of freshness inventory

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110363454A (en) * 2018-04-09 2019-10-22 杉数科技(北京)有限公司 For determining the method and device of commodity replenishment quantity
CN110689157A (en) * 2018-07-04 2020-01-14 北京京东尚科信息技术有限公司 Method and device for determining call relation
CN111325490A (en) * 2018-12-14 2020-06-23 顺丰科技有限公司 Replenishment method and device
CN111815198A (en) * 2020-07-27 2020-10-23 名创优品（横琴）企业管理有限公司 Method, device and equipment for replenishing goods in store

Also Published As

Publication number Publication date
CN113592153A (en) 2021-11-02

Similar Documents

Publication Publication Date Title
Haijema et al. Improved ordering of perishables: The value of stock-age information
CA2972893C (en) Apparatus and method of maintaining accurate perpetual inventory information
US8515835B2 (en) Systems and methods for multi-echelon inventory planning with lateral transshipment
CN111242524B (en) Method, system, equipment and storage medium for determining single product replenishment quantity
US6643556B1 (en) Method for optimizing a supply-consumption operation
CN107963385A (en) The processing method and system of cargo in a kind of logistic storage field
CN113743862B (en) Product target inventory determining method and system based on product classification
CN113762885B (en) Method, apparatus, device, storage medium and program product for determining replenishment quantity
Lototsky et al. Model of the automated warehouse management and forecasting system in the conditions of transition to industry 4.0
CN113592153B (en) Goods distribution method, device, medium and computer equipment
CN112884393A (en) Inventory replenishment method and device and electronic equipment
CN114819831B (en) Method, equipment and storage medium for replenishment and dispatch based on Amazon FBA warehouse
CN111784223B (en) Cargo allocation data processing method, device and storage medium
CN113762859B (en) Inventory binning method, device, system and storage medium
CN113962416A (en) Engineering machinery part stock prediction method, management method and system
CN112712222A (en) Article scheduling method and system
CN114358874A (en) Replenishment method and device and electronic equipment
CN116071000B (en) Control method and control device for article inventory
Kawanaka et al. Inventory satisfaction discrimination method utilizing images and deep learning
CN114169557A (en) Inventory replenishment method, computer-readable storage medium and terminal device
CN114841634A (en) Warehouse goods management method
CN113743733B (en) Replenishment method and system
CN106537442A (en) Collection amount regulation assist apparatus, collection amount regulation assist method, and computer-readable recording medium
CN113762842B (en) Warehouse scheduling method, server and system
CN115034709A (en) Inventory management 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
GR01 Patent grant
GR01 Patent grant