A method and device for generating configuration information

Abstract

The present invention discloses a method and device for generating configuration information, and relates to the field of computer technology. A specific implementation of the method includes: determining the optimized price, upper limit of demand, sales volume forecast value and order volume forecast value of a commodity in a specified channel; inputting the optimized price, upper limit of demand, sales volume forecast value and order volume forecast value into an optimization model for maximizing the profit and order fulfillment rate of the order in which the commodity is located in the channel, and solving the optimization model to determine the inventory allocation and order dispatch configuration of the commodity in the specified channel. This implementation can perform effective commodity configuration management for the omni-channel retail model.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and device for generating configuration information. Background Art

[0002] At present, the omni-channel retail model is gaining wide attention and adoption. For customers, omni-channel means being able to make purchases at any time, in any place, and in any way, and receiving the goods in the most convenient way possible. For retailers, omni-channel means integrating all channels to provide customers with a seamless shopping experience across all channels. These channels include: physical stores, online stores, direct mail, mobile phone stores, etc. In the omni-channel retail model, customers' consumption behavior is not limited to using only one of the offline and online channels, but can also appear alternately in multiple channels. The sales model adopted by retailers includes not only a single online / offline sales channel, but also sales channels for online ordering and offline fulfillment, and sales channels for offline ordering and online fulfillment. The same consumer has a unified and unique identity in any channel. Because of the sharing of information across channels, customers can enjoy a unified service experience and will not feel the existence of barriers between channels.

[0003] In the process of realizing the present invention, the inventors found that there are at least the following problems in the prior art: Reasonable configuration of inventory product distribution and capacity can maximize the profits of retailers, avoid the phenomenon of inventory shortage during product sales, and provide customers with a good user experience. However, since the omni-channel retail model is currently in its early stages of development, the configuration management of products is relatively weak, and there is currently no effective method for generating configuration information for the omni-channel retail model.

[0004] Therefore, there is an urgent need for a method and device for generating configuration information that can effectively configure commodities for an omni-channel retail model. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method and apparatus for generating configuration information, which can effectively manage product configuration for an omni-channel retail model.

[0006] To achieve the above object, according to one aspect of an embodiment of the present invention, a method for generating configuration information is provided, comprising:

[0007] Determine the optimized price, demand limit, sales forecast, and order volume forecast for the product in a specified channel;

[0008] The optimized price, demand upper limit, sales volume forecast value and order volume forecast value are input into the optimization model for maximizing the profit and order fulfillment rate of the order of the product under the channel, and the optimization model is solved to determine the inventory allocation and order dispatch configuration of the product under the specified channel.

[0009] Optionally, when the inventory of the product does not meet the demand for the product in the order, no delivery is issued for the order, the optimization model for maximizing the profit of the order to which the product belongs and the order fulfillment rate includes:

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[0018] Among them, K c is the set of commodity categories that can be stored in channel c, numbered k, G c is the set of products in all categories of channel c, numbered g, G k c is the set of products in category k under channel c, k belongs to K c , J c Place an order set for channel c, numbered j, N k c is the maximum inventory quantity of category k under channel c, B j c A is the category set of goods required by channel c to place order j, g c is the predicted sales volume of product g under channel c, g belongs to G c , a gj c The demand for product g by order j from channel c, q g c is the upper limit of demand for product g under channel c, M c is the inventory limit of all products in channel c, p g c is the offline sales price of product g in channel c, rg c is the gross profit margin of product g under channel c, η g c is the monthly interest rate of funds for commodity g under channel c, δ g c is the provision rate of product g under channel c, ζ j c is the revenue of delivering order j under channel c, C c is the inventory cost of channel c, w g c Indicates whether product g is stored in the warehouse corresponding to channel c, u gj c Indicates whether to send product g from channel c to satisfy order j, o j c Indicates whether to issue a delivery for order j placed by channel c, s g c Indicates the inventory quantity of product g in the warehouse under channel c,

[0019] w g c 、u gj c , o j c and g c is the decision variable, according to the decision variable w g c and g c Determine the inventory allocation of product g under channel c, based on the decision variable u gj c and j c Determine the dispatch configuration for order j containing product g.

[0020] Optionally, when the inventory of the product does not meet the demand for the product in the order, delivery for the order can still be issued, the optimization model for maximizing the profit of the order to which the product belongs and the order fulfillment rate includes:

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[0030] Among them, K c is the set of commodity categories that can be stored in channel c, numbered k, G c is the set of products in all categories of channel c, numbered g, G k c is the set of products in category k under channel c, k belongs to K c , J c Place an order set for channel c, numbered j, N k c is the maximum inventory quantity of category k under channel c, B j c A is the category set of goods required by channel c to place order j, g c is the predicted sales volume of product g under channel c, g belongs to G c , a gj c The demand for product g by order j from channel c, q g c is the upper limit of demand for product g under channel c, M c is the inventory limit of all products in channel c, p g c is the offline sales price of product g in channel c, r g c is the gross profit margin of product g under channel c, η g c is the monthly interest rate of funds for commodity g under channel c, δ g c is the provision rate of product g under channel c, ζ j c is the revenue of delivering order j under channel c, C c is the inventory cost of channel c, w g c Indicates whether product g is stored in the warehouse corresponding to channel c, u gj c Indicates whether to send product g from channel c to satisfy order j, o j c Indicates whether to issue a delivery for order j placed by channel c, s g c represents the inventory quantity of product g in the warehouse under channel c, x gjc represents the actual delivery quantity of item g in order j,

[0031] x gj c 、w g c 、u gj c , o j c and g c is the decision variable, according to the decision variable w g c and g c Determine the inventory allocation of product g under channel c, based on the decision variable u gj c 、x gj c and j c Determine the dispatch configuration for order j containing product g.

[0032] Furthermore, determining the optimal price and demand limit of a product in a specified channel includes:

[0033] Determine the category to which the commodity belongs, where the category to which the commodity belongs includes: seasonal commodity, perishable commodity and general commodity, wherein the residual value of the perishable commodity is zero, and the residual value of the general commodity is greater than the residual value of the perishable commodity and less than the residual value of the seasonal commodity;

[0034] Determine the demand function, production cost, logistics cost, demand price elasticity coefficient and demand inflation factor of the product under the specified channel;

[0035] The demand function, production cost, logistics cost, demand price elasticity coefficient and demand inflation factor are input into an optimization model for maximizing the profit of the product in all channels of the category to which the product belongs, and the optimization model is solved to determine the optimal price and demand limit of the product in the specified channel.

[0036] Optionally, when the product is a seasonal product, the optimization model for maximizing the product profit of all channels includes:

[0037]

[0038] M i 0 (p i ,z i )=(p i -cd i )E[min{q i ,D i (pi )}];

[0039] When the commodity is a perishable commodity, the optimization model for maximizing the commodity profit of all channels includes:

[0040]

[0041] M i 1 (p i ,z i )=(p i -d i )E[min{q i ,D i (p i )}]-cy i (p i ) i ;

[0042] When the commodity is a general commodity, the optimization model for maximizing the commodity profit of all channels includes:

[0043]

[0044] M i 2 (p i ,z i )=(p i -cd i )y i (p i )[z i -Λ i (z i )]-γcE[max{0,q i -D i (p i )}];

[0045] in, M 0 (p,z),M 1 (p,z) and M 2 (p, z) are the expected total profits of seasonal goods, perishable goods and general goods in all channels, respectively. i 0 (p i ,z i )、M i 1 (p i ,z i ) and M i 2 (p i ,z i) are the expected profits of seasonal goods, perishable goods and general goods under channel i, c is the production cost of each unit of goods, d i is the logistics cost per unit of goods in channel i, p i is the price per unit of goods in channel i, q i is the upper limit of the demand for the product under channel i, p i and q i is the decision variable, D i (p i ) is the demand function of the commodity under channel i, b i is the price elasticity coefficient of demand for the product in channel i, z i is the inventory factor of the product in channel i, z i =q i / y i (p i ), y i (p i ) is the specified decreasing function of the product under channel i.

[0046] Optionally, determining the demand function and demand inflation factor of a product in a specified channel includes:

[0047] Determine the demand inflation factor ∈ under channel i i For [A i ,B i ], 0≤A i i ,∈ i The mean μ i >0, let h i (∈ i ) = f i (∈ i ) / (1-F i (∈ i )), then d(∈ i h i (∈ i )) / d∈ i =h i (∈ i )+∈ i dh i (∈ i ) / d∈ i >0,f i (∈ i ) is the demand expansion factor ∈ i The probability density function, F i (∈ i ) is the demand expansion factor ∈ i The distribution function of

[0048] ​Determine the demand function D of the product under channel i i (p i )=y i (p i )+∈ i , or D i (p i )=y i (p i )∈ i ,y i (p i )=a i p i -bi , a i >0,b i >1.

[0049] Furthermore, the channel of the product includes one or more of the following channels: a single online channel, a single offline channel, an online ordering and offline fulfillment channel, and an offline ordering and online fulfillment channel.

[0050] Furthermore, the logistics cost of goods in the online ordering and offline fulfillment channels is 3 Equal to the logistics cost of a single offline channel d 1 , the logistics cost of goods in offline order and online fulfillment channels d 4 Equal to the logistics cost of a single online channel d 2 ;

[0051] The price elasticity coefficient of demand for goods in the online ordering and offline fulfillment channels b 3 and demand inflation factor ∈ 3 Equal to the price elasticity coefficient of demand for a single online channel b 2 and demand inflation factor ∈ 2 , the price elasticity coefficient of demand for goods in the offline order and online fulfillment channels b 4 and demand inflation factor ∈ 4 Equal to the price elasticity coefficient of demand for a single offline channel b 1 and demand inflation factor ∈ 1 .

[0052] To achieve the above object, according to another aspect of an embodiment of the present invention, there is also provided a device for generating configuration information, including:

[0053] The pricing module is used to determine the optimal price and demand limit of the product in a specified channel;

[0054] The forecasting module is used to determine the sales volume forecast and order volume forecast of the product in the specified channel;

[0055] The inventory optimization module is used to input the optimized price, demand upper limit, sales volume forecast value and order quantity forecast value into the optimization model that maximizes the profit and order fulfillment rate of the order of the product in the channel, and solve the optimization model to determine the inventory allocation and order dispatch configuration of the product in the specified channel.

[0056] Optionally, when the inventory of the product does not meet the demand for the product in the order, no delivery is issued for the order, the optimization model for maximizing the profit of the order to which the product belongs and the order fulfillment rate includes:

[0057]

[0058]

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[0060]

[0061]

[0062]

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[0065] Among them, K c is the set of commodity categories that can be stored in channel c, numbered k, G c is the set of products in all categories of channel c, numbered g, G k c is the set of products in category k under channel c, k belongs to K c , J c Place an order set for channel c, numbered j, N k c is the maximum inventory quantity of category k under channel c, B j c A is the category set of goods required by channel c to place order j, g c is the predicted sales volume of product g under channel c, g belongs to G c , a gj c The demand for product g by order j from channel c, q g c is the upper limit of demand for product g under channel c, M c is the inventory limit of all products in channel c, p g cis the offline sales price of product g in channel c, r g c is the gross profit margin of product g under channel c, η g c is the monthly interest rate of funds for commodity g under channel c, δ g c is the provision rate of product g under channel c, ζ j c is the revenue of delivering order j under channel c, C c is the inventory cost of channel c, w g c Indicates whether product g is stored in the warehouse corresponding to channel c, u gj c Indicates whether to send product g from channel c to satisfy order j, o j c Indicates whether to issue a delivery for order j placed by channel c, s g c Indicates the inventory quantity of product g in the warehouse under channel c,

[0066] w g c 、u gj c , o j c and g c is the decision variable, according to the decision variable w g c and g c Determine the inventory allocation of product g under channel c, based on the decision variable u gj c and j c Determine the dispatch configuration for order j containing product g.

[0067] Optionally, when the inventory of the product does not meet the demand for the product in the order, delivery for the order can still be issued, the optimization model for maximizing the profit of the order to which the product belongs and the order fulfillment rate includes:

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[0077] Among them, K c is the set of commodity categories that can be stored in channel c, numbered k, G c is the set of products in all categories of channel c, numbered g, G k c is the set of products in category k under channel c, k belongs to K c , J c Place an order set for channel c, numbered j, N k c is the maximum inventory quantity of category k under channel c, B j c A is the category set of goods required by channel c to place order j, g c is the predicted sales volume of product g under channel c, g belongs to G c , a gj c The demand for product g by order j from channel c, q g c is the upper limit of demand for product g under channel c, M c is the inventory limit of all products in channel c, p g c is the offline sales price of product g in channel c, r g c is the gross profit margin of product g under channel c, η g c is the monthly interest rate of funds for commodity g under channel c, δ g c is the provision rate of product g under channel c, ζ j c is the revenue of delivering order j under channel c, C c is the inventory cost of channel c, w g c Indicates whether product g is stored in the warehouse corresponding to channel c, u gj c Indicates whether to send product g from channel c to satisfy order j, o j c Indicates whether to issue a delivery for order j placed by channel c, s g crepresents the inventory quantity of product g in the warehouse under channel c, x gj c represents the actual delivery quantity of item g in order j,

[0078] x gj c 、w g c 、u gj c , o j c and g c is the decision variable, according to the decision variable w g c and g c Determine the inventory allocation of product g under channel c, based on the decision variable u gj c 、x gj c and j c Determine the dispatch configuration for order j containing product g.

[0079] Optionally, the pricing module is further used to determine the category to which the commodity belongs, and the category to which the commodity belongs includes: seasonal commodities, perishable commodities and general commodities, wherein the residual value of perishable commodities is zero, and the residual value of general commodities is greater than the residual value of perishable commodities and less than the residual value of seasonal commodities;

[0080] Determine the demand function, production cost, logistics cost, demand price elasticity coefficient and demand inflation factor of the product under the specified channel;

[0081] The demand function, production cost, logistics cost, demand price elasticity coefficient and demand inflation factor are input into an optimization model for maximizing the profit of the product in all channels of the category to which the product belongs, and the optimization model is solved to determine the optimal price and demand limit of the product in the specified channel.

[0082] Optionally, when the product is a seasonal product, the optimization model for maximizing the product profit of all channels includes:

[0083]

[0084] M i 0 (p i ,z i )=(p i -cd i )E[min{q i ,D i (pi )}];

[0085] When the commodity is a perishable commodity, the optimization model for maximizing the commodity profit of all channels includes:

[0086]

[0087] M i 1 (p i ,z i )=(p i -d i )E[min{q i ,D i (p i )}]-cy i (p i ) i ;

[0088] When the commodity is a general commodity, the optimization model for maximizing the commodity profit of all channels includes:

[0089]

[0090] M i 2 (p i ,z i )=(p i -cd i )y i (p i )[z i -Λ i (z i )]-γcE[max{0,q i -D i (p i )}];

[0091] in, M 0 (p,z),M 1 (p,z) and M 2 (p, z) are the expected total profits of seasonal goods, perishable goods and general goods in all channels, respectively. i 0 (p i ,z i )、M i 1 (p i ,z i ) and M i 2 (p i ,z i) are the expected profits of seasonal goods, perishable goods and general goods under channel i, c is the production cost of each unit of goods, d i is the logistics cost per unit of goods in channel i, p i is the price per unit of goods in channel i, q i is the upper limit of the demand for the product under channel i, p i and q i is the decision variable, D i (p i ) is the demand function of the commodity under channel i, b i is the price elasticity coefficient of demand for the product in channel i, z i is the inventory factor of the product in channel i, z i =q i / y i (p i ), y i (p i ) is the specified decreasing function of the product under channel i.

[0092] Optionally, the pricing module is further used to determine the demand inflation factor ∈ under channel i i For [A i ,B i ], 0≤A i i ,∈ i The mean μ i >0, let h i (∈ i ) = f i (∈ i ) / (1-F i (∈ i )), then d(∈ i h i (∈ i )) / d∈ i =h i (∈ i )+∈ i dh i (∈ i ) / d∈ i >0,f i (∈ i ) is the demand expansion factor ∈ i The probability density function, F i (∈ i ) is the demand expansion factor ∈ i The distribution function of

[0093] Determine the demand function D of the product under channel i i (p i )=y​i (p i )+∈ i , or D i (p i )=y i (p i )∈ i ,y i (p i )=a i p i -bi , a i >0,b i >1.

[0094] Furthermore, it is characterized in that the channel of the product includes one or more of the following channels: a single online channel, a single offline channel, an online ordering and offline fulfillment channel, and an offline ordering and online fulfillment channel.

[0095] Furthermore, the logistics cost of goods in the online ordering and offline fulfillment channels is 3 Equal to the logistics cost of a single offline channel d 1 , the logistics cost of goods in offline order and online fulfillment channels d 4 Equal to the logistics cost of a single online channel d 2 ;

[0096] The price elasticity coefficient of demand for goods in the online ordering and offline fulfillment channels b 3 and demand inflation factor ∈ 3 Equal to the price elasticity coefficient of demand for a single online channel b 2 and demand inflation factor ∈ 2 , the price elasticity coefficient of demand for goods in the offline order and online fulfillment channels b 4 and demand inflation factor ∈ 4 Equal to the price elasticity coefficient of demand for a single offline channel b 1 and demand inflation factor ∈ 1 .

[0097] To achieve the above object, according to another aspect of an embodiment of the present invention, there is also provided an electronic device for generating configuration information, including:

[0098] one or more processors;

[0099] a storage device for storing one or more programs,

[0100] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for generating configuration information provided by the embodiment of the present invention.

[0101] To achieve the above objective, according to another aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method for generating configuration information provided by an embodiment of the present invention is implemented.

[0102] The method and device for generating configuration information provided by the embodiment of the present invention coordinates many factors in the omni-channel unbounded retail model, and realizes reasonable inventory allocation and order dispatch configuration of goods under the premise of maximizing profits and the whole order fulfillment rate. In the present invention, firstly, seasonal goods, perishable goods and general goods are distinguished, and price optimization models are established for the three categories of goods respectively, so as to obtain the optimal price under different channels. Secondly, a mathematical optimization model is established based on the principle of maximizing order profits and the whole order fulfillment rate to allocate suitable goods to omni-channel retailers.

[0103] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0105] Figure 1 is a schematic diagram of the main process of the method for generating configuration information provided by an embodiment of the present invention;

[0106] Figure 2 is a schematic diagram of main modules of an apparatus for generating configuration information provided by an embodiment of the present invention;

[0107] Figure 3 is a schematic diagram of a system for generating configuration information provided by an embodiment of the present invention;

[0108] Figure 4 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0109] Figure 5 It is a schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present invention. DETAILED DESCRIPTION

[0110] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0111] The embodiment of the present invention provides a method for generating configuration information. The configuration of goods refers to a retailer starting from analyzing customer needs to make a comprehensive analysis and plan of the product combination, pricing method, promotional activities, as well as the use of funds, inventory goods and other operating indicators. The method for generating configuration information provided by the present invention mainly realizes the allocation of goods, pricing of goods, inventory selection of goods and dispatching selection of goods orders.

[0112] like Figure 1 As shown, the method for generating configuration information provided by the embodiment of the present invention includes: step S101 and step S102. In step S101, the optimized price, upper limit of demand, sales volume forecast value and order volume forecast value of the commodity in the specified channel are determined.

[0113] Then in step S102, the optimized price, demand upper limit, sales volume forecast value and order volume forecast value are input into the optimization model that maximizes the profit and order fulfillment rate of the order containing the product under the channel, and the optimization model is solved to determine the inventory allocation and order dispatch configuration of the product under the specified channel.

[0114] In one embodiment of the present invention, inventory allocation refers to whether the product is stored in the warehouse corresponding to the channel, and if so, the inventory quantity of the product in the warehouse under the channel. Order dispatch configuration refers to whether a certain product under the channel is sent to meet an order, and whether a delivery is issued for the order under the channel.

[0115] The method for generating configuration information provided by the embodiment of the present invention can be applied to the inventory allocation and dispatch configuration of goods in the omni-channel retail model. In the omni-channel retail model, the channels of goods include one or more of the following channels: a single online channel, a single offline channel, an online order and offline fulfillment channel, and an offline order and online fulfillment channel. The method of the present invention coordinates many factors in the omni-channel unbounded retail model, and establishes a mathematical optimization model to allocate suitable goods to omni-channel retailers based on the principle of maximizing order profit and order satisfaction rate. Under the premise of maximizing profit and order satisfaction rate, reasonable inventory allocation and dispatch configuration of goods are achieved.

[0116] The method for generating configuration information provided by the embodiment of the present invention further includes the following steps before determining the optimized price and inventory factor of the commodity:

[0117] The first step is to determine the category of the goods based on the goods' life, saleable period, residual value, etc. The categories of goods include: seasonal goods, perishable goods and general goods. Among them, the residual value of perishable goods is zero, the residual value of general goods is greater than the residual value of perishable goods and less than the residual value of seasonal goods.

[0118] Usually, after the sales period of goods ends, the unsold goods may still have a certain value for retailers. For example, in order to coordinate the supply chain, suppliers will sign a repurchase contract with retailers to provide retailers with certain protection and guide them to increase their purchase volume, so that the risks caused by demand uncertainty are shared by both parties. If the retailer signs a repurchase contract with the supplier when purchasing goods, the supplier will purchase the goods that the retailer has not sold out at a certain price after the sales season ends, and the remaining goods will have an uncertain residual value. For another example, some goods have significant seasonality, and the remaining goods can be left in the next sales period for continued sale, and they also retain a certain value, such as mosquito nets, cold-proof gloves, etc. However, some food products have no value once they expire. Therefore, for retailers, different goods have different values ​​after the sales end, which is an important element that cannot be ignored when considering the retailer's revenue.

[0119] Seasonal goods usually have significant seasonality, and the remaining goods can be left for sale in the next sales period, such as mosquito nets, cold-proof gloves, etc. The characteristics of these goods make the remaining value of the unsold goods at the end of the sales period not much different from their original value.

[0120] Perishable goods mainly refer to perishable food products. Under the influence of temperature and humidity in a natural temperature environment, animal food may die or deteriorate, and plant food may rot or mold. For example, fresh fish, livestock and poultry meat, fruits, vegetables, flowers and dairy products, etc. These commodities have strict requirements on the environment of transportation and storage, and once expired, they have no value. In this case, the goods that are not sold after the sales period ends are no longer of any value.

[0121] General merchandise refers to merchandise that has not been sold after the sales period ends. It has a certain value to the retailer, but the residual value of the merchandise is somewhat lower than its original value. The most typical case is that after the sales period ends, a promotion is carried out, and the retailer sells the remaining merchandise at a certain discount price, such as clothing.

[0122] The second step is to estimate the production cost and logistics cost of goods in different channels through finance, procurement, sales, logistics and other means.

[0123] The third step is to determine the demand function of the product and estimate the price elasticity coefficient of demand for the product in different channels. The demand function is used to express the relationship between the demand quantity of the product and the various factors that affect the demand quantity.

[0124] Price elasticity of demand i Indicates the degree to which the quantity demanded of a commodity responds to changes in its price. i The larger the value, the more sensitive the demand is to price changes. iWhen it is greater than 1, it is called elastic or highly elastic demand. i When it is equal to 1, it is called unitary elasticity of demand, b i When it is less than 1, the demand is called inelastic or low elasticity. There are many existing methods for estimating the price elasticity of demand. For commodities with sufficient price and sales volume change data to directly estimate the elasticity, the elasticity definition calculation method is used. For commodities with insufficient price and sales volume change data to directly estimate the elasticity, the elasticity prediction calculation method is used. In one embodiment of the present invention, it is assumed that the demand is highly elastic, that is, b i is greater than 1, because if b i <1, then the optimal price obtained in the subsequent step is 0. If b i =1, then the optimal price is infinite.

[0125] The fourth step is to estimate the demand inflation factor of the product under different channels. The demand inflation factor refers to the degree of change of actual demand with respect to demand estimates or the random part in the demand function.

[0126] After completing the above steps, the demand function, production cost, logistics cost, demand price elasticity coefficient and demand inflation factor are input into the optimization model of maximizing the profit of the product in all channels of the category to which the product belongs, and the optimization model is solved to determine the optimal price and demand limit of the product in the specified channel.

[0127] In one embodiment of the present invention, determining the demand function and demand inflation factor of a commodity in a specified channel specifically includes:

[0128] When considering pricing issues, it is usually assumed that the commodity demand function is random and price-related, and the randomness can be expressed in the form of addition or multiplication. Therefore, in one embodiment of the present invention, the demand function D of the commodity under channel i is determined as i (p i )=y i (p i )+∈ i , or D i (p i )=y i (p i )∈ i , where y i (p i ) is a decreasing function y i (p i )=a i p i -bi , a i >0,b i >1, y is commonly used in practice i (p i) can also be in the form of y i (p i )=a i -b i p i (a i >0,b i >1) and y i (p i )=a i p i -b i ,(a i >0,b i >1), the former represents a linear demand curve, and the latter is a constant elasticity demand curve, that is, the price elasticity of the demand curve is always a constant.

[0129] Determine the demand inflation factor ∈ under channel i i For [A i ,B i ], 0≤A i i ,∈ i The mean μ i >0, it is required to set h i (∈ i ) = f i (∈ i ) / (1-F i (∈ i )), then d(∈ i h i (∈ i )) / d∈ i =h i (∈ i )+∈ i dh i (∈ i ) / d∈ i >0,f i (∈ i ) is the demand expansion factor ∈ i The probability density function, F i (∈ i ) is the demand expansion factor ∈ i The distribution function of .

[0130] In one embodiment of the present invention, the surplus value of seasonal goods is defined as c, the surplus value of perishable goods is 0, and the surplus value of general goods is (1-γ)c, where γ takes values ​​in [0,1].

[0131] More specifically, in the embodiment of the present invention, when the product is a seasonal product, the optimization model for maximizing the product profit of all channels includes:

[0132] ​

[0133] M i 0 (p i ,z i )=(p i -cd i )E[min{q i ,D i (p i )}].

[0134] When the product is perishable, the optimization model for maximizing the product profit of all channels includes:

[0135]

[0136] M i 1 (p i ,z i )=(p i -d i )E[min{q i ,D i (p i )}]-cy i (p i ) i .

[0137] When the product is a general product, the optimization model for maximizing the product profit of all channels includes:

[0138]

[0139] M i 2 (p i ,z i )=(p i -cd i )y i (p i )[z i -Λ i (z i )]-γcE[max{0,q i -D i (p i )}].

[0140] in, M 0 (p,z),M 1 (p,z) and M 2 (p, z) are the expected total profits of seasonal goods, perishable goods and general goods in all channels, respectively. i0 (p i ,z i )、M i 1 (p i ,z i ) and M i 2 (p i ,z i ) are the expected profits of seasonal goods, perishable goods and general goods under channel i, c is the production cost of each unit of goods, d i is the logistics cost per unit of goods in channel i, p i is the price per unit of goods in channel i, q i is the upper limit of the demand for the product under channel i, p i and q i is the decision variable, D i (p i ) is the demand function of the commodity under channel i, b i is the price elasticity coefficient of demand for the product in channel i, z i is the inventory factor of the product in channel i, z i =q i / y i (p i ).

[0141] In one embodiment of the present invention, in order to facilitate the solution, the inventory factor z under channel i is defined as i =q i / y i (p i ). In this embodiment, the inventory factor z is used i Instead of q i As a decision variable, the nature of the problem to be solved does not change.

[0142] For the optimization model of maximizing the commodity profits of all channels for seasonal commodities, given any z i ,

[0143]

[0144] Because (z i -Λ i (z i ))a i p i -bi-1 >0 and b i >1, so M i 0 (p i ,z i ) is about p iA single-peak function, and at p~ i 0 (z i )=(c+d i )b i / (b i -1) reaches the maximum value. i 0 (z i ) is independent of z i .

[0145] Assuming there is no capacity constraint, determine the optimal price per seasonal product in channel i Determine the optimal inventory factor for seasonal goods per unit under channel i as

[0146] For the optimization model of maximizing the commodity profit of all channels for perishable goods, it is assumed that there is no capacity constraint and the condition 2h i (z i )(1-F i (z i ))+(z i -Λ i (z i ))dh i (z i ) / dz i >0 is established.

[0147] Yes i ,B i Any z in ] i , then determine the optimal price p of seasonal goods per unit under channel i~ i 1 (z i )=[(z i -Λ i (z i ))d i b i +cb i z i ] / (b i -1)(z i -Λ i (z i ), the optimal inventory factor z i 1* is uniquely determined by:

[0148]

[0149] For the optimization model of maximizing the profit of all channels for perishable goods, let γ = 0, then M 2 (p,z)=M 0(p,z). Let γ=1, then M 2 (p,z)=M 1 (p,z).

[0150] Assume there is no capacity limit and the condition is 2h i (z i )(1-F i (z i ))+(z i -Λ i (z i ))dh i (z i ) / dz i >0 for any z i Belong to [A i ,B i ] and i=1,2 are both true, then determine the optimal price of general goods per unit under channel i

[0151] The optimal inventory factor of general goods per unit in channel i is uniquely determined by the following formula:

[0152] In the present invention, the steps of determining the inventory allocation and dispatch configuration of a product under a specified channel are specifically as follows: First, determine the predicted sales volume and order quantity of online historical sales data. The historical sales data includes information on product sales, sales volume, order quantity, and price. The existing methods can be used to obtain and process features that have a significant impact on sales forecasts using sales data, and machine learning methods can be used to predict sales volume and order quantity of products in different channels.

[0153] In the second step, the optimized price, upper limit of demand, sales volume forecast value, order volume forecast value and commodity allocation business constraints are taken as input, and the optimal channel inventory allocation and dispatch configuration are output by solving the integer linear programming model under multiple objective requirements such as maximizing profit and minimizing order splitting rate. Channel inventory information limits the upper limit of inventory capacity, which is a number of commodity allocation business constraints in the model.

[0154] In the present invention, when the inventory of goods does not meet the demand for the goods in the order, whether the order can be split is considered, and an optimization model for maximizing the profit of the order containing the goods and the satisfaction rate of the entire order is provided.

[0155] When the inventory of the product does not meet the demand for the order, no delivery is issued for the order, that is, the order cannot be split for delivery, the optimization model that maximizes the profit of the order and the order fulfillment rate of the product includes:

[0156]

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164] Among them, K c It is a collection of commodity categories that can be stored in channel c, numbered as k. In the present invention, commodities refer to single product SKUs.

[0165] G c It is the set of products in all categories of channel c, numbered as g.

[0166] G k c is the set of products in category k under channel c, k belongs to K c ,

[0167] J c Place an order set for channel c, numbered j,

[0168] N k c is the maximum inventory quantity of category k under channel c,

[0169] B j c The category set of goods required for channel c to place order j,

[0170] A g c is the predicted sales volume of product g under channel c, g belongs to G c ,

[0171] a gj c The demand for product g by order j from channel c,

[0172] q g c is the upper limit of demand for product g under channel c,

[0173] M c is the inventory limit of all products in channel c,

[0174] p g cis the offline sales price of product g in channel c,

[0175] r g c is the gross profit margin of product g under channel c,

[0176] η g c is the monthly interest rate of funds for product g under channel c,

[0177] δ g c is the provision rate of product g under channel c,

[0178] ζ j c is the revenue of delivering order j under channel c,

[0179] C c is the inventory cost of channel c,

[0180] w g c Indicates whether product g is stored in the warehouse corresponding to channel c.

[0181] u gj c Indicates whether to send product g from channel c to satisfy order j.

[0182] o j c Indicates whether to issue a delivery for order j placed by channel c.

[0183] s g c Indicates the inventory quantity of product g in the warehouse under channel c,

[0184] w g c 、u gj c , o j c and g c is the decision variable, according to the decision variable w g c and g c Determine the inventory allocation of product g under channel c, based on the decision variable u gj c and j c Determine the dispatch configuration for order j of product g. Decision variable u gj c Whether to send product g from channel c to satisfy order j to maximize the order fulfillment rate of the product.

[0185] The objective components of this optimization model are: Distribution gross profit: Gross profit from sales: Cost of capital: Loss of tail goods: The goal of this optimization model is to maximize the order profit and order fulfillment rate of omni-channel retailers. Order profit = delivery gross profit + sales gross profit - inventory cost - capital cost - tail goods loss.

[0186] In the present invention, since the demand is less than the inventory, and the sign of the tail goods loss in the objective is negative, it can be known from the objective of maximizing the order profit that the optimal solution of the model corresponds to a tail goods loss of zero, so the optimization model can be simplified as follows:

[0187]

[0188] The constraint (1) of this model is the number of products contained in a category, which should not exceed |I k |; Constraint (2) indicates that the upper limit of the number of products in the same category is N k ; Constraint (3) limits the quantity of commodity g shipped by the omni-channel retailer for all orders to no more than the maximum inventory quantity of commodity g; Constraint (4) indicates that the premise that commodity g is shipped from the omni-channel retailer to meet order j is that all the commodities g required are stored in the warehouse of the omni-channel retailer; Constraint (5) indicates that the inventory quantity of commodity g at the omni-channel retailer does not exceed the upper limit of the demand for commodity g; Constraint (6) indicates that the premise that the omni-channel retailer sends the delivery for order j is that all the commodities g required are shipped from the omni-channel retailer to meet order j.

[0189] In one embodiment of the present invention, when the inventory of the product does not meet the demand for the product in the order, the delivery for the order can still be issued, that is, the order can be split for delivery, the optimization model for maximizing the profit of the order to which the product belongs and the order fulfillment rate includes:

[0190]

[0191]

[0192]

[0193]

[0194]

[0195]

[0196]

[0197]

[0198]

[0199] Among them, x gj c is a decision variable, which represents the actual delivery quantity of item g in order j. g c and g c Determine the inventory allocation of product g under channel c, based on the decision variable u gj c 、x gj c and j c Determine the dispatch configuration for order j containing product g.

[0200] Taking the application scenario of the omni-channel retail model as an example, the constraints 1, 2, 4 and 5 of this model are the same as the constraints of the model in the case where orders cannot be split. Constraint (3) restricts the actual delivery quantity of product g issued by the omni-channel retailer for all orders to not exceed the maximum inventory quantity of product g; constraint (6) restricts the number of product types dispatched by the omni-channel retailer when partial fulfillment is allowed, not to exceed the total number of product types contained in the order. Constraint (7) restricts the actual delivery quantity of product g in order j by the omni-channel retailer to not exceed the demand for product g in order j, and is greater than or equal to 0.

[0201] Similar to the above model where orders cannot be split, this model can be simplified as follows:

[0202]

[0203] In one embodiment of the present invention, when solving the process of optimizing the price and the upper limit of the demand for commodities, in order to facilitate the solution, the inventory factor is used instead of the upper limit of the demand as the decision variable. The q in the constraint condition (5) of the above two models is g c Can be replaced by y g c (p g c ) g c , z g c That is, the inventory factor of product g under channel c, y g c (p g c ) is a decreasing function that reflects the correlation between demand and price.

[0204] The method for generating configuration information provided by the present invention can be applied to a single-channel retail model, a multi-channel retail model, and an omni-channel retail model. When applied to a single-channel retail model, in the above algorithm, n is equal to 1, and i takes a value of 1 to represent a single offline / online channel. When applied to a multi-channel retail model or an omni-channel retail model, n is greater than 1. For example, for an omni-channel retail model that includes four sales channels, that is, n=4, i takes a value of 1 to represent a single offline channel, i takes a value of 2 to represent a single online channel, i takes a value of 3 to represent an online order and offline fulfillment channel, and i takes a value of 4 to represent an offline order and online fulfillment channel.

[0205] For the omni-channel retail model, in one embodiment of the present invention, in order to simplify the calculation process, the following parameter configuration can be performed: the logistics cost of the goods in the online order and offline fulfillment channels d 3 Equal to the logistics cost of a single offline channel d 1 , the logistics cost of goods in offline order and online fulfillment channels d 4 Equal to the logistics cost of a single online channel d 2 .

[0206] The price elasticity coefficient of demand for goods in the online ordering and offline fulfillment channels b 3 and demand inflation factor ∈ 3 Equal to the price elasticity coefficient of demand for a single online channel b 2 and demand inflation factor ∈ 2 , the price elasticity coefficient of demand for goods in the offline order and online fulfillment channels b 4 and demand inflation factor ∈ 4 Equal to the price elasticity coefficient of demand for a single offline channel b 1 and demand inflation factor ∈ 1 .

[0207] Of course, in other embodiments of the present invention, the demand price elasticity coefficient and demand inflation factor of the goods in the online ordering and offline fulfillment channel and the goods in the offline ordering and online fulfillment channel may also be calculated separately.

[0208] In the specific application process, the optimization model for maximizing commodity profits for seasonal commodities, perishable commodities, and general commodities can be configured with corresponding capacity constraints according to the application scenario. For example, for an omni-channel retail model including four sales channels, the following constraints can be configured for the optimization model for maximizing commodity profits: 1 (p 1 ) 1 +y 3 (p 3 ) 3 ≤Q 1 ,y 2 (p 2 )2 +y 4 (p 4 ) 4 ≤Q 2 , Q 1 is the omni-channel offline inventory capacity, Q 2 It is the omni-channel online inventory capacity, that is, the sum of the upper limits of the inventory of the products in the online ordering and offline fulfillment channels and the upper limits of the inventory of the single offline channel is less than or equal to the omni-channel offline inventory capacity, and the sum of the upper limits of the inventory of the products in the offline ordering and online fulfillment channels and the upper limits of the inventory of the single online channel is less than or equal to the omni-channel online inventory capacity.

[0209] In the present invention, the outputted commodity allocation, price suggestion, inventory suggestion and dispatch suggestion can be presented to the user through graphs and / or tables, and commodity allocation, dynamic pricing and inventory suggestion reports can be automatically generated. In addition, various indicators as shown in Table 1 below (taking the omni-channel retail model as an example) can be further calculated based on the commodity allocation, dynamic pricing and inventory configuration results to provide a reference for the actual production of omni-channel retailers. After the retailers actually implement the configuration suggestions, they can compare the current sales data (sales volume, sales revenue, etc.) with the historical sales data to evaluate the effect after the actual implementation.

[0210]

[0211]

[0212] Table 1

[0213] The embodiment of the present invention also provides a device for generating configuration information, such as Figure 2 As shown, the device 200 includes: a pricing module 201, a forecasting module 202 and an inventory optimization module 203.

[0214] The pricing module 201 is used to determine the optimized price and upper limit of demand for the product in the specified channel. The forecasting module 202 is used to determine the sales forecast value and order quantity forecast value of the product in the specified channel. The inventory optimization module 203 is used to input the optimized price, upper limit of demand, sales forecast value and order quantity forecast value into the optimization model that maximizes the profit and order fulfillment rate of the order in which the product is located in the channel, and solve the optimization model to determine the inventory allocation and order dispatch configuration of the product in the specified channel.

[0215] In the present invention, the pricing module is further used to determine the category to which the commodity belongs, and the categories to which the commodity belongs include: seasonal commodities, perishable commodities and general commodities, wherein the residual value of perishable commodities is zero, and the residual value of general commodities is greater than the residual value of perishable commodities and less than the residual value of seasonal commodities. Then the demand function, production cost, logistics cost, demand price elasticity coefficient and demand inflation factor of the commodity under the specified channel are determined. The demand function, production cost, logistics cost, demand price elasticity coefficient and demand inflation factor are input into the optimization model of maximizing the commodity profit of all channels of the category to which the commodity belongs, and the optimization model is solved to determine the optimized price and demand upper limit of the commodity under the specified channel.

[0216] In the present invention, when the product is a seasonal product, the optimization model for maximizing the product profit of all channels includes:

[0217]

[0218] M i 0 (p i ,z i )=(p i -cd i )E[min{q i ,D i (p i )}];

[0219] When the product is perishable, the optimization model for maximizing the product profit of all channels includes:

[0220]

[0221] M i 1 (p i ,z i )=(p i -d i )E[min{q i ,D i (p i )}]-cy i (p i ) i ;

[0222] When the product is a general product, the optimization model for maximizing the product profit of all channels includes:

[0223]

[0224] M i 2 (p i ,zi )=(p i -cd i )y i (p i )[z i -Λ i (z i )]-γcE[max{0,q i -D i (p i )}];

[0225] in, M 0 (p,z),M 1 (p,z) and M 2 (p, z) are the expected total profits of seasonal goods, perishable goods and general goods in all channels, respectively. i 0 (p i ,z i )、M i 1 (p i ,z i ) and M i 2 (p i ,z i ) are the expected profits of seasonal goods, perishable goods and general goods under channel i, c is the production cost of each unit of goods, d i is the logistics cost per unit of goods in channel i, p i is the price per unit of goods in channel i, q i is the upper limit of the demand for the product under channel i, p i and q i is the decision variable, D i (p i ) is the demand function of the commodity under channel i, b i is the price elasticity coefficient of demand for the product in channel i, z i is the inventory factor of the product in channel i, z i =q i / y i (p i ), y i (p i ) is the specified decreasing function of the product under channel i.

[0226] In the present invention, the pricing module is further used to determine the demand inflation factor ∈ under channel i i For [A i ,B i ], 0≤A i i ​,∈ i The mean μ i >0, let h i (∈ i ) = f i (∈ i ) / (1-F i (∈ i )), then d(∈ i h i (∈ i )) / d∈ i =h i (∈ i )+∈ i dh i (∈ i ) / d∈ i >0,f i (∈ i ) is the demand expansion factor ∈ i The probability density function, F i (∈ i ) is the demand expansion factor ∈ i The distribution function of .

[0227] Determine the demand function D of the product under channel i i (p i )=y i (p i )+∈ i , or D i (p i )=y i (p i )∈ i ,y i (p i )=a i p i -bi , a i >0,b i >1.

[0228] In the present invention, the channel of the commodity includes one or more of the following channels: a single online channel, a single offline channel, an online ordering and offline fulfillment channel, and an offline ordering and online fulfillment channel.

[0229] In the present invention, the logistics cost of goods in the online order and offline fulfillment channels is d 3 Equal to the logistics cost of a single offline channel d 1 , the logistics cost of goods in offline order and online fulfillment channels d 4 Equal to the logistics cost of a single online channel d 2 .

[0230] The price elasticity coefficient of demand for goods in the online ordering and offline fulfillment channels b3 and demand inflation factor ∈ 3 Equal to the price elasticity coefficient of demand for a single online channel b 2 and demand inflation factor ∈ 2 , the price elasticity coefficient of demand for goods in the offline order and online fulfillment channels b 4 and demand inflation factor ∈ 4 Equal to the price elasticity coefficient of demand for a single offline channel b 1 and demand inflation factor ∈ 1 .

[0231] In the present invention, when the inventory of the goods does not meet the demand for the goods in the order, no delivery for the order is issued, the optimization model for maximizing the profit of the order where the goods are located and the entire order satisfaction rate includes:

[0232]

[0233]

[0234]

[0235]

[0236]

[0237]

[0238]

[0239]

[0240] Among them, K c is the set of commodity categories that can be stored in channel c, numbered k, G c is the set of products in all categories of channel c, numbered g, G k c is the set of products in category k under channel c, k belongs to K c , J c Place an order set for channel c, numbered j, N k c is the maximum inventory quantity of category k under channel c, B j c The category set of goods required for channel c to place order j, q g c is the upper limit of demand for product g under channel c, A g c is the predicted sales volume of product g under channel c, g belongs to G c , a gjc is the demand for product g by order j from channel c, M c is the inventory limit of all products in channel c, p g c is the offline sales price of product g in channel c, r g c is the gross profit margin of product g under channel c, η g c is the monthly interest rate of funds for commodity g under channel c, δ g c is the provision rate of product g under channel c, ζ j c is the revenue of delivering order j under channel c;

[0241] C c is the inventory cost of channel c, w g c Indicates whether product g is stored in the warehouse corresponding to channel c, u gj c Indicates whether to send product g from channel c to satisfy order j, o j c Indicates whether to issue a delivery for order j placed by channel c, s g c represents the inventory quantity of product g in the warehouse under channel c, w g c 、u gj c , o j c and g c is the decision variable, according to the decision variable w g c and g c Determine the inventory allocation of product g under channel c, based on the decision variable u gj c and j c Determine the dispatch configuration for order j containing product g.

[0242] In the present invention, when the inventory of the goods does not meet the demand for the goods in the order, the delivery for the order can still be issued, and the optimization model for maximizing the profit of the order where the goods are located and the entire order satisfaction rate includes:

[0243]

[0244]

[0245]

[0246]

[0247]

[0248]

[0249]

[0250]

[0251]

[0252] Among them, x gj c is a decision variable, which represents the actual delivery quantity of item g in order j. g c and g c Determine the inventory allocation of product g under channel c, based on the decision variable u gj c 、x gj c and j c Determine the dispatch configuration for order j containing product g.

[0253] The embodiment of the present invention also provides a system for generating configuration information, such as Figure 3 As shown, the system includes: a channel commodity pricing unit, a commodity allocation unit and a result reporting unit, wherein the function of the channel commodity pricing unit is implemented by a pricing module, and the commodity allocation unit includes: an optimization database, a prediction component and an optimizer. The optimization database is used to store basic data, such as commodity cost data, inventory data and commodity sales history data, etc. The optimization database is also used to store inventory allocation data and dispatch suggestion data generated by the system.

[0254] The channel commodity pricing unit obtains the commodity production cost / logistics cost data and commodity price elasticity data from the optimization database, and then inputs them into the channel commodity classification pricing model (i.e., the optimization model that maximizes the commodity profit of all channels in the category to which the commodity belongs), thereby obtaining the optimal price and inventory factor of the commodity as the input of the optimizer. The forecasting component obtains the commodity sales data from the optimization database to predict the sales volume and order volume, and inputs the predicted data into the optimizer. The optimizer obtains the above integer linear programming input, obtains the optimal commodity allocation, recommended inventory and recommended dispatch configuration through the integer programming solution algorithm, and stores them in the optimization database.

[0255] The result reporting unit can query and visualize the calculation results stored in the optimization database, and automatically generate a report on the results of commodity allocation, dynamic pricing, and inventory. It can further calculate various indicators based on the results of commodity allocation, dynamic pricing, and inventory configuration to provide a reference for the actual production of omni-channel retailers. After the retailers actually implement the configuration recommendations, they can compare the current sales data (sales volume, sales, etc.) with the historical sales data to evaluate the effect after the actual implementation and generate an effect evaluation report.

[0256] The method and device for generating configuration information provided by the embodiment of the present invention coordinates many factors in the omni-channel unbounded retail model, and realizes reasonable inventory allocation and order dispatch configuration of goods under the premise of maximizing profits and the whole order fulfillment rate. In the present invention, firstly, seasonal goods, perishable goods and general goods are distinguished, and price optimization models are established for the three categories of goods respectively, so as to obtain the optimal price under different channels. Secondly, a mathematical optimization model is established based on the principle of maximizing order profits and the whole order fulfillment rate to allocate suitable goods to omni-channel retailers.

[0257] Figure 4 An exemplary system architecture 400 is shown to which the method for generating configuration information or the apparatus for generating configuration information according to the embodiments of the present invention can be applied.

[0258] like Figure 4 As shown, system architecture 400 may include terminal devices 401, 402, 403, network 404 and server 405. Network 404 is used to provide a medium for communication links between terminal devices 401, 402, 403 and server 405. Network 404 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0259] Users can use terminal devices 401, 402, 403 to interact with server 405 through network 404 to receive or send messages, etc.

[0260] The terminal devices 401 , 402 , and 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0261] The server 405 may be a server that provides various services.

[0262] It should be noted that the method for generating configuration information provided in the embodiment of the present invention is generally executed by the server 405 , and accordingly, the device for generating configuration information is generally set in the server 405 .

[0263] It should be understood that Figure 4The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0264] Reference below Figure 5 , which shows a schematic diagram of the structure of a computer system 500 of a terminal device suitable for implementing an embodiment of the present invention. Figure 5 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0265] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0266] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0267] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present invention are executed.

[0268] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0269] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0270] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor. For example, they may be described as: a processor includes a pricing module, a forecasting module, and an inventory optimization module. The names of these modules do not, in some cases, constitute limitations on the modules themselves. For example, the pricing module may also be described as a "module for determining the category to which a commodity belongs."

[0271] As another aspect, the present invention further provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes:

[0272] Determine the optimized price, demand limit, sales forecast, and order volume forecast for the product in a specified channel;

[0273] The optimized price, demand upper limit, sales volume forecast value and order volume forecast value are input into the optimization model for maximizing the profit and order fulfillment rate of the order of the product under the channel, and the optimization model is solved to determine the inventory allocation and order dispatch configuration of the product under the specified channel.

[0274] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for generating configuration information, characterized in that: include: Determine the optimized price, demand limit, sales forecast, and order volume forecast for the product in a specified channel; Input the optimized price, demand upper limit, sales volume forecast value and order volume forecast value into an optimization model for maximizing the profit and order fulfillment rate of the order of the product under the channel, and solve the optimization model to determine the inventory allocation and order dispatch configuration of the product under the specified channel; When the inventory of the product does not meet the demand for the product in the order, no delivery is issued for the order, the optimization model for maximizing the profit of the order and the order fulfillment rate of the product includes: Among them, K c is the set of commodity categories that can be stored in channel c, numbered k, G c is the set of products in all categories of channel c, numbered g, G k c is the set of products in category k under channel c, k belongs to K c , J c Place an order set for channel c, numbered j, N k c is the maximum inventory quantity of category k under channel c, B j c A is the category set of goods required by channel c to place order j, g c is the predicted sales volume of product g under channel c, g belongs to G c , a gj c The demand for product g by order j from channel c, q g c is the upper limit of demand for product g under channel c, M c is the inventory limit of all products in channel c, p g c is the offline sales price of product g in channel c, r g c is the gross profit margin of product g in channel c, η g c is the monthly interest rate of funds for commodity g under channel c, δ g c is the provision rate of product g under channel c, ζ j c is the revenue of delivering order j under channel c, C c is the inventory cost of channel c, w g c Indicates whether product g is stored in the warehouse corresponding to channel c, u gj c Indicates whether to send product g from channel c to satisfy order j, o j c Indicates whether to issue a delivery for order j placed by channel c, s g c represents the inventory quantity of product g in the warehouse under channel c, w g c 、u gj c , o j c and g c is the decision variable, according to the decision variable w g c and g c Determine the inventory allocation of product g under channel c, based on decision variables u gj c and j c Determine the dispatch configuration for order j containing product g.

2. The method according to claim 1, characterized in that When the inventory of the product does not meet the demand for the product in the order, the delivery for the order can still be issued, the optimization model for maximizing the profit of the order where the product is located and the entire order fulfillment rate includes: Among them, K c is the set of commodity categories that can be stored in channel c, numbered k, G c is the set of products in all categories of channel c, numbered g, G k c is the set of products in category k under channel c, k belongs to K c , J c Place an order set for channel c, numbered j, N k c is the maximum inventory quantity of category k under channel c, B j c A is the category set of goods required by channel c to place order j, g c is the predicted sales volume of product g under channel c, g belongs to G c , a gj c The demand for product g by order j from channel c, q g c is the upper limit of demand for product g under channel c, M c is the inventory limit of all products in channel c, p g c is the offline sales price of product g in channel c, r g c is the gross profit margin of product g in channel c, η g c is the monthly interest rate of funds for commodity g under channel c, δ g c is the provision rate of product g under channel c, ζ j c is the revenue of delivering order j under channel c, C c is the inventory cost of channel c, w g c Indicates whether product g is stored in the warehouse corresponding to channel c, u gj c Indicates whether to send product g from channel c to satisfy order j, o j c Indicates whether to issue a delivery for order j placed by channel c, s g c represents the inventory quantity of product g in the warehouse under channel c, x gj c represents the actual delivery quantity of item g in order j, x gj c 、w g c 、u gj c , o j c and g c is the decision variable, according to the decision variable w g c and g c Determine the inventory allocation of product g under channel c, based on decision variables u gj c 、x gj c and j c Determine the dispatch configuration for order j containing product g.

3. The method according to claim 1 or 2, characterized in that: Determining the optimal price and demand limit for a product in a given channel includes: Determine the category to which the commodity belongs, where the category to which the commodity belongs includes: seasonal commodity, perishable commodity and general commodity, wherein the residual value of the perishable commodity is zero, and the residual value of the general commodity is greater than the residual value of the perishable commodity and less than the residual value of the seasonal commodity; Determine the demand function, production cost, logistics cost, demand price elasticity coefficient and demand inflation factor of the product in a specified channel; The demand function, production cost, logistics cost, demand price elasticity coefficient and demand inflation factor are input into an optimization model for maximizing the profit of the product in all channels of the category to which the product belongs, and the optimization model is solved to determine the optimal price and demand limit of the product in the specified channel.

4. The method according to claim 3, characterized in that When the product is a seasonal product, the optimization model for maximizing the product profit of all channels includes: M i 0 (p i ,z i )=(p i -c-d i )E[min{q i ,D i (p i )}]; When the commodity is a perishable commodity, the optimization model for maximizing the commodity profit of all channels includes: M i 1 (p i ,z i )=(p i -d i )E[min{q i ,D i (p i )}]-cy i (p i )z i ; When the commodity is a general commodity, the optimization model for maximizing the commodity profit of all channels includes: M i 2 (p i ,With i )=(p i -continued i )y i (p i )[With i -Λ i (With i )]-γcE[max{0,q i -D i (p i )}]; in, M 0 (p,z),M 1 (p,z) and M 2 (p, z) are the expected total profits of seasonal goods, perishable goods and general goods in all channels, respectively. i 0 (p i ,z i )、M i 1 (p i ,z i ) and M i 2 (p i ,z i ) are the expected profits of seasonal goods, perishable goods and general goods under channel i, c is the production cost of each unit of goods, d i is the logistics cost per unit of goods in channel i, p i is the price per unit of goods in channel i, q i is the upper limit of the demand for the product under channel i, p i and q i is the decision variable, D i (p i ) is the demand function of the commodity under channel i, b i is the price elasticity coefficient of demand for the product in channel i, z i is the inventory factor of the product in channel i, z i =q i / y i (p i ), y i (p i ) is the specified decreasing function of the product under channel i.

5. The method according to claim 4, characterized in that Determine the demand function and demand inflation factor of a product in a specified channel including: Determine the demand inflation factor ∈ under channel i i For [A i ,B i ], 0≤A i i ,∈ i The mean μ i >0, let h i (∈ i ) = f i (∈ i ) / (1-F i (∈ i )), then d(∈ i h i (∈ i )) / d∈ i =h i (∈ i )+∈ i dh i (∈ i ) / d∈ i >0,f i (∈ i ) is the demand expansion factor ∈ i The probability density function, F i (∈ i ) is the demand expansion factor ∈ i The distribution function of​ Determine the demand function D of the product under channel i i (p i )=y i (p i )+∈ i , or D i (p i )=y i (p i )∈ i ,y i (p i )=a i p i -bi , a i >0,b i >1.

6. The method according to claim 3, characterized in that: The channels for the goods include one or more of the following channels: a single online channel, a single offline channel, an online ordering and offline fulfillment channel, and an offline ordering and online fulfillment channel.

7. The method according to claim 6, characterized in that The logistics cost d3 of goods in the online order and offline fulfillment channel is equal to the logistics cost d1 of the single offline channel, and the logistics cost d4 of goods in the offline order and online fulfillment channel is equal to the logistics cost d2 of the single online channel; The price elasticity coefficient b3 and demand inflation factor ∈3 of commodities in the online ordering and offline fulfillment channel are equal to the price elasticity coefficient b2 and demand inflation factor ∈2 of commodities in the single online channel, and the price elasticity coefficient b4 and demand inflation factor ∈4 of commodities in the offline ordering and online fulfillment channel are equal to the price elasticity coefficient b1 and demand inflation factor ∈1 of commodities in the single offline channel.

8. A device for generating configuration information, characterized in that: include: The pricing module is used to determine the optimal price and demand limit of the product in a specified channel; The forecasting module is used to determine the sales volume forecast and order volume forecast of the product in the specified channel; An inventory optimization module is used to input the optimized price, demand upper limit, sales volume forecast value and order volume forecast value into an optimization model for maximizing the profit and order fulfillment rate of the order of the product in the channel, and solve the optimization model to determine the inventory allocation and order dispatch configuration of the product in the specified channel; When the inventory of the product does not meet the demand for the product in the order, no delivery is issued for the order, the optimization model for maximizing the profit of the order and the order fulfillment rate of the product includes: Among them, K c is the set of commodity categories that can be stored in channel c, numbered k, G c is the set of products in all categories of channel c, numbered g, G k c is the set of products in category k under channel c, k belongs to K c , J c Place an order set for channel c, numbered j, N k c is the maximum inventory quantity of category k under channel c, B j c A is the category set of goods required by channel c to place order j, g c is the predicted sales volume of product g under channel c, g belongs to G c , a gj c The demand for product g by order j from channel c, q g c is the upper limit of demand for product g under channel c, M c is the inventory limit of all products in channel c, p g c is the offline sales price of product g in channel c, r g c is the gross profit margin of product g in channel c, η g c is the monthly interest rate of funds for commodity g under channel c, δ g c is the provision rate of product g under channel c, ζ j c is the revenue of delivering order j under channel c, C c is the inventory cost of channel c, w g c Indicates whether product g is stored in the warehouse corresponding to channel c, u gj c Indicates whether to send product g from channel c to satisfy order j, o j c Indicates whether to issue a delivery for order j placed by channel c, s g c represents the inventory quantity of product g in the warehouse under channel c, w g c 、u gj c , o j c and g c is the decision variable, according to the decision variable w g c and g c Determine the inventory allocation of product g under channel c, based on decision variables u gj c and j c Determine the dispatch configuration for order j containing product g.

9. The device according to claim 8, characterized in that When the inventory of the product does not meet the demand for the product in the order, the delivery for the order can still be issued, the optimization model for maximizing the profit of the order where the product is located and the entire order fulfillment rate includes: Among them, K c is the set of commodity categories that can be stored in channel c, numbered k, G c is the set of products in all categories of channel c, numbered g, G k c is the set of products in category k under channel c, k belongs to K c , J c Place an order set for channel c, numbered j, N k c is the maximum inventory quantity of category k under channel c, B j c A is the category set of goods required by channel c to place order j, g c is the predicted sales volume of product g under channel c, g belongs to G c , a gj c The demand for product g by order j from channel c, q g c is the upper limit of demand for product g under channel c, M c is the inventory limit of all products in channel c, p g c is the offline sales price of product g in channel c, r g c is the gross profit margin of product g in channel c, η g c is the monthly interest rate of funds for commodity g under channel c, δ g c is the provision rate of product g under channel c, ζ j c is the revenue of delivering order j under channel c, C c is the inventory cost of channel c, w g c Indicates whether product g is stored in the warehouse corresponding to channel c, u gj c Indicates whether to send product g from channel c to satisfy order j, o j c Indicates whether to issue a delivery for order j placed by channel c, s g c represents the inventory quantity of product g in the warehouse under channel c, x gj c represents the actual delivery quantity of item g in order j, x gj c 、w g c 、u gj c , o j c and g c is the decision variable, according to the decision variable w g c and g c Determine the inventory allocation of product g under channel c, based on decision variables u gj c 、x gj c and j c Determine the dispatch configuration for order j containing product g.

10. The device according to claim 8 or 9, characterized in that The pricing module is further used to determine the category to which the commodity belongs, and the category to which the commodity belongs includes: seasonal commodities, perishable commodities and general commodities, wherein the residual value of perishable commodities is zero, and the residual value of general commodities is greater than the residual value of perishable commodities and less than the residual value of seasonal commodities; Determine the demand function, production cost, logistics cost, demand price elasticity coefficient and demand inflation factor of the product in a specified channel; The demand function, production cost, logistics cost, demand price elasticity coefficient and demand inflation factor are input into an optimization model for maximizing the profit of the product in all channels of the category to which the product belongs, and the optimization model is solved to determine the optimal price and demand limit of the product in the specified channel.

11. The device according to claim 10, characterized in that When the product is a seasonal product, the optimization model for maximizing the product profit of all channels includes: M i 0 (p i ,z i )=(p i -c-d i )E[min{q i ,D i (p i )}]; When the commodity is a perishable commodity, the optimization model for maximizing the commodity profit of all channels includes: M i 1 (p i ,z i )=(p i -d i )E[min{q i ,D i (p i )}]-cy i (p i )z i ; When the commodity is a general commodity, the optimization model for maximizing the commodity profit of all channels includes: M i 2 (p i ,With i )=(p i -continued i )y i (p i )[With i -Λ i (With i )]-γcE[max{0,q i -D i (p i )}]; in, M 0 (p,z),M 1 (p,z) and M 2 (p, z) are the expected total profits of seasonal goods, perishable goods and general goods in all channels, respectively. i 0 (p i ,z i )、M i 1 (p i ,z i ) and M i 2 (p i ,z i ) are the expected profits of seasonal goods, perishable goods and general goods under channel i, c is the production cost of each unit of goods, d i is the logistics cost per unit of goods in channel i, p i is the price per unit of goods in channel i, q i is the upper limit of the demand for the product under channel i, p i and q i is the decision variable, D i (p i ) is the demand function of the commodity under channel i, b i is the price elasticity coefficient of demand for the product in channel i, z i is the inventory factor of the product in channel i, z i =q i / y i (p i ), y i (p i ) is the specified decreasing function of the product under channel i.

12. The device according to claim 11, characterized in that The pricing module is further used to determine the demand expansion factor ∈ under channel i i For [A i ,B i ], 0≤A i i ,∈ i The mean μ i >0, let h i (∈ i ) = f i (∈ i ) / (1-F i (∈ i )), then d(∈ i h i (∈ i )) / d∈ i =h i (∈ i )+∈ i dh i (∈ i ) / d∈ i >0,f i (∈ i ) is the demand expansion factor ∈ i The probability density function, F i (∈ i ) is the demand expansion factor ∈ i The distribution function of​ Determine the demand function D of the product under channel i i (p i )=y i (p i )+∈ i , or D i (p i )=y i (p i )∈ i ,y i (p i )=a i p i -bi , a i >0,b i >1.

13. The device according to claim 10, characterized in that The channels for the goods include one or more of the following channels: a single online channel, a single offline channel, an online ordering and offline fulfillment channel, and an offline ordering and online fulfillment channel.

14. The device according to claim 13, characterized in that The logistics cost d3 of goods in the online order and offline fulfillment channel is equal to the logistics cost d1 of the single offline channel, and the logistics cost d4 of goods in the offline order and online fulfillment channel is equal to the logistics cost d2 of the single online channel; The price elasticity coefficient b3 and demand inflation factor ∈3 of commodities in the online ordering and offline fulfillment channel are equal to the price elasticity coefficient b2 and demand inflation factor ∈2 of commodities in the single online channel, and the price elasticity coefficient b4 and demand inflation factor ∈4 of commodities in the offline ordering and online fulfillment channel are equal to the price elasticity coefficient b1 and demand inflation factor ∈1 of commodities in the single offline channel.

15. An electronic device for generating configuration information, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

16. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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