Multi-commodity joint pricing method and device, storage medium and processor

Abstract

The embodiment of the invention provides a multi-commodity joint pricing method and device, a storage medium and a processor, and belongs to the technical field of information processing. The multi-commodity joint pricing method comprises the steps of firstly determining a commodity set to be sold, then determining corresponding constraint conditions according to different factors influencing commodity pricing, establishing a calculation model of a suggested retail price and a predicted sales volume of each commodity in the commodity set, and then solving the calculation model based on an integer programming method, therefore, the gross profit rate or the total sales volume obtained by selling the commodities is maximized, and finally the purpose of quickly and effectively performing joint pricing on the multiple commodities by integrating multiple price influence factors is achieved. In the application, different factors influencing commodity pricing can be one or more of the following factors: commodity category, price fluctuation, market demand, minimum gross profit, inventory, shelf life and price-quantity relationship.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a multi-commodity joint pricing method, device, storage medium and processor. Background Art

[0002] In chain retail stores, shelf prices are one of the most important factors in marketing sales. If they are too high or too low, they will affect sales profits. Since chain retail companies have a wide variety of products, they usually use cost-oriented pricing, incremental analysis pricing, and target pricing. However, in scenarios where geographical locations are dispersed, target populations vary greatly, the number of products is large, and prices change frequently, such as gas station chain convenience stores, there are few research results on intelligent pricing strategies in the retail field, and joint pricing is difficult.

[0003] The inventors of the present application discovered during the process of implementing the present invention that the existing problems have not been effectively solved. Summary of the invention

[0004] The purpose of the embodiment of the present invention is to provide a pricing method that can comprehensively consider multiple price influencing factors to quickly and effectively jointly price multiple commodities.

[0005] In order to achieve the above object, an embodiment of the present invention provides a multi-commodity joint pricing method, including:

[0006] Determine the collection of goods to be sold;

[0007] Determine the corresponding constraints for different factors that affect product pricing, and establish a calculation model for the suggested retail price and predicted sales volume of each product in the product set; and

[0008] Based on the integer programming method, the calculation model is solved to maximize the gross profit margin or total sales volume obtained from selling goods.

[0009] Among them, the different factors that affect commodity pricing include one or more of the following: commodity category, price fluctuation, market demand, minimum gross profit, inventory, shelf life, and quantity-price relationship.

[0010] Preferably, a calculation model for the suggested retail price and predicted sales volume of each commodity in the commodity set is established, including:

[0011] For each product in the product set, a price band set including multiple closed and open price ranges is established, wherein the relationship between the predicted sales volume and the suggested retail price of the product in each closed and open price range satisfies a linear relationship;

[0012] An objective function for maximizing gross profit margin or total sales is established based on the cost price, predicted sales volume, and suggested retail price of each commodity in the commodity set.

[0013] Preferably, the objective function for maximizing gross profit margin is: Max∑ i∈1 (x i -c i )y i , and / or, the objective function for maximizing total sales is: Max∑ i∈I y i x i , where I is the set of commodities, x i is the suggested retail price of product i, y i is the predicted sales volume c of product i i is the cost price of commodity i, i∈I, and x i ≥0,y i ≥0.

[0014] Preferably, for a product category, determining corresponding constraint conditions includes:

[0015] According to the commodity categories, the commodities in the commodity set are divided into different commodity subsets, and the intersection of every two different commodity subsets is an empty set;

[0016] The constraint function for determining the corresponding product category of each product subset is: In the formula, G is the set of commodity categories included in the commodity set, s g is the upper limit of the total sales volume of product category g.

[0017] Preferably, for price fluctuations, determining corresponding constraints includes: In the formula, l i is the lower limit of the suggested retail price of product i, u i is the upper limit of the suggested retail price of product i.

[0018] Preferably, according to market demand, determining corresponding constraints includes: In the formula, a i It is the maximum market sales volume of commodity i in the same period of history.

[0019] Preferably, for the minimum gross profit, the corresponding constraints include: Where b i is the historical lowest gross profit value of product i.

[0020] Preferably, for inventory, determining corresponding constraints includes: and / or Where, d i is the current inventory balance of product i, l i is the lower limit of the suggested retail price of product i, u i is the upper limit of the suggested retail price of product i, vi is the remaining inventory percentage of product i.

[0021] Preferably, for the shelf life, determining corresponding constraints includes: In the formula, l i is the lower limit of the suggested retail price of product i, f i is the remaining percentage of the shelf life of product i.

[0022] Preferably, for the quantity-price relationship, determining corresponding constraints includes: In the formula, is the slope of the linear relationship between the predicted sales volume and the suggested retail price corresponding to the kth price band in the price band set of product i, The intercept of the linear relationship between the predicted sales volume and the suggested retail price corresponding to the kth price band in the price band set of product i.

[0023] On the other hand, an embodiment of the present invention provides a multi-commodity joint pricing device, including: a calculation model determination module and a calculation model solution module,

[0024] The calculation model determination module is configured to: determine a set of commodities to be sold, and determine corresponding constraints for different factors affecting commodity pricing, and establish a calculation model for the suggested retail price and predicted sales volume of each commodity in the commodity set;

[0025] The calculation model solving module is configured to solve the calculation model based on an integer programming method to maximize the gross profit margin or total sales volume obtained from selling goods.

[0026] Among them, the different factors that affect commodity pricing include one or more of the following: commodity category, price fluctuation, market demand, minimum gross profit, inventory, shelf life, and quantity-price relationship.

[0027] Preferably, the calculation model determination module is further configured to:

[0028] For each product in the product set, a price band set including multiple closed and open price ranges is established, wherein the relationship between the predicted sales volume and the suggested retail price of the product in each closed and open price range satisfies a linear relationship;

[0029] An objective function for maximizing gross profit margin or total sales is established based on the cost price, predicted sales volume, and suggested retail price of each commodity in the commodity set.

[0030] Furthermore, the calculation model determination module is further configured as follows: the objective function for maximizing the gross profit margin is: Max∑ i∈I (x i -c i )y i, and / or, the objective function for maximizing total sales is: Max∑ i∈I y i x i , where I is the set of commodities, x i is the suggested retail price of product i, y i is the predicted sales volume c of product i i is the cost price of commodity i, i∈I, and x i ≥0,y i ≥0.

[0031] Furthermore, the computational model solving module is further configured as follows: the constraint condition includes one or more of the following:

[0032] According to the commodity categories, the commodities in the commodity set are divided into different commodity subsets, and the intersection of every two different commodity subsets is an empty set. The constraint function for determining the corresponding commodity category of each commodity subset is: In the formula, G is the set of commodity categories included in the commodity set, s g is the upper limit of the total sales volume of product category g;

[0033] In the formula, l i is the lower limit of the suggested retail price of product i, u i is the upper limit of the suggested retail price of product i;

[0034] In the formula, a i is the maximum market sales volume of commodity i in the same period of history;

[0035] Where b i is the historical lowest gross profit value of commodity i;

[0036] and / or Where, d i is the current inventory balance of product i, l i is the lower limit of the suggested retail price of product i, u i is the upper limit of the suggested retail price of product i, v i is the remaining inventory percentage of product i;

[0037] In the formula, l i is the lower limit of the suggested retail price of product i, f i is the remaining percentage of the shelf life of product i;

[0038] In the formula, is the slope of the linear relationship between the predicted sales volume and the suggested retail price corresponding to the kth price band in the price band set of product i, The intercept of the linear relationship between the predicted sales volume and the suggested retail price corresponding to the kth price band in the price band set of product i.

[0039] On the other hand, the present invention provides a machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the multi-commodity joint pricing method of the present application.

[0040] On the other hand, the present invention provides a processor for running a program, wherein the program, when run, is used to execute: the multi-commodity joint pricing method of the present application.

[0041] On the other hand, the present invention provides a computer program product, including a computer program, which implements the multi-commodity joint pricing method of the present application when executed by a processor.

[0042] Through the above technical solution, the set of goods to be sold is first determined, and then the corresponding constraints are determined for the different factors that affect the pricing of the goods, and a calculation model for the suggested retail price and predicted sales volume of each commodity in the commodity set is established. Then, based on the integer programming method, the calculation model is solved to maximize the gross profit margin or total sales volume obtained by selling the goods, and finally achieve the purpose of quickly and effectively pricing multiple commodities by integrating multiple price influencing factors. In this application, the different factors that affect the pricing of goods can be one or more of the following: commodity category, price fluctuation, market demand, minimum gross profit, inventory, shelf life, and quantity-price relationship.

[0043] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0045] Figure 1 is a flow chart of an embodiment of a multi-commodity joint pricing method of the present invention;

[0046] Figure 2 It is a composition diagram of an embodiment of a multi-commodity joint pricing device of the present invention. DETAILED DESCRIPTION

[0047] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0048] If there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0049] The acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. In addition, it should be noted that in the embodiments of this application, some existing solutions in the industry such as certain software, components, and models may be mentioned, which should be considered as exemplary, and their purpose is only to illustrate the feasibility of the implementation of the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0050] Figure 1 The flowchart of an embodiment of the multi-commodity joint pricing method of the present application is schematically shown. The embodiment is intended to be applied to a chain convenience store project operated by a chain of gas stations, but the present invention is not limited thereto. Figure 1 As shown, the multi-commodity joint pricing method includes the following steps:

[0051] Step S101, determining a set of commodities to be sold;

[0052] Step S102, determining corresponding constraints for different factors affecting commodity pricing, and establishing a calculation model for the suggested retail price and predicted sales volume of each commodity in the commodity set;

[0053] Step S103, solving the calculation model based on an integer programming method to maximize the gross profit margin or total sales volume obtained from selling goods.

[0054] Specifically, in step S101, a commodity set I including all considered commodities is established according to the actual situation of the convenience store; in step S102, different factors affecting commodity pricing include one or more of the following: commodity category, price fluctuation, market demand, minimum gross profit, inventory, shelf life, and quantity-price relationship.

[0055] More specifically, in step S102, a calculation model for the suggested retail price and predicted sales volume of each commodity in the commodity set is established, which also includes the following sub-steps:

[0056] Step S1021: for each commodity in the commodity set, a price band set including multiple closed and open price ranges is established, wherein the relationship between the predicted sales volume and the suggested retail price of the commodity in each closed and open price range satisfies a linear relationship;

[0057] Step S1022: Establish an objective function of maximizing gross profit margin or total sales based on the cost price, predicted sales volume, and suggested retail price of each commodity in the commodity set.

[0058] In some embodiments, the objective function for maximizing gross profit margin is: Max∑ i∈I (x i -c i )y i , and / or, the objective function for maximizing total sales is: Max∑ i∈I y i x i , where I is the set of commodities, x i is the suggested retail price of product i, y i is the predicted sales volume c of product i i is the cost price of commodity i, i∈I, and x i ≥0,y i ≥0.

[0059] Based on the conversion of the nonlinear quantity-price relationship into a piecewise quantity-price linear relationship in step S1021, each linear interval is further determined in step S103 based on a detailed analysis of the relationship between sales volume and price to ensure the prediction accuracy of the model in each interval.

[0060] In some embodiments, for a product category, determining corresponding constraint conditions includes:

[0061] According to the commodity categories, the commodities in the commodity set are divided into different commodity subsets, and the intersection of every two different commodity subsets is an empty set;

[0062] The constraint function for determining the corresponding product category of each product subset is: In the formula, G is the set of commodity categories included in the commodity set, s gis the upper limit of the total sales volume of product category g.

[0063] In some embodiments, with respect to price fluctuations, determining corresponding constraint conditions includes: In the formula, l i is the lower limit of the suggested retail price of product i, u i is the upper limit of the suggested retail price of product i.

[0064] In some embodiments, determining corresponding constraints according to market demand includes: In the formula, a i It is the maximum market sales volume of commodity i in the same period of history.

[0065] In some embodiments, for the minimum gross profit, determining corresponding constraint conditions includes: Where b i is the historical lowest gross profit value of product i.

[0066] In some embodiments, for inventory, determining corresponding constraints includes: and / or Where, d i is the current inventory balance of product i, l i is the lower limit of the suggested retail price of product i, u i is the upper limit of the suggested retail price of product i, v i is the remaining inventory percentage of product i.

[0067] In some embodiments, for the shelf life, determining corresponding constraint conditions includes: In the formula, l i is the lower limit of the suggested retail price of product i, f i is the remaining percentage of the shelf life of product i.

[0068] In some embodiments, with respect to the quantity-price relationship, determining corresponding constraint conditions includes: In the formula, is the slope of the linear relationship between the predicted sales volume and the suggested retail price corresponding to the kth price band in the price band set of product i, The intercept of the linear relationship between the predicted sales volume and the suggested retail price corresponding to the kth price band in the price band set of product i.

[0069] Compared with the prior art, the technical advantages of this embodiment are:

[0070] 1. Determine the corresponding constraints for the multiple factors that affect the price, which is equivalent to considering multiple price decision variables and the interdependence between the variables at the same time, and find the overall optimal solution. It can respond to market changes in real time and enhance the flexibility and adaptability of pricing;

[0071] 2. By establishing a set of price bands that satisfy the linear relationship between the predicted sales volume and the suggested retail price, the difficulty of solving the problem is reduced while the speed of solving the problem is increased, thus ensuring the accuracy of the solution.

[0072] The present invention also provides another embodiment of the multi-commodity joint pricing method to realize intelligent joint pricing of chain convenience stores. The commodity information is first obtained, and then an integer programming model is established and solved based on the obtained commodity information.

[0073] The product information acquisition stage includes the following steps:

[0074] Step 1: According to the actual situation of convenience stores, establish a product set I containing all the considered products;

[0075] Step 2: Group the products together to create different category sets G, each category g corresponds to a product subset S g , ensuring that there is no intersection between subsets;

[0076] Step 3: Create a price range set P based on product i i , the price band set of commodity i, the price band set consists of multiple closed and open intervals, and the closed and open intervals are continuous and have no overlap;

[0077] Since each commodity has different price characteristics, the granularity of the price band is different for each commodity to ensure greater accuracy in the linearization of the quantity-price relationship.

[0078] Step 4: Based on product i and its price range set P i Build k i Index, used to identify product i in its price range set P i A specific segment in

[0079] Step 5: For each item i in set I, determine the parameters required for the following calculation:

[0080] 1) Establish a price floor l for commodity i based on its historical prices i and the price ceiling u for good i i As an input parameter, the calculated price cannot be lower than the lower price limit and cannot be higher than the upper price limit to ensure that the suggested retail price of product i is within a reasonable range;

[0081] 2) According to its historical sales data, establish the maximum market sales value a of product i in the same period in history i It is an input parameter used to ensure that the sales forecast is within a reasonable range and to prevent the pricing algorithm from exceeding the actual sales capacity.

[0082] 3) Set the cost price c of commodity i based on the purchase cost of the commodity iIt is an input parameter, which is the basis for calculating the gross profit margin of the product;

[0083] 4) Establish the historical minimum gross profit b of product i based on the historical purchase cost and sales price of the product i It is an input parameter that ensures that the pricing strategy meets the minimum profit requirement;

[0084] 5) According to the shelf life information of the product, establish the remaining shelf life percentage f of product i i The shelf life has a significant impact on the price of the product: the higher the remaining shelf life percentage, the higher the price of the product may be; conversely, the shorter the remaining shelf life, the lower the price may be. However, even in the later period of the shelf life, the price of the product should not be lower than its lower price limit. i ;

[0085] 6) Set the inventory balance d of product i based on the product inventory information i As an input parameter, inventory balance has a direct impact on commodity prices;

[0086] By analyzing the relationship between the quantity and price of goods, the inventory percentage can be segmented: when the inventory percentage is in a higher range, the price may need to be lowered to promote sales; on the contrary, when the inventory percentage is in a lower range, the price can be increased. However, even in the case of excess inventory, the price of the product should not be lower than its lower price limit. i .

[0087] 7) Establish sales volume upper limit s for category g products based on product category information and historical sales g is the input parameter;

[0088] s g Ensure that the total sales volume of all products in category g does not exceed the preset upper limit, thereby achieving mutual restraint and balance in sales volume of products within the category, adapting to changes in market demand, avoiding excessive concentration of sales of a single product, and maintaining product diversity.

[0089] 8) To achieve better results, you can also use the store's latitude and longitude information and store type information to analyze regional characteristics and consumer behavior to adjust product pricing strategies;

[0090] 9) Adjust the composition and focus of the product category collection based on the market demand and seasonal changes of the product category. Combined with customer flow data, evaluate the pricing differences between peak and off-peak hours to attract more customers;

[0091] 10) According to the historical sales volume and historical price of the commodity, the quantity-price relationship is fitted and a nonlinear quantity-price relationship is formed to determine the relationship between commodity profit and sales volume. This model can more accurately reflect the market's response to price changes;

[0092] In order to ensure the speed and efficiency of model solution without sacrificing practicality and accuracy, the present invention further includes the step of converting the nonlinear quantity-price relationship into a piecewise quantity-price linear relationship: through mathematical processing and market analysis, the nonlinear model is decomposed into multiple linear intervals P according to different commodities. i , the relationship between sales volume and price in each interval is approximately linear. This piecewise linearization process simplifies the computational complexity of the model while retaining sensitivity to market changes, making the model more efficient and easy to operate in practical applications. The determination of each linear interval is based on a detailed analysis of the relationship between sales volume and price to ensure the prediction accuracy of the model in each interval.

[0093] In this embodiment, the target model is established by integer programming method, with maximizing gross profit margin and sales as the objective function. The model considers constraints such as price fluctuation, market demand, minimum gross profit, inventory balance, shelf life and quantity-price relationship, and is solved by the operations optimization algorithm to obtain the optimal commodity price and sales volume.

[0094] Compared with the prior art, the technical advantages of this embodiment are:

[0095] 1. Optimize the recommended retail price of goods: The system uses advanced data analysis technology, combined with the store's existing product sales data, historical prices and sales information, to output scientific pricing suggestions for each product. The recommended retail price is designed to help retailers predict sales response at different price levels, thereby maximizing profits.

[0096] 2. Dynamic real-time pricing: When the purchase price of goods changes, the system can quickly capture the purchase price change information and adjust the current pricing of the goods accordingly, ensuring that the pricing strategy keeps pace with market conditions and reflects the latest cost structure.

[0097] 3. Reduce human intervention and errors: Through automated data collection and processing mechanisms, the system eliminates the need for manual input of relevant data, greatly reducing calculation errors caused by human operations and significantly reducing the calculation burden on business personnel.

[0098] 4. Improve pricing efficiency: Compared with traditional manual pricing methods, the system of the present invention greatly improves the efficiency of commodity pricing through a highly automated process, allowing stores to quickly respond to market changes and make price adjustments.

[0099] 5. Establish a mathematical model: The system establishes a preset mathematical model based on the historical data of the product. The model can describe and predict the relationship between the price and sales volume of the product, further provide quantity-price elasticity analysis, and determine the optimal price point in a scientific way.

[0100] 6. Ensure and improve store profits: By comprehensively considering various internal and external factors, the system provides stores with a comprehensive pricing strategy to ensure the maximization of store profits under various market conditions.

[0101] The embodiment of the present invention provides a multi-commodity joint pricing device, the composition structure of which is as follows: Figure 2 As shown, it includes: a calculation model determination module and a calculation model solution module,

[0102] The calculation model determination module is configured to: determine a set of commodities to be sold, and determine corresponding constraints for different factors affecting commodity pricing, and establish a calculation model for the suggested retail price and predicted sales volume of each commodity in the commodity set;

[0103] The calculation model solving module is configured to solve the calculation model based on an integer programming method to maximize the gross profit margin or total sales volume obtained from selling goods.

[0104] Among them, the different factors that affect commodity pricing include one or more of the following: commodity category, price fluctuation, market demand, minimum gross profit, inventory, shelf life, and quantity-price relationship.

[0105] In some implementations, the computing model determination module is further configured to:

[0106] For each product in the product set, a price band set including multiple closed and open price ranges is established, wherein the relationship between the predicted sales volume and the suggested retail price of the product in each closed and open price range satisfies a linear relationship;

[0107] An objective function for maximizing gross profit margin or total sales is established based on the cost price, predicted sales volume, and suggested retail price of each commodity in the commodity set.

[0108] In some embodiments, the calculation model determination module is further configured as follows: the objective function for maximizing the gross profit margin is: Max∑ i∈I (x i -c i )y i , and / or, the objective function for maximizing total sales is: Max∑ i∈I y i x i , where I is the set of commodities, x i is the suggested retail price of product i, y i is the predicted sales volume c of product i i is the cost price of commodity i, i∈I, and x i ≥0,y i ≥0.

[0109] In some embodiments, the computational model solving module is further configured to: the constraint condition includes one or more of the following:

[0110] According to the commodity categories, the commodities in the commodity set are divided into different commodity subsets, and the intersection of every two different commodity subsets is an empty set. The constraint function for determining the corresponding commodity category of each commodity subset is: In the formula, G is the set of commodity categories included in the commodity set, s g is the upper limit of the total sales volume of product category g;

[0111] In the formula, l i is the lower limit of the suggested retail price of product i, u i is the upper limit of the suggested retail price of product i;

[0112] In the formula, a i is the maximum market sales volume of commodity i in the same period of history;

[0113] Where b i is the historical lowest gross profit value of commodity i;

[0114] and / or Where, d i is the current inventory balance of product i, l i is the lower limit of the suggested retail price of product i, u i is the upper limit of the suggested retail price of product i, v i is the remaining inventory percentage of product i;

[0115] In the formula, l i is the lower limit of the suggested retail price of product i, f i is the remaining percentage of the shelf life of product i;

[0116] In the formula, is the slope of the linear relationship between the predicted sales volume and the suggested retail price corresponding to the kth price band in the price band set of product i, The intercept of the linear relationship between the predicted sales volume and the suggested retail price corresponding to the kth price band in the price band set of product i.

[0117] An embodiment of the present invention provides a multi-commodity joint pricing device, which includes a processor and a memory. The above-mentioned calculation model determination module and the calculation model solution module are both stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to implement corresponding functions.

[0118] The processor contains a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and multiple price influencing factors can be adjusted to quickly and effectively price multiple commodities together.

[0119] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0120] An embodiment of the present invention provides a storage medium on which a program is stored. When the program is executed by a processor, the multi-commodity joint pricing method of the present application is implemented.

[0121] An embodiment of the present invention provides a processor, which is used to run a program, wherein the multi-commodity joint pricing method of the present application is executed when the program is run.

[0122] The embodiment of the present invention provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements the steps of the multi-commodity joint pricing method of the present application when executing the program. The device in this article can be a server, a PC, a PAD, a mobile phone, etc.

[0123] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the steps of the multi-commodity joint pricing method of the present application.

[0124] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0125] Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0126] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions.

[0127] These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0131] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0132] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0133] According to the definition in this article, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0134] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0135] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A multi-commodity joint pricing method, characterized in that: include: Determine the collection of goods to be sold; Determine corresponding constraints for different factors affecting commodity pricing, and establish a calculation model for the suggested retail price and predicted sales volume of each commodity in the commodity set; and Based on the integer programming method, the calculation model is solved to maximize the gross profit margin or total sales volume obtained from selling goods. Among them, the different factors that affect commodity pricing include one or more of the following: commodity category, price fluctuation, market demand, minimum gross profit, inventory, shelf life, and quantity-price relationship.

2. The multi-commodity joint pricing method according to claim 1, characterized in that: The step of establishing a calculation model for the suggested retail price and predicted sales volume of each commodity in the commodity set includes: For each commodity in the commodity set, a price band set including a plurality of closed and open price ranges is established, wherein the relationship between the predicted sales volume and the suggested retail price of the commodity in each closed and open price range satisfies a linear relationship; An objective function of maximizing gross profit margin or total sales is established based on the cost price of each commodity in the commodity set, the predicted sales volume, and the suggested retail price.

3. The multi-commodity joint pricing method according to claim 2, characterized in that: The objective function for maximizing the gross profit margin is: Max∑ i∈I (x i -c i )y i , and / or The objective function for maximizing the total sales is: Max∑ i∈I y i x i , In the formula, I is the product set, x i is the suggested retail price of product i, y i is the predicted sales volume c of product i i is the cost price of commodity i, i∈I, and x i ≥0,y i ≥0.

4. The multi-commodity joint pricing method according to claim 3, characterized in that: For the commodity category, the corresponding constraints are determined to include: According to the commodity categories to which they belong, the commodities in the commodity set are divided into different commodity subsets, and the intersection of every two different commodity subsets is an empty set; The constraint function for determining the corresponding product category of each product subset is: In the formula, G is the set of commodity categories included in the commodity set, s g is the upper limit of the total sales volume of product category g.

5. The multi-commodity joint pricing method according to claim 3, characterized in that: In view of the price fluctuation, the corresponding constraints are determined to include: In the formula, l i is the lower limit of the suggested retail price of product i, u i is the upper limit of the suggested retail price of product i.

6. The multi-commodity joint pricing method according to claim 3, characterized in that: According to the market demand, the corresponding constraints are determined to include: In the formula, a i It is the maximum market sales volume of commodity i in the same period of history.

7. The multi-commodity joint pricing method according to claim 3, characterized in that: For the minimum gross profit, the corresponding constraints include: Where b i is the historical lowest gross profit value of product i.

8. The multi-commodity joint pricing method according to claim 3, characterized in that: For the inventory, the corresponding constraints are determined to include: and / or Where, d i is the current inventory balance of product i, l i is the lower limit of the suggested retail price of product i, u i is the upper limit of the suggested retail price of product i, v i is the remaining inventory percentage of product i.

9. The multi-commodity joint pricing method according to claim 3, characterized in that: For the shelf life, the corresponding constraints are determined to include: In the formula, l i is the lower limit of the suggested retail price of product i, f i is the remaining percentage of the shelf life of product i.

10. The multi-commodity joint pricing method according to claim 3, characterized in that: In view of the quantity-price relationship, the corresponding constraints are determined to include: In the formula, is the slope of the linear relationship between the predicted sales volume and the suggested retail price corresponding to the kth price band in the price band set of product i, The intercept of the linear relationship between the predicted sales volume and the suggested retail price corresponding to the kth price band in the price band set of product i.

11. A multi-commodity joint pricing device, characterized in that: include: Computational model determination module and computational model solution module, The calculation model determination module is configured to: determine a set of commodities to be sold, and determine corresponding constraints for different factors affecting commodity pricing, and establish a calculation model for the suggested retail price and predicted sales volume of each commodity in the commodity set; The computing model solving module is configured to solve the computing model based on an integer programming method to maximize the gross profit margin or total sales amount obtained from selling goods. Among them, the different factors that affect commodity pricing include one or more of the following: commodity category, price fluctuation, market demand, minimum gross profit, inventory, shelf life, and quantity-price relationship.

12. The multi-commodity joint pricing device according to claim 11, characterized in that: The computing model determination module is further configured to: For each commodity in the commodity set, a price band set including a plurality of closed and open price ranges is established, wherein the relationship between the predicted sales volume and the suggested retail price of the commodity in each closed and open price range satisfies a linear relationship; An objective function of maximizing gross profit margin or total sales is established based on the cost price of each commodity in the commodity set, the predicted sales volume, and the suggested retail price.

13. The multi-commodity joint pricing device according to claim 12, characterized in that: The computing model determination module is further configured to: The objective function for maximizing the gross profit margin is: Max∑ i∈I (x i -c i )y i , and / or The objective function for maximizing the total sales is: Max∑ i∈I y i x i , In the formula, I is the product set, x i is the suggested retail price of product i, y i is the predicted sales volume c of product i i is the cost price of commodity i, i∈I, and x i ≥0,y i ≥0.

14. The multi-commodity joint pricing device according to claim 13, characterized in that: The computational model solving module is further configured such that: the constraint condition includes one or more of the following: According to the commodity categories, the commodities in the commodity set are divided into different commodity subsets, and the intersection of every two different commodity subsets is an empty set. The constraint function for determining the corresponding commodity category of each commodity subset is: In the formula, G is the set of commodity categories included in the commodity set, s g is the upper limit of the total sales volume of product category g; In the formula, l i is the lower limit of the suggested retail price of product i, u i is the upper limit of the suggested retail price of product i; In the formula, a i is the maximum market sales volume of commodity i in the same period of history; Where b i is the historical lowest gross profit value of commodity i; and / or Where, d i is the current inventory balance of product i, l i is the lower limit of the suggested retail price of product i, u i is the upper limit of the suggested retail price of product i, v i is the remaining inventory percentage of product i; In the formula, l i is the lower limit of the suggested retail price of product i, f i is the remaining percentage of the shelf life of product i; In the formula, is the slope of the linear relationship between the predicted sales volume and the suggested retail price corresponding to the kth price band in the price band set of product i, The intercept of the linear relationship between the predicted sales volume and the suggested retail price corresponding to the kth price band in the price band set of product i.

15. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions for causing the machine to execute: the multi-commodity joint pricing method according to any one of claims 1-10.

16. A processor, characterized in that: Used to run a program, wherein the program, when run, is used to execute: a multi-commodity joint pricing method according to any one of claims 1-10.

17. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the multi-commodity joint pricing method according to any one of claims 1 to 10.

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