A method and system for dynamically dividing the distribution range of multiple warehouses in the same city

Abstract

Multiple embodiments of this specification relate to the field of information technology, and specifically to a method and system for dynamically dividing the distribution range of multiple warehouses in the same city. The method includes the following steps: calculating the purchase volume parameters of retail outlets on each date in the annual cycle; setting the warehouse's daily throughput coefficient equal to the weighted product of the number of retail outlets distributed by the warehouse on that day and the distribution volume, and the cargo distance equal to the weighted product of the distribution volume and the distribution distance; based on the purchase volume parameters, calculating the number of days when the throughput coefficient of each warehouse exceeds the limit and the cargo distance of the warehouse; calculating the weighted product of the number of days and the cargo distance as the warehouse index; attempting to generate the distribution retail outlet range of each warehouse multiple times, and obtaining the distribution retail outlet range that minimizes the warehouse index and the minimum for all warehouses as a monthly plan; daily statistics on the warehouse's cargo age, calculating the equivalent warehouse age based on the age, and generating a daily adjustment plan for the next day.

Description

Technical Field

[0001] Multiple embodiments of this specification relate to the field of information technology, and specifically to a method and system for dynamically dividing the delivery range of multiple warehouses in the same city. Background Art

[0002] With the rapid development of e-commerce and consumers' increasing demands for faster logistics, the traditional single-warehouse distribution model is no longer able to meet market demand. Against this backdrop, the multi-warehouse model has emerged. This refers to the establishment of multiple warehousing nodes within a city or its surrounding areas. The most obvious advantage of this model is its significant improvement in delivery efficiency. By establishing warehouses in different parts of a city, goods can be stored closer to target customers, significantly reducing order processing time and delivery distances. This close-range delivery not only speeds up goods' arrival at the consumer's doorstep but also reduces the uncertainties associated with long-distance transportation, such as traffic congestion and weather fluctuations, thereby improving overall delivery reliability. In the face of a rapidly changing market environment, the multi-warehouse model provides merchants with greater delivery flexibility and faster responsiveness. When demand in a particular area suddenly increases, goods can be quickly transferred from neighboring warehouses to replenish the supply. Conversely, inventory levels can be adjusted based on actual conditions, avoiding overinvestment. Summary of the Invention

[0003] Multiple embodiments of this specification describe a method and system for dynamically dividing the delivery range of multiple warehouses in the same city.

[0004] In a first aspect, embodiments of this specification provide a method for dynamically dividing a same-city multi-warehouse delivery range, which is used to generate a range of warehouse delivery retail outlets, including the following steps:

[0005] Calculate the purchase quantity parameters of the retail outlets on each date in the monthly cycle;

[0006] Set the warehouse's daily throughput coefficient to the weighted product of the number of retail outlets delivered by the warehouse on that day and the delivery volume, and set the warehouse's cargo distance to the weighted product of the delivery volume and the delivery distance;

[0007] Randomly generate the distribution retail outlet range for each warehouse in the next month, and calculate the number of days that each warehouse's throughput coefficient exceeds the limit and the distance of the goods to the warehouse based on the purchase volume parameters;

[0008] Calculate a weighted product of the number of days and the distance of the goods as a warehouse index;

[0009] Try to generate the distribution retail outlet range for each warehouse multiple times to obtain the warehouse index of all warehouses and the distribution retail outlet range with the minimum, which will be used as the monthly plan;

[0010] During the execution of the monthly plan, the age of the goods in the warehouse is counted daily, and the equivalent age is calculated based on the age. When the equivalent age exceeds a preset first threshold, a daily adjustment plan for the next day is generated to ensure that the expected value of the equivalent age does not exceed a preset second threshold. The expected value of the equivalent age is calculated based on the purchase volume parameters of the retail outlets on the next day.

[0011] In a second aspect, the embodiments of this specification provide a system for dynamically dividing the delivery range of multiple warehouses in the same city, including:

[0012] The equivalent module calculates the purchase quantity parameters of the retail outlets on each date in the monthly cycle;

[0013] In the setup module, set the warehouse's daily throughput coefficient to the weighted product of the number of retail outlets delivered by the warehouse on that day and the delivery volume, and set the warehouse's cargo distance to the weighted product of the delivery volume and the delivery distance;

[0014] The first calculation module randomly generates the distribution retail outlet range for each warehouse in the next month, and calculates the number of days when the throughput coefficient of each warehouse exceeds the limit and the distance of the goods to the warehouse based on the purchase volume parameter;

[0015] A second calculation module calculates a weighted product of the number of days and the distance of the goods as a warehouse index;

[0016] The monthly module attempts to generate the distribution retail outlet range for each warehouse multiple times, and obtains the warehouse index of all warehouses and the minimum distribution retail outlet range as the monthly plan;

[0017] The daily adjustment module counts the age of goods in the warehouse every day during the execution of the monthly plan, calculates the equivalent age based on the age, and generates a daily adjustment plan for the next day when the equivalent age exceeds a preset first threshold, so that the expected value of the equivalent age does not exceed a preset second threshold. The expected value of the equivalent age is calculated based on the purchase volume parameters of the retail outlets on the next day in the monthly cycle.

[0018] In a third aspect, embodiments of this specification provide an electronic device, including a processor and a memory;

[0019] The processor is connected to the memory;

[0020] The memory is used to store executable program code;

[0021] The processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to execute the method described in any one of the above aspects.

[0022] In a fourth aspect, an embodiment of this specification provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method described in any one of the above aspects is implemented.

[0023] In a fifth aspect, embodiments of this specification provide a computer program product, including a computer program, which implements the method described in any of the above aspects when executed by a processor.

[0024] The beneficial effects of the technical solutions provided by some embodiments of this specification include at least:

[0025] In multiple embodiments of this specification, the method for dynamically dividing the distribution range is provided. By generating a monthly plan, the range of distribution retail outlets for each monthly warehouse is achieved, so that the warehouse throughput coefficient and the distance of goods reach a better level. The daily adjustment plan is also used to expand the range of distribution retail outlets for warehouses with goods with a longer equivalent storage age, so that goods with a longer equivalent storage age can be quickly distributed to retail outlets for sale, which helps to ensure the quality of goods sold at retail outlets through coordination. The average historical purchase volume ratio of the date is calculated through the annual cycle, and then the monthly predicted purchase volume is used to calculate the purchase volume parameters of each retail outlet, so that the generation of daily purchase volume parameters for retail outlets is more accurate. By generating a daily adjustment plan based on the equivalent storage age, a more reasonable daily adjustment plan can be achieved by adjusting the equivalent storage age, ensuring that the storage age of goods in the warehouse is more reasonable.

[0026] Other features and advantages of the various embodiments of this specification will be further disclosed in the following detailed description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of this specification, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0028] Figure 1 This is a schematic diagram of the warehouse distribution range used in the embodiments of this specification.

[0029] Figure 2 A schematic diagram of an application scenario of a method for dynamically dividing a delivery range is provided for the embodiments of this specification.

[0030] Figure 3 This is a flow chart of the method for dynamically dividing the delivery range of multiple warehouses in the same city according to an embodiment of this specification.

[0031] Figure 4 This is a schematic diagram of equivalent storage age calculation for the embodiments of this specification.

[0032] Figure 5 This is a schematic diagram of the system for dynamically dividing the delivery range of multiple warehouses in the same city according to the embodiment of this specification.

[0033] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0034] The following is an explanation and description of the technical solutions of the embodiments of this specification in conjunction with the drawings of the embodiments of this specification. However, the following embodiments are only preferred embodiments of this specification and are not exhaustive. Based on the embodiments in the implementation mode, other embodiments obtained by those skilled in the art without making any creative work are all within the scope of protection of this specification.

[0035] Throughout this specification, the claims, and the accompanying drawings, the terms "first," "second," "third," and the like are used to distinguish between different items, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may include other steps or elements inherent to the process, method, product, or apparatus.

[0036] In the following description, terms such as "inside", "outside", "up", "down", "left", "right", etc. that indicate directions or positional relationships are only used to facilitate the description of the embodiments and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limitations on this specification.

[0037] The data involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection of relevant data complies with the relevant laws, regulations and standards of relevant countries and regions.

[0038] Before describing the technical solution in this specification, an introduction to the application scenarios and related technologies of the technical solution is given.

[0039] Urban traffic congestion and traffic restrictions significantly impact the delivery of goods with a short shelf life. Specifically, urban traffic congestion causes delivery vehicles to spend more time on the road, directly impacting delivery times. For perishable goods or items with a short shelf life, delayed delivery can cause them to lose their optimal state or even spoil, impacting the consumer experience and the merchant's reputation. Examples include flowers and cakes. Prolonged stoppages caused by traffic congestion increase fuel consumption and labor costs. Furthermore, traffic restrictions may force logistics companies to take detours to avoid restricted areas, further increasing transportation costs. In urban delivery, fresh vegetables, milk, prepared foods, baked bread, flowers, and pre-prepared dishes all require time-sensitive delivery. For example, for pre-prepared marinated chicken cutlets, extended delivery times can lead to changes in taste and flavor, potentially posing food safety risks.

[0040] Specifically, if the various spices and seasonings added during the marinating process are not presented within the optimal time, their flavor may not be fully released. Prolonged storage may lead to bacterial growth, increasing food safety risks. Fried chicken that is spoiled or tastes bad can easily lead to customer loss and affect the business's operating profits. Please refer to the attached Figure 1 By establishing multiple warehouses within a city, each retail outlet 20 no longer needs to travel long distances to purchase goods from warehouse 10, effectively alleviating the aforementioned issues. This ensures that even in traffic congestion, products can be delivered quickly from the nearest warehouse 10. Furthermore, this multi-warehouse layout allows for flexible adjustments to delivery routes based on real-time traffic conditions, avoiding congested roads and minimizing the impact of traffic restrictions, thus maintaining product freshness and optimal consumption.

[0041] In the delivery industry, especially within-city delivery, for products requiring time-sensitive delivery, such as cakes, vegetables, fruits, flowers, and baked goods, or products requiring rapid delivery, such as newly released electronics and home appliances, establishing multiple warehouses within the same city to complete delivery not only improves delivery efficiency but also ensures product quality, significantly enhancing delivery performance.

[0042] However, multiple warehouses within a city also present challenges such as complex management, high initial investment, and difficult inventory management. Due to regional fluctuations in product demand, some warehouses' inventory may not be cleared as expected, resulting in longer product aging and impacting product quality. Currently, the delivery ranges of multiple warehouses within a city are typically manually set and generally remain unchanged. However, this fixed delivery range approach is clearly not effective for delivering products with shorter shelf lives. Therefore, research is needed to optimize scheduling techniques for intra-city delivery using multiple warehouses within a city.

[0043] Please see the attached Figure 2 The system architecture of the technical solution recorded in this specification includes a server 30 and a device end. Multiple device ends are set in the warehouse 10 and are used to upload the inventory quantity, incoming quantity, outgoing quantity, the current distribution retail outlet range of the warehouse 10 and the distribution records.

[0044] The operation interface is deployed on the device side, wherein the operation interface can be run on the device side in the form of a browser, or in the form of an independent application (APP), etc. The specific display form of the operation interface is not limited here. The server 30 involved in this application can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms. The device side can be a smart phone, tablet computer, laptop computer, PDA, personal computer, smart TV, smart watch, car-mounted device, wearable device, etc., but is not limited to this. The device side and the server 30 can be directly or indirectly connected by wired or wireless communication, and this application does not limit this. The number of servers 30 and device sides is also not limited.

[0045] Since this application involves some professional terms, these professional terms will be introduced below.

[0046] retail outlets

[0047] A retail outlet is any physical location where goods are sold or services are provided directly to consumers. These locations are typically fixed commercial facilities such as shops, supermarkets, convenience stores, specialty stores, and shopping malls. They can also be non-fixed points of sale, such as mobile vending carts and market stalls. Characteristics of retail outlets 20 include direct access to consumers, a diverse range of goods and services, strategic locations, and the ability to showcase and experience the experience. Retail outlets 20 can take the form of traditional brick-and-mortar stores, shopping malls / malls, specialty stores / exclusive stores, convenience stores, direct sales outlets, and non-fixed points of sale.

[0048] Storage age

[0049] Stock age (or inventory age) refers to the length of time between the time an item enters the warehouse and the current moment. It's a crucial metric for measuring inventory management efficiency, particularly in industries like retail, manufacturing, and logistics. Inventory age can help companies assess inventory turnover, identify products at risk of slow sales, and develop appropriate inventory management strategies to optimize inventory structure, reduce capital tied up, and improve inventory turnover.

[0050] Weighted product

[0051] Weighted product refers to a method that assigns different weights to variables and then performs a product calculation. The weight reflects the importance of each indicator to the overall evaluation; the larger the weight, the greater the influence of the indicator on the final decision. Since the total product is calculated by multiplying multiple weight coefficients by their corresponding multipliers, it does not affect the result. Therefore, this manual only uses a single weight coefficient, that is, the weight coefficient is the product of all weight coefficients.

[0052] This specification first provides a method for dynamically dividing the distribution range of multiple warehouses in the same city, which is used to generate the distribution range of warehouse 10 to retail outlets 20. Figure 3 , including the steps of:

[0053] Step S01) Calculate the purchase quantity parameters of the retail outlet 20 on each date in the annual cycle.

[0054] The method for calculating the purchase quantity parameter of the retail outlet 20 on each date in the annual cycle includes:

[0055] Read the historical purchase volume for each date and calculate the average historical purchase volume;

[0056] Calculate the sum of the average historical purchase volume of all dates in the month, and calculate the average historical purchase volume percentage of each date in the month, where the average historical purchase volume percentage is equal to the ratio of the average historical purchase volume of the date to the sum;

[0057] The predicted purchase volume of the retail outlet 20 for the next month is read, and the purchase volume parameter is obtained according to the product of the average historical purchase volume ratio of each date in the month and the predicted purchase volume.

[0058] For example, taking ambient temperature packaged milk as an example, one of the retail outlets A made one purchase of 15 cartons on the first day of the year, January 1, in the past five years; on the second day of the year, January 2, it made no purchases; on the 36th day of the year, February 5, it made two purchases of 10 cartons and 12 cartons respectively; on the 42nd day of the year, February 11, it made one purchase of 20 cartons; and on the 50th day of the year, February 19, it made one purchase of 5 cartons. The average historical purchase volume on the first day of the year is 3 cartons (i.e., 15 ÷ 5 = 3), the average historical purchase volume on the second day of the year is 0 cartons, the average historical purchase volume on the 36th day of the year is 2.2 cartons (i.e., (10 + 12) ÷ 5 = 4.4), the average historical purchase volume on the 42nd day of the year is 4 cartons (i.e., 20 ÷ 5 = 4), and the average historical purchase volume on the 50th day of the year is 1 carton (i.e., 5 ÷ 5 = 1). The average historical purchase volume for other days of the year is calculated in the same way.

[0059] To avoid excessive length, let's assume that retail outlet A has only purchased goods four times in the past five years, as described above. The sum of the average historical purchase volume for all days in January is calculated as 3 + 0 = 3, and the sum of the average historical purchase volume for all days in February is calculated as 4.4 + 4 = 8.4. This is the ratio of total purchase volume to the number of years. Next, calculate the average historical purchase volume percentage for each day within the month. The average historical purchase volume percentage for day 1 of the year is 3 ÷ 3 = 100%, the average historical purchase volume percentage for day 0 of the year is 0%, the average historical purchase volume percentage for day 36 of the year is 4.4 ÷ 9.4 = 46.80%, the average historical purchase volume percentage for day 42 of the year is 4 ÷ 9.4 = 42.55%, and the average historical purchase volume percentage for day 50 of the year is 1 ÷ 9.4 = 10.65%.

[0060] Read the forecasted purchase volume for retail outlet A for the next month. Assuming February is next month and the volume is 60 boxes, the purchase volume parameters for February 5 are 60 × 46.80% = 28.08, February 11 are 60 × 42.55% = 25.53, and February 19 are 60 × 10.65% = 6.39. The purchase volume parameters for the remaining days are 0. In practice, based on more abundant data, you can use the calculation method given in this example.

[0061] Step S02) The daily throughput coefficient of the warehouse 10 is set to be equal to the weighted product of the number of retail outlets delivered by the warehouse 10 on that day and the delivery volume, and the cargo distance of the warehouse 10 is set to be equal to the weighted product of the delivery volume and the delivery distance.

[0062] For example, Warehouse A delivered to three retail outlets on January 3rd, with delivery volumes of 5, 8, and 3 cartons, respectively. The throughput coefficient is then k10×3×k11×5×k12×8×k13×3. Among them, k10, k11, k12, and k13 are coefficients of the multiplier. Since k10×k11×k12×k13 is still a coefficient, the coefficient k1 is set to k10×k11×k12×k13, so the throughput coefficient is k1×3×5×8×3=k1×360. The value of the coefficient k1 can be a pre-set function of the number of retail outlets delivered, such as k1=2.5×the number of retail outlets delivered (i.e., the coefficients corresponding to k11, k12, k13, and other retail outlets are all constants and equal to 2.5). It can also be a function of the total delivery volume, such as k1=f(5+8+3)=f(16), where function f() is a pre-set function. For example, the function f(x) = 2x. For another example, the function f(x) = x^2. When the function f(x) = 2x, the throughput coefficient = k1 × 360 = 2 × (5 + 8 + 3) × 120 = 11520. When the function f(x) = x^2, the throughput coefficient = k1 × 120 = (5 + 8 + 3)^2 × 360 = 92160. In this case, the weight coefficients of individual retail outlets can no longer be distinguished.

[0063] The distances between Warehouse A and the three retail outlets it delivered to on January 3rd are 1.2 km, 2 km, and 0.5 km, respectively. Therefore, the cargo distance = k21 × 1.2 × 5 × k22 × 2 × 8 × k23 × 0.5 × 3 = 144 × k21 × k22 × k23. Setting the weight coefficient k2 = k21 × k22 × k23, the cargo distance = 144 × k2. The weight coefficients for individual retail outlets do not need to be differentiated. Therefore, setting the weight coefficient k2 as a function of the number of retail outlets delivered to, for example, k2 = 2 × the number of retail outlets delivered to, results in a cargo distance of 144 × 2 × 3 = 864.

[0064] Step S03) randomly generates the distribution retail outlet range for each warehouse in the next month, and calculates the number of days when the throughput coefficient of each warehouse exceeds the limit and the cargo distance of the warehouse based on the purchase volume parameter.

[0065] For example, only one distribution retail outlet is generated for Warehouse A for the next month (assuming it is February), assuming it is Retail Outlet A.

[0066] The method for calculating the number of days that the throughput coefficient of each warehouse exceeds the limit based on the purchase quantity parameter includes:

[0067] According to the randomly generated range of retail outlets for each warehouse for the next month, all retail outlets distributed by each warehouse are obtained;

[0068] Calculate the sum of the purchase volume parameters of all retail outlets of the warehouse on each date in the annual cycle, record it as the daily distribution volume, and calculate the warehouse throughput coefficient on each date;

[0069] According to the throughput coefficient of the warehouse on each date and the preset lower and upper thresholds of the throughput coefficient, the number of days on which the throughput coefficient of each warehouse exceeds the limit is obtained.

[0070] Read the purchase quantity parameters for retail outlet A for the next month. For example, the purchase quantity parameters for February 5th are 28.08, February 11th are 25.53, and February 19th are 6.39. Calculate the throughput coefficients for Warehouse A on these three days. Warehouse A's throughput coefficient on February 5th is k1 × 1 × 28.08 = 2 × (28.08) × 1 × 28.08 = 1576.9728. Warehouse A's throughput coefficient on February 11th is 2 × (25.53) × 1 × 25.53 = 1303.5618. Warehouse A's throughput coefficient on February 19th is 2 × (6.39) × 1 × 6.39 = 81.6642.

[0071] Suppose Warehouse A's throughput coefficient is set to an upper threshold of 1500 and a lower threshold of 150. Warehouse A's throughput coefficient on February 5th exceeded the upper threshold, indicating that Warehouse A's delivery workload was too heavy that day. On February 19th, the throughput coefficient fell below the lower threshold, indicating that Warehouse A's delivery workload was too light, resulting in a waste of resources. Therefore, Warehouse A's throughput coefficient exceeded the upper threshold on two days. The solution is to re-divide Warehouse A's delivery range and assign it to retail outlets with more balanced purchase volume parameters. Due to space limitations, this example only assigns Warehouse A to one retail outlet. Implementation will be based on the retail outlet's data, and the upper and lower thresholds for the throughput coefficient will be set according to the warehouse's delivery capacity.

[0072] The method for calculating the distance of goods to each warehouse according to the incoming goods quantity parameter includes:

[0073] According to the randomly generated range of retail outlets for each warehouse for the next month, all retail outlets distributed by each warehouse are obtained;

[0074] Calculate the total purchase volume parameters of each retail outlet of the warehouse in the annual cycle;

[0075] Obtaining the distance between the warehouse and the retail outlet according to the product of the total purchase quantity parameter of the retail outlet and the distance between the warehouse and the retail outlet;

[0076] The cargo distance of the warehouse is obtained by adding up the cargo distances of all retail outlets distributed by the warehouse.

[0077] Specifically, the delivery distance between Warehouse A and Retail Outlet A is 1.5 km, and the delivery volume is directly derived from the purchase volume parameter. Warehouse A's cargo distance on February 5th = k² × delivery volume × delivery distance = 2 × number of retail outlets delivered × delivery volume × delivery distance = 2 × 1 × 28.08 × 1.5 = 84.24. Warehouse A's cargo distance on February 11th = 2 × 1 × 25.53 × 1.5 = 76.59. Warehouse A's cargo distance on February 19th = 2 × 1 × 6.39 × 1.5 = 19.17.

[0078] Step S04) Calculate a weighted product of the number of days and the distance to the goods as the warehouse index. The weighted product is the product of a weight coefficient, the number of days, and the distance to the goods. The weight coefficient is an increasing function of the number of days, and the first-order derivative of the function increases with the increase in the number of days.

[0079] Therefore, within this range division, for Warehouse A distributing to Retail Outlet A, the Warehouse Index for Warehouse A on February 5th is calculated as k3 × 1576.9728 × 84.24, with the weight coefficient k3 set to 1.25 × the square of the number of days. In this case, the weight coefficient k3 satisfies the increasing function, and the first-order derivative of the function increases with the number of days. Thus, the Warehouse Index for Warehouse A on February 5th is 1.25 × (2^2) × 1576.9728 × 84.24 = 664220, the Warehouse Index for Warehouse A on February 11th is 1.25 × (2^2) × 1303.5618 × 76.59 = 499198, and the Warehouse Index for Warehouse A on February 19th is 1.25 × (2^2) × 81.6642 × 19.17 = 7827. Decimals are discarded in the calculation.

[0080] Step S05) Attempt to generate the distribution retail outlet range for each warehouse multiple times to obtain the distribution retail outlet range that minimizes the warehouse index and the sum of all warehouses as the monthly plan.

[0081] When generating distribution retail outlet ranges for each warehouse multiple times, an optimization algorithm can be used to more quickly find the distribution retail outlet range that minimizes the warehouse index for all warehouses. For example, a publicly available optimization algorithm in the art, such as a simulated annealing algorithm, a genetic algorithm, an ant colony search algorithm, a particle swarm optimization algorithm, or a differential evolution algorithm, can be used.

[0082] Step S06) During the execution of the monthly plan, the warehouse's goods age is counted daily, and an equivalent age is calculated based on the age. When the equivalent age exceeds a preset first threshold, a daily adjustment plan for the next day is generated to ensure that the expected value of the equivalent age does not exceed a preset second threshold. The expected value of the equivalent age is calculated based on the retail outlet's purchase volume parameters for the next day.

[0083] Because the monthly plan is calculated based on the retail outlets' incoming goods parameters, it doesn't take warehouse inventory into account. Furthermore, the retail outlets' incoming goods parameters are forecasted. If a retail outlet's actual incoming goods deviate significantly from the incoming goods parameters, or if warehouse incoming goods fluctuate significantly, this can lead to a backlog of goods in the warehouse, compromising the quality of some goods. Therefore, in step S06, the equivalent inventory age of the goods is calculated daily to provide data support for adjusting the distribution retail outlet range.

[0084] Specifically, the warehouse age is counted daily, and the method for calculating the equivalent warehouse age based on the warehouse age includes:

[0085] When the storage age does not exceed a preset first reference value, the equivalent storage age is equal to a preset constant;

[0086] When the storage age exceeds the first reference value and does not exceed the second reference value, the equivalent storage age is equal to the storage age;

[0087] When the storage age exceeds the second reference value, the equivalent storage age is equal to the product of the storage age and the adjustment coefficient;

[0088] Among them, the second reference value is equal to the product of the reference proportion, the preset coefficient and the preset inventory age parameter of the goods, the reference proportion is equal to the ratio of the number of warehouses whose inventory age exceeds the preset third reference value to the total number of warehouses, and the adjustment coefficient is greater than 1.

[0089] When a warehouse contains goods that were entered on multiple dates, the oldest entry in the warehouse is used as the age. For example, 20 cartons of milk were entered on January 1st, and 10 cartons were entered on January 8th. By January 8th, 18 cartons had been delivered. On January 8th, the warehouse had 2 cartons of milk entered on January 1st and 10 cartons of milk that had just been entered. At this point, the age of the milk is based on the 2 cartons of milk entered on January 1st, meaning it is 7 days old. On January 9th, 5 more cartons of milk were delivered. On January 9th, the warehouse had 7 cartons of milk entered on January 8th, meaning it has an age of 1 day.

[0090] The set first reference value means that when the storage age is lower than the first reference value, the quality of the goods is basically unaffected. For example, if the shelf life of room temperature milk is 6 months, the first reference value can be set to 2 months (i.e. 60 days). Milk within 2 months has little impact on consumers and is very easy to accept.

[0091] The set second reference value means that when the equivalent inventory age is equal to the second reference value, it is best if the goods can be sold immediately (or it can be understood that it is best if they can be delivered to retail outlets immediately). When the equivalent inventory age exceeds the second reference value, there is a greater risk that the goods cannot be sold within the shelf life.

[0092] For milk stored in a warehouse with a storage age of no more than 2 months, the equivalent storage age is set to a constant of 1 day (or other value). If the storage age is between 2 and 4 months, the equivalent storage age is equal to the storage age.

[0093] When the shelf life exceeds 4 months (at this time, there are only 2 months left in the 6-month shelf life. This embodiment does not take into account the time between the completion of the goods and their storage in the warehouse. It is considered that the time consumed before storage is relatively short for the shelf life of the goods. For example, milk with a shelf life of 6 months is considered to take several days to be stored in the warehouse. Food goods with a shelf life of about 5 days are stored in the warehouse on the same day or the next day), it is necessary to distribute these milk with a shelf life of more than 4 months to retail outlets as soon as possible. At this time, the range of retail outlets distributed by the warehouse is dynamically adjusted so that the warehouse can distribute to more retail outlets. The basis for adjustment is the equivalent shelf age. However, when the shelf age is really too long, such as 5 and a half months, there is no need to deliberately adjust the shelf age of the goods, because the probability that they cannot be sold within the shelf life is relatively high. It should be handled by other means, such as transporting them to discounted stores for sale, which is beyond the scope of discussion in this manual.

[0094] Furthermore, if a large number of warehouses have inventory ages exceeding five months, there's no need to significantly increase the equivalent inventory age of any one warehouse, as many other warehouses also need to increase their equivalent inventory age to expand their distribution network. In this case, there's no need to significantly adjust the equivalent inventory age. Therefore, assuming the current second reference value is four months, and the third reference value is also set to four months. When inventory age exceeds four months, the equivalent inventory age is first increased by multiplying the inventory age by the adjustment coefficient to obtain the equivalent inventory age.

[0095] The warehouse's inventory age is counted daily, so a second reference value is updated daily. This second reference value is the product of the reference ratio, the preset coefficient, and the preset inventory age parameter. On January 5th, the reference ratio was 0.4, the preset coefficient was 1.5, and the preset inventory age parameter for milk was 180 days (i.e., 6 months). Therefore, on January 5th (actually used to generate the daily adjustment plan for January 6th), the second reference value = 0.4 × 1.5 × 180 = 108 (i.e., 3.6 months). On January 6th, the reference ratio changed to 0.5, and the second reference value at this time = 0.5 × 1.5 × 180 = 135 (i.e., 4.5 months). Therefore, when the reference ratio changed to 0.5 on January 6th, the inventory age would need to exceed 4.5 months for the inventory age to be multiplied by the adjustment coefficient when calculating the equivalent inventory age. On the previous day (i.e. January 5), the storage age only needs to be more than 4 months. When calculating the equivalent storage age, the storage age will be multiplied by the adjustment coefficient, as shown in the attached figure. Figure 4 shown.

[0096] Assume that on January 7th, the reference ratio reaches 0.7 (indicating that 70% of warehouses have milk older than the third reference value). The second reference value at this point is 0.7 × 1.5 × 180 = 189 (or six months). In reality, no milk will be older than 189 days, meaning no adjustment factor will be applied when calculating the equivalent age. This is because when the reference ratio reaches 0.7, most warehouses will need to expand their distribution network, eliminating the need for adjustments.

[0097] On the other hand, in another embodiment, the method for generating a daily adjustment plan for the next day includes:

[0098] Arrange the warehouses whose equivalent inventory age exceeds a preset first threshold in descending order of equivalent inventory age, select warehouses in the arrangement in turn, and perform the following steps:

[0099] The warehouse's closest retail outlets, which are also distributed by other warehouses, are sequentially read and added to the warehouse's distribution range until the warehouse's expected equivalent inventory age does not exceed a preset second threshold or the sum of the equivalent inventory ages of all warehouses reaches a minimum. When the sum of the equivalent inventory ages of all warehouses reaches a minimum, it indicates that inventory age control can no longer be achieved by adjusting the warehouse's distribution range of retail outlets. The generated daily adjustment plan can only minimize the sum of the equivalent inventory ages of all warehouses.

[0100] The new technical effects achieved by this embodiment include: by generating a monthly plan, the range of retail outlets distributed by each warehouse is achieved, so that the warehouse throughput coefficient and cargo distance both reach a relatively good level; and by adjusting the plan daily, the range of retail outlets distributed by warehouses with goods of a relatively long equivalent storage age is expanded, thereby quickly distributing goods of a relatively long equivalent storage age to retail outlets for sale, which helps to ensure the quality of goods sold at retail outlets through coordination. By calculating the average historical purchase volume ratio of the date by annual cycle, and then using the monthly predicted purchase volume, the purchase volume parameters of each retail outlet are calculated, making the generation of daily purchase volume parameters for retail outlets more accurate. By generating a daily adjustment plan based on the equivalent storage age, a more reasonable daily adjustment plan can be achieved by adjusting the equivalent storage age, ensuring that the storage age of goods in the warehouse is more reasonable.

[0101] On the other hand, this manual provides a system for dynamically dividing the delivery range of multiple warehouses in the same city. Figure 5 ,include:

[0102] The equivalent module 100 calculates the purchase quantity parameters of the retail outlets on each date in the annual cycle;

[0103] Setting module 200, setting the daily throughput coefficient of the warehouse to be equal to the weighted product of the number of retail outlets delivered by the warehouse on that day and the delivery volume, and setting the cargo distance of the warehouse to be equal to the weighted product of the delivery volume and the delivery distance;

[0104] The first calculation module 300 randomly generates a distribution retail outlet range for each warehouse in the next month, and calculates the number of days when the throughput coefficient of each warehouse exceeds the limit and the distance of the goods between the warehouses based on the purchase volume parameters;

[0105] The second calculation module 400 calculates a weighted product of the number of days and the distance of the goods as a warehouse index;

[0106] Monthly module 500 attempts to generate the distribution retail outlet range for each warehouse multiple times, and obtains the distribution retail outlet range that minimizes the warehouse index of all warehouses as a monthly plan;

[0107] The daily adjustment module 600 counts the age of goods in the warehouse daily during the execution of the monthly plan, calculates the equivalent age based on the age, and generates a daily adjustment plan for the next day when the equivalent age exceeds a preset first threshold, so that the expected value of the equivalent age does not exceed a preset second threshold. The expected value of the equivalent age is calculated based on the purchase volume parameters of the retail outlets on the next day.

[0108] See also Figure 6 A schematic structural diagram of an electronic device provided in an embodiment of this specification is shown.

[0109] like Figure 6As shown, the electronic device 1100 may include: at least one processor 1101, at least one network interface 1104, a user interface 1103, a memory 1105, and at least one communication bus 1102. The communication bus 1102 may be used to implement communication between the aforementioned components. The user interface 1103 may include buttons, and optionally may also include a standard wired interface or a wireless interface. The network interface 1104 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc. The processor 1101 may include one or more processing cores. The processor 1101 utilizes various interfaces and circuits to connect the various components within the entire electronic device 1100. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 1105 and accessing data stored in the memory 1105, it performs various functions of the routing device 1100 and processes data. Optionally, the processor 1101 may be implemented in hardware using at least one of a DSP, an FPGA, and a PLA. The processor 1101 may integrate one or a combination of a CPU, a GPU, and a modem. The CPU primarily processes the operating system, user interface, and applications; the GPU is responsible for rendering and drawing content displayed on the display; and the modem handles wireless communications.

[0110] It is understandable that the above-mentioned modem may not be integrated into the processor 1101, but may be implemented by a separate chip.

[0111] Memory 1105 may include either RAM or ROM. Optionally, memory 1105 may include non-transitory computer-readable media. Memory 1105 may be used to store instructions, programs, codes, code sets, or instruction sets. Memory 1105 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, sound playback function, image playback function, etc.), instructions for implementing the aforementioned method embodiments, etc.; the data storage area may store data related to the aforementioned method embodiments, etc. Memory 1105 may also optionally be at least one storage device located remotely from the aforementioned processor 1101. Memory 1105, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and application programs. Processor 1101 may be configured to invoke the application programs stored in memory 1105 and execute the methods described in the aforementioned embodiments.

[0112] The embodiments of this specification also provide a computer-readable storage medium having instructions stored therein that, when executed on a computer or processor, cause the computer or processor to perform the steps of the aforementioned embodiments. If the components of the aforementioned electronic device are implemented as software functional units and sold or used as independent products, they may be stored in the computer-readable storage medium.

[0113] The embodiments of this specification also provide a computer program product, including a computer program, which implements multiple steps in the above embodiments when executed by a processor.

[0114] In the absence of conflict, the technical features in this embodiment and implementation scheme can be combined arbitrarily.

[0115] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product comprises multiple computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible by a computer or a data storage device such as a server or data center that integrates multiple available media. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state drive (SSD)).

[0116] When implemented via hardware or firmware, the aforementioned method flow is programmed into the hardware circuit to obtain the corresponding hardware circuit structure and realize the corresponding function. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit, whose logical function is determined by the user's device programming. Designers can "integrate" a digital system on a PLD through self-programming, eliminating the need for chip manufacturers to design and manufacture dedicated integrated circuit chips. Moreover, today, instead of manually manufacturing integrated circuit chips, this programming is often performed using "logic compiler" software. This is similar to the software compiler used in program development. Before compilation, the original code must also be written in a specific programming language, called a hardware description language (HDL). There are not just one HDL, but many. Those skilled in the art will also understand that simply by programming the method flow in one of the aforementioned hardware description languages ​​and programming it into the integrated circuit, a hardware circuit that implements the logical method flow can be easily obtained.

[0117] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Without departing from the design spirit of this specification, various modifications and improvements made to the technical solutions of this specification by ordinary technicians in this field should fall within the scope of protection determined by the claims of this specification.

Claims

1. A method for dynamically dividing the distribution range of multiple warehouses in the same city, used to generate the range of warehouse distribution retail outlets, characterized by: Including steps: Calculate the purchase volume parameters of the retail outlets on each date in the annual cycle; Set the warehouse's daily throughput coefficient to the weighted product of the number of retail outlets delivered by the warehouse on that day and the delivery volume, and set the warehouse's cargo distance to the weighted product of the delivery volume and the delivery distance; Randomly generate the distribution retail outlet range for each warehouse in the next month, and calculate the number of days that each warehouse's throughput coefficient exceeds the limit and the distance of the goods to the warehouse based on the purchase volume parameters; Calculate a weighted product of the number of days and the distance of the goods as a warehouse index; Try to generate the distribution retail outlet range for each warehouse multiple times to obtain the warehouse index of all warehouses and the distribution retail outlet range with the minimum, which will be used as the monthly plan; During the execution of the monthly plan, the warehouse's goods age is counted daily, and an equivalent age is calculated based on the age. When the equivalent age exceeds a preset first threshold, a daily adjustment plan for the next day is generated to ensure that the expected value of the equivalent age does not exceed a preset second threshold. The expected value of the equivalent age is calculated based on the retail outlet's purchase volume parameters for the next day. Methods for calculating the purchase volume parameters of a retail outlet on each date in an annual cycle include: Read the historical purchase volume for each date and calculate the average historical purchase volume; Calculate the sum of the average historical purchase volume of all dates in the month, and calculate the average historical purchase volume percentage of each date in the month, where the average historical purchase volume percentage is equal to the ratio of the average historical purchase volume of the date to the sum; Read the predicted purchase volume of the retail outlets for the next month, and obtain the purchase volume parameter based on the product of the average historical purchase volume ratio of each date in the month and the predicted purchase volume; Methods for calculating the number of days that the throughput coefficient of each warehouse exceeds the limit based on the purchase volume parameters include: According to the randomly generated range of retail outlets for each warehouse for the next month, all retail outlets distributed by each warehouse are obtained; Calculate the sum of the purchase volume parameters of all retail outlets of the warehouse on each date in the annual cycle, record it as the daily distribution volume, and calculate the warehouse throughput coefficient on each date; Based on the warehouse's throughput coefficient on each date and the preset lower and upper thresholds of the throughput coefficient, the number of days on which the throughput coefficient of each warehouse exceeds the limit is obtained; The method for calculating the distance of goods to each warehouse according to the incoming goods quantity parameter includes: According to the randomly generated range of retail outlets for each warehouse for the next month, all retail outlets distributed by each warehouse are obtained; Calculate the total purchase volume parameters of each retail outlet of the warehouse in the annual cycle; Obtaining the distance between the warehouse and the retail outlet according to the product of the total purchase quantity parameter of the retail outlet and the distance between the warehouse and the retail outlet; The cargo distance of the warehouse is obtained by adding up the cargo distances of all retail outlets distributed by the warehouse; The warehouse age is counted daily. Methods for calculating equivalent age based on the age include: When the storage age does not exceed a preset first reference value, the equivalent storage age is equal to a preset constant; When the storage age exceeds the first reference value and does not exceed the second reference value, the equivalent storage age is equal to the storage age; When the storage age exceeds the second reference value, the equivalent storage age is equal to the product of the storage age and the adjustment coefficient; Among them, the second reference value is equal to the product of the reference proportion, the preset coefficient and the preset inventory age parameter of the goods, the reference proportion is equal to the ratio of the number of warehouses whose inventory age exceeds the preset third reference value to the total number of warehouses, and the adjustment coefficient is greater than 1.

2. A method for dynamically dividing the delivery range of multiple warehouses in the same city according to claim 1, characterized in that: Methods for calculating the weighted product of the number of days and the distance to the goods as a warehouse index include: The weighted product is the product of a weight coefficient, the number of days, and the cargo distance. The weight coefficient is a function of the number of days. The function is an increasing function, and the first-order derivative of the function increases with the increase of the number of days.

3. A method for dynamically dividing the delivery range of multiple warehouses in the same city according to claim 1 or 2, characterized in that: Methods for generating a daily adjustment plan for the next day include: Arrange the warehouses whose equivalent inventory age exceeds a preset first threshold in descending order of equivalent inventory age, select warehouses in the arrangement in turn, and perform the following steps: The retail outlets closest to the warehouse and distributed by other warehouses are read in sequence, and the retail outlets are included in the distribution range of the warehouse until the expected value of the equivalent inventory age of the warehouse does not exceed the preset second threshold or reaches the minimum sum of the equivalent inventory ages of all warehouses.

4. A system for dynamically dividing the delivery range of multiple warehouses in the same city, characterized by: include: The equivalent module calculates the purchase volume parameters of the retail outlets on each date in the annual cycle; In the setup module, set the warehouse's daily throughput coefficient to the weighted product of the number of retail outlets delivered by the warehouse on that day and the delivery volume, and set the warehouse's cargo distance to the weighted product of the delivery volume and the delivery distance; The first calculation module randomly generates the distribution retail outlet range for each warehouse in the next month, and calculates the number of days when the throughput coefficient of each warehouse exceeds the limit and the distance of the goods to the warehouse based on the purchase volume parameter; A second calculation module calculates a weighted product of the number of days and the distance of the goods as a warehouse index; The monthly module attempts to generate the distribution retail outlet range for each warehouse multiple times, and obtains the warehouse index of all warehouses and the minimum distribution retail outlet range as the monthly plan; A daily adjustment module, during the execution of the monthly plan, counts the inventory age of the goods in the warehouse daily, calculates the equivalent inventory age based on the inventory age, and generates a daily adjustment plan for the next day when the equivalent inventory age exceeds a preset first threshold, so that the expected value of the equivalent inventory age does not exceed a preset second threshold. The expected value of the equivalent inventory age is calculated based on the purchase volume parameters of the retail outlet on the next day; Methods for calculating the purchase volume parameters of a retail outlet on each date in an annual cycle include: Read the historical purchase volume for each date and calculate the average historical purchase volume; Calculate the sum of the average historical purchase volume of all dates in the month, and calculate the average historical purchase volume percentage of each date in the month, where the average historical purchase volume percentage is equal to the ratio of the average historical purchase volume of the date to the sum; Read the predicted purchase volume of the retail outlets for the next month, and obtain the purchase volume parameter based on the product of the average historical purchase volume ratio of each date in the month and the predicted purchase volume; Methods for calculating the number of days that the throughput coefficient of each warehouse exceeds the limit based on the purchase volume parameters include: According to the randomly generated range of retail outlets for each warehouse for the next month, all retail outlets distributed by each warehouse are obtained; Calculate the sum of the purchase volume parameters of all retail outlets of the warehouse on each date in the annual cycle, record it as the daily distribution volume, and calculate the warehouse throughput coefficient on each date; Based on the warehouse's throughput coefficient on each date and the preset lower and upper thresholds of the throughput coefficient, the number of days on which the throughput coefficient of each warehouse exceeds the limit is obtained; The method for calculating the distance of goods to each warehouse according to the incoming goods quantity parameter includes: According to the randomly generated range of retail outlets for each warehouse for the next month, all retail outlets distributed by each warehouse are obtained; Calculate the total purchase volume parameters of each retail outlet of the warehouse in the annual cycle; Obtaining the distance between the warehouse and the retail outlet according to the product of the total purchase quantity parameter of the retail outlet and the distance between the warehouse and the retail outlet; The cargo distance of the warehouse is obtained by adding up the cargo distances of all retail outlets distributed by the warehouse; The warehouse age is counted daily. Methods for calculating equivalent age based on the age include: When the storage age does not exceed a preset first reference value, the equivalent storage age is equal to a preset constant; When the storage age exceeds the first reference value and does not exceed the second reference value, the equivalent storage age is equal to the storage age; When the storage age exceeds the second reference value, the equivalent storage age is equal to the product of the storage age and the adjustment coefficient; Among them, the second reference value is equal to the product of the reference proportion, the preset coefficient and the preset inventory age parameter of the goods, the reference proportion is equal to the ratio of the number of warehouses whose inventory age exceeds the preset third reference value to the total number of warehouses, and the adjustment coefficient is greater than 1.

5. An electronic device, characterized in that including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

7. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 3 when the computer program is executed by a processor.

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