Method and system for dynamically dividing intra-city multi-warehouse distribution range

Abstract

The embodiment of the invention relates to the technical field of information, in particular to a city-wide multi-warehouse distribution range dynamic division method and a city-wide multi-warehouse distribution range dynamic division system. The method comprises the following steps: calculating an incoming quantity parameter of a retail network on each date in an annual period; the daily throughput coefficient of the warehouse is set to be equal to the weighted product of the number of retail network points distributed by the warehouse on that day and the distribution amount, and the cargo distance is set to be equal to the weighted product of the distribution amount of cargoes and the distribution distance; calculating the number of days when the throughput coefficient of each warehouse exceeds the limit and the cargo distance of the warehouse according to the purchase quantity parameter; calculating a weighted product of the number of days and the cargo distance as a warehouse index; trying to generate the distribution and retail network point range of each warehouse for multiple times, and obtaining the distribution and retail network point range with the minimum warehouse index sum of all the warehouses as a monthly plan; counting the age of the goods in the warehouse every day, calculating the equivalent age according to the age, and generating a daily adjustment plan of 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 distribution range of multiple warehouses in the same city. Background Art

[0002] With the rapid development of e-commerce and the increasing demand of consumers for logistics speed, the traditional single warehouse distribution model can no longer meet market demand. In this context, the same-city multi-warehouse distribution model came into being. Same-city multi-warehouse refers to setting up multiple storage nodes in the same city or its surrounding areas. The most intuitive advantage of same-city multi-warehouse is that it can significantly improve distribution efficiency. By setting up warehouses in different areas of the city, goods can be stored closer to target customers, thereby greatly shortening order processing time and distribution distance. Short-distance distribution not only speeds up the speed of goods reaching consumers, but also reduces the uncertainty factors caused by long-distance transportation, such as traffic jams and weather changes, thereby improving the reliability of overall distribution. Faced with the ever-changing market environment, the same-city multi-warehouse model gives merchants higher distribution flexibility and faster response capabilities. When demand in a certain area suddenly increases, goods can be quickly allocated from neighboring warehouses to replenish them; conversely, inventory levels can be adjusted according to actual conditions to avoid 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, an embodiment of this specification provides a method for dynamically dividing a distribution range of multiple warehouses in a same city, which is used to generate a range of warehouse distribution retail outlets, including the steps of: Calculate the purchase volume parameters of the retail outlets on each date in the monthly cycle; Set the warehouse's daily throughput coefficient equal 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 equal to the weighted product of the delivery volume and the delivery distance; Randomly generate the distribution retail outlet range of each warehouse for the next month, and calculate the number of days when the throughput coefficient of each warehouse exceeds the limit and the cargo distance of the warehouse according to the purchase volume parameters; Calculate the 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 of each warehouse multiple times to obtain the warehouse index of all warehouses and the minimum distribution retail outlet range as a monthly plan; During the execution of the monthly plan, the age of 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 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.

[0005] 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: The equivalent module calculates the purchase volume parameters of the retail outlets on each date in the monthly cycle; Setting module, set the daily throughput coefficient of the warehouse equal to the weighted product of the number of retail outlets and the delivery volume delivered by the warehouse on that day, and set the cargo distance of the warehouse equal to the weighted product of the delivery volume and the delivery distance; The first calculation module randomly generates the distribution retail outlet range of 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 according to 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; Monthly module attempts to generate the distribution retail outlet range of each warehouse multiple times, and obtains the warehouse index of all warehouses and the minimum distribution retail outlet range as the monthly plan; 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.

[0006] In a third aspect, an embodiment of this specification provides an electronic device, 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 described in any one of the above aspects.

[0007] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any of the above aspects.

[0008] In a fifth aspect, an embodiment of this specification provides a computer program product, including a computer program, which implements the method described in any of the above aspects when executed by a processor.

[0009] The beneficial effects brought by the technical solutions provided by some embodiments of this specification include at least: In multiple embodiments of the present specification, the dynamic division method of the distribution range is provided to achieve the range of distribution retail outlets of each monthly warehouse by generating a monthly plan, so that the throughput coefficient and cargo distance of the warehouse reach a better level, and the distribution retail outlets of the warehouse with goods with a larger equivalent storage age are expanded through the daily adjustment plan, so that the goods with a larger equivalent storage age can be quickly distributed to the retail outlets for sale, which helps to ensure the quality of the goods sold by the retail outlets through coordination. The average historical purchase volume ratio of the date is calculated by 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 the daily purchase volume parameters of the 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 the goods in the warehouse is more reasonable.

[0010] Other features and advantages of the various embodiments of the present specification will be further disclosed in the following detailed description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of this specification, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

[0012] Figure 1 This is a schematic diagram of the warehouse distribution range used in the embodiments of this specification.

[0013] 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.

[0014] Figure 3 This is a flow chart of a method for dynamically dividing the delivery range of multiple warehouses in the same city according to an embodiment of this specification.

[0015] Figure 4 This is a schematic diagram of equivalent storage age calculation for the embodiments of this specification.

[0016] Figure 5 This is a schematic diagram of a system for dynamically dividing the delivery range of multiple warehouses in the same city according to an embodiment of this specification.

[0017] Figure 6A schematic diagram of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0018] 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, but the following embodiments are only preferred embodiments of this specification, not all. Based on the embodiments in the implementation mode, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of this specification.

[0019] The terms "first", "second", "third", etc. in the description and claims of this specification and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0020] 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, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation of this specification.

[0021] 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.

[0022] Before describing the technical solution in this specification, the application scenarios and related technologies of the technical solution are introduced.

[0023] Traffic congestion and traffic restrictions in cities have a significant impact on the delivery of goods with a short shelf life. Specifically, urban traffic congestion will cause delivery vehicles to stay on the road for a longer time, which directly affects the delivery time of goods. For perishable goods or goods with a short shelf life, delayed delivery may cause the product to lose its best edible state or even deteriorate, thus affecting the consumer experience and the reputation of the merchant. For example, products such as flowers and cakes. Due to the long-term stagnation caused by traffic congestion, the fuel consumption of vehicles increases, and labor costs are also increased. In addition, traffic restrictions may force logistics companies to take detours and other methods to avoid restricted areas, further increasing transportation costs. In urban distribution, fresh vegetables, milk, cooked food products, baked bread, flowers, semi-finished dishes, etc. all need to control the timeliness of delivery. Taking the semi-finished marinated chicken steak as an example, if the delivery time is extended, the taste may change, the flavor may be lost, and there may be food safety risks.

[0024] Specifically, the various spices and condiments added during the marinating process may not fully release their flavor if they are not presented within the optimal time. Long-term storage may lead to bacterial growth and increase food safety risks. Fried chicken steaks that are spoiled or have a bad taste are likely to cause customer loss and affect the business's operating profits. Please refer to the attached Figure 1 By setting up multiple warehouses in the same city, each retail outlet 20 does not need to travel a long distance to purchase goods from the warehouse 10, thus effectively alleviating the above-mentioned problem. In this way, even in the case of traffic congestion, it can ensure that the product departs from the nearest warehouse 10 and arrives at the destination as soon as possible. In addition, the multi-warehouse layout can also flexibly adjust the distribution route according to real-time traffic conditions, avoid congested sections, and reduce the impact of traffic restrictions. Keep the product fresh and in the best edible state.

[0025] In the distribution industry, especially in the same-city distribution business, for products that need to control the delivery time, such as cakes, vegetables, fruits, flowers, baked products, etc., or products that want to respond to delivery quickly, such as newly released electronic products, household appliances, etc., being able to complete distribution by setting up multiple warehouses in the same city can not only improve distribution efficiency, but also ensure product quality, which has a more significant improvement effect on completing distribution work.

[0026] However, multiple warehouses in the same city also bring challenges such as complex management, high initial investment, and high difficulty in inventory management. Due to the volatility of regional demand for products, the inventory of some warehouses 10 may not be cleared as expected, resulting in a long product age and affecting product quality. The current distribution range of multiple warehouses in the same city is usually set manually and generally remains unchanged after setting. However, it is obvious that this fixed distribution range method cannot effectively complete the distribution of products with a shorter shelf life. Therefore, it is necessary to study the scheduling optimization technology of same-city distribution under multiple warehouses 10 in the same city.

[0027] Please see attached Figure 2 The system architecture of the technical solution recorded in this specification includes a server 30 and a device terminal. Multiple device terminals are set in the warehouse 10 to upload the inventory, incoming and outgoing quantities of goods in the warehouse 10, the scope of the current distribution retail outlets of the warehouse 10, and the distribution records.

[0028] 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, vehicle-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, which is not limited in this application. The number of servers 30 and device sides is also not limited.

[0029] Since this application involves some professional terms, these professional terms will be introduced below.

[0030] Retail outlets Retail outlets refer to any physical location where goods can be sold directly to consumers or services can be provided. These locations are usually fixed commercial facilities, such as shops, supermarkets, convenience stores, specialty stores, shopping malls, etc., or they can be non-fixed sales points, such as mobile sales trucks, market stalls, etc. The characteristics of retail outlets 20 are direct to consumers, diversified goods and services, important geographical location, and display and experience functions. Retail outlets 20 can be in the form of traditional physical stores, shopping malls / mall, franchise stores / specialty stores, convenience stores, direct sales stores, non-fixed sales points, etc.

[0031] Storage age Stock Age (or Inventory Age) refers to the length of time from when the inventory items were put into storage to the current moment. Stock Age is an important indicator to measure the efficiency of inventory management, especially in the retail, manufacturing and logistics industries. Stock Age can help companies evaluate the speed of inventory turnover, identify which products may be at risk of being unsalable, and help formulate corresponding inventory management strategies to optimize inventory structure, reduce capital occupation, and improve inventory turnover.

[0032] Weighted Product Weighted Product refers to a method of multiplying variables by assigning different weights. The weight reflects the importance of each indicator to the overall evaluation. The greater the weight, the greater the influence of the indicator in the final decision. Since the total product is calculated after multiple weight coefficients are multiplied by the corresponding multipliers, it does not affect the result to multiply multiple weight coefficients first, then multiply them by the multiplier, and then calculate the total product. Therefore, only one weight coefficient is set in this manual, that is, the weight coefficient is the product of all weight coefficients.

[0033] 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: Step S01) Calculate the purchase quantity parameters of the retail outlet 20 on each date in the annual cycle.

[0034] The method for calculating the purchase volume parameter of the retail outlet 20 on each date in the annual cycle includes: Read the historical purchase volume for each date and calculate the average historical purchase volume; Count the sum of the average historical purchase volumes of all dates in a 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; The predicted purchase volume of the retail outlet 20 in 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.

[0035] For example, taking ambient temperature packaged milk as an example, one of the retail outlets A made one purchase on the first day of the year, January 1, in the past five years, with a purchase volume of 15 cartons; on the second day of the year, January 2, there were no purchases; on the 36th day of the year, February 5, there were two purchases, with 10 cartons and 12 cartons respectively; on the 42nd day of the year, February 11, there was one purchase, with a purchase volume of 20 cartons; and on the 50th day of the year, February 19, there was one purchase, with a purchase volume of 5 cartons. Then the average historical purchase volume on the first day of the year is 3 (i.e. 15÷5=3) boxes, the average historical purchase volume on the second day of the year is 0 boxes, the average historical purchase volume on the 36th day of the year is 2.2 boxes (i.e. (10+12)÷5=4.4), the average historical purchase volume on the 42nd day of the year is 4 boxes (i.e. 20÷5=4), and the average historical purchase volume on the 50th day of the year is 1 box (i.e. 5÷5=1). The calculation method of the average historical purchase volume on other days of the year is the same.

[0036] To avoid too much space, assume that retail outlet A has purchased goods 4 times in the past 5 years, as mentioned above. Then the sum of the average historical purchase volume of all dates in January is 3+0=3, and the sum of the average historical purchase volume of all dates in February is 4.4+4=8.4. That is, the ratio of the total purchase volume to the number of years. Then calculate the average historical purchase volume ratio of each date in the month, and obtain the average historical purchase volume ratio of the first day of the year as 3÷3=100%, the average historical purchase volume ratio of the 0th day of the year as 0%, the average historical purchase volume ratio of the 36th day of the year as 4.4÷9.4=46.80%, the average historical purchase volume ratio of the 42nd day of the year as 4÷9.4=42.55%, and the average historical purchase volume ratio of the 50th day of the year as 1÷9.4=10.65%.

[0037] Read the predicted purchase volume of retail outlet A in the next month. Assuming that the next month is February and the volume is 60 boxes, the purchase volume parameter on February 5 is 60×46.80%=28.08, the purchase volume parameter on February 11 is 60×42.55%=25.53, the purchase volume parameter on February 19 is 60×10.65%=6.39, and the purchase volume parameters on other dates are 0. In implementation, you can calculate according to the method given in this example based on more abundant data.

[0038] Step S02) The daily throughput coefficient of the warehouse 10 is set equal to the weighted product of the number of retail outlets and the delivery volume delivered by the warehouse 10 on that day, and the cargo distance of the warehouse 10 is set equal to the weighted product of the delivery volume and the delivery distance.

[0039] For example, warehouse A delivered to three retail outlets on January 3, with the delivery volumes of 5 boxes, 8 boxes, and 3 boxes, respectively. Then the throughput coefficient = 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, and the throughput coefficient = 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 (that is, 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), and the function f () is a preset function. Exemplarily, function f(x)=2x. In another exemplary embodiment, function f(x)=x^2. When function f(x)=2x, throughput coefficient=k1×360=2×(5+8+3)×120=11520. When function f(x)=x^2, throughput coefficient=k1×120=(5+8+3)^2×360=92160. At this time, the weight coefficients of individual retail outlets may no longer be distinguished.

[0040] The distances between Warehouse A and the three retail outlets delivered on January 3 are 1.2km, 2km, and 0.5km respectively, so the cargo distance = k21×1.2×5×k22×2×8×k23×0.5×3=144×k21×k22×k23. Set the weight coefficient k2=k21×k22×k23, then the cargo distance = 144×k2. The weight coefficient of a single retail outlet can be ignored. Therefore, the weight coefficient k2 is set as a function of the number of retail outlets delivered, such as k2=2×the number of retail outlets delivered, then the cargo distance = 144×2×3=864.

[0041] Step S03) randomly generates the distribution retail outlet range of each warehouse for 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 according to the purchase volume parameter.

[0042] 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.

[0043] Wherein, according to the purchase quantity parameter, the method for calculating the number of days when the throughput coefficient of each warehouse exceeds the limit includes: According to the randomly generated distribution retail outlet range of each warehouse in 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 throughput coefficient of the warehouse on each date; 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 when the throughput coefficient of each warehouse exceeds the limit is obtained.

[0044] Read the purchase quantity parameters of retail outlet A in the next month. For example, the purchase quantity parameters on February 5 are 28.08, on February 11 are 25.53, and on February 19 are 6.39. Calculate the throughput coefficients of warehouse A on these three days. On February 5, the throughput coefficient of warehouse A = k1×1×28.08=2×(28.08)×1×28.08=1576.9728, on February 11, the throughput coefficient of warehouse A = 2×(25.53)×1×25.53=1303.5618, and on February 19, the throughput coefficient of warehouse A = 2×(6.39)×1×6.39=81.6642.

[0045] The upper threshold of the throughput coefficient of Warehouse A is set to 1500, and the lower threshold is set to 150. The throughput coefficient of Warehouse A on February 5th exceeded the upper threshold, indicating that the distribution task of Warehouse A on that day was too heavy, while the throughput coefficient on February 19th was lower than the lower threshold, indicating that the distribution task of Warehouse A on that day was too few, resulting in a waste of resources. That is, the number of days that the throughput coefficient of Warehouse A exceeded the limit was 2. The solution is to re-divide the distribution range of Warehouse A and select retail outlets with more balanced purchase volume parameters to allocate to Warehouse A. Due to space limitations, the example here only allocates one retail outlet to Warehouse A. During implementation, calculations are performed based on the data of the retail outlets, and the upper and lower thresholds of the throughput coefficient are set based on the distribution capacity of the warehouse.

[0046] Wherein, according to the incoming goods quantity parameter, the method for calculating the distance of goods in each warehouse includes: According to the randomly generated range of retail outlets for distribution of each warehouse in 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.

[0047] Specifically, the delivery distance between warehouse A and retail outlet A is 1.5 km, and the delivery volume is directly taken from the purchase volume parameter. The cargo distance of warehouse A on February 5 = k2 × delivery volume × delivery distance = 2 × number of retail outlets delivered × delivery volume × delivery distance = 2 × 1 × 28.08 × 1.5 = 84.24, the cargo distance of warehouse A on February 11 = 2 × 1 × 25.53 × 1.5 = 76.59, and the cargo distance of warehouse A on February 19 = 2 × 1 × 6.39 × 1.5 = 19.17.

[0048] Step S04) Calculate the weighted product of the number of days and the distance of the goods as the warehouse index. The weighted product is the product of the weight coefficient, the number of days and the distance of the goods, 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.

[0049] Thus, it can be calculated that in this range division, that is, warehouse A distributes retail outlet A, the warehouse index of warehouse A on February 5 = k3 × 1576.9728 × 84.24, and the weight coefficient k3 is set to 1.25 × the square of the number of days. At this time, the weight coefficient k3 satisfies that the function is an increasing function, and the first-order derivative of the function increases with the increase of the number of days. Then the warehouse index of warehouse A on February 5 = 1.25 × (2^2) × 1576.9728 × 84.24 = 664220, the warehouse index of warehouse A on February 11 = 1.25 × (2^2) × 1303.5618 × 76.59 = 499198, and the warehouse index of warehouse A on February 19 = 1.25 × (2^2) × 81.6642 × 19.17 = 7827. The decimal part is directly discarded in the calculation.

[0050] Step S05) Attempt to generate the distribution retail outlet range of each warehouse multiple times to obtain the distribution retail outlet range that minimizes the warehouse index of all warehouses as a monthly plan.

[0051] When generating the distribution retail outlet range of each warehouse multiple times, an optimization algorithm can be used to find the warehouse index of all warehouses and the distribution retail outlet range with the minimum more quickly. For example, an optimization algorithm disclosed 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.

[0052] Step S06) During the execution of the monthly plan, the inventory age of the goods in the warehouse is counted daily, and the equivalent inventory age is calculated based on the inventory age. When the equivalent inventory age exceeds a preset first threshold, a daily adjustment plan for the next day is generated 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 outlets on the next day.

[0053] Since the monthly plan is calculated based on the purchase volume parameters of the retail outlets, the warehouse's warehousing situation is not taken into account, and the purchase volume parameters of the retail outlets are predicted. When the actual purchase volume of the retail outlets deviates greatly from the purchase volume parameters, or the warehouse's warehousing volume changes significantly, it may cause a backlog of goods in the warehouse, making it difficult to ensure the quality of some goods. Therefore, in step S06, the equivalent warehouse age of the goods is calculated daily to provide data support for adjusting the scope of the distribution retail outlets.

[0054] Specifically, the storage age of the goods in the warehouse is counted daily, and the method for calculating the equivalent storage age based on the storage age includes: 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.

[0055] When there are goods that were put into the warehouse on multiple dates, the age of the goods that were put into the warehouse earliest shall prevail. For example, 20 cartons of milk were put into the warehouse on January 1, and 10 cartons of milk were put into the warehouse on January 8. As of January 8, a total of 18 cartons were delivered. On January 8, there were 2 cartons of milk put into the warehouse on January 1 and 10 cartons of milk that had just been put into the warehouse. At this time, the age of the milk is based on the 2 cartons of milk put into the warehouse on January 1, that is, the age is 7 days. On January 9, another 5 cartons of milk were delivered. On January 9, there were 7 cartons of milk put into the warehouse on January 8. At this time, the age of the milk is 1 day.

[0056] The first reference value that is set means that when the inventory age is lower than the first reference value, the quality of the goods is basically unaffected. For example, the shelf life of ambient temperature milk is 6 months, and the first reference value can be set to 2 months (ie 60 days). Milk within 2 months has little impact on consumers and is very easy to accept.

[0057] The set second reference value means that when the 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.

[0058] That is, for milk stored in the warehouse, when the storage age does not exceed 2 months, the equivalent storage age is set to a constant of 1 day (or other value). When the storage age is between 2 months and 4 months, the equivalent storage age is equal to the storage age.

[0059] When the warehouse age 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 the storage in the warehouse. It is believed 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, and 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 warehouse age 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 more retail outlets. The basis for adjustment is the equivalent warehouse age. However, when the warehouse age is really too large, such as 5 and a half months, there is no need to deliberately adjust the warehouse age of the goods, because the probability that it cannot be sold within the shelf life is relatively high. It should be handled in other ways, such as transporting to discounted shops for sale, which is not within the scope of discussion in this specification.

[0060] In addition, when there are a large number of warehouses with a storage age of 5 months, there is no need to significantly increase the equivalent storage age of the goods in one of the warehouses, because there are many other warehouses that also need to expand the range of retail outlets for distribution by increasing the equivalent storage age. At this time, there is no need to significantly adjust the equivalent storage age of the goods. Therefore, assuming that the current second reference value is 4 months, and the third reference value is set to 4 months. Then, when the storage age exceeds 4 months, the equivalent storage age is first increased, that is, the adjustment coefficient is multiplied by the storage age to obtain the equivalent storage age.

[0061] The age of the goods in the warehouse needs to be counted every day, so a second reference value is updated every day. The second reference value is equal to the product of the reference ratio, the preset coefficient, and the preset age parameter of the goods. On January 5, the reference ratio is 0.4, the preset coefficient is 1.5, and the preset age parameter of milk is 180 days (i.e. 6 months). On January 5 (actually used to generate the daily adjustment plan on January 6), the second reference value = 0.4×1.5×180=108 (i.e. 3.6 months). On January 6, the reference ratio changes to 0.5, and the second reference value at this time = 0.5×1.5×180=135 (i.e. 4.5 months). That is, when the reference ratio changes to 0.5 on January 6, the age needs to exceed 4.5 months before the age is multiplied by the adjustment coefficient when calculating the equivalent age. On the previous day (i.e. January 5), the inventory age only needs to be more than 4 months. When calculating the equivalent inventory age, the inventory age is multiplied by the adjustment factor, as shown in the attached figure. Figure 4 shown.

[0062] Assume that on January 7, the reference ratio becomes 0.7 (which means that 70% of warehouses have milk with an age exceeding the third reference value). At this time, the second reference value = 0.7 × 1.5 × 180 = 189 (i.e. 6 months). In fact, there will be no milk with an age exceeding 189 days. In other words, when calculating the equivalent age, there will be no adjustment factor. This is because when the reference ratio becomes 0.7, most warehouses need to increase the range of retail outlets for distribution, and there is no need to make adjustments.

[0063] On the other hand, in another embodiment, the method for generating a daily adjustment plan for the next day includes: The warehouses whose equivalent inventory age exceeds the preset first threshold are arranged in descending order according to the equivalent inventory age, and the warehouses in the arrangement are selected in turn to 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 the sum of the equivalent inventory ages of all warehouses is minimized. When the sum of the equivalent inventory ages of all warehouses is minimized, it means that the inventory age cannot be controlled by adjusting the range of retail outlets distributed by the warehouse. At this time, the generated daily adjustment plan can only reduce the sum of the equivalent inventory ages of all warehouses as much as possible.

[0064] The new technical effects achieved by this embodiment include: by generating a monthly plan, the range of retail outlets for distribution of each monthly warehouse is realized, so that the warehouse throughput coefficient and cargo distance are both at an optimal level, and by adjusting the plan on a daily basis, the range of retail outlets for distribution of warehouses with goods of a relatively long equivalent storage age is expanded, so that goods of a relatively long 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 by 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 the daily purchase volume parameters of the 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 the goods in the warehouse is more reasonable.

[0065] 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: The equivalent module 100 calculates the purchase volume parameters of the retail outlets on each date in the annual cycle; Setting module 200, setting the daily throughput coefficient of the warehouse equal to the weighted product of the number of retail outlets and the delivery volume delivered by the warehouse on that day, and setting the cargo distance of the warehouse equal to the weighted product of the delivery volume and the delivery distance of the cargo; The first calculation module 300 randomly generates the distribution retail outlet range of 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 goods of the warehouse according to the purchase quantity parameter; The second calculation module 400 calculates a weighted product of the number of days and the distance of the goods as a warehouse index; Monthly module 500 attempts to generate the distribution retail outlet range of each warehouse multiple times, and obtains the distribution retail outlet range that minimizes the warehouse index of all warehouses as a monthly plan; The daily adjustment module 600 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.

[0066] See also Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification is shown.

[0067] like Figure 6 As 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. Among them, the communication bus 1102 can be used to realize the connection and communication of the above-mentioned components. Among them, the user interface 1103 may include a button, and the optional user interface may also include a standard wired interface and a wireless interface. Among them, the network interface 1104 may include but is not limited to a Bluetooth module, an NFC module, a Wi-Fi module, etc. Among them, the processor 1101 may include one or more processing cores. The processor 1101 uses various interfaces and lines to connect various parts in the entire electronic device 1100, and executes various functions and processes data of the routing device 1100 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1105, and calling data stored in the memory 1105. Optionally, the processor 1101 can be implemented in at least one hardware form of DSP, FPGA, and PLA. The processor 1101 may integrate one or a combination of CPU, GPU and modem, etc. Among them, the CPU mainly processes the operating system, user interface and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication.

[0068] It is understandable that the above-mentioned modem may not be integrated into the processor 1101, but may be implemented by a separate chip.

[0069] Among them, the memory 1105 may include RAM or ROM. Optionally, the memory 1105 includes a non-transitory computer-readable medium. The memory 1105 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1105 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1105 may also be at least one storage device located away from the aforementioned processor 1101. The memory 1105 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application. The processor 1101 may be used to call the application stored in the memory 1105 and execute the methods in the above-mentioned multiple embodiments.

[0070] The embodiments of this specification also provide a computer-readable storage medium, which stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes the multiple steps in the above embodiments. If the components of the above electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0071] 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.

[0072] In the absence of conflict, the technical features in this embodiment and implementation scheme can be combined arbitrarily.

[0073] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes a plurality of computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of this specification is generated in whole or in part. 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 through the computer-readable storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by 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 that a computer can access or a data storage device such as a server or a data center that includes multiple available media integrated. 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)).

[0074] When implemented by 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, and its logic function is determined by the user programming the device. The designer programs by himself to "integrate" a digital system on a PLD, without asking a chip manufacturer to design and make a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when writing program development, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL), and HDL is not just one, but many. Those skilled in the art should also be aware that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit to easily obtain the hardware circuit that implements the logic method flow.

[0075] 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 in that: Includes steps: Calculate the purchase volume parameters of the retail outlets on each date in the annual cycle; Set the warehouse's daily throughput coefficient equal 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 equal to the weighted product of the delivery volume and the delivery distance; Randomly generate the distribution retail outlet range of each warehouse for the next month, and calculate the number of days when the throughput coefficient of each warehouse exceeds the limit and the cargo distance of the warehouse according to the purchase volume parameters; Calculate the 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 of each warehouse multiple times to obtain the warehouse index of all warehouses and the minimum distribution retail outlet range as a monthly plan; During the execution of the monthly plan, the age of 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 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.

2. A method for dynamically dividing the distribution range of multiple warehouses in the same city according to claim 1, characterized in that: The method for calculating the purchase volume parameters of a retail outlet on each date in an annual cycle includes: Read the historical purchase volume for each date and calculate the average historical purchase volume; Count the sum of the average historical purchase volumes of all dates in a 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; The predicted purchase volume of the retail outlets in 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.

3. A method for dynamically dividing the distribution range of multiple warehouses in the same city according to claim 1 or 2, characterized in that: According to the purchase quantity parameter, the method for calculating the number of days when the throughput coefficient of each warehouse exceeds the limit includes: According to the randomly generated range of retail outlets for distribution of each warehouse in 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 throughput coefficient of the warehouse on each date; 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 when the throughput coefficient of each warehouse exceeds the limit is obtained; According to the incoming goods quantity parameter, the method for calculating the distance of goods for each warehouse includes: According to the randomly generated range of retail outlets for distribution of each warehouse in 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.

4. A method for dynamically dividing the distribution range of multiple warehouses in the same city according to claim 1 or 2, characterized in that: The method of calculating the weighted product of the number of days and the distance of the goods as the warehouse index includes: The weighted product is the product of the 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.

5. A method for dynamically dividing the distribution range of multiple warehouses in the same city according to claim 1 or 2, characterized in that: The warehouse age of goods is counted daily, and the method for calculating the equivalent warehouse age based on the warehouse age includes: 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.

6. A method for dynamically dividing the distribution 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: The warehouses whose equivalent inventory age exceeds the preset first threshold are arranged in descending order according to the equivalent inventory age, and the warehouses in the arrangement are selected in turn to 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.

7. A system for dynamically dividing the distribution range of multiple warehouses in the same city, characterized in that: include: The equivalent module calculates the purchase volume parameters of the retail outlets on each date in the annual cycle; Setting module, set the daily throughput coefficient of the warehouse equal to the weighted product of the number of retail outlets and the delivery volume delivered by the warehouse on that day, and set the cargo distance of the warehouse equal to the weighted product of the delivery volume and the delivery distance; The first calculation module randomly generates the distribution retail outlet range of 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 according to 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; Monthly module attempts to generate the distribution retail outlet range of each warehouse multiple times, and obtains the warehouse index of all warehouses and the minimum distribution retail outlet range as the monthly plan; 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.

8. 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 6.

9. 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 6 is implemented.

10. 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 6 when being executed by a processor.

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