Intelligent Carton Production Management System and Method

Abstract

This invention provides an intelligent cardboard box production management system and method. The system includes: an acquisition module for acquiring the production cost per unit cardboard box, the production volume over a time period, the storage cost per unit cardboard box per unit time, the inventory volume for the current time period, and the transportation volume for the current time period; a calculation module, including a first calculation unit, for calculating the optimal total cost for the current time period based on the production cost per unit cardboard box, the production volume over a time period, the storage cost per unit cardboard box per unit time, the inventory volume for the current time period, and the transportation volume for the current time period, combined with a first preset algorithm; and an execution module for determining the optimal transportation volume corresponding to the optimal total cost, and scheduling the transportation of finished cardboard boxes based on the optimal transportation volume. This invention solves the problem in related technologies where the total cost of cardboard box production increases due to the inability to transport finished cardboard boxes in a timely manner.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of intelligent management, and more specifically, to an intelligent cardboard box production management system and method. Background Technology

[0002] With the rapid development of e-commerce and the fast growth of the logistics industry, cardboard boxes, as an environmentally friendly and economical packaging material, are receiving increasing attention for their production and application. Cardboard boxes not only provide basic protection and transportation functions, but also, through the integration of smart technologies such as sensors and RFID tags, enable real-time tracking and monitoring of goods, greatly improving logistics efficiency and cargo security.

[0003] Currently, cardboard box production has shifted from traditional manual manufacturing to automated and intelligent production. Modern production lines, by introducing advanced robotic arms, automated cutting and folding equipment, and real-time monitoring systems, have significantly improved production efficiency and cardboard box quality. Furthermore, by integrating information technologies such as ERP (Enterprise Resource Planning) and MES (Manufacturing Execution System), each stage of the production process is effectively managed and optimized, resulting in more flexible production scheduling and more precise inventory control.

[0004] Despite significant advancements in cardboard box production technology, manufacturers still face challenges in controlling total costs during the implementation of intelligent transformation. Rising production costs, including fluctuating raw material prices, increased labor costs, and increasingly stringent environmental regulations, place higher demands on companies' cost control capabilities. Simultaneously, the variability of market demand and the complexity of production make cost forecasting and control more difficult. In particular, with increased production efficiency, if finished cardboard boxes cannot be transported in a timely manner, storage costs will rise, easily leading to inventory backlogs and consequently increasing the total cost of cardboard box production. Summary of the Invention

[0005] This invention provides an intelligent cardboard box production management system and method, which at least solves the problem in related technologies that the total cost of cardboard box production increases due to the inability to transport finished cardboard boxes in a timely manner.

[0006] According to one embodiment of the present invention, an intelligent carton production management system is provided, comprising:

[0007] The acquisition module is used to acquire the production cost per unit carton, the production volume over a time period, the storage cost per unit carton per unit time, the inventory level for the current time period, and the transportation volume for the current time period.

[0008] The calculation module includes a first calculation unit, which is used to calculate the optimal total cost for the current time period based on the production cost per unit carton, the production volume for the time period, the storage cost per unit carton per unit time, the inventory for the current time period, the transportation volume for the current time period, and a first preset algorithm.

[0009] The execution module is used to determine the optimal transportation volume corresponding to the optimal total cost, so as to schedule the transportation of the finished carton based on the optimal transportation volume.

[0010] According to another embodiment of the present invention, an intelligent carton production management method is provided, comprising:

[0011] Obtain the production cost per unit carton, the production volume over a time period, the storage cost per unit carton per unit time, the inventory level for the current time period, and the transportation volume for the current time period;

[0012] Based on the production cost per unit carton, the production volume over a time period, the storage cost per unit carton per unit time, the inventory volume for the current time period, and the transportation volume for the current time period, and in conjunction with the first preset algorithm, the optimal total cost for the current time period is calculated.

[0013] Determine the optimal transportation volume corresponding to the optimal total cost, and schedule the transportation of finished cardboard boxes based on the optimal transportation volume.

[0014] In one embodiment of the present invention, firstly, through the acquisition module, the system can acquire in real time the production cost per unit carton, the production volume over a time period, the storage cost per unit carton per unit time, the inventory volume for the current time period, and the transportation volume for the current time period. This real-time data acquisition is the foundation for optimizing production and transportation scheduling. Secondly, the calculation module uses the acquired data and a preset algorithm (first preset algorithm) to calculate the optimal total cost for the current time period. This algorithm may consider multiple factors such as production efficiency, storage cost, and transportation cost to ensure that the total cost is minimized. Next, the execution module determines the optimal transportation volume based on the calculated optimal total cost and performs transportation scheduling accordingly. This means that the system can dynamically adjust the transportation plan based on real-time data and cost calculation results to reduce inventory backlog and lower storage costs. Furthermore, by optimizing transportation scheduling, the system can reduce the inventory backlog of finished cartons because the optimal transportation volume is calculated based on the current inventory and production volume. This ensures that the produced cartons can be transported out in a timely manner, reducing storage time and thus lowering storage costs. Secondly, through real-time monitoring and calculation, the system can more accurately predict costs and make dynamic adjustments during the production process to cope with the variability of market demand and the complexity of production. Furthermore, through the intelligent management system, enterprises can utilize resources such as raw materials, labor, and transportation more effectively, reducing waste and improving overall production and transportation efficiency.

[0015] In summary, this invention, through an intelligent management system, achieves real-time monitoring and optimization of the cardboard box production and transportation process, thereby reducing the overall cost increase caused by the inability to transport finished products in a timely manner, and realizing effective cost control and reduction. This method not only improves the economic benefits of enterprises but also meets current requirements for environmental protection and efficient resource utilization. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of the intelligent carton production management system according to an embodiment of the present invention;

[0017] Figure 2 This is a schematic diagram of the structure of the computing module according to an embodiment of the present invention;

[0018] Figure 3 This is a schematic diagram of the acquisition module according to an embodiment of the present invention;

[0019] Figure 4 This is a schematic diagram of the structure of the first computing unit according to an embodiment of the present invention;

[0020] Figure 5 This is a schematic diagram of the structure of the second computing unit according to an embodiment of the present invention;

[0021] Figure 6This is a flowchart of an intelligent cardboard box production management method according to an embodiment of the present invention;

[0022] Figure 7 This is a flowchart of a method for calculating the optimal transport volume according to an embodiment of the present invention;

[0023] Figure 8 This is a flowchart of a method for automatically obtaining quotation data from transportation companies according to an embodiment of the present invention;

[0024] Figure 9 This is a flowchart of a method for determining the optimal transportation company according to an embodiment of the present invention.

[0025] Explanation of reference numerals in the attached drawings: 1. Acquisition module; 11. First data interface unit; 12. Data storage unit; 2. Calculation module; 21. First calculation unit; 211. First calculation subunit; 212. Second calculation subunit; 22. Second calculation unit; 221. Third calculation subunit; 222. Fourth calculation subunit; 223. Fifth calculation subunit; 3. Execution module. Detailed Implementation

[0026] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0028] This embodiment provides an intelligent cardboard box production management system. Figure 1 This is a schematic diagram of the intelligent cardboard box production management system according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the computing module according to an embodiment of the present invention, such as... Figure 1 and Figure 2 As shown, the system includes: acquisition module 1, calculation module 2, and execution module 3. Among them,

[0029] Module 1 is used to obtain the production cost per unit carton, the production volume for a time period, the storage cost per unit carton per unit time, the inventory for the current time period, and the transportation volume for the current time period.

[0030] In one exemplary implementation, the following method can be used:

[0031] Sensors and automation equipment: Install sensors and automation equipment, such as weight sensors, counters, barcode or RFID scanners, on the production line to automatically collect data such as production volume and inventory levels.

[0032] Data interface: Through interfaces with information systems such as Enterprise Resource Planning (ERP) systems and Manufacturing Execution System (MES), it automatically obtains data such as production costs, inventory levels, and transportation volumes.

[0033] Data Validation: To ensure the accuracy of the data, the acquisition module 1 may include a data validation mechanism, such as data anomaly detection and error prompts, to ensure that the input data conforms to the expected range and logic.

[0034] Data storage: The collected data needs to be stored in a database for historical data analysis and real-time data processing.

[0035] User interface: Provides a user interface that allows operators to monitor the data collection process and make manual adjustments or interventions as needed.

[0036] For example, production cost data can be automatically retrieved from the financial module by integrating with the ERP system to obtain the production cost per unit of cardboard box. Production volume data can be automatically recorded by sensors and counters on the production line, recording the number of cardboard boxes produced in each time period. Storage cost data can be automatically calculated based on storage space occupancy and storage time, using system-set rates to calculate the storage cost per unit of cardboard box per unit of time. Inventory data can be obtained in real-time through a warehouse management system (WMS) for the current time period. Transportation volume data can be obtained through a transportation management system (TMS) to obtain the volume of transportation already arranged or completed within the current time period.

[0037] The calculation module 2 includes a first calculation unit 21, which is used to calculate the optimal total cost for the current time period based on the production cost per unit carton, the production volume of the time period, the storage cost per unit carton per unit time, the inventory of the current time period, the transportation volume of the current time period, and a first preset algorithm.

[0038] Execution module 3 is used to determine the optimal transportation volume corresponding to the optimal total cost, so as to schedule the transportation of finished carton products based on the optimal transportation volume.

[0039] By adopting the above technical solution, firstly, through module 1, the system can obtain in real time the production cost per unit carton, the production volume over a time period, the storage cost per unit carton per unit time, the inventory level for the current time period, and the transportation volume for the current time period. This real-time data acquisition is the foundation for optimizing production and transportation scheduling.

[0040] Secondly, the calculation module 2 uses the acquired data and a preset algorithm (first preset algorithm) to calculate the optimal total cost for the current time period. This algorithm may consider multiple factors such as production efficiency, storage costs, and transportation costs to ensure that the total cost is minimized.

[0041] Secondly, execution module 3 determines the optimal transportation volume based on the calculated optimal total cost and performs transportation scheduling accordingly. This means that the system can dynamically adjust the transportation plan based on real-time data and cost calculation results to reduce inventory backlog and lower storage costs.

[0042] Secondly, by optimizing transportation scheduling, the system can reduce the backlog of finished cardboard boxes, because the optimal transportation volume is calculated based on the current inventory and production volume. This ensures that the produced cardboard boxes can be transported out in a timely manner, reducing storage time and thus lowering storage costs.

[0043] Secondly, through real-time monitoring and calculation, the system can more accurately predict costs and make dynamic adjustments during the production process to cope with the variability of market demand and the complexity of production.

[0044] Furthermore, through intelligent management systems, enterprises can utilize resources such as raw materials, labor, and transportation tools more effectively, reduce waste, and improve overall production and transportation efficiency.

[0045] In summary, this invention, through an intelligent management system, achieves real-time monitoring and optimization of the cardboard box production and transportation process, thereby reducing the overall cost increase caused by the inability to transport finished products in a timely manner, and realizing effective cost control and reduction. This method not only improves the economic benefits of enterprises but also meets current requirements for environmental protection and efficient resource utilization.

[0046] Figure 3 This is a schematic diagram of the acquisition module according to an embodiment of the present invention. In one implementation, such as... Figure 3 As shown, the acquisition module 1 includes: a first data interface unit 11 and a data storage unit 12. Wherein,

[0047] The first data interface unit 11 is used to call the API of the transportation company with which it has a continuous cooperation to obtain quotation data periodically. The quotation data includes: price, service level, and transportation time.

[0048] In one exemplary embodiment, the main function of the first data interface unit 11 is to interact with the API of an external transportation company and periodically obtain transportation quotation data.

[0049] The specific implementation method can be:

[0050] API Calls: The first data interface unit 11 is designed to interact with the APIs of multiple transportation companies. This is typically achieved via HTTP requests, such as using GET or POST methods.

[0051] Data Acquisition: Regularly (e.g., daily or whenever production plans are updated) call these APIs to obtain the latest shipping quote data.

[0052] Data parsing: The quotation data obtained from the API will be parsed into a format that the system can process, such as JSON or XML, and key information, including price, service level and delivery time, will be extracted.

[0053] For example, the system needs to obtain quotes from three different transportation companies. The first data interface unit 11 is configured with three API endpoints and sends requests at predetermined time intervals. The data returned by each request may include prices, service levels (such as standard, expedited, etc.), and estimated transit times for multiple transportation options. This data is parsed and prepared for subsequent cost calculations and transportation scheduling decisions.

[0054] Data storage unit 12 is used to store quotation data.

[0055] In one exemplary embodiment, the main function of the data storage unit 12 is to store the transportation quotation data obtained from the first data interface unit 11.

[0056] The specific implementation method can be:

[0057] Database Design: Design a database table to store the transportation company's quotation data, including fields such as price, service level, and transit time.

[0058] Data writing: Whenever the first data interface unit 11 obtains new quotation data, this data will be written to the database.

[0059] Data management: Enables CRUD operations on data to ensure data integrity and consistency.

[0060] Data access: Provides interfaces for other modules (such as Calculation Module 2) to access these stored quotation data for cost calculation and transportation scheduling.

[0061] For example, data storage unit 12 may use a relational database management system (RDBMS) such as MySQL or PostgreSQL to store data. The database tables may be designed to contain fields such as: shipping company ID, price, service level, transit time, and data update time. Whenever new quote data is available, the system automatically updates the corresponding record in the database to ensure data timeliness and accuracy.

[0062] Figure 4 This is a schematic diagram of the structure of the first computing unit according to an embodiment of the present invention. In one embodiment, such as... Figure 4As shown, the first calculation unit 21 includes: a first calculation subunit 211 and a second calculation subunit 212. Wherein,

[0063] The first calculation subunit 211 is used to recursively calculate the first optimal total cost and the first optimal transportation volume for each previous time period, starting from the last time period.

[0064] The second calculation subunit 212 is used to update the inventory of the next time period, the second optimal transportation volume of the current time period, and the second optimal total cost of the current time period in a positive direction based on the first optimal transportation volume, starting from the first time period.

[0065] Execution module 3 is also used for:

[0066] Starting from the first time period, output the second optimal transportation volume and the second optimal total cost for each time period, so as to schedule the transportation of finished cardboard boxes based on the optimal transportation volume.

[0067] In one implementation, the first preset algorithm is:

[0068] V t (I t )=min Tt (C p *P t +C s *I t *Δt+k*T t +V t+1 (I t+1 ));

[0069] I t+1 =I t +P t -T t ;

[0070] Among them, C p P represents the production cost per unit of cardboard box. t Let C be the production quantity of cardboard boxes during time period t. s The storage cost per unit carton per unit time, I t Let I be the inventory of cardboard boxes in the current time period t. t+1 Let T be the inventory of cardboard boxes for the next time period t+1, k be the transportation cost per unit cardboard box, and T be the inventory of cardboard boxes for the next time period t+1. t V represents the volume of cardboard boxes transported over a time period t. t (I t For time period t and inventory status I t The optimal total cost, V t+1 (I t+1 ) represents the optimal total cost for the next time period t+1.

[0071] The following examples will be used to explain the above content:

[0072] The first calculation subunit 211 performs the function of recursively calculating the optimal total cost and optimal transportation volume for each time period.

[0073] The specific implementation method can be:

[0074] Recursive process: Starting from the last time period, use the backward recursive method of dynamic programming to calculate the optimal total cost V for each time period. t (I t ) and optimal transport volume (T) t This process is based on the algorithm's recursive formula, calculating from the end to the beginning until the first time period.

[0075] Recursive Formula: Using the given recursive formula, combined with production costs (C... p ), production volume (P) t Storage cost (C) s ), current inventory (I) t ), transportation costs (k) and inventory levels for the next time period (I) t+1 This is used to calculate the optimal total cost for each time period.

[0076] Therefore, by recursively calculating backwards, the system can determine the optimal transportation strategy that should be adopted at the end of each time period in order to achieve the globally optimal total cost.

[0077] The second calculation subunit 212 performs the following function: positively updating the inventory, optimal transportation volume, and optimal total cost for each time period.

[0078] The specific implementation method can be:

[0079] Update process: Starting from the first time period, based on the optimal transportation volume calculated by the first calculation subunit 211, update the inventory volume (I) for the next time period. t+1 ), and calculate the optimal total cost (V) for the current time period. t (I t )) and optimal transport volume (T) t ).

[0080] Inventory Update: Using Formula I t+1 =I t +P t -T t To update the inventory level for each time period.

[0081] Therefore, through positive updates, the system can ensure that the inventory and transportation volume in each time period are adjusted based on the globally optimal strategy, thereby minimizing costs.

[0082] The execution module 3 is responsible for outputting the optimal transport volume and optimal total cost for each time period, and performing transport scheduling based on this information.

[0083] The specific implementation method can be:

[0084] Output: Starting from the first time period, output the optimal transportation volume and optimal total cost for each time period. This information will be used to guide actual transportation scheduling.

[0085] Guided transportation scheduling: Based on the calculated optimal transportation volume, the execution module 3 will schedule the corresponding transportation resources, such as vehicles and routes, to ensure that the cartons can be transported according to the optimal strategy.

[0086] Therefore, the execution module 3 ensures that the transportation scheduling of cartons matches the calculated optimal strategy, thereby minimizing costs and maximizing efficiency throughout the entire production and transportation cycle.

[0087] In summary, through the reverse recursion of the first calculation subunit 211 and the forward update of the second calculation subunit 212, the system can determine the optimal transportation strategy for each time period. The execution module 3 then performs actual transportation scheduling based on these strategies, ensuring optimal cost efficiency throughout the entire production and logistics process. This method not only improves resource utilization but also reduces unnecessary storage and transportation costs, ultimately achieving intelligent and automated production and transportation processes.

[0088] In one implementation, such as Figure 2 As shown, the calculation module 2 also includes:

[0089] The second calculation unit 22 is used to determine the optimal transportation company based on the quotation data and in combination with the second preset algorithm.

[0090] Figure 5 This is a schematic diagram of the structure of the second computing unit according to an embodiment of the present invention. In one embodiment, such as... Figure 5 As shown, in one embodiment, the second computing unit 22 includes:

[0091] The third calculation subunit 221 is used to determine a comprehensive scoring function based on price, service level, and transportation time to obtain a score for each transportation company;

[0092] The fourth calculation subunit 222 is used to determine the weighting factor for each transportation company based on the scoring results;

[0093] The fifth calculation subunit 223 is used to determine the optimal transportation company based on the weighting factors.

[0094] In one implementation, the comprehensive scoring function is:

[0095] F i =α* +β*S i +γ* ;

[0096] Among them, P i Let S be the price of the i-th transportation company. i For the service level of the i-th transportation company, T i Let F be the transit time for the i-th transportation company, α be the weight of price, β be the weight of service class, γ be the weight of transit time, and F be the weight of transit time. i The rating result for the i-th transportation company.

[0097] In one implementation, the formula for calculating the weighting factor is:

[0098] W i = ;

[0099] The formula for determining the optimal transportation company based on weighting factors is as follows:

[0100] i * =argmax i W i ;

[0101] Among them, W i Let i be the weighting factor for transportation company i. * The transportation company with the largest weighting factor.

[0102] In summary, the second calculation unit 22 selects the optimal transportation company by comprehensively considering price, service level, and transit time using mathematical models and algorithms. The third calculation subunit 221 is responsible for calculating the comprehensive score of each transportation company, the fourth calculation subunit 222 determines the weighting factors based on the scores, and the fifth calculation subunit 223 selects the optimal transportation company based on these weighting factors. This method not only improves the objectivity and scientific nature of transportation decisions but also helps reduce transportation costs, improve service quality, and ultimately optimize the entire logistics process.

[0103] The above process has been explained with examples below:

[0104] For example, there are three transportation companies, A, B, and C. You need to decide which company to partner with based on their prices, service levels, and delivery times. Below are some data points for each company:

[0105] Company A: Price: P A =100 yuan, service level: S A =8, Transportation time: T A =3 days;

[0106] Company B: Price: P B =120 yuan, Service level: S B =7, Transportation time: T B =2 days;

[0107] Company C: Price: P C =90 yuan, Service level: S C =9, Transportation time: T C =4 days;

[0108] Set the weighting coefficients as follows: α=0.5, β=1.0, γ=0.5.

[0109] The method for calculating the comprehensive score of each transportation company in the third calculation subunit 221 is as follows:

[0110] Company A's rating: F A =0.5* +1.0*8+0.5* ;

[0111] Company B's rating: F B =0.5* +1.0*7+0.5* ;

[0112] Company C's rating: F C =0.5* +1.0*9+0.5* ;

[0113] The fourth calculation subunit 222 implements the following method to determine the weighting factor for each transportation company based on the scoring results:

[0114] For company A's weighting factor W A = ;

[0115] For company B, the weighting factor W B = ;

[0116] For company C, the weighting factor W C = ;

[0117] For the fifth computational subunit 223, the implementation is based on comparison W. A W B W CThe method for selecting the company with the largest weighting factor as the optimal transportation company is as follows:

[0118] F A =0.005+8+0.1667=8.1717, W A = ;

[0119] F B =0.00417 + 7 + 0.5 = 7.50417, W B = ;

[0120] F C =0.00556+9+0.25=9.25556, W C = .

[0121] Compare W A W B , W, for example W C If the value is maximized, then company C is determined to be the optimal choice.

[0122] In summary, the aforementioned intelligent cardboard box production management system, by comprehensively considering the price, service level, and transportation time of transportation companies, and using mathematical models and algorithms to determine the optimal transportation company, significantly contributes to solving the problem of increased total costs caused by the inability to transport finished cardboard boxes on time. For example:

[0123] Contribution 1: Accurate cost-benefit analysis.

[0124] Through a comprehensive scoring function, the system can accurately assess the cost-effectiveness of each transportation company. This assessment considers not only price but also service level and transit time, ensuring that the selected transportation company not only offers reasonable prices but also guarantees service quality and transportation efficiency.

[0125] Contribution 2: Optimize transportation scheduling.

[0126] By identifying the optimal shipping company, the system ensures that finished cardboard boxes can be transported at the lowest cost, with the highest service level, and the fastest delivery time. This directly reduces inventory buildup caused by shipping delays, thereby lowering storage costs and avoiding additional costs incurred due to stockpiling.

[0127] Contribution 3: Dynamically adjust transportation strategies.

[0128] The system not only selects the optimal transportation company in the initial stage, but also dynamically adjusts transportation strategies based on real-time data and market changes. For example, if a transportation company's service level declines or its transit time increases, the system can reassess and select a new optimal transportation company, ensuring that transportation efficiency and cost control are always at their best.

[0129] Contribution 4: Improve overall logistics efficiency.

[0130] By selecting the optimal transportation company, the system can improve overall logistics efficiency, including reducing transit time and increasing the reliability of goods delivery. This not only reduces time costs in the logistics process but also enhances customer satisfaction, contributing to the building of long-term customer relationships and market competitiveness.

[0131] Contribution 5: Reduce operational risks.

[0132] Choosing a shipping company with high service standards and short transit times can reduce risks during transportation, such as the risk of damage or loss of goods. This not only reduces potential compensation costs but also protects the company's reputation.

[0133] In summary, through the comprehensive scoring and weighting factor calculations in the aforementioned intelligent cardboard box production management system, enterprises can select the most suitable transportation company, thereby optimizing transportation costs, improving transportation efficiency, and enhancing service quality. This method not only solves the problem of untimely transportation of finished cardboard boxes but also reduces overall production costs, improving the enterprise's market competitiveness and economic benefits.

[0134] This embodiment also provides an intelligent cardboard box production management method. Figure 6 This is a flowchart of an intelligent cardboard box production management method according to an embodiment of the present invention, such as... Figure 6 As shown, the method includes:

[0135] Step S601: Obtain the production cost per unit carton, the production volume for the time period, the storage cost per unit carton per unit time, the inventory for the current time period, and the transportation volume for the current time period.

[0136] By adopting the above technical solution, in the background technology, due to the lack of accurate data support, enterprises find it difficult to accurately predict and control costs. Step S601, by acquiring these key data in real time, provides a foundation for subsequent cost calculation and optimization.

[0137] Step S602: Calculate the optimal total cost for the current time period based on the production cost per unit carton, the production volume for the time period, the storage cost per unit carton per unit time, the inventory for the current time period, the transportation volume for the current time period, and the first preset algorithm.

[0138] By adopting the above technical solution, the difficulties in cost prediction and control mentioned in the background section are resolved through the application of algorithms. Algorithms can help enterprises select the lowest-cost option from multiple production and transportation plans, thereby achieving cost optimization.

[0139] Step S603: Determine the optimal transportation volume corresponding to the optimal total cost, and schedule the transportation of the finished cardboard boxes based on the optimal transportation volume.

[0140] By adopting the above technical solution, the problems of untimely transportation of finished cardboard boxes and inventory backlog mentioned in the background technology are effectively solved through step S603. Precise transportation scheduling ensures that the produced cardboard boxes can be transported out in a timely manner, reducing inventory backlog, thereby lowering storage costs and avoiding additional costs caused by backlog.

[0141] Through steps S601 to S603, comprehensive optimization of the carton production and transportation process is achieved. By accurately acquiring data, advanced cost calculation, and intelligent transportation scheduling, the total cost control problem mentioned in the background art is effectively solved. This method not only improves production and logistics efficiency but also reduces cost increases caused by inventory backlog and transportation delays, thereby reducing the overall cost of carton production.

[0142] Figure 7 This is a flowchart of a method for calculating the optimal transport volume according to an embodiment of the present invention. In one embodiment, such as... Figure 7 As shown, the method also includes:

[0143] Step S701: Starting from the last time period, recursively calculate the first optimal total cost and the first optimal transportation volume for each previous time period.

[0144] Step S702: Starting from the first time period, update the inventory level of the next time period, the second optimal transportation volume of the current time period, and the second optimal total cost of the current time period in a positive direction based on the first optimal transportation volume;

[0145] Step S703: Starting from the first time period, output the second optimal transportation volume and the second optimal total cost for each time period, so as to schedule the transportation of finished carton products based on the optimal transportation volume.

[0146] In one implementation, the first preset algorithm is:

[0147] V t (I t )=min Tt (C p *P t +C s *I t *Δt+k*Tt +V t+1 (I t+1 ));

[0148] I t+1 =I t +P t -T t ;

[0149] Among them, C p P represents the production cost per unit of cardboard box. t Let C be the production quantity of cardboard boxes during time period t. s The storage cost per unit carton per unit time, I t Let I be the inventory of cardboard boxes in the current time period t. t+1 Let T be the inventory of cardboard boxes for the next time period t+1, k be the transportation cost per unit cardboard box, and T be the inventory of cardboard boxes for the next time period t+1. t V represents the volume of cardboard boxes transported over a time period t. t (I t For time period t and inventory status I t The optimal total cost, V t+1 (I t+1 ) represents the optimal total cost for the next time period t+1.

[0150] Figure 8 This is a flowchart of a method for automatically obtaining price quotes from transportation companies according to an embodiment of the present invention. In one embodiment, such as... Figure 8 The method further includes:

[0151] Step S801: Call the API of the transportation company with which we have a continuous partnership to obtain quotation data periodically. The quotation data includes: price, service level, and transit time.

[0152] Step S802: Store the quotation data.

[0153] In one implementation, the method further includes: determining the optimal transportation company based on the quotation data and in conjunction with a second preset algorithm.

[0154] Figure 9 This is a flowchart of a method for determining the optimal transportation company according to an embodiment of the present invention. In one embodiment, such as... Figure 9 The process of determining the optimal transportation company based on the quoted price data and in conjunction with a second preset algorithm includes:

[0155] Step S901: Determine a comprehensive scoring function based on price, service level, and transit time to obtain a score for each transportation company;

[0156] In one implementation, the comprehensive scoring function is:

[0157] Fi=α* +β*Si+γ* ;

[0158] Where Pi is the price of the i-th transportation company, Si is the service level of the i-th transportation company, Ti is the transportation time of the i-th transportation company, α is the weight of price, β is the weight of service level, γ is the weight of transportation time, and Fi is the rating result of the i-th transportation company.

[0159] Step S902: Determine the weighting factor for each transportation company based on the scoring results;

[0160] Step S903: Determine the optimal transportation company based on the weighting factors.

[0161] In one implementation, the formula for calculating the weighting factor is:

[0162] Wi= ;

[0163] The formula for determining the optimal transportation company based on weighting factors is as follows:

[0164] i*=argmaxiWi;

[0165] Where Wi is the weight factor of transportation company i, and i* is the transportation company with the largest weight factor.

[0166] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by adding necessary general-purpose hardware platforms with the aid of software. Of course, they can also be implemented using hardware, but in many cases, the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0167] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0168] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0169] Embodiments of the present invention also provide an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0170] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0171] Embodiments of the present invention also provide a computer program product, including computer instructions that, when executed by a processor, implement the steps of the methods described in various embodiments of the present application.

[0172] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0173] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0174] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An intelligent cardboard box production management system, characterized in that, include: The acquisition module is used to acquire the production cost per unit carton, the production volume over a time period, the storage cost per unit carton per unit time, the inventory level for the current time period, and the transportation volume for the current time period. The calculation module includes a first calculation unit, which is used to calculate the optimal total cost and optimal transportation volume for the current time period based on the production cost per unit carton, the production volume for the time period, the storage cost per unit carton per unit time, the inventory for the current time period, and the transportation volume for the current time period, combined with a first preset algorithm. The execution module is used to schedule the transportation of finished cardboard boxes based on the optimal transportation volume. The first preset algorithm is as follows: ; ； in, The production cost per unit of cardboard box, Let t be the production volume of cardboard boxes during the time period t. The storage cost per unit carton per unit time. Let represent the inventory of cardboard boxes in the current time period t. Let k be the inventory of cardboard boxes for the next time period t+1, and k be the transportation cost per unit cardboard box. Let t be the volume of cardboard boxes transported over a time period t. For time period t and inventory status The optimal total cost is as follows. The optimal total cost for the next time period t+1; The first computing unit includes: The first calculation subunit is used to recursively calculate the first optimal total cost and the first optimal transportation volume for each previous time period, starting from the last time period. The second calculation subunit is used to update the inventory of the next time period, the second optimal transportation volume of the current time period, and the second optimal total cost of the current time period in a positive direction based on the first optimal transportation volume, starting from the first time period. The execution module is also used for: Starting from the first time period, output the second optimal transportation volume and the second optimal total cost for each time period, so as to schedule the transportation of finished cardboard boxes based on the optimal transportation volume; The acquisition module includes: The first data interface unit is used to call the API of the transportation company with which it has a continuous cooperation to obtain quotation data periodically. The quotation data includes: price, service level, and transportation time. Data storage unit for storing the quotation data; The computing module also includes: The second calculation unit is used to determine the optimal transportation company based on the quoted price data and in combination with the second preset algorithm; The second computing unit includes: The third calculation subunit is used to determine a comprehensive scoring function based on the price, service level, and transportation time to obtain a scoring result for each of the transportation companies. The fourth calculation subunit is used to determine the weighting factor for each of the transportation companies based on the scoring results; The fifth calculation subunit is used to determine the optimal transportation company based on the weighting factors; The comprehensive scoring function is as follows: ； in, Let i be the price of the i-th transportation company. For the service level of the i-th transportation company, Let i be the transportation time for the i-th transportation company. As a weight for price, As a weight for service level, As a weight for transportation time, The rating result for the i-th transportation company; The formula for calculating the weighting factor is: ； The calculation formula for determining the optimal transportation company based on the weighting factors is as follows: ； in, For i transportation companies, The transportation company with the largest weighting factor.

2. A method for intelligent cardboard box production management, characterized in that, Applied to the system as described in claim 1.

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