Inventory distribution and scheduling method based on supply chain collaboration

Abstract

The invention discloses an inventory distribution and scheduling method based on supply chain collaboration, and relates to the technical field of inventory scheduling, and the method comprises the following steps: dividing a first target region with the capability of producing first target materials into sub-regions, and obtaining the material demand prediction data of each sub-region based on big data analysis; according to the material demand prediction data of each sub-region, in combination with existing material inventory data, obtaining material gap prediction data of each sub-region; obtaining the excitation state capacity of the first target area, setting the excitation state time length, and obtaining the predicted inventory allowance of each sub-area according to an inventory allowance prediction model in combination with the material gap prediction data of each sub-area; obtaining material dispatching efficiency data among the sub-regions, and obtaining excitation state cost according to excitation state productivity; and according to the predicted inventory allowance of each sub-region, the inter-sub-region material scheduling efficiency data and the excitation state cost, scheduling the material inventory of each sub-region. The method and the device are used for solving the problem of insufficient supply chain collaborative consideration of inventory scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of inventory scheduling, and more specifically, to an inventory allocation and scheduling method based on supply chain collaboration. Background Art

[0002] Supply chain collaboration can improve overall operational efficiency, reduce inventory costs, increase response speed, enhance market competitiveness, and ensure product quality and delivery accuracy by strengthening information sharing, resource integration and process optimization among all parties. In addition, collaboration can also strengthen cooperation among all links in the supply chain, reduce risks and improve flexibility, helping companies to quickly adjust and respond to changes in a dynamic market environment.

[0003] Supply chain collaboration enables inventory scheduling to be more precise, flexible and efficient through information sharing, joint decision-making and resource integration, thereby improving the operational efficiency and market responsiveness of the entire supply chain.

[0004] For example, the invention patent announcement with announcement number: CN109961306B discloses a method and device for allocating inventory of items, which relates to the field of computer technology. A specific implementation of the method includes: according to the delivery dimension information of each type of items to be delivered, obtaining historical statistical data related to the delivery dimension of the various types of items to be delivered; according to the historical statistical data and the pre-delivery quantity of the various types of items to be delivered, respectively calculating the delivery forecast data of the various types of items to be delivered; and allocating corresponding inventory to the various types of items to be delivered according to the delivery forecast data. This implementation can clarify the delivery time of various types of items, facilitate the scheduling and delivery of multiple types of items, achieve the optimal inventory allocation of various types of items to be delivered, improve the utilization rate of warehouse space, and improve the spot rate and delivery rate of various types of items to be delivered.

[0005] For example, the invention patent with announcement number: CN113919687B announces a method for allocating the inventory of electric energy metering materials, which involves the allocation of the inventory of electric energy metering materials. The method includes S1, statistics on the actual monthly demand quantity of electric energy metering materials in each warehouse; S2, predicting the demand quantity in the future period based on historical data; S3, calculating the optimal allocation quantity of electric energy metering materials in each warehouse based on the future demand forecast value and volatility. This method is mainly used for optimizing the allocation of storage capacity in each warehouse when the total number of electric energy metering materials is certain. The present invention can take into account the uncertainty of the demand for electric energy metering materials, and comprehensively consider the uncertainty of the predicted value and historical data to allocate the demand of each warehouse.

[0006] The above disclosed technical solutions have at least the following technical problems: The existing inventory allocation and scheduling methods schedule and allocate materials based on the information of materials entering and exiting the warehouse combined with the prediction model, which effectively improves the utilization rate of warehouses. However, there is still room for improvement in the overall coordination of the supply chain in allocating and scheduling inventory, and insufficient consideration is given to the dynamic changes and overall costs on the production side.

[0007] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0008] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an inventory allocation and scheduling method based on supply chain collaboration, by introducing incentive state capacity, selecting appropriate incentive state duration to incentivize production, and selecting appropriate scheduling scheme to schedule material inventory, so as to solve the problem that inventory scheduling does not take supply chain collaboration into consideration enough.

[0009] To achieve the above object, the present invention provides the following technical solutions: The inventory allocation and scheduling method based on supply chain collaboration includes the following steps: dividing a first target area with the ability to produce a first target material into no less than one sub-area, obtaining material demand forecast data for each sub-area based on big data analysis; obtaining material gap forecast data for each sub-area based on the material demand forecast data for each sub-area in combination with existing material inventory data obtained based on a statistical method; obtaining the incentive state capacity of the first target area, setting the incentive state duration, combining the material gap forecast data for each sub-area, and obtaining the predicted inventory balance of each sub-area according to a preset inventory balance forecast model; obtaining material scheduling efficiency data between sub-areas, and obtaining the incentive state cost of the first target area based on the incentive state capacity of the first target area; scheduling the material inventory of each sub-area based on the predicted inventory balance of each sub-area, the material scheduling efficiency data between sub-areas, and the incentive state cost of the first target area.

[0010] In a preferred embodiment, the incentive state capacity includes the capacity achieved when the equipment is operating at full capacity; the incentive state cost includes the wear and maintenance costs and energy consumption costs caused by the full operation of the equipment, as well as the employee expense costs; the material demand forecast data includes the material demand forecast amount, the material gap forecast data includes the material gap forecast amount, and the existing material inventory data includes the existing material inventory amount.

[0011] In a preferred embodiment, the incentive state capacity of the first target area is obtained, the incentive state duration is set, and the predicted inventory balance of each sub-area is obtained in combination with the material gap prediction data of each sub-area according to a preset inventory balance prediction model. Specifically, the incentive state capacity of the first target area is obtained, and the average incentive state capacity of each sub-area is obtained according to the incentive state capacity of the first target area; according to the average incentive state capacity and the incentive state duration of each sub-area, in combination with the material gap prediction data of each sub-area, the predicted inventory balance of each sub-area is obtained according to the preset inventory balance prediction model.

[0012] In a preferred embodiment, the obtaining of material dispatch efficiency data between sub-regions is specifically as follows: obtaining the transportation distance of material dispatch between sub-regions; obtaining the type and quantity of available transportation tools for material dispatch between sub-regions, and obtaining the efficiency factor of the available transportation tools according to the type of available transportation tools; obtaining the road condition information of the material dispatch path between sub-regions, and obtaining the road condition influencing factor according to the road condition information of the path; obtaining the number of approval nodes of the material dispatch path between sub-regions; obtaining the material dispatch efficiency data between sub-regions according to the transportation distance of material dispatch between sub-regions, the type and quantity of available transportation tools, the road condition information of the material dispatch path between sub-regions, and the number of approval nodes of the path, in combination with a preset calculation formula for material dispatch efficiency data between sub-regions.

[0013] In a preferred embodiment, the material inventory of each sub-region is scheduled based on the predicted inventory balance of each sub-region, the material scheduling efficiency data between sub-regions and the incentive state cost of the first target region, specifically: the scheduling is completed within a preset time t; based on the predicted inventory balance of each sub-region, combined with the material scheduling efficiency data between sub-regions, the optimal material scheduling plan between sub-regions that minimizes the scheduling cost within the time t is obtained, and the scheduling cost includes the transportation cost, labor cost and scheduling management cost of the material scheduling between sub-regions; based on the incentive state cost of the first target region and the minimum scheduling cost, the first total cost is obtained, and the first total cost includes the incentive state cost of the first target region and the corresponding minimum scheduling cost of the material scheduling between sub-regions; the incentive state duration T is adjusted, with the minimum first total cost as the goal, the incentive state cost of the first target region that minimizes the first total cost and the corresponding optimal material scheduling plan for each sub-region are obtained, and the material inventory of each sub-region is scheduled.

[0014] In a preferred embodiment, the predicted inventory balance of each sub-region is combined with the material scheduling efficiency data between sub-regions to obtain the optimal material scheduling plan between sub-regions that minimizes the scheduling cost within time t, specifically: according to the predicted inventory balance of each sub-region, all material scheduling plans between sub-regions are obtained; the material scheduling plans that exceed the preset time t are eliminated; according to the material scheduling efficiency data between each sub-region, combined with the screened material scheduling plans between sub-regions, the scheduling cost corresponding to the plan is obtained; and the material scheduling plan between sub-regions with the smallest scheduling cost is selected as the optimal material scheduling plan.

[0015] In a preferred embodiment, the incentive state duration T is adjusted with the goal of minimizing the first total cost, the incentive state cost of the first target area that minimizes the first total cost and the corresponding optimal material scheduling plan for each sub-area are obtained, and the material inventory of each sub-area is scheduled, specifically: the incentive state duration T is adjusted within a preset incentive state duration value range; according to the incentive state capacity of the first target area and the adjusted incentive state duration, the incentive state cost of the first target area and the predicted inventory balance of each sub-area are obtained; the optimal material scheduling plan between each sub-area that minimizes the scheduling cost within time t is obtained; according to the incentive state cost of the first target area and the minimum scheduling cost of the corresponding material scheduling between each sub-area, the first total cost is obtained; the incentive state duration T corresponding to the minimum first total cost and the optimal material scheduling plan for each sub-area are used as the final scheduling plan, and the material inventory of each sub-area is scheduled.

[0016] The technical effects and advantages of the inventory allocation and scheduling method based on supply chain collaboration of the present invention are as follows: 1. The present invention introduces incentive state capacity, thereby incorporating the impact of production-side collaboration on inventory into inventory scheduling considerations, effectively improving the flexibility of inventory scheduling.

[0017] 2. The present invention uses an inventory surplus prediction model to comprehensively consider the production-side incentive capacity and consumer-side demand, predicts inventory surplus, and combines the material scheduling efficiency data between each sub-region, with the goal of minimizing the sum of the incentive state cost and the scheduling cost, selects a suitable incentive state duration to incentivize production, and selects a suitable scheduling plan to schedule the material inventory, thereby synergizing the supply chain as a whole in the material inventory scheduling, effectively improving the accuracy of material inventory scheduling control, quickly and accurately meeting consumer-side demand, and reducing inventory shortages and backlog risks. In addition, the minimum sum of the incentive state cost and the scheduling cost is used as the goal to control production and select scheduling plans, effectively reducing overall costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1It is a flow chart of the inventory allocation and scheduling method based on supply chain collaboration of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Embodiment 1, Figure 1 The present invention provides an inventory allocation and scheduling method based on supply chain collaboration, which includes the following steps: Divide the first target area with the ability to produce the first target material into at least one sub-area, and obtain material demand forecast data for each sub-area based on big data analysis; Based on the material demand forecast data of each sub-region and the existing material inventory data obtained based on statistical methods, the material gap forecast data of each sub-region is obtained; Obtain the incentive state capacity of the first target area, set the incentive state duration, combine the material gap forecast data of each sub-area, and obtain the predicted inventory balance of each sub-area according to the preset inventory balance forecast model; Obtain the material dispatch efficiency data between sub-regions, and obtain the incentive state cost of the first target region according to the incentive state capacity of the first target region; The material inventory of each sub-region is scheduled based on the predicted inventory balance of each sub-region, the material scheduling efficiency data between sub-regions and the incentive state cost of the first target region.

[0021] In regional demand forecasting, big data analysis can accurately capture the changing trend of regional demand through time series analysis, regression analysis, machine learning, GIS spatial analysis and other methods, combined with massive, multi-dimensional data. These methods can improve the accuracy and real-time nature of forecasts, while providing personalized and flexible demand forecasts, helping decision makers better respond to market fluctuations, optimize resource allocation, and reduce costs and risks. They are especially suitable for comparisons between sub-regions and responding to rapidly changing demand environments.

[0022] Common statistical methods can provide companies with accurate inventory status and optimize inventory management processes by analyzing existing material inventory data. Descriptive statistics (such as mean, standard deviation, etc.) can help companies understand the distribution and fluctuation of inventory levels, reveal which products have too much or too little inventory, and adjust procurement and sales strategies in a timely manner. In addition, statistical methods can also help companies improve inventory liquidity and reduce unnecessary inventory backlogs, reduce capital occupation and storage costs by calculating inventory turnover rates and identifying slow-moving goods. Overall, statistical methods can achieve accurate management of inventory information under data-driven conditions, enhance the flexibility and responsiveness of companies to market changes, and thus achieve more efficient supply chain management.

[0023] In this embodiment, the incentive state capacity includes the capacity achieved when the equipment is in full-load operation state; The incentive status costs include wear and tear maintenance costs and energy consumption costs caused by full-load operation of equipment, as well as employee cost costs; The material demand forecast data includes material demand forecast quantity, the material gap forecast data includes material gap forecast quantity, and the existing material inventory data includes existing material inventory quantity.

[0024] In this embodiment, the incentive state capacity of the first target area is obtained, the incentive state duration is set, and the predicted inventory balance of each sub-area is obtained according to the preset inventory balance prediction model in combination with the material gap prediction data of each sub-area, specifically: Obtain the incentive state capacity of the first target area, and obtain the incentive state average capacity of each sub-area according to the incentive state capacity of the first target area; According to the average production capacity and duration of the incentive state of each sub-region, combined with the material gap forecast data of each sub-region, the predicted inventory balance of each sub-region is obtained according to the preset inventory balance forecast model.

[0025] In this embodiment, the specific calculation formula of the preset inventory balance prediction model is:

[0026] The specific calculation formula for the sub-region material gap forecast data is:

[0027] In the formula, is the predicted inventory balance of the z-th sub-region, is the capacity of the first target area in the incentive state, T is the duration of the first target area in the incentive state, is the number of sub-regions, is the predicted material gap in the zth sub-region, is the current inventory of the first target material in the z-th sub-area, It is the forecast quantity of the first target material demand in the zth sub-region.

[0028] It should be noted that a negative sign for the predicted material gap indicates that a material gap is predicted, and the negative value indicates the missing material inventory; a positive sign for the predicted material gap indicates that the existing material inventory is sufficient to meet the predicted material demand, and the positive value indicates the remaining material inventory after the predicted material demand is met.

[0029] Furthermore, the predicted inventory balance of each sub-region is recorded as a value with a positive or negative sign; when the sign of the predicted sub-region inventory balance is positive, it means that it is predicted that there will be inventory balance of materials in this sub-region after being replenished by the production end, and when the sign is negative, it means that it is predicted that the materials in this sub-region will still be insufficient to fill the material gap after being replenished by the production end.

[0030] In this embodiment, the acquisition of material dispatch efficiency data between sub-regions is specifically as follows: Obtain the transportation distance for material dispatch between sub-regions; Obtain the types and quantities of available transportation tools for material dispatch between sub-regions, and obtain the efficiency factors of available transportation tools according to the types of available transportation tools; Obtain the road condition information of the material dispatch path between sub-regions, and obtain the road condition influencing factor based on the road condition information of the path; Get the number of approval nodes for material dispatch paths between sub-regions; According to the transportation distance of inter-sub-regional material dispatch, the types and quantity of available transportation tools, the road condition information of the inter-sub-regional material dispatch path, and the number of approval nodes of the path, combined with the preset inter-sub-regional material dispatch efficiency data calculation formula, the inter-sub-regional material dispatch efficiency data is obtained.

[0031] In this embodiment, the preset calculation formula for the material dispatch efficiency data between sub-regions is specifically as follows:

[0032] In the formula, is the material dispatch efficiency between sub-regions, is the efficiency factor of the ith means of transport, is the number of the i-th means of transport, is the number of transport types, is the transportation distance between the two sub-areas, is the road condition influencing factor, The number of approval nodes in the path.

[0033] It should be noted that the road condition impact factor is obtained according to the road condition information of the path. If the road condition is bad, the value of the road condition impact factor is large, and if the road condition is good, the value of the road condition impact factor is small.

[0034] In this embodiment, the material inventory of each sub-region is scheduled according to the predicted inventory balance of each sub-region, the material scheduling efficiency data between sub-regions and the incentive state cost of the first target region, specifically: The scheduling is completed within the preset time t; According to the predicted inventory balance of each sub-region, combined with the material dispatch efficiency data between sub-regions, the optimal material dispatch scheme between sub-regions is obtained to minimize the dispatch cost within time t. The dispatch cost includes the transportation cost, labor cost and dispatch management cost of the material dispatch between sub-regions. Obtaining a first total cost according to the first target area incentive state cost and the minimum scheduling cost, wherein the first total cost includes the first target area incentive state cost and the corresponding minimum scheduling cost of material scheduling between each sub-area; Adjust the incentive state duration T, take the minimum first total cost as the goal, obtain the first target area incentive state cost that minimizes the first total cost and the corresponding optimal material scheduling plan for each sub-area, and schedule the material inventory of each sub-area.

[0035] In this embodiment, the predicted inventory balance of each sub-region is combined with the material dispatch efficiency data between sub-regions to obtain the optimal material dispatch solution between sub-regions that minimizes the dispatch cost within time t, specifically: Obtain material dispatch plans among all sub-regions based on the predicted inventory balance of each sub-region; Eliminate material dispatch plans that exceed the preset time t; According to the material dispatch efficiency data between each sub-region, combined with the selected material dispatch plan between sub-regions, the dispatch cost corresponding to the plan is obtained; The material scheduling plan among sub-regions with the smallest scheduling cost is selected as the optimal material scheduling plan.

[0036] It should be noted that the material dispatch plans among all sub-regions refer to all the optional plans for dispatching materials among the sub-regions to fill the predicted material gaps, not the material dispatch plans among all the sub-regions individually.

[0037] Furthermore, there is a positive correlation between the dispatching cost and the amount of materials dispatched, and a negative correlation between the dispatching cost and the efficiency of material dispatching between sub-regions.

[0038] In this embodiment, the incentive state duration T is adjusted with the goal of minimizing the first total cost, obtaining the incentive state cost of the first target area that minimizes the first total cost and the corresponding optimal material scheduling plan for each sub-area, and scheduling the material inventory of each sub-area, specifically: Adjust the incentive state duration T within the preset incentive state duration value range; According to the incentive state capacity of the first target area and the adjusted incentive state duration, the incentive state cost of the first target area and the predicted inventory balance of each sub-area are obtained; Obtain the optimal material dispatching plan among sub-regions that minimizes the dispatching cost within time t; Obtaining a first total cost according to the incentive state cost of the first target area and the minimum scheduling cost of material scheduling between the corresponding sub-areas; Adjust the incentive state duration T, and repeat the above steps until the first total cost corresponding to all incentive state durations T is obtained; The incentive state duration T corresponding to the minimum first total cost and the optimal material scheduling plan for each sub-region are used as the final scheduling plan to schedule the material inventory of each sub-region.

[0039] It should be noted that the duration of the incentive state until the predicted material demand moment e is the upper limit of the preset incentive state duration value range, and the upper limit of the duration should ensure that no irreversible damage is caused to the equipment; the duration of the incentive state to produce enough materials to fill the material gaps in all sub-areas is the lower limit of the preset incentive state duration value range.

[0040] This embodiment uses an inventory surplus prediction model to comprehensively consider the production-side incentive capacity and consumer-side demand, predicts inventory surplus, and combines the material scheduling efficiency data between each sub-region, with the goal of minimizing the sum of the incentive state cost and the scheduling cost, selects the appropriate incentive state duration to incentivize production, and selects the appropriate scheduling plan to schedule the material inventory, thereby synergizing the supply chain as a whole in the material inventory scheduling, effectively improving the accuracy of material inventory scheduling control, quickly and accurately meeting consumer-side demand, and reducing inventory out-of-stock and backlog risks. In addition, taking the minimum sum of the incentive state cost and the scheduling cost as the goal to control production and select scheduling plans effectively reduces the overall cost.

[0041] Embodiment 2 includes the following steps: dividing a first target area having the ability to produce a first target material into at least one sub-area, and obtaining material demand forecast data for each sub-area based on big data analysis; Based on the material demand forecast data of each sub-region and the existing material inventory data obtained based on statistical methods, the material gap forecast data of each sub-region is obtained; Obtain the incentive state capacity of the first target area, set the incentive state duration, combine the material gap forecast data of each sub-area, and obtain the predicted inventory balance of each sub-area according to the preset inventory balance forecast model; Obtain the material dispatch efficiency data between sub-regions, and obtain the incentive state cost of the first target region according to the incentive state capacity of the first target region; The material inventory of each sub-region is scheduled based on the predicted inventory balance of each sub-region, the material scheduling efficiency data between sub-regions and the incentive state cost of the first target region.

[0042] In this embodiment, the first target area A is divided into three sub-areas: , and , based on big data analysis, the material demand forecast data of each sub-region was obtained, and the material demand forecasts of the three sub-regions were 4, 6 and 10 respectively.

[0043] In this embodiment, based on the material demand forecast data of each sub-region and combined with the existing material inventory data obtained based on statistical methods, the existing material inventory of the three sub-regions is obtained to be 2, 3 and 3 respectively; the material gap forecast data of each sub-region is obtained, and the material gap forecast amounts of the three sub-regions are obtained to be -2, -3 and -7 respectively.

[0044] In this embodiment, the incentive state production capacity of the first target area is obtained as 3, and the incentive state duration T is set to 4. Combined with the material gap forecast data of each sub-area, the predicted inventory balance of each sub-area is obtained according to the preset inventory balance forecast model, which are +2, +1 and -3 respectively.

[0045] In this embodiment, the material dispatch efficiency data between sub-regions is obtained. and The material dispatch efficiency is , sub-area and The material dispatch efficiency is , sub-area and The material dispatch efficiency is , In this embodiment, the incentive state cost of the first target area is obtained according to the incentive state capacity of the first target area, and the cost is increased by 1 if the capacity is increased by P, so the incentive state cost is 12P (3*P*4).

[0046] In this embodiment, the material inventory of each sub-region is scheduled according to the predicted inventory balance of each sub-region, the material scheduling efficiency data between sub-regions and the incentive state cost of the first target region, specifically: The scheduling is completed within the preset time t; According to the predicted inventory balance of each sub-region, combined with the material dispatch efficiency data between sub-regions, the optimal material dispatch scheme between sub-regions is obtained to minimize the dispatch cost within time t. The dispatch cost includes the transportation cost, labor cost and dispatch management cost of the material dispatch between sub-regions. Obtaining a first total cost according to the first target area incentive state cost and the minimum scheduling cost, wherein the first total cost includes the first target area incentive state cost and the corresponding minimum scheduling cost of material scheduling between each sub-area; Adjust the incentive state duration T, take the minimum first total cost as the goal, obtain the first target area incentive state cost that minimizes the first total cost and the corresponding optimal material scheduling plan for each sub-area, and schedule the material inventory of each sub-area.

[0047] In this embodiment, based on the predicted inventory balance of each sub-region and the material dispatching efficiency data between sub-regions, the optimal material dispatching scheme between sub-regions that minimizes the dispatching cost within time t is obtained, which is specifically: Obtain material dispatch plans among all sub-regions based on the predicted inventory balance of each sub-region; Eliminate material dispatch plans that exceed the preset time t; According to the material dispatch efficiency data between each sub-region, combined with the selected material dispatch plan between sub-regions, the dispatch cost corresponding to the plan is obtained; The material scheduling plan among sub-regions with the smallest scheduling cost is selected as the optimal material scheduling plan.

[0048] It should be noted that there is a positive correlation between the dispatching cost and the amount of materials dispatched, and a negative correlation between the dispatching cost and the efficiency of material dispatch between sub-regions.

[0049] In this embodiment, the optional inter-sub-region material dispatching solutions are: 1. Sub-region To sub-area Dispatch material inventory 2, sub-area To sub-area The dispatch material inventory is 3, then the dispatch cost is expressed as ; 2. Sub-region To sub-area Dispatch material inventory 2, sub-area To sub-area The dispatch material inventory is 1, then the dispatch cost is expressed as ; The plan with the smaller scheduling cost is selected as the optimal material scheduling plan when the incentive state duration T=4, which is recorded as plan ,plan The scheduling cost is recorded as .

[0050] In this embodiment, the incentive state duration T is adjusted, and the first total cost is minimized as the goal, the incentive state cost of the first target area that minimizes the first total cost and the corresponding optimal material scheduling plan for each sub-area are obtained, and the material inventory of each sub-area is scheduled, specifically: Adjust the incentive state duration T within the preset incentive state duration value range; According to the incentive state capacity of the first target area and the adjusted incentive state duration, the incentive state cost of the first target area and the predicted inventory balance of each sub-area are obtained; Obtain the optimal material dispatching plan among sub-regions that minimizes the dispatching cost within time t; Obtaining a first total cost according to the incentive state cost of the first target area and the minimum scheduling cost of material scheduling between the corresponding sub-areas; Adjust the incentive state duration T, and repeat the above steps until the first total cost corresponding to all incentive state durations T is obtained; The incentive state duration T corresponding to the minimum first total cost and the optimal material scheduling plan for each sub-region are used as the final scheduling plan to schedule the material inventory of each sub-region.

[0051] In this embodiment, the incentive state duration T is adjusted to 5. According to the method for obtaining the inventory balance of each sub-region and the incentive state cost at T=4, the predicted inventory balances of each sub-region are +3, +2 and -2 respectively, and the incentive state cost is 15P (3*P*5).

[0052] In this embodiment, according to the method for obtaining the optimal material scheduling solution when the excitation state duration T=4, the optimal material scheduling solution when T=5 is obtained, which is recorded as solution ,plan The scheduling cost is recorded as .

[0053] In this embodiment, the first total cost when the excitation state duration T=4 is , the first total cost when the incentive state duration T=5 is , and so on to obtain the first total cost corresponding to the duration T of all incentive states.

[0054] In this embodiment, the first total costs corresponding to all incentive state durations T are compared, and the incentive state duration T corresponding to the smallest first total cost is selected to incentivize the production end. The optimal material scheduling plan between sub-regions corresponding to the incentive state duration T is selected as the final scheduling plan, and the material inventory of each sub-region is scheduled.

[0055] It should be noted that when the incentive state duration is too long, the incentive state cost generated by maintaining the incentive state capacity tends to increase significantly.

[0056] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0057] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0058] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0059] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0060] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0061] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Inventory allocation and scheduling method based on supply chain collaboration, characterized by: The steps include: Divide the first target area with the ability to produce the first target material into at least one sub-area, and obtain material demand forecast data for each sub-area based on big data analysis; Based on the material demand forecast data of each sub-region and the existing material inventory data obtained based on statistical methods, the material gap forecast data of each sub-region is obtained; Obtain the incentive state capacity of the first target area, set the incentive state duration, combine the material gap forecast data of each sub-area, and obtain the predicted inventory balance of each sub-area according to the preset inventory balance forecast model; Obtain the material dispatch efficiency data between sub-regions, and obtain the incentive state cost of the first target region according to the incentive state capacity of the first target region; The material inventory of each sub-region is scheduled based on the predicted inventory balance of each sub-region, the material scheduling efficiency data between sub-regions and the incentive state cost of the first target region.

2. The inventory allocation and scheduling method based on supply chain collaboration according to claim 1 is characterized in that: The incentive state capacity includes the capacity achieved when the equipment is in full operation state; The incentive status costs include wear and tear maintenance costs and energy consumption costs caused by full-load operation of equipment, as well as employee cost costs; The material demand forecast data includes material demand forecast quantity, the material gap forecast data includes material gap forecast quantity, and the existing material inventory data includes existing material inventory quantity.

3. The inventory allocation and scheduling method based on supply chain collaboration according to claim 2 is characterized in that: The method of obtaining the incentive state capacity of the first target area, setting the incentive state duration, combining the material gap forecast data of each sub-area, and obtaining the predicted inventory balance of each sub-area according to the preset inventory balance forecast model is specifically as follows: Obtain the incentive state capacity of the first target area, and obtain the incentive state average capacity of each sub-area according to the incentive state capacity of the first target area; According to the average production capacity and duration of the incentive state of each sub-region, combined with the material gap forecast data of each sub-region, the predicted inventory balance of each sub-region is obtained according to the preset inventory balance forecast model.

4. The inventory allocation and scheduling method based on supply chain collaboration according to claim 3 is characterized in that: The acquisition of material dispatch efficiency data between sub-regions is specifically as follows: Obtain the transportation distance for material dispatch between sub-regions; Obtain the types and quantities of available transportation tools for material dispatch between sub-regions, and obtain the efficiency factors of available transportation tools according to the types of available transportation tools; Obtain the road condition information of the material dispatch path between sub-regions, and obtain the road condition influencing factor based on the road condition information of the path; Get the number of approval nodes for material dispatch paths between sub-regions; According to the transportation distance of inter-sub-regional material dispatch, the types and quantity of available transportation tools, the road condition information of the inter-sub-regional material dispatch path, and the number of approval nodes of the path, combined with the preset inter-sub-regional material dispatch efficiency data calculation formula, the inter-sub-regional material dispatch efficiency data is obtained.

5. The inventory allocation and scheduling method based on supply chain collaboration according to claim 4 is characterized in that: The material inventory of each sub-region is scheduled according to the predicted inventory balance of each sub-region, the material scheduling efficiency data between sub-regions and the incentive state cost of the first target region, specifically: The scheduling is completed within the preset time t; According to the predicted inventory balance of each sub-region, combined with the material dispatch efficiency data between sub-regions, the optimal material dispatch scheme between sub-regions is obtained to minimize the dispatch cost within time t. The dispatch cost includes the transportation cost, labor cost and dispatch management cost of the material dispatch between sub-regions. Obtaining a first total cost according to the first target area incentive state cost and the minimum scheduling cost, wherein the first total cost includes the first target area incentive state cost and the corresponding minimum scheduling cost of material scheduling between each sub-area; Adjust the incentive state duration T, take the minimum first total cost as the goal, obtain the first target area incentive state cost that minimizes the first total cost and the corresponding optimal material scheduling plan for each sub-area, and schedule the material inventory of each sub-area.

6. The inventory allocation and scheduling method based on supply chain collaboration according to claim 5 is characterized in that: According to the predicted inventory balance of each sub-region, combined with the material dispatch efficiency data between sub-regions, the optimal material dispatch solution between sub-regions that minimizes the dispatch cost within time t is obtained, specifically: Obtain material dispatch plans among all sub-regions based on the predicted inventory balance of each sub-region; Eliminate material dispatch plans that exceed the preset time t; According to the material dispatch efficiency data between each sub-region, combined with the selected material dispatch plan between sub-regions, the dispatch cost corresponding to the plan is obtained; The material scheduling plan among sub-regions with the smallest scheduling cost is selected as the optimal material scheduling plan.

7. The inventory allocation and scheduling method based on supply chain collaboration according to claim 6 is characterized in that: The incentive state duration T is adjusted, with the first total cost minimized as the goal, to obtain the first target area incentive state cost that minimizes the first total cost and the corresponding optimal plan for material scheduling in each sub-area, and to schedule the material inventory in each sub-area, specifically: Adjust the excitation state duration T within the preset excitation state duration value range; According to the incentive state capacity of the first target area and the adjusted incentive state duration, the incentive state cost of the first target area and the predicted inventory balance of each sub-area are obtained; Obtain the optimal material dispatching plan among sub-regions that minimizes the dispatching cost within time t; Obtaining a first total cost according to the incentive state cost of the first target area and the minimum scheduling cost of material scheduling between the corresponding sub-areas; Adjust the incentive state duration T, and repeat the above steps until the first total cost corresponding to all incentive state durations T is obtained; The incentive state duration T corresponding to the minimum first total cost and the optimal material scheduling plan for each sub-region are used as the final scheduling plan to schedule the material inventory of each sub-region.

8. The inventory allocation and scheduling method based on supply chain collaboration according to claim 7 is characterized in that: The specific calculation formula of the preset inventory balance prediction model is: The specific calculation formula for the sub-region material gap forecast data is: In the formula, is the predicted inventory balance of the z-th sub-region, is the capacity of the first target area in the incentive state, T is the duration of the first target area in the incentive state, is the number of sub-regions, is the predicted material gap in the zth sub-region, is the current inventory of the first target material in the z-th sub-area, It is the forecast quantity of the first target material demand in the zth sub-region.

9. The inventory allocation and scheduling method based on supply chain collaboration according to claim 8 is characterized in that: The preset calculation formula for material dispatch efficiency data between sub-regions is as follows: In the formula, is the material dispatch efficiency between sub-regions, is the efficiency factor of the ith means of transport, is the number of the i-th means of transport, is the number of transport types, is the transportation distance between the two sub-areas, is the road condition influencing factor, The number of approval nodes in the path.

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