Material management intelligent warehouse logistics planning and distribution plan overall scheduling method and system

Abstract

The invention belongs to the technical field of loading and maintenance material intelligent warehouses, and particularly discloses a material management intelligent warehouse logistics planning and distribution plan overall scheduling method and system, and the method comprises the following steps: collecting the dynamic data of logistics and distribution in real time, constructing a demand prediction model, and generating the material demand of a point location; generating a replenishment plan and a loading list of the materials according to the material demand quantity and inventory data of the point locations, and generating a global optimal distribution route through an optimization algorithm; and dynamically adjusting a global optimal distribution route by using real-time traffic information and distribution priority in the dynamic data. By adopting the technical scheme, the distribution meter is generated based on the demand prediction and the inventory data, and accurate distribution and path optimization of materials are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent warehouses for installation and maintenance materials, and relates to a method and system for coordinating and dispatching logistics planning and distribution plans for intelligent warehouses for material management. Background Art

[0002] With the application of intelligent warehouses for installation and maintenance materials, the distribution areas and locations are becoming more and more numerous and complex. Currently, the quantity of materials required at each location is reported manually based on experience. The existing logistics and distribution management of installation and maintenance materials mainly rely on manual operations, including the following methods:

[0003] 1. Demand reporting and distribution plan:

[0004] The material demand of each smart warehouse location is manually reported by the location personnel based on their experience. This lack of data support can easily lead to inaccurate demand estimation.

[0005] The distribution plan is manually determined by the central warehouse manager without considering the relationship between historical data, inventory status and actual demand.

[0006] 2. Logistics Planning:

[0007] Logistics transportation routes are determined by drivers based on their experience, without fully considering the point address, traffic conditions and loading capacity, resulting in a lack of optimization in route planning.

[0008] 3. Temporary transfer and return transportation:

[0009] When inventory at a certain location is insufficient or demand changes temporarily, goods need to be temporarily adjusted or re-delivered, increasing logistics costs and time consumption.

[0010] The distribution plan is manually determined by the central warehouse manager, and the logistics planning is determined by the logistics driver, resulting in the materials at each point being unable to meet the needs of the installation and maintenance personnel. Temporary transfers and re-delivery often occur, leading to a huge waste of manpower, time and logistics. Summary of the invention

[0011] The purpose of the present invention is to provide a method and system for coordinating and scheduling logistics planning and distribution plans for intelligent warehouses for material management, dynamically adjust logistics and distribution plans, and achieve accurate distribution and path optimization of materials.

[0012] In order to achieve the above-mentioned purpose, the basic scheme of the present invention is: a method for coordinating and dispatching logistics planning and distribution plan of intelligent warehouse for material management, comprising the following steps:

[0013] Collect dynamic data of logistics and distribution in real time;

[0014] Based on the dynamic data of logistics and distribution, a demand forecasting model for the point is constructed, and the material demand for the point is generated;

[0015] Generate material replenishment plans and loading lists based on material demand and inventory data at each location;

[0016] Based on the replenishment plan and loading list of materials, the global optimal delivery route is generated through optimization algorithm;

[0017] Utilize real-time traffic information and delivery priorities in dynamic data to dynamically adjust the globally optimal delivery route.

[0018] The working principle and beneficial effects of this basic solution are: This technical solution realizes accurate distribution and route optimization of materials through data collection, demand forecasting, logistics optimization and dynamic adjustment. It integrates the comprehensive data of each point to realize overall scheduling and reduce manual dependence.

[0019] Furthermore, the dynamic data of logistics and distribution is collected in real time, specifically:

[0020] Deploy barcode scanners or RFID readers at each smart warehouse location to automatically record the current inventory quantity of each type of material;

[0021] Call the inbound and outbound logs recorded in the smart warehouse system and extract usage data by time dimension;

[0022] Collect traffic information between points in real time, including real-time road conditions and whether there are any restrictions on the routes;

[0023] The geographical coordinates of the collection points, as well as loading restrictions.

[0024] Collect data on each point’s historical usage, current inventory, future demand, point address, loading quantity, traffic conditions, etc. to facilitate overall scheduling.

[0025] Furthermore, based on the dynamic data of logistics and distribution, a demand forecasting model for the point is constructed as follows:

[0026] Dfi=αDhi+ β Ds+γEt

[0027] Among them, D fi is the predicted material demand at point i; D hi represents the historical usage data of point i over a period of time; Ds is the seasonal factor; E t is a special event factor; α, β, γ are adjustable weight parameters;

[0028] If the inventory at a location is higher than the predicted material demand at that location, the delivery will be skipped;

[0029] If the inventory at a location is lower than the predicted material demand at that location, replenishment will be prioritized.

[0030] Demand forecasting is based on dynamic data of logistics and distribution, which requires less calculation data and is easy to operate.

[0031] Furthermore, according to the material demand and inventory data of the point, the steps to generate the material replenishment plan and loading list are as follows:

[0032] Combined with the predicted material demand D at point i fi And the current inventory Ici, calculate the replenishment quantity of point i:

[0033] Qpi=max(0,Dfi-Ici)

[0034] Among them, Q pi is the replenishment quantity at point i; if Q pi ≧Is, triggering the emergency purchase plan, where Is is the central warehouse inventory;

[0035] Consider the loading capacity C of each delivery vehicle υ and material volume V m , calculate the loading quantity of each material:

[0036]

[0037] Among them, N m is the loading quantity of each material, rounded down;

[0038] Generate a loading list, including: material types and quantities.

[0039] Goods are distributed based on the material demand and inventory data at each point, without the need for complex algorithm models and making calculations easy.

[0040] Furthermore, based on the replenishment plan and loading list of materials, the global optimal delivery route is generated through the optimization algorithm. The specific steps are as follows:

[0041] The model is a vehicle routing problem (VRP), which is solved by linear programming:

[0042]

[0043] Among them, C ij is the transportation cost from point i to point j; x ij A binary variable indicating whether to go from point i to point j;

[0044] Set up constraints:

[0045] The total load for each delivery route does not exceed the vehicle capacity:

[0046]

[0047] Each point must be visited once:

[0048]

[0049] Among them, Vm is the volume of materials, Cv is the loading capacity of each delivery vehicle, M, m; n represents the total number of points.

[0050] Based on the optimization algorithm, the route is adjusted according to the real-time road conditions and point requirements to achieve coordinated scheduling of logistics plans.

[0051] Furthermore, the real-time traffic information and delivery priority in the dynamic data are used to dynamically adjust the global optimal delivery route. The specific method is as follows:

[0052] Dynamically adjust the route based on the traffic condition index Tr and delivery priority Pu:

[0053] R new =f(T r ,P u )

[0054] f(T r ,P u )=ω1P u -ω2T r

[0055] Among them, R new Indicates the new priority of the point, f is a function expression; W1 and W2 are manually adjustable weight parameters; the delivery priority Pu of the point is determined according to the point delivery order, and the traffic condition index Tr of the point is obtained based on the map API service;

[0056] Real-time monitoring of inventory changes at each location. When inventory Ic is less than the threshold It, priority is given to replenishment:

[0057]

[0058] If the traffic condition index Tr>0, re-plan the route and notify the driver.

[0059] Provides real-time dynamic adjustment capabilities to quickly respond to inventory changes and traffic conditions.

[0060] The present invention also provides a material management intelligent warehouse logistics planning and distribution plan coordinated scheduling system based on the method of the present invention, including a data acquisition module, a demand forecasting module, a distribution plan module, a logistics optimization module, a dynamic adjustment module and a visual scheduling platform connected in sequence;

[0061] The data collection module is used to collect dynamic data related to logistics and distribution in real time, and the demand forecasting module is used to predict future demand by combining historical usage, inventory and special events to generate material demand at a point;

[0062] The distribution planning module generates a replenishment plan and loading list for materials based on demand forecasts and inventory data;

[0063] The logistics optimization module generates the global optimal delivery route through the optimization algorithm, and the dynamic adjustment module is used to monitor dynamic data such as inventory and traffic in real time and adjust the logistics and distribution plan;

[0064] The visual scheduling platform receives dynamically adjusted global optimal delivery route information and displays it.

[0065] This system achieves accurate distribution and route optimization of materials through modules such as data collection, demand forecasting, logistics optimization and dynamic adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flow chart of the coordinated dispatching method of the material management intelligent warehouse logistics planning and distribution plan of the present invention. DETAILED DESCRIPTION

[0067] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0068] In the description of the present invention, it is necessary to understand that the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0069] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0070] The present invention discloses a coordinated dispatching method for logistics planning and distribution plan of intelligent warehouse for material management, aiming to solve the problems of low efficiency of manual judgment, waste of resources and insufficient dynamic adjustment in the existing logistics and distribution process. The method of the present invention integrates the data of historical usage, current inventory, future demand, address, loading quantity, traffic conditions, etc. of each point to achieve coordinated dispatching and reduce manual dependence.

[0071] like Figure 1 As shown in the figure, the coordinated scheduling method of logistics planning and distribution plan of material management intelligent warehouse includes the following steps:

[0072] Collect dynamic data of logistics and distribution in real time;

[0073] Based on the dynamic data of logistics and distribution, a demand forecasting model for the point is constructed, and the material demand for the point is generated;

[0074] Generate material replenishment plans and loading lists based on material demand and inventory data at each location;

[0075] Based on the replenishment plan and loading list of materials, the global optimal delivery route is generated through optimization algorithm;

[0076] Utilize real-time traffic information and delivery priorities in dynamic data to dynamically adjust the globally optimal delivery route.

[0077] In a preferred embodiment of the present invention, the dynamic data of logistics and distribution is collected in real time, specifically:

[0078] Inventory data collection:

[0079] Deploy barcode scanners, RFID readers or QR code scanners at each smart warehouse location (attach QR code labels to materials at each location, obtain material information by scanning the QR code, and the warehouse management system updates material inventory in real time through handheld terminals or fixed scanning devices) to automatically record the current inventory quantity of each type of material;

[0080] Collection content: material ID, name, specifications; current inventory; maximum storage capacity and remaining storage space of the warehouse;

[0081] Data storage: regularly uploaded to the central warehouse database for subsequent algorithm calls.

[0082] Historical usage data collection:

[0083] Call the inbound and outbound logs recorded in the smart warehouse system and extract usage data by time dimension;

[0084] Data content: Daily / weekly / monthly material consumption, and peak usage periods (such as morning rush hour or specific projects).

[0085] Real-time traffic data collection:

[0086] Call third-party traffic APIs or vehicle-mounted GPS systems to collect real-time traffic information between points, including real-time road conditions (congestion level, travel time, etc.) and whether there are any restrictions such as closures or construction on the route;

[0087] Point attribute collection:

[0088] The geographical coordinates of the collection points, as well as loading restrictions (such as maximum load, door width, etc.).

[0089] In a preferred embodiment of the present invention, a demand forecasting model for a point is constructed based on the dynamic data of logistics and distribution, which is:

[0090] Dfi= α Dhi+ β Ds+γEt

[0091] Among them, D fi is the predicted material demand at point i; D hi Indicates the historical consumption data of point i over a period of time (such as the average consumption in the past 30 days); Fs is the seasonal factor (statistics of installation and maintenance demand data in the past few years, calculate the average value by quarter, and identify the cyclical change trend of demand, such as the increase in demand for materials in winter); E t is a special event factor (large-scale promotional activities of operators will significantly increase the demand for installation and maintenance, and new policies of the government or operators (such as broadband upgrade subsidies) may trigger installation peaks, such as large-scale network transformation projects); α, β, and γ are adjustable weight parameters. The proportion is adjusted according to historical data, actual business experience and data, and different weight combinations are tried. The weight parameters are gradually adjusted manually by verifying the accuracy of the results;

[0092] If the inventory at a location is higher than the predicted material demand at that location, the delivery will be skipped;

[0093] If the inventory at a location is lower than the predicted demand for materials at that location, replenishment will be prioritized;

[0094] The system is updated every hour in real time based on the latest data f , to adapt to changing needs.

[0095] In a preferred embodiment of the present invention, the steps of generating a replenishment plan and a loading list of materials according to the material demand and inventory data of the point are as follows:

[0096] Combined with the predicted material demand D at point i fi And the current inventory Ici, calculate the replenishment quantity of point i:

[0097] Qpi=max(0,Dfi-Ici)

[0098] Among them, Q pi is the replenishment quantity at point i; if Q pi ≧Is, triggering the emergency purchase plan, where Is is the central warehouse inventory;

[0099] Consider the loading capacity C of each delivery vehicle υ and material volume V m , calculate the loading quantity of each material:

[0100]

[0101] Among them, N m is the loading quantity of each material, rounded down;

[0102] Generate a loading list, including: material types and quantities.

[0103] In a preferred embodiment of the present invention, based on the replenishment plan and loading list of materials, a global optimal delivery route is generated through an optimization algorithm, and the specific steps are as follows:

[0104] The model is a vehicle routing problem (VRP), which is solved by linear programming:

[0105]

[0106] Among them, C ij is the transportation cost (distance, time, etc.) from point i to point j; ij A binary variable indicating whether to go from point i to point j;

[0107] Set up constraints:

[0108] The total load for each delivery route does not exceed the vehicle capacity:

[0109]

[0110] Each point must be visited once:

[0111]

[0112] Among them, Vm is the volume of materials, Cv is the loading capacity of each delivery vehicle, M, m; n represents the total number of points;

[0113] Use genetic algorithm to solve the point path optimization problem.

[0114] In a preferred embodiment of the present invention, the global optimal delivery route is dynamically adjusted by utilizing the real-time traffic information and delivery priority in the dynamic data. The specific method is as follows:

[0115] Dynamically adjust the route based on the traffic condition index Tr and delivery priority Pu:

[0116] R new =f(T r ,P u )

[0117] f(T r ,P u )=ω1P u -ω2T r

[0118] Among them, R new Indicates the new priority of the point (connecting the route according to the priority order), f is a function expression; W1 and W2 are manually adjustable weight parameters; the delivery priority Pu of the point is determined according to the delivery order of the point (the first delivery has the highest priority), and the traffic condition index Tr of the point is obtained based on the map API service (such as Amap, etc.);

[0119] The system obtains data such as traffic flow, vehicle speed, and accident information to determine whether the route needs to be adjusted. If the traffic situation deteriorates, the system will call the path planning algorithm (the previous VRP) to recalculate the optimal route, avoid congested sections, and choose an alternative route. In addition, the delivery priority (Pu) will also affect the route selection, giving priority to the delivery needs of urgent orders. The adjusted route will be notified to the driver in real time through the communication system.

[0120] Monitor the inventory changes of the point in real time. When the inventory I c is less than the threshold I t, give priority to replenishment:

[0121]

[0122] If the traffic condition index Tr>0, re-plan the route and notify the driver.

[0123] The present invention reduces the subjectivity of manual reporting and distribution planning, and provides a data-driven demand forecasting model. It also optimizes multi-point logistics distribution routes to solve the problems of inexperienced drivers or unreasonable route selection. It also provides real-time dynamic adjustment functions to quickly respond to inventory changes and traffic conditions, reducing the time and resource waste caused by temporary transfers or repeated distribution.

[0124] The present invention also provides a material management intelligent warehouse logistics planning and distribution plan coordinated scheduling system based on the method described in the present invention, including a data acquisition module, a demand forecasting module, a distribution plan module, a logistics optimization module, a dynamic adjustment module and a visual scheduling platform connected in sequence.

[0125] The data collection module is used to collect dynamic data related to logistics and distribution in real time. The demand forecasting module is used to combine historical usage, inventory and special events to predict future demand and generate material demand at each point. The distribution planning module generates replenishment plans and loading lists for materials based on demand forecasts and inventory data.

[0126] The logistics optimization module generates the globally optimal delivery route through optimization algorithms. The dynamic adjustment module is used to monitor dynamic data such as inventory and traffic in real time and adjust logistics and distribution plans.

[0127] The visual scheduling platform receives and displays the globally optimal delivery route information that is dynamically adjusted. It can also display inventory status, forecast values, and logistics routes in real time, and support administrators to manually modify replenishment plans or logistics routes. It also sets up data visualization tools, uses heat maps to display point demand, and dynamic maps to display vehicle locations and estimated arrival times.

[0128] The demand forecast of the present invention is more accurate: demand forecast is carried out based on multi-dimensional data to reduce the imbalance between supply and demand. Logistics efficiency is greatly improved: the transportation route is planned by using optimization algorithm to reduce cost and time consumption. Strong dynamic response ability: real-time adjustment of distribution and logistics plan to quickly adapt to demand and traffic changes. Management visualization: the whole process is made transparent through the scheduling platform to improve management efficiency.

[0129] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0130] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for coordinating and scheduling logistics planning and distribution plans for intelligent warehouses for material management, characterized in that: The steps include: Collect dynamic data of logistics and distribution in real time; Based on the dynamic data of logistics and distribution, a demand forecasting model for the point is constructed, and the material demand for the point is generated; Generate material replenishment plans and loading lists based on material demand and inventory data at each location; Based on the replenishment plan and loading list of materials, the global optimal delivery route is generated through optimization algorithm; Utilize real-time traffic information and delivery priorities in dynamic data to dynamically adjust the globally optimal delivery route.

2. The material management intelligent warehouse logistics planning and distribution plan coordinated scheduling method as claimed in claim 1 is characterized in that: Real-time collection of dynamic data on logistics and distribution, specifically: Deploy barcode scanners or RFID readers at each smart warehouse location to automatically record the current inventory quantity of each type of material; Call the inbound and outbound logs recorded in the smart warehouse system and extract usage data by time dimension; Collect traffic information between points in real time, including real-time road conditions and whether there are any restrictions on the routes; The geographical coordinates of the collection points, as well as loading restrictions.

3. The material management intelligent warehouse logistics planning and distribution plan coordinated scheduling method as claimed in claim 1 is characterized in that: Based on the dynamic data of logistics and distribution, a demand forecasting model for the point is constructed as follows: Dfi=αDhi+βDs+ γ Et Among them, D fi is the predicted material demand at point i; D hi represents the historical usage data of point i over a period of time; Ds is the seasonal factor; E t is a special event factor; α, β, γ are adjustable weight parameters; If the inventory at the point is higher than the predicted material demand at the point, the delivery will be skipped; If the inventory at a location is lower than the predicted material demand at that location, replenishment will be prioritized.

4. The material management intelligent warehouse logistics planning and distribution plan coordinated scheduling method as claimed in claim 1 is characterized in that: According to the material demand and inventory data of the point, the steps to generate the material replenishment plan and loading list are as follows: Combined with the predicted material demand D at point i fi And the current inventory Ici, calculate the replenishment quantity of point i: Qpi=max(0,Dfi-Ici) Among them, Q pi is the replenishment quantity at point i; if Q pi ≧Is, triggering the emergency purchase plan, where Is is the central warehouse inventory; Consider the loading capacity C of each delivery vehicle υ and material volume V m , calculate the loading quantity of each material: Among them, N m is the loading quantity of each material, rounded down; Generate a loading list, including: material types and quantities.

5. The material management intelligent warehouse logistics planning and distribution plan coordinated scheduling method as claimed in claim 1 is characterized in that: Based on the replenishment plan and loading list of materials, the global optimal distribution route is generated through the optimization algorithm. The specific steps are as follows: The model is a vehicle routing problem (VRP), which is solved by linear programming: Among them, C ij is the transportation cost from point i to point j; x ij A binary variable indicating whether to go from point i to point j; Set up constraints: The total load for each delivery route does not exceed the vehicle capacity: Each point must be visited once: Among them, Vm is the volume of materials, Cv is the loading capacity of each delivery vehicle, M represents the number of material types, m represents the number of the currently traversed material type ranging from 1 to M, and n represents the total number of points.

6. The material management intelligent warehouse logistics planning and distribution plan coordinated scheduling method as claimed in claim 1 is characterized in that: Using real-time traffic information and delivery priorities in dynamic data, dynamically adjust the global optimal delivery route. The specific method is as follows: Based on the traffic condition index Tr and delivery priority Pu, the route is adjusted dynamically: R new =f(T r ,P u ) f(T r ,P u )=ω1P u -ω2T r Among them, R new Indicates the new priority of the point, f is a function expression; W1 and W2 are manually adjustable weight parameters; the delivery priority Pu of the point is determined according to the point delivery order, and the traffic condition index Tr of the point is obtained based on the map API service; Real-time monitoring of inventory changes at each location. When inventory Ic is less than the threshold It, priority is given to replenishment: If the traffic condition index Tr>0, re-plan the route and notify the driver.

7. A material management intelligent warehouse logistics planning and distribution plan coordination and dispatching system based on the method of any one of claims 1 to 6, characterized in that: It includes a data collection module, a demand forecasting module, a distribution planning module, a logistics optimization module, a dynamic adjustment module and a visual scheduling platform connected in sequence; The data collection module is used to collect dynamic data related to logistics and distribution in real time, and the demand forecasting module is used to predict future demand by combining historical usage, inventory and special events to generate material demand at a point; The distribution planning module generates a replenishment plan and loading list for materials based on demand forecasts and inventory data; The logistics optimization module generates the global optimal delivery route through the optimization algorithm, and the dynamic adjustment module is used to monitor dynamic data such as inventory and traffic in real time and adjust the logistics and distribution plan; The visual scheduling platform receives dynamically adjusted global optimal delivery route information and displays it.

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