A material management method and system based on intelligent warehousing

Abstract

The present invention provides a material management method and system based on intelligent warehousing, which relates to the technical field of management systems. The method comprises: constructing an intelligent material storage model with the goal of reducing the material warehousing cost; acquiring data of materials to be warehousing; solving the intelligent material storage model to determine the optimal storage position of each material to be warehousing; warehousing the materials according to the optimal storage position; determining the material demand data of each workstation this time according to the current inventory in the buffer area; dynamically determining the earliest service time and the latest service time of each workstation; setting a unit time penalty cost when a vehicle arrives at the workstation in advance and a unit time penalty cost when a vehicle arrives at the workstation late; constructing an intelligent material distribution model with the goal of reducing material distribution cost and penalty cost; solving the intelligent material distribution model to determine the optimal material distribution plan; and dispatching materials according to the optimal material distribution plan.

Description

Technical Field

[0001] The present invention relates to the technical field of management systems, and in particular to a material management method and system based on intelligent warehousing. Background Art

[0002] Smart warehousing realizes automation and intelligence of warehousing management by combining advanced technologies. It can dynamically monitor inventory status, optimize material warehousing and distribution processes, effectively reduce operating costs, improve resource utilization and work efficiency, and at the same time improve the response speed and accuracy of the supply chain, providing important support for modern logistics and production systems.

[0003] Existing smart warehousing solutions mainly use RFID, sensors and monitoring equipment to obtain inventory data and material locations in real time, and take materials on demand when they are needed.

[0004] However, most existing technologies rely on static planning models and lack the ability to dynamically optimize for changes in real-time inventory and production demand. They often replenish materials at each workstation at a fixed time frequency and lack accurate monitoring of the real-time inventory and consumption rate of production stations, which can easily lead to material accumulation or material shortages, and the mismatch between material distribution and production demand, affecting production efficiency. Summary of the invention

[0005] In order to solve the technical problems that most of the current planning models rely on static planning models and lack the ability to dynamically optimize the real-time inventory and production demand changes, materials are often replenished at each workstation at a fixed time frequency, and there is a lack of accurate monitoring of the real-time inventory and consumption rate of the production workstation, which easily leads to material accumulation or material shortage, and the mismatch between material distribution and production demand, thereby affecting production efficiency, the present invention provides a material management method and system based on intelligent warehousing.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect:

[0008] An embodiment of the present invention provides a material management method based on intelligent warehousing, comprising:

[0009] S1: Build an intelligent material storage model with the goal of reducing material warehousing costs;

[0010] S2: Get the data of materials to be put into storage;

[0011] S3: Solving the intelligent material storage model to determine the optimal storage location for each material to be stored;

[0012] S4: Putting materials into storage according to the optimal storage position;

[0013] S5: Get the current inventory of the buffer area of ​​each workstation;

[0014] S6: Determine the material demand data of each workstation based on the current inventory in the buffer area;

[0015] S7: According to the material demand data of each workstation, dynamically determine the earliest service time and the latest service time of each workstation;

[0016] S8: Set the unit time penalty cost when the vehicle arrives at the workstation ahead of time and the unit time penalty cost when the vehicle arrives at the workstation late;

[0017] S9: constructing an intelligent material distribution model with the goal of reducing material distribution costs and the penalty costs;

[0018] S10: Solving the intelligent material distribution model to determine the optimal material distribution plan;

[0019] S11: According to the optimal material distribution plan, materials are shipped out.

[0020] Second aspect:

[0021] An embodiment of the present invention provides a material management system based on intelligent warehousing, comprising:

[0022] processor;

[0023] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the material management method based on intelligent warehousing as described in the first aspect is implemented.

[0024] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0025] In the present invention, the real-time inventory and consumption rate of the production stations are accurately monitored, and the earliest service time and the latest service time of each station can be dynamically determined according to the material demand data of each station. The optimal material distribution plan is determined with the goal of reducing the material distribution cost and the penalty cost, thereby improving the dynamic optimization capability for real-time inventory and production demand changes, avoiding material accumulation or material shortages, improving the matching degree of material distribution with production demand, and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A schematic diagram of a flow chart of a material management method based on intelligent warehousing provided by an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of the structure of a material management system based on intelligent warehousing provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0030] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0031] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0032] Reference Manual Attached Figure 1 , showing a flow chart of a material management method based on intelligent warehousing provided in an embodiment of the present invention.

[0033] The embodiment of the present invention provides a material management method based on intelligent warehousing, which may include the following steps:

[0034] S1: Build an intelligent material storage model with the goal of reducing material warehousing costs.

[0035] In a possible implementation, S1 specifically includes sub-steps S101 and S102:

[0036] S101: With the goal of minimizing the material warehousing cost, construct the objective function of the intelligent material storage model.

[0037] Optionally, the objective function of the intelligent material storage model is specifically:

[0038]

[0039] Where min means minimization, f1 means the material storage objective function, G means the material storage plan, G={g uv}, guv indicates whether the u-th material is stored in the v-th storage area. When the u-th material is stored in the v-th storage area, g uv=1, when the uth material is not stored in the vth storage area g uv =0,H uv represents the transportation cost of the u-th material to the v-th storage area, B v represents the space balance penalty cost of the vth storage area, z v Indicates whether the vth storage area is selected as the storage area. When the vth storage area is selected as the storage area, z v = 1, when the vth storage area is not selected as the storage area z v =0, U represents the total number of materials to be stored, and V represents the total number of storage areas.

[0040] Optionally, the handling cost is specifically:

[0041] H uv =w u d v h0

[0042] Among them, w u Indicates the weight of the u-th material, d v It represents the length of the transportation path from the warehouse entrance to the vth storage area, and h0 represents the cost of transporting material of unit weight per unit distance.

[0043] Optionally, the spatial balance penalty cost is specifically:

[0044]

[0045] Among them, γ represents the penalty factor, P u represents the volume of the u-th material, S v Represents the available volume of the vth storage area.

[0046] It should be noted that the calculation of handling costs takes into account the weight of materials and the length of the route, effectively reducing transportation consumption. The spatial balance penalty cost achieves balanced use of warehouse resources by adjusting the utilization rate of the storage area to avoid overcrowding or waste of resources.

[0047] In the present invention, by constructing an accurate objective function, the warehousing cost is decomposed into the handling cost and the space balance penalty cost, and the material storage location can be dynamically optimized. It can not only significantly reduce the overall cost of material warehousing, but also improve the utilization efficiency of storage space, and at the same time create a more efficient basic condition for subsequent logistics operations.

[0048] S102: Set constraints for the intelligent material storage model.

[0049] Optionally, the constraints of the intelligent material storage model include: a first storage constraint and a second storage constraint.

[0050] Furthermore, the first storage constraint condition is specifically:

[0051]

[0052] Among them, g uv Indicates whether the u-th material is stored in the v-th storage area. When the u-th material is stored in the v-th storage area, g uv =1, when the uth material is not stored in the vth storage area g uv =0, V represents the total number of storage areas.

[0053] It should be noted that the first storage constraint ensures that each material must and can only be assigned to one storage area. This constraint prevents materials from being stored repeatedly or not stored, helps maintain the integrity and accuracy of storage operations, and ensures that all materials have a clear storage location.

[0054] Furthermore, the second storage constraint condition is specifically:

[0055]

[0056] Among them, P u represents the volume of the u-th material, S v Represents the available volume of the vth storage area.

[0057] It should be noted that the second storage constraint condition limits the total volume of materials stored in each storage area to not exceed the available capacity of the area. This constraint ensures the rational use of storage areas, avoids overloading, improves storage safety and resource management efficiency, and ensures the feasibility of the allocation plan.

[0058] S2: Get the data of materials to be put into storage.

[0059] S3: Solve the intelligent material storage model to determine the optimal storage location for each material to be stored.

[0060] Optionally, heuristic algorithms such as genetic algorithms, particle swarm optimization algorithms, simulated annealing algorithms, etc. can be used to solve the intelligent material storage model to determine the best storage location for each material to be stored.

[0061] Since traditional genetic algorithms are good at global search but lack local search capabilities, it is difficult to fully explore the local optimal solution in the solution space within a limited time. Therefore, the present invention introduces a flower pollination algorithm based on the genetic algorithm, and after the introduction of the flower pollination algorithm, the search strategy can be flexibly adjusted according to the complexity of the problem and the optimization stage.

[0062] The optimization objective function can be used as the fitness function of flower pollination combined with genetic optimization algorithm.

[0063] Initialize the population, which contains multiple individuals. Each individual represents a feasible storage location solution. Each individual has multiple components, and each component represents a storage location for a material to be stored.

[0064] An elite selection strategy is adopted to remove the 40% of individuals with the lowest fitness values ​​to form the first population.

[0065] In the present invention, adopting an elite selection strategy and removing the 40% individuals with the lowest fitness values ​​helps to quickly improve the overall quality of the population and focus computing resources on more promising solutions, thereby accelerating the optimization process. By eliminating inferior individuals, the population can be prevented from falling into local searches for low-quality solutions, and the global search capability and convergence efficiency of the algorithm can be enhanced. At the same time, this strategy can also improve the diversity of the population, reduce redundant searches, enhance adaptability to complex problems, and ensure that the optimization process is more efficient and robust.

[0066] Perform a crossover operation on the individuals in the first population to form a second population:

[0067] Y1=rand×X1+(1-rand)×X2

[0068] Y2=rand×X2+(1-rand)×X1

[0069] Among them, Y1 and Y2 represent new individuals, X1 represents the first parent, X2 represents the second parent, and rand represents a random number between 0 and 1.

[0070] In the present invention, by performing a crossover operation on the individuals in the first population, the excellent characteristics of different parents can be combined together through genetic recombination to generate new individuals with greater potential, thereby enhancing the diversity and adaptability of the population.

[0071] Perform mutation operations on individuals in the second population to form a third population:

[0072]

[0073] Among them, Y3 represents the new individual, X3 represents the parent, and X max represents the individual with the largest fitness value, X min represents the individual with the smallest fitness value, and rand represents a random number between 0 and 1.

[0074] In the present invention, mutation operation is performed on individuals in the second population to guide them to approach the optimal solution, and random factors are introduced to increase the exploration diversity of the solution space. It can not only enhance the local search ability and promote the convergence of individuals to high-quality solutions, but also break the singleness of the population through mutation to prevent the optimization process from falling into the local optimum too early. Mutation operation plays an important role in balancing the quality and diversity of the population, and helps to improve the global optimization performance of the algorithm.

[0075] The individuals in the third population are updated according to the flower pollination optimization algorithm to form the fourth population. A random number is randomly generated to determine whether the conversion probability is greater than the random number. If so, cross-pollination is performed. Otherwise, self-pollination is performed.

[0076] When cross-pollination occurs, the individual positions are updated according to the Levy flight mechanism:

[0077]

[0078] in, represents the position of the i-th individual at the t+1th iteration, represents the position of the i-th individual at the t-th iteration, θ represents the step length influencing factor, L represents the step length, represents the global optimal solution at the tth iteration.

[0079]

[0080] Among them, Γ represents the standard Gamma function, λ represents the exponential parameter, and s represents the scale parameter.

[0081] In the present invention, the Lévy flight mechanism is used to update the individual position during the cross-pollination process, and the long-tail distribution characteristics of the Lévy flight can be used to generate a large random step length, thereby realizing a wide range of jumping search. This search method can effectively explore new areas in the solution space and avoid premature convergence to the local optimal solution. At the same time, the step length is guided by the global optimal solution and the step length influencing factor, so that the search has directionality and gradually approaches the global optimal solution. By combining the advantages of global search and random exploration, the Lévy flight mechanism can significantly improve the global exploration ability and convergence efficiency of the optimization algorithm.

[0082] Optionally, the step length influencing factor is specifically:

[0083]

[0084] Among them, θ t It represents the step size influencing factor at the tth iteration, q represents the scaling factor, and T represents the maximum number of iterations.

[0085] In the present invention, the step size is dynamically adjusted so that the step size gradually decreases as the number of iterations increases. In the early stage of optimization, a larger step size helps to explore the solution space extensively and improve the global search capability; while in the later stage of optimization, a smaller step size facilitates the refinement of local search and improves the accuracy of the solution.

[0086] When self-pollination occurs, the individual positions are updated according to the golden sine mechanism:

[0087]

[0088] x1=-π+2π(1-τ)

[0089] x2=-π+2πτ

[0090] in, represents the position of the jth randomly selected individual at the tth iteration, represents the position of the randomly selected kth individual at the tth iteration, r1 and r2 represent random numbers between 0 and 2π, x1 and x2 represent the self-pollination coefficients, and τ represents the golden section number.

[0091] In the present invention, the sinusoidal fluctuations of the golden sine mechanism introduce periodic changes, avoid the monotonicity of individual position updates, and can more flexibly explore the possibility of different solutions, thereby improving the convergence accuracy and stability of the optimization algorithm, and is particularly suitable for local optimization problems in complex solution spaces.

[0092] The third and fourth populations are combined to form the fifth population.

[0093] Determine whether the current number of iterations has reached the maximum number of iterations; if so, output the storage location plan for the individual representative with the highest fitness in the fifth population; otherwise, return to continue iterating.

[0094] In the present invention, the introduction of the flower pollination algorithm and the combination of the genetic algorithm solution method can effectively improve the balance between global optimization and local search. Through the global exploration characteristics of the genetic algorithm, a diverse solution space is generated, and the search strategy is flexibly adjusted in combination with the flower pollination algorithm. The cross-pollination mechanism of Levy flight is used to enhance the jumping search capability, and the self-pollination mechanism of the golden sine is used to finely optimize the local solution, thereby improving the convergence speed and the quality of the solution. In addition, elite selection and grouping operations ensure that the high-quality solutions of the population are retained, further enhancing the robustness and adaptability of the algorithm. This improved method can efficiently find solutions close to the global optimal in complex optimization problems, and is particularly suitable for dynamic and multi-constrained material storage scenarios.

[0095] S4: Put materials into storage according to the best storage location.

[0096] S5: Get the current inventory of the buffer area of ​​each workstation.

[0097] S6: Determine the material demand data for each workstation based on the current inventory in the buffer area.

[0098] In a possible implementation, S6 specifically includes: determining the material demand data of each workstation this time according to the current inventory and the safety inventory in the buffer area:

[0099] Q it =SS i -NOW i

[0100] Among them, Q it represents the material demand for the tth delivery at the i-th station, SS i represents the safety stock of the ith workstation, NOW i Represents the real-time inventory of the i-th workstation.

[0101] In the present invention, the material demand of the workstation is determined dynamically, which can reflect the inventory situation of the buffer area in real time and ensure that the material supply of the workstation is highly matched with the actual demand.

[0102] S7: According to the material demand data of each workstation, dynamically determine the earliest service time and the latest service time of each workstation.

[0103] In a possible implementation, S7 specifically includes sub-steps S701 and S702:

[0104] S701: Determine the time window length of each workstation:

[0105]

[0106] Among them, Length i Indicates the time window length of the i-th station, MIN i represents the minimum inventory of the ith station, r i Represents the material consumption rate of the i-th workstation.

[0107] In the present invention, the time window length is calculated based on the minimum inventory and consumption rate of the workstation to ensure that the material supply meets the workstation demand within a reasonable time.

[0108] S702: Determine the earliest service time and the latest service time of each workstation based on the minimum inventory, safety inventory, and the amount of material last allocated to the workstation:

[0109]

[0110] L it=E it +Length i

[0111] Among them, E it represents the earliest service time of the tth delivery of the i-th station, E i,t-1 represents the earliest service time of the t-1th delivery of the i-th station, Q i,t-1 represents the amount of material delivered to the ith station for the t-1th time, L it It represents the latest service time of the tth delivery to the i-th station.

[0112] In the present invention, the earliest and latest service times are dynamically adjusted in combination with historical delivery data and safety inventory to ensure the accuracy and flexibility of delivery.

[0113] S8: Set the unit time penalty cost when the vehicle arrives at the workstation ahead of schedule and the unit time penalty cost when the vehicle arrives at the workstation late.

[0114] In the present invention, setting the unit time penalty cost for vehicles arriving early or late at the workstation can effectively guide the optimization algorithm to pay more attention to the time matching with the workstation demand when planning the delivery route and time. The penalty cost of early arrival can reduce the waiting time and resource waste caused by premature arrival, while the penalty cost of late arrival encourages on-time delivery and avoids production stagnation or efficiency reduction due to delays. By quantifying the cost impact of time deviation, this method can balance delivery efficiency and production demand, and improve the timeliness and economy of the entire logistics system.

[0115] S9: Build an intelligent material distribution model with the goal of reducing material distribution costs and penalty costs.

[0116] In a possible implementation, S9 specifically includes sub-steps S901 and S902:

[0117] S901: Construct the objective function of the intelligent material distribution model with the goal of minimizing the material distribution cost and penalty cost.

[0118] Optionally, the objective function of the intelligent material distribution model is specifically:

[0119]

[0120] Where min means minimization, f means the objective function, X means the material distribution plan, X={x ijk}, x ijk Indicates whether the kth vehicle moves from the ith station to the jth station. When the kth vehicle moves from the ith station to the jth station, x ijk =1, when the kth vehicle does not move from the ith station to the jth station xijk =0, C F represents the fixed cost of each delivery vehicle, C V represents the vehicle transportation cost per unit distance, d ij represents the driving distance from the i-th station to the j-th station, a represents the penalty cost per unit time when the vehicle arrives at the station ahead of schedule, max represents the maximum value, and E it represents the earliest service time of the tth delivery of the i-th station, t i represents the time when the vehicle actually arrives at the i-th station, b represents the penalty cost per unit time when the vehicle is delayed in arriving at the station, and L it represents the latest service time of the tth delivery at the i-th station, N represents the total number of stations, and K represents the total number of vehicles.

[0121] In the present invention, the objective function combines fixed costs and transportation costs with time penalty costs to ensure that the distribution plan is not only efficient in path selection, but also accurately matches the time requirements of the production station. The penalty costs of early arrival and late arrival further promote the optimization algorithm to be more reasonable in time scheduling, avoiding waiting waste caused by premature arrival and production interruptions caused by delays. This method can improve the timeliness and accuracy of material supply while reducing logistics operating costs, ensuring smooth production.

[0122] S902: Set constraints for the intelligent material distribution model.

[0123] Optionally, the constraints of the intelligent material distribution model include: a first distribution constraint, a second distribution constraint, a third distribution constraint, a fourth distribution constraint, a fifth distribution constraint, a sixth distribution constraint and a seventh distribution constraint.

[0124] Furthermore, the first delivery constraint condition is specifically:

[0125]

[0126] Among them, x 0jk indicates that the kth vehicle departs from the warehouse and arrives at the jth station, x i0k Indicates that the kth vehicle returns to the warehouse from the i-th workstation.

[0127] It should be noted that the first delivery constraint ensures that each delivery vehicle departs from the warehouse and returns to the warehouse after completing the task, ensuring the integrity of the delivery process and avoiding the situation where the vehicle task is not assigned or returned.

[0128] Furthermore, the second delivery constraint condition is specifically:

[0129]

[0130] Among them, y ik It means that the delivery task of the i-th station is performed by the k-th vehicle.

[0131] It should be noted that the second delivery constraint ensures that the delivery task of each workstation can only be performed by one vehicle, avoiding duplicate delivery or non-delivery, and improving the accuracy and efficiency of task allocation.

[0132] Furthermore, the third delivery constraint condition is specifically:

[0133]

[0134] It should be noted that the third delivery constraint ensures that each workstation can only be visited once, avoiding vehicles from repeatedly arriving at the same workstation or missing a workstation, and ensuring the rationality of the delivery path.

[0135] Furthermore, the fourth delivery constraint condition is specifically:

[0136]

[0137] Among them, y jk It means that the delivery task of the j-th workstation is performed by the k-th vehicle.

[0138] It should be noted that the fourth delivery constraint ensures that the vehicle's path is consistent with the task allocation, maintains the logical continuity between the delivery task and the actual path, and avoids the disconnection between path planning and task allocation.

[0139] Furthermore, the fifth delivery constraint condition is specifically:

[0140]

[0141] Among them, Q it represents the material demand for the tth delivery at the i-th workstation, and Q represents the maximum load capacity of the vehicle.

[0142] It should be noted that the fifth delivery constraint ensures that the material delivery volume of the vehicle does not exceed its maximum load capacity, avoiding safety risks and mission failures caused by overloading, and ensuring the feasibility of the delivery mission.

[0143] Furthermore, the sixth delivery constraint condition is specifically:

[0144]

[0145] in, represents the time when the vehicle arrives at the i-th station, represents the time when the vehicle leaves the i-th station, tw i represents the waiting time of the vehicle at the i-th station, tf irepresents the service time of the vehicle at the i-th station, t ij It represents the travel time of the vehicle from the i-th station to the j-th station.

[0146] It should be noted that the sixth delivery constraint ensures the accuracy of time scheduling, avoids time conflicts, and improves delivery efficiency by defining the time relationship between the vehicle's arrival and departure from the workstation.

[0147] Furthermore, the seventh delivery constraint condition is specifically:

[0148]

[0149] Among them, d ij represents the driving distance from the i-th station to the j-th station, and S represents the maximum driving distance of the vehicle.

[0150] It should be noted that the seventh delivery constraint condition limits the driving distance of the vehicle within a delivery cycle to no more than its maximum feasible range, ensuring the operability of the path planning and avoiding affecting the completion of the task due to exceeding the driving capacity.

[0151] S10: Solve the intelligent material distribution model to determine the optimal material distribution plan.

[0152] Optionally, heuristic algorithms such as genetic algorithms, particle swarm optimization algorithms, simulated annealing algorithms, etc. can be used to solve the intelligent material distribution model and determine the best material distribution plan.

[0153] Furthermore, referring to the solution method for the intelligent material storage model described above, a flower pollination algorithm can be introduced on the basis of the genetic algorithm to solve the intelligent material distribution model. To avoid repetition, the present invention will not go into details.

[0154] S11: Ship materials out of the warehouse according to the best material distribution plan.

[0155] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0156] In the present invention, the real-time inventory and consumption rate of the production stations are accurately monitored, and the earliest service time and the latest service time of each station can be dynamically determined according to the material demand data of each station. The optimal material distribution plan is determined with the goal of reducing the material distribution cost and the penalty cost, thereby improving the dynamic optimization capability for real-time inventory and production demand changes, avoiding material accumulation or material shortages, improving the matching degree of material distribution with production demand, and improving production efficiency.

[0157] Reference Manual Attached Figure 2 , showing a structural schematic diagram of a material management system based on intelligent warehousing provided by the present invention.

[0158] The present invention also provides a material management system 20 based on intelligent warehousing, comprising:

[0159] Processor 201;

[0160] The memory 202 stores computer-readable instructions, and when the computer-readable instructions are executed by the processor 201, the material management method based on intelligent warehousing as described in the method embodiment is implemented.

[0161] The smart warehousing-based material management system 20 provided in the present invention can execute the above-mentioned smart warehousing-based material management method and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0162] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0163] In the present invention, the real-time inventory and consumption rate of the production stations are accurately monitored, and the earliest service time and the latest service time of each station can be dynamically determined according to the material demand data of each station. The optimal material distribution plan is determined with the goal of reducing the material distribution cost and the penalty cost, thereby improving the dynamic optimization capability for real-time inventory and production demand changes, avoiding material accumulation or material shortages, improving the matching degree of material distribution with production demand, and improving production efficiency.

[0164] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0165] There are a few points to note:

[0166] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0167] (2) For the sake of clarity, the thickness of a film or region in the drawings used to describe the embodiments of the present invention is enlarged or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a film, a region or a substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0168] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0169] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A material management method based on intelligent warehousing, characterized in that: include: S1: Build an intelligent material storage model with the goal of reducing material warehousing costs; S2: Get the data of materials to be put into storage; S3: Solving the intelligent material storage model to determine the optimal storage location for each material to be stored; S4: Putting materials into storage according to the optimal storage position; S5: Get the current inventory of the buffer area of ​​each workstation; S6: Determine the material demand data of each workstation based on the current inventory in the buffer area; S7: According to the material demand data of each workstation, dynamically determine the earliest service time and the latest service time of each workstation; S8: Set the unit time penalty cost when the vehicle arrives at the workstation ahead of time and the unit time penalty cost when the vehicle arrives at the workstation late; S9: constructing an intelligent material distribution model with the goal of reducing material distribution costs and the penalty costs; S10: Solving the intelligent material distribution model to determine the optimal material distribution plan; S11: According to the optimal material distribution plan, materials are shipped out of the warehouse.

2. The material management method based on intelligent warehousing according to claim 1 is characterized in that: The S1 specifically includes: S101: constructing an objective function of the intelligent material storage model with the goal of minimizing the material storage cost; S102: Setting constraints of the intelligent material storage model.

3. The material management method based on intelligent warehousing according to claim 2 is characterized in that: The objective function of the intelligent material storage model is specifically: Where min means minimization, f1 means the material storage objective function, G means the material storage plan, G={g uv }, guv indicates whether the u-th material is stored in the v-th storage area. When the u-th material is stored in the v-th storage area, g uv =1, when the u-th material is not stored in the v-th storage area g uv =0,H uv represents the transportation cost of the u-th material to the v-th storage area, B v represents the space balance penalty cost of the vth storage area, z v Indicates whether the vth storage area is selected as the storage area. When the vth storage area is selected as the storage area, z v = 1, when the vth storage area is not selected as the storage area z v =0, U represents the total number of materials to be stored, and V represents the total number of storage areas.

4. The material management method based on intelligent warehousing according to claim 3 is characterized in that: The constraints of the intelligent material storage model include: a first storage constraint and a second storage constraint; The first storage constraint condition is specifically: Among them, g uv Indicates whether the u-th material is stored in the v-th storage area. When the u-th material is stored in the v-th storage area, g uv =1, when the u-th material is not stored in the v-th storage area g uv =0, V represents the total number of storage areas; The second storage constraint condition is specifically: Among them, P u represents the volume of the u-th material, S v Represents the available volume of the vth storage area.

5. The material management method based on intelligent warehousing according to claim 1 is characterized in that: The S6 is specifically: Based on the current inventory and safety stock in the buffer area, determine the material demand data for each workstation: Q it =SS i -NOW i Among them, Q it represents the material demand for the tth delivery at the i-th station, SS i represents the safety stock of the ith workstation, NOW i Represents the real-time inventory of the i-th workstation.

6. The material management method based on intelligent warehousing according to claim 5 is characterized in that: The S7 specifically includes: S701: Determine the time window length of each workstation: Among them, Length i Indicates the time window length of the i-th station, MIN i represents the minimum inventory of the ith station, r i represents the material consumption rate of the i-th station; S702: Determine the earliest service time and the latest service time of each workstation based on the minimum inventory, safety inventory, and the amount of material last allocated to the workstation: L it =E it +Length i Among them, E it represents the earliest service time of the tth delivery of the i-th station, E i,t-1 represents the earliest service time of the t-1th delivery of the i-th station, Q i,t-1 represents the amount of material delivered to the ith station for the t-1th time, L it It represents the latest service time of the tth delivery to the i-th station.

7. The material management method based on intelligent warehousing according to claim 1 is characterized in that: The S9 specifically includes: S901: constructing an objective function of the intelligent material distribution model with the goal of minimizing the material distribution cost and the penalty cost; S902: Setting constraints of the intelligent material distribution model.

8. The material management method based on intelligent warehousing according to claim 7 is characterized in that: The objective function of the intelligent material distribution model is specifically: Where min means minimization, f means the objective function, X means the material distribution plan, X={x ijk }, x ijk Indicates whether the kth vehicle moves from the ith station to the jth station. When the kth vehicle moves from the ith station to the jth station, x ijk =1, when the kth vehicle does not move from the ith station to the jth station x ijk =0, C F represents the fixed cost of each delivery vehicle, C V represents the vehicle transportation cost per unit distance, d ij represents the driving distance from the i-th station to the j-th station, a represents the penalty cost per unit time when the vehicle arrives at the station ahead of schedule, max represents the maximum value, and E it represents the earliest service time of the tth delivery of the i-th station, t i represents the time when the vehicle actually arrives at the i-th station, b represents the penalty cost per unit time when the vehicle arrives at the station late, and L it represents the latest service time of the tth delivery at the i-th station, N represents the total number of stations, and K represents the total number of vehicles.

9. The material management method based on intelligent warehousing according to claim 8 is characterized in that: The constraints of the intelligent material distribution model include: a first distribution constraint, a second distribution constraint, a third distribution constraint, a fourth distribution constraint, a fifth distribution constraint, a sixth distribution constraint and a seventh distribution constraint; The first delivery constraint condition is specifically: Among them, x 0jk indicates that the kth vehicle departs from the warehouse and arrives at the jth station, x i0k Indicates that the kth vehicle returns to the warehouse from the i-th station; The second delivery constraint condition is specifically: Among them, y ik Indicates that the delivery task of the i-th station is performed by the k-th vehicle; The third delivery constraint condition is specifically: The fourth delivery constraint condition is specifically: Among them, y jk It means that the delivery task of the j-th station is performed by the k-th vehicle; The fifth delivery constraint condition is specifically: Among them, Q it represents the material demand for the tth delivery at the i-th station, and Q represents the maximum load of the vehicle; The sixth delivery constraint condition is specifically: in, represents the time when the vehicle arrives at the i-th station, represents the time when the vehicle leaves the i-th station, tw i represents the waiting time of the vehicle at the i-th station, tf i represents the service time of the vehicle at the i-th station, t ij represents the travel time of the vehicle from the i-th station to the j-th station; The seventh delivery constraint condition is specifically: Among them, d ij represents the driving distance from the i-th station to the j-th station, and S represents the maximum driving distance of the vehicle.

10. A material management system based on intelligent warehousing, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the material management method based on intelligent warehousing as described in any one of claims 1 to 7 is implemented.

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