A method and system for optimizing material allocation strategy

Abstract

The present invention discloses a method and system for optimizing material allocation strategy, which includes the following steps: collecting and preprocessing data related to material allocation; predicting material demand; formulating inventory optimization strategy; based on the above content, generating an optimal material allocation plan based on the critical path priority scheduling algorithm. The present invention can achieve more accurate demand forecasting, more efficient inventory management and more optimized allocation decision-making.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics network, and in particular to a method and system for optimizing material allocation strategy. Background Art

[0002] The existing technology mainly includes the following methods in terms of material allocation strategy, each of which has its limitations or shortcomings:

[0003] 1. Allocation method based on experience

[0004] The allocation decision of this method mainly relies on the experience and intuition of managers. Its shortcomings include:

[0005] 1. Subjectivity: Due to reliance on personal experience, it is easily influenced by subjective judgment and lacks objectivity.

[0006] 2. Instability: Different managers have different experiences, which may lead to inconsistency in transfer strategies.

[0007] 3. Inefficiency: Lack of systematic data analysis makes it difficult to cope with the complex and changing material demand environment.

[0008] 2. Fixed cycle inventory method

[0009] This method counts inventory regularly and allocates materials according to preset rules. The shortcomings include:

[0010] 1. Delayed response: Fixed-cycle inventory cannot respond to changes in demand in a timely manner, which may lead to inventory backlogs or shortages.

[0011] 2. Inventory waste: Due to the inability to accurately predict demand, it is often necessary to maintain a high safety stock, resulting in a waste of resources.

[0012] 3. Rule-based transfer system

[0013] This system guides the allocation of materials by setting a series of rules. Its limitations are as follows:

[0014] 1. Poor flexibility: Once the rules are set, it is difficult to change them and it is difficult to adapt to the dynamic changes in market demand.

[0015] 2. Local optimization: Rules are usually designed for specific situations, which may result in the overall allocation strategy being suboptimal.

[0016] 4. Allocation method based on simple statistical model

[0017] This method uses statistical models such as moving average, exponential smoothing, etc. to forecast demand. Its shortcomings include:

[0018] 1. Limited accuracy: Simple statistical models have difficulty handling complex demand patterns such as nonlinearity and seasonality.

[0019] 2. Data requirements: These models usually require a large amount of stable historical data, which is difficult to meet in many cases.

[0020] In summary, the prior art has the following deficiencies:

[0021] 1. Lack of real-time and dynamic performance: Existing technologies are difficult to respond to changes in demand in real time, and allocation strategies are not updated in a timely manner.

[0022] 2. Limited forecasting accuracy: Simple statistical models cannot accurately predict complex and changing demand.

[0023] 3. Low system integration: There is a lack of effective integration between different allocation methods, making it difficult to form a unified optimization strategy.

[0024] 4. Low resource utilization: Inadequate forecasting and decision-making often lead to inventory backlogs or reduced service levels. Summary of the invention

[0025] The purpose of the present invention is to provide a method for optimizing material allocation strategy in order to address the problems existing in the above-mentioned prior art, thereby achieving more accurate demand forecasting, more efficient inventory management and more optimized allocation decision-making.

[0026] The technical solution to achieve the purpose of the present invention is: a method for optimizing material allocation strategy, the method comprising the following steps:

[0027] Step 1: Collect and pre-process data related to material allocation;

[0028] Step 2: Forecast material demand;

[0029] Step 3: Develop inventory optimization strategy;

[0030] Step 4: Generate the optimal material allocation plan based on the above content.

[0031] Furthermore, the data related to material allocation in step 1 includes:

[0032] Material demand data, including historical consumption, seasonal factors and market trends;

[0033] Inventory data, including on-hand inventory, inventory turnover, and safety stock;

[0034] Transportation capacity data, including transportation distance, transportation time, transportation cost and transportation vehicle capacity.

[0035] Furthermore, the preprocessing in step 1 includes:

[0036] Data cleaning: remove invalid, erroneous and duplicate data;

[0037] Data standardization: converting data of different dimensions into a unified format;

[0038] Data integration: Merge data from different sources to form a unified data set.

[0039] Furthermore, the predicted material demand in step 2 specifically includes:

[0040] Construct a training set based on the material demand data collected in step 1, train a machine learning-based method, and obtain a material demand model;

[0041] The material demand model is used to predict material demand:

[0042] D_p = F(D_h, T)

[0043] In the formula, D_h represents historical material demand data, T represents the forecast period, and D_p represents the forecast demand.

[0044] Furthermore, the inventory optimization strategy in step 3 includes:

[0045] (1) Determine safety stock:

[0046] S = Z * σ

[0047] In the formula, S is the safety stock, Z is the service level coefficient, and σ is the standard deviation of material demand;

[0048] (2) Determine the reorder point

[0049] R = D_p + S

[0050] Where D_p is the forecast demand and R is the reorder point.

[0051] Furthermore, step 4 specifically includes:

[0052] Step 4-1, calculate the transfer demand:

[0053] Q = R - I

[0054] In the formula, Q is the demand, R is the reorder point, and I is the existing inventory;

[0055] Step 4-2, construct a critical path priority scheduling algorithm, through which the critical path, i.e., the optimal material allocation plan, is obtained; the critical path priority scheduling algorithm must meet the following requirements:

[0056] (1) Each scheduling tool is treated as a different entity;

[0057] (2) At the same time, a single dispatching tool can only carry the maximum amount of materials it can transport;

[0058] (3) Each scheduled task must be started after all its associated predecessor tasks are completed.

[0059] Furthermore, the critical path priority scheduling algorithm described in step 4-2 specifically includes:

[0060] Algorithm input: Job set X = { x 1 , x 2 , . . . , x n}, x n Indicates n task; predecessor task set Before = { Before 1 , Before 2 , . . . , Pre n}, Before n Indicates the job x n Corresponding predecessor activity; transfer activity time set T ={ t 1 , t 2 , . . . , t n}, t n Indicates the job x n Corresponding transfer time; Scheduling toolset NO = { with 1 , not 2 , . . . , not m}, with m Indicates m Scheduling tools; Scheduling tool set corresponding to the job XMA = { wax 1 , wax 2 , . . . , wax n}, wax n Indicates the job x n Corresponding scheduling tools;

[0061] Step 4-2-1, initialize the dispatch capacity and start operation time set MAT = { mat 1, mat 2, . . . , matn}, mat i = 0, j = 1, 2, . . . , n , mat i Indicates i The available start time of each job; the set of start scheduling times for each job is S = { s 1, s 2, . . . , s n}, No. i Job start scheduling time s i = 0, i = 1, 2, .. . , n , the completion time set of each job scheduling is F = { f 1, f 2, . . . , f n}, No. i Job scheduling completion time f i = 0, i = 1, 2, . . . , n , the set of jobs to be scheduled is SX = { x 1, x 2, . . . , x n’}, x n’ Indicates the first n’ jobs to be scheduled;

[0062] Step 4-2-2, calculate the earliest scheduling start time set of the job through the earliest scheduling start and completion algorithm ES ={ yes 1, yes 2, . . . , yes n}, and the earliest dispatch completion time of the repair list Bef ;in, yes n Indicates n The earliest scheduled start time of a job;

[0063] Step 4-2-3, calculate the latest start scheduling time set of the job through the latest scheduling start and completion algorithm LS ={ls 1, ls 2, . . . , ls n}, ls n Indicates n The latest scheduled start time of a job;

[0064] Step 4-2-4, calculate the total time difference set of the job through the total time difference algorithm RTD = { rtd 1, rtd 2, . . . , rtd n}, rtd n Indicates n The total time difference of each job;

[0065] Step 4-2-5, calculate the critical path set CP according to the critical path algorithm = { CP 1, CP 2, . . . , CP q}, CP q Indicates q Critical paths;

[0066] Step 4-2-6, determine whether the critical path set CP includes only one critical path. If so, the priority scheduling job is the starting job of the critical path, recorded as x l∗ , whose subscript in the job set is l ∗; otherwise, the priority scheduling job is the starting job with the longest operation time among all the starting jobs of the critical path, recorded as x l∗ ;

[0067] Step 4-2-7, prioritize the jobs x l∗ The preceding task Before l∗ The subscript set of all jobs in is set to ;

[0068] Step 4-2-8, prioritize the jobs x l∗ The corresponding scheduling tool wax l∗ In the Scheduling Toolset NO The lower corner of the l ♯;

[0069] Step 4-2-9, calculate the priority scheduling job x l∗Start time:

[0070] ;

[0071] That is, the maximum value is taken for the completion time of the immediate predecessor and the time when the scheduling tool can start scheduling;

[0072] Step 4-2-10, calculate the priority scheduling job x l∗ Completion time:

[0073] f l∗ = s l∗ + t l∗

[0074] In the formula, t l∗ Prioritize job scheduling x l∗ The transfer time;

[0075] Step 4-2-11, Calculation and Scheduling Tools wax l∗ The available scheduling time is:

[0076] mat l♯ = f l∗

[0077] Step 4-2-12, remove from the set of jobs to be scheduled and the set of jobs x l∗ , removed from the predecessor set Before l∗ , and update the predecessor activities of the remaining activities, removing them from the activity time set t l∗ , remove from the set of required scheduling tools corresponding to the job wax l∗ ;

[0078] Step 4-2-13, repeat steps 4-2-2 to 4-2-12 until the set of jobs to be scheduled is SX is empty;

[0079] Step 4-2-14, output the start time of each job S , the completion time of each task F , and the scheduling tool set corresponding to each operation together constitute the optimal material allocation plan.

[0080] Furthermore, the latest scheduling start and completion algorithm described in step 4-2-3 is specifically as follows:

[0081] Algorithm input: Job set X = { x 1 , x 2 , . . . , x n}, transfer operation time set T = { t 1 , t 2 , . . . , t n}, the next job set Location = { Location 1 , Location 2 , . . . , Location n}, Location n Indicates the job x n The corresponding next task; the earliest completion time of the transfer plan Bef ;

[0082] Step 4-2-3-1, initialize the uncalculated job set to Unfinish ={ x 1 , x 2 , . . . , x n}, the calculated job set is Finish = φ , the latest transfer start time set for the job is LS = { ls 1, ls 2, . . . , ls n}, among which i The latest transfer start time of each job ls i = 0, i = 1, 2, . . . , n ; The latest completion time of the operation LF = { lf 1, lf 2, . . . , lf n}, among which i The latest completion time of the operation lf i = 0;

[0083] Step 4-2-3-2, through the calculated job set Finish Finding Uncalculated Job Sets Unfinish Computable tasks in x l∗ , and itsX The corresponding subscript l ∗;

[0084] Step 4-2-3-3, the work x l∗ The next job Location l∗ The subscript set of all jobs in is set to The Place l∗ ;

[0085] Step 4-2-3-4, never calculate the job set Unfinish Remove job x l∗ ;

[0086] Step 4-2-3-5, judgement Location l∗ Is it empty? If so, calculate the job. x l∗ The latest transfer completion time ; Otherwise, calculate the job x l∗ The latest transfer completion time , that is, the minimum value of the latest transfer start time of all subsequent operations;

[0087] Step 4-2-3-6, calculation work x l∗ The latest start time ls l∗ :

[0088] ;

[0089] Homework x l∗ The latest transfer completion time minus the transfer time of the activity;

[0090] Step 4-2-3-7, in the calculated job set Finish Add a job x l∗ ;

[0091] Step 4-2-3-8, repeat steps 4-2-3-2 to 4-2-3-7 until no job set is calculated Unfinish is empty;

[0092] Step 4-2-3-9, output the latest transfer start time set of the job LS , the latest completion time of the operation LF.

[0093] Compared with the prior art, the present invention has the following significant advantages:

[0094] (1) The resource-constrained critical path method is introduced to consider resource availability and constraints when calculating the critical path. Resource conflict detection: It can identify and solve the problem of over-allocation of resources. More realistic schedule: The schedule after considering resource constraints is more in line with the actual situation. Resource balancing: By adjusting the start time of tasks, the balance of resource utilization is achieved.

[0095] (2) Consider multiple goals in critical path calculation, such as cost, quality, risk, etc. Comprehensive decision-making: Balance multiple goals such as time, cost, quality, etc. to achieve the best project results. Maximize value: Not just the shortest time, but the maximization of overall value. Multi-dimensional analysis: Provide a more comprehensive evaluation perspective.

[0096] (3) The algorithms are more applicable, accurate, and flexible, which helps to plan, execute, and control solutions more effectively.

[0097] The present invention is further described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 The present invention is a flow chart of the method for optimizing material allocation strategy. DETAILED DESCRIPTION

[0099] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0100] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0101] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0102] In one embodiment, a method for optimizing material allocation strategy is provided, the method comprising the following steps:

[0103] Step 1: Collect and pre-process data related to material allocation;

[0104] Step 2: Forecast material demand;

[0105] Step 3: Develop inventory optimization strategy;

[0106] Step 4: Generate the optimal material allocation plan based on the above content.

[0107] Furthermore, in one embodiment, the data related to material allocation in step 1 includes:

[0108] Material demand data, including historical consumption, seasonal factors, and market trends;

[0109] Inventory data, including on-hand inventory, inventory turnover, and safety stock;

[0110] Transportation capacity data, including transportation distance, transportation time, transportation cost and transportation vehicle capacity.

[0111] Further, in one embodiment, the preprocessing in step 1 includes:

[0112] Data cleaning: remove invalid, erroneous and duplicate data;

[0113] Data standardization: converting data of different dimensions into a unified format;

[0114] Data integration: Merge data from different sources to form a unified data set.

[0115] Furthermore, in one embodiment, the predicting of material demand in step 2 specifically includes:

[0116] Construct a training set based on the material demand data collected in step 1, train a machine learning-based method, and obtain a material demand model;

[0117] The material demand model is used to predict material demand:

[0118] D_p = F(D_h, T)

[0119] In the formula, D_h represents historical material demand data, T represents the forecast period, and D_p represents the forecast demand.

[0120] Furthermore, in one embodiment, the inventory optimization strategy in step 3 includes:

[0121] (1) Determine safety stock:

[0122] S = Z * σ

[0123] In the formula, S is the safety stock, Z is the service level coefficient, and σ is the standard deviation of material demand;

[0124] (2) Determine the reorder point

[0125] R = D_p + S

[0126] Where D_p is the forecast demand and R is the reorder point.

[0127] Further, in one embodiment, step 4 specifically includes:

[0128] Step 4-1, calculate the transfer demand:

[0129] Q = R - I

[0130] In the formula, Q is the demand, R is the reorder point, and I is the existing inventory;

[0131] Step 4-2, construct a critical path priority scheduling algorithm, through which the critical path, i.e., the optimal material allocation plan, is obtained; the critical path priority scheduling algorithm must meet the following requirements:

[0132] (1) Each scheduling tool is treated as a different entity;

[0133] (2) At the same time, a single dispatching tool can only carry the maximum amount of materials it can transport;

[0134] (3) Each scheduled task must be started after all its associated predecessor tasks are completed.

[0135] Further, in one embodiment, the critical path priority scheduling algorithm described in step 4-2 specifically includes:

[0136] Algorithm input: Job set X = { x 1 , x 2 , . . . , x n}, x n Indicates n task; predecessor task set Before = { Before 1 , Before 2 , . . . , Pre n}, Before n Indicates the job x n Corresponding predecessor activity; transfer activity time set T ={t 1 , t 2 , . . . , t n}, t n Indicates the job x n Corresponding transfer time; Scheduling toolset NO = { with 1 , not 2 , . . . , not m}, with m Indicates m Scheduling tools; Scheduling tool set corresponding to the job XMA = { wax 1 , wax 2 , . . . , wax n}, wax n Indicates the job x n Corresponding scheduling tools;

[0137] Step 4-2-1, initialize the dispatch capacity and start operation time set MAT = { mat 1, mat 2, . . . , mat n}, mat i = 0, j = 1, 2, . . . , n , mat i Indicates i The available start time of each job, and the start scheduling time set of each job is S = { s 1, s 2, . . . , s n}, No. i Job start scheduling time s i = 0, i = 1, 2, . . . , n , the completion time set of each job scheduling is F = { f 1, f 2, . . . , f n}, No. i Job scheduling completion time fi = 0, i = 1, 2, . . . , n , the set of jobs to be scheduled is SX = { x 1, x 2, . . . , x n’}, x n’ Indicates the first n’ jobs to be scheduled;

[0138] Step 4-2-2, calculate the earliest scheduling start time set of the job through the earliest scheduling start and completion algorithm ES ={ yes 1, yes 2, . . . , yes n}, and the earliest dispatch completion time of the repair list Bef ;in, yes n Indicates n The earliest scheduled start time of a job;

[0139] Step 4-2-3, calculate the latest start scheduling time set of the job through the latest scheduling start and completion algorithm LS ={ ls 1, ls 2, . . . , ls n}, ls n Indicates n The latest scheduled start time of a job;

[0140] Step 4-2-4, calculate the total time difference set of the job through the total time difference algorithm RTD = { rtd 1, rtd 2, . . . , rtd n}, rtd n Indicates n The total time difference of each job;

[0141] Step 4-2-5, calculate the critical path set CP according to the critical path algorithm = { CP 1, CP 2, . . . , CP q}, CP q Indicates q Critical paths;

[0142] Step 4-2-6, determine whether the critical path set CP includes only one critical path. If so, the priority scheduling job is the starting job of the critical path, recorded as x l∗ , whose subscript in the job set is l ∗; otherwise, the priority scheduling job is the starting job with the longest operation time among all the starting jobs of the critical path, recorded as x l∗ ;

[0143] Step 4-2-7, prioritize the jobs x l∗ The preceding task Before l∗ The subscript set of all jobs in is set to ;

[0144] Step 4-2-8, prioritize the jobs x l∗ The corresponding scheduling tool wax l∗ In the Scheduling Toolset NO The lower corner of the l ♯;

[0145] Step 4-2-9, calculate the priority scheduling job x l∗ Start time:

[0146]

[0147] That is, the maximum value is taken for the completion time of the immediate predecessor and the time when the scheduling tool can start scheduling;

[0148] Step 4-2-10, calculate the priority scheduling job x l∗ Completion time:

[0149] f l∗ = s l∗ + t l∗

[0150] In the formula, t l∗ Prioritize job scheduling x l∗ The transfer time;

[0151] Step 4-2-11, Calculation and Scheduling Tools wax l∗ The available scheduling time is:

[0152] mat l♯ = f l∗

[0153] Step 4-2-12, remove from the set of jobs to be scheduled and the set of jobs x l∗ , removed from the predecessor set Before l∗ , and update the predecessor activities of the remaining activities, removing them from the activity time set t l∗ , remove from the set of required scheduling tools corresponding to the job wax l∗ ;

[0154] Step 4-2-13, repeat steps 4-2-2 to 4-2-12 until the set of jobs to be scheduled is SX is empty;

[0155] Step 4-2-14, output the start time of each job S , the completion time of each task F , and the scheduling tool set corresponding to each operation together constitute the optimal material allocation plan.

[0156] Furthermore, in one embodiment, the latest scheduling start and completion algorithm in step 4-2-3 is specifically:

[0157] Algorithm input: Job set X = { x 1 , x 2 , . . . , x n}, transfer operation time set T = { t 1 , t 2 , . . . , t n}, the next job set Location = { Location 1 , Location 2 , . . . , Location n}, Location n Indicates the job x n The corresponding next task; the earliest completion time of the transfer plan Bef ;

[0158] Step 4-2-3-1, initialize the uncalculated job set to Unfinish ={ x 1 , x 2 , . . . , xn}, the calculated job set is Finish = φ , the latest transfer start time set for the job is LS = { ls 1, ls 2, . . . , ls n}, among which i The latest transfer start time of each job ls i = 0, i = 1, 2, . . . , n ; The latest completion time of the operation LF = { lf 1, lf 2, . . . , lf n}, among which i The latest completion time of the operation lf i = 0;

[0159] Step 4-2-3-2, through the calculated job set Finish Finding Uncalculated Job Sets Unfinish Computable tasks in x l∗ , and its X The corresponding subscript l ∗;

[0160] Step 4-2-3-3, the work x l∗ The next job Location l∗ The subscript set of all jobs in is set to The Place l∗ ;

[0161] Step 4-2-3-4, never calculate the job set Unfinish Remove job x l∗ ;

[0162] Step 4-2-3-5, judgement Location l∗ Is it empty? If so, calculate the job. x l∗ The latest transfer completion time ; Otherwise, calculate the job x l∗ The latest transfer completion time , that is, the minimum value of the latest transfer start time of all subsequent operations;

[0163] Step 4-2-3-6, calculation work x l∗ The latest start time ls l∗ :

[0164] ;

[0165] Homework x l∗ The latest transfer completion time minus the transfer time of the activity;

[0166] Step 4-2-3-7, in the calculated job set Finish Add a job x l∗ ;

[0167] Step 4-2-3-8, repeat steps 4-2-3-2 to 4-2-3-7 until no job set is calculated Unfinish is empty;

[0168] Step 4-2-3-9, output the latest transfer start time set of the job LS , the latest completion time of the operation LF.

[0169] In one embodiment, a system for optimizing material allocation strategy is provided, the system comprising:

[0170] The first module is used to collect data related to material allocation and perform pre-processing;

[0171] The second module is used to predict material needs;

[0172] The third module is used to develop inventory optimization strategies;

[0173] The fourth module is used to generate the optimal material allocation plan based on the above content.

[0174] For the specific definition of the optimized material allocation strategy system, please refer to the definition of the optimized material allocation strategy system method above, which will not be repeated here. Each module in the above-mentioned optimized material allocation strategy system can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0175] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following is achieved:

[0176] Step 1: Collect and pre-process data related to material allocation;

[0177] Step 2: Forecast material demand;

[0178] Step 3: Develop inventory optimization strategy;

[0179] Step 4: Generate the optimal material allocation plan based on the above content.

[0180] For the specific limitations of each step, please refer to the limitations of the method for optimizing material allocation strategy mentioned above, which will not be repeated here.

[0181] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements:

[0182] Step 1: Collect and pre-process data related to material allocation;

[0183] Step 2: Forecast material demand;

[0184] Step 3: Develop inventory optimization strategy;

[0185] Step 4: Generate the optimal material allocation plan based on the above content.

[0186] For the specific limitations of each step, please refer to the limitations of the method for optimizing material allocation strategy mentioned above, which will not be repeated here.

[0187] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing material allocation strategy, characterized in that: The method comprises the following steps: Step 1: Collect and pre-process data related to material allocation; Step 2: Forecast material demand; Step 3: Develop inventory optimization strategy; Step 4: Generate the optimal material allocation plan based on the above content; specifically including: Step 4-1, calculate the transfer demand: Q = R - I In the formula, Q is the demand, R is the reorder point, and I is the existing inventory; Step 4-2, construct a critical path priority scheduling algorithm, through which the critical path, i.e., the optimal material allocation plan, is obtained; the critical path priority scheduling algorithm must meet the following requirements: (1) Each scheduling tool is treated as a different entity; (2) At the same time, a single dispatching tool can only carry the maximum amount of materials it can transport; (3) Each scheduled task can only be started after all its associated predecessor tasks are completed; The critical path priority scheduling algorithm described in step 4-2 specifically includes: Algorithm input: Job set X = { x 1 , x 2 , . . . , x n }, x n Indicates n task; predecessor task set Pre ={ Pre 1 , Pre 2 , . . . , Pre n }, Pre n Indicates the job x n Corresponding predecessor activity; transfer activity time set T = { t 1 , t 2 , . . . , t n }, t n Indicates the job x n Corresponding transfer time; Scheduling toolset MA = { ma 1 , ma 2 , . . . , ma m }, ma m Indicates m Scheduling tools; Scheduling tool set corresponding to the job XMA = { x 1 , xma 2 , . . . , xma n }, x n Indicates the job x n Corresponding scheduling tools; Step 4-2-1, initialize the dispatch capacity and start operation time set MAT = { mat 1, mat 2, . . . , mat n }, mat i = 0, j = 1, 2, . . . , n , mat i Indicates i The available start time of each job; the set of start scheduling times for each job is S = { s 1, s 2, . . . , s n }, No. i Job start scheduling time s i = 0, i = 1, 2, . .. , n , the completion time set of each job scheduling is F = { f 1, f 2, . . . , f n }, No. i Job scheduling completion time f i = 0, i = 1, 2, . . . , n , the set of jobs to be scheduled is SX = { x 1, x 2, . . . , x n }, x n’ Indicates the first n’ jobs to be scheduled; Step 4-2-2, calculate the earliest scheduling start time set of the job through the earliest scheduling start and completion algorithm ES = { es 1, es 2, . . . , es n }, and the earliest dispatch completion time of the repair list Bef ;in, es n Indicates n The earliest scheduled start time of a job; Step 4-2-3, calculate the latest start scheduling time set of the job through the latest scheduling start and completion algorithm LS = { ls 1, ls 2, . . . , ls n }, ls n Indicates n The latest scheduled start time of a job; Step 4-2-4, calculate the total time difference set of the job through the total time difference algorithm RTD = { rtd 1, rtd 2, . . . , rtd n }, rtd n Indicates n The total time difference of each job; Step 4-2-5, calculate the critical path set CP according to the critical path algorithm = { CP 1, CP 2, . . . , CP q }, CP q Indicates q Critical paths; Step 4-2-6, determine whether the critical path set CP includes only one critical path. If so, the priority scheduling job is the starting job of the critical path, recorded as x l∗ , whose subscript in the job set is l ∗; otherwise, the priority scheduling job is the starting job with the longest operation time among all the starting jobs of the critical path, recorded as x l∗ ; Step 4-2-7, prioritize the jobs x l∗ The preceding task Pre l∗ The subscript set of all jobs in is set to ; Step 4-2-8, prioritize the jobs x l∗ The corresponding scheduling tool x l∗ In the Scheduling Toolset MA The lower corner of the l ♯; Step 4-2-9, calculate the priority scheduling job x l∗ Start time: ； That is, the maximum value is taken for the completion time of the immediate predecessor and the time when the scheduling tool can start scheduling; Step 4-2-10, calculate the priority scheduling job x l∗ Completion time: f l∗ = s l∗ + t l∗ In the formula, t l∗ Prioritize job scheduling x l∗ The transfer time; Step 4-2-11, Calculation and Scheduling Tools x l∗ The available scheduling time is: mat l♯ = f l∗ Step 4-2-12, remove from the set of jobs to be scheduled and the set of jobs x l∗ , removed from the predecessor set Pre l∗ , and update the predecessor activities of the remaining activities, removing them from the activity time set t l∗ , remove from the set of required scheduling tools corresponding to the job x l∗ ; Step 4-2-13, repeat steps 4-2-2 to 4-2-12 until the set of jobs to be scheduled is SX is empty; Step 4-2-14, output the start time of each job S , the completion time of each task F , and the scheduling toolset corresponding to each operation together constitutes the optimal material allocation plan; The latest scheduling start and completion algorithm described in step 4-2-3 is specifically as follows: Algorithm input: Job set X = { x 1 , x 2 , . . . , x n }, transfer operation time set T = { t 1 , t 2 , . . . , t n }, the next job set Post = { Post 1 , Post 2 , . . . , Post n }, Post n Indicates the job x n The corresponding next task; the earliest completion time of the transfer plan Bef ; Step 4-2-3-1, initialize the uncalculated job set to Unfinish ={ x 1 , x 2 , . . . , x n }, the calculated job set is Finish = φ , the latest transfer start time set for the job is LS = { ls 1, ls 2, . . . , ls n }, among which i The latest transfer start time of each job ls i = 0, i = 1, 2, . . . , n ; The latest completion time of the operation LF ={ lf 1, lf 2, . . . , lf n }, among which i The latest completion time of the operation lf i = 0; Step 4-2-3-2, through the calculated job set Finish Finding Uncalculated Job Sets Unfinish Computable tasks in x l∗ , and its X The corresponding subscript l ∗; Step 4-2-3-3, the work x l∗ The next job Post l∗ The subscript set of all jobs in is set to LPost l∗ ; Step 4-2-3-4, never calculate the job set Unfinish Remove job x l∗ ; Step 4-2-3-5, judgement Post l∗ Is it empty? If so, calculate the job. x l∗ The latest transfer completion time ; Otherwise, calculate the job x l∗ The latest transfer completion time , that is, the minimum value of the latest transfer start time of all subsequent operations; Step 4-2-3-6, calculation work x l∗ The latest start time ls l∗ : ； Homework x l∗ The latest transfer completion time minus the transfer time of the activity; Step 4-2-3-7, in the calculated job set Finish Add a job x l∗ ; Step 4-2-3-8, repeat steps 4-2-3-2 to 4-2-3-7 until no job set is calculated Unfinish is empty; Step 4-2-3-9, output the latest transfer start time set of the job LS , the latest completion time of the operation LF.

2. The method for optimizing material allocation strategy according to claim 1, characterized in that: The data related to material transfer described in step 1 includes: Material demand data, including historical consumption, seasonal factors and market trends; Inventory data, including on-hand inventory, inventory turnover, and safety stock; Transportation capacity data, including transportation distance, transportation time, transportation cost and transportation vehicle capacity.

3. The method for optimizing material allocation strategy according to claim 2 is characterized in that: The preprocessing in step 1 includes: Data cleaning: remove invalid, erroneous and duplicate data; Data standardization: converting data of different dimensions into a unified format; Data integration: Merge data from different sources to form a unified data set.

4. The method for optimizing material allocation strategy according to claim 2 is characterized in that: The predicted material demand in step 2 specifically includes: Construct a training set based on the material demand data collected in step 1, train a machine learning-based method, and obtain a material demand model; The material demand model is used to predict material demand: D_p = F(D_h, T) In the formula, D_h represents historical material demand data, T represents the forecast period, and D_p represents the forecast demand.

5. The method for optimizing material allocation strategy according to claim 1, characterized in that: The inventory optimization strategy described in step 3 includes: (1) Determine safety stock: S = Z * σ In the formula, S is the safety stock, Z is the service level coefficient, and σ is the standard deviation of material demand; (2) Determine the reorder point R = D_p + S Where D_p is the forecast demand and R is the reorder point.

6. A material allocation strategy optimization system based on the method according to any one of claims 1 to 5, characterized in that: The system comprises: The first module is used to collect data related to material allocation and perform pre-processing; The second module is used to predict material needs; The third module is used to develop inventory optimization strategies; The fourth module is used to generate the optimal material allocation plan based on the above content.

7. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

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