A method, apparatus, equipment, and storage medium for inventory management of railway spare parts.

Abstract

This invention relates to the field of spare parts management technology, and provides a method, apparatus, equipment, and storage medium for railway spare parts inventory management. The method includes: constructing an objective function; using the objective function to calculate the minimum total cost corresponding to at least one inventory management strategy; determining inventory management constraints for railway spare parts; iteratively updating a preset number of first chromosome individuals based on the objective function, inventory management constraints, and a preset iteration termination condition to obtain a target chromosome individual at iteration termination; the first chromosome individuals and the target chromosome individual represent the corresponding inventory management strategies; and determining the target inventory management strategy based on the target chromosome individual. This invention improves the reliability of railway spare parts inventory management.

Description

Technical Field

[0001] This invention relates to the field of spare parts management technology, and in particular to a method, apparatus, equipment and storage medium for inventory management of railway spare parts. Background Technology

[0002] Urban populations are growing rapidly, leading to the continuous expansion of urban areas and the ever-increasing travel demands of residents. Railways, as a mode of transportation that enhances the convenience of residents' travel, are a key focus of urban development and construction. To ensure the systematic and orderly operation of railways after they are put into service, safety must be guaranteed. To meet the needs of routine planned maintenance and repairs, and to minimize equipment downtime and losses, it is necessary to procure spare parts in advance. Insufficient spare parts inventory may be insufficient to handle emergency repairs; excessive inventory will increase ordering and storage costs. Therefore, scientifically and rationally controlling and optimizing spare parts inventory, and improving inventory management efficiency, is of great significance for improving the overall operational level of rail transit.

[0003] In existing technologies, railway spare parts management mostly relies on manual experience and qualitative analysis to determine inventory management strategies. However, relying on manual experience to determine railway spare parts inventory management strategies has poor reliability. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and storage medium for railway spare parts inventory management, which addresses the shortcomings of poor reliability in existing technologies and improves the reliability of railway spare parts inventory management.

[0005] In a first aspect, the present invention provides a method for inventory management of railway spare parts, the method comprising:

[0006] Construct an objective function; the objective function is used to calculate the minimum total cost corresponding to at least one inventory management strategy.

[0007] Determine the constraints for railway spare parts inventory management;

[0008] Based on the objective function, the inventory management constraints, and the preset iteration termination condition, a preset number of first chromosome individuals are iteratively updated to obtain the target chromosome individual at the time of iteration termination; the first chromosome individual and the target chromosome individual represent the corresponding inventory management strategy.

[0009] Based on the target chromosome individuals, a target inventory management strategy is determined.

[0010] According to the present invention, a method for managing railway spare parts inventory includes an inventory management strategy determined based on a first ordering point and a first order quantity of spare parts within an ordering cycle. The inventory management strategy characterizes ordering the first order quantity of spare parts when the spare parts inventory quantity is less than or equal to the first ordering point. The method involves iteratively updating a preset number of first chromosome individuals based on the objective function, the inventory management constraints, and a preset iteration termination condition to obtain the target chromosome individual at the time of iteration termination.

[0011] Initialize the preset number of first chromosome individuals; the inventory management strategy is represented by a first code and a second code, the first code being used to characterize the first order point corresponding to the inventory management strategy, and the second code being used to characterize the first order quantity corresponding to the inventory management strategy.

[0012] Using the objective function, the first fitness score of the inventory management strategy corresponding to each individual of the first chromosome is calculated;

[0013] Based on the first fitness score of the inventory management strategy corresponding to each first chromosome individual and the inventory management constraints, determine the preset number of second chromosome individuals corresponding to the current iteration number; the inventory management constraints include at least one of the following: the ordering cycle duration is greater than or equal to the order lead time duration, the first reorder point is greater than or equal to 0, the first order quantity is greater than or equal to the number of spare parts failures, and the spare parts availability rate within the ordering cycle is greater than a preset availability rate threshold.

[0014] For any given iteration, the preset number of second chromosome individuals corresponding to the current iteration are determined as the third chromosome individuals corresponding to the next iteration.

[0015] Based on the preset iteration termination condition and the third chromosome individual corresponding to the next iteration, determine the preset number of second chromosome individuals corresponding to the iteration termination;

[0016] The target chromosome individual is determined based on the preset number of second chromosome individuals corresponding to the termination of the iteration and the target function.

[0017] According to the present invention, a method for inventory management of railway spare parts, wherein determining the target chromosome individual based on the preset number of second chromosome individuals corresponding to the termination of the iteration and the objective function includes:

[0018] Determine the respective inventory management strategies corresponding to each individual of the second chromosome;

[0019] Using the objective function, calculate the second fitness score corresponding to each of the inventory management strategies for each individual of the second chromosome;

[0020] The individuals with the highest second fitness scores are sorted from largest to smallest, and the individual with the highest second fitness score is identified as the target chromosome individual.

[0021] According to a railway spare parts inventory management method provided by the present invention, the step of determining the preset number of second chromosome individuals corresponding to the current iteration number based on the first fitness score of the inventory management strategy corresponding to each first chromosome individual and the inventory management constraints includes:

[0022] Using a preset crossover algorithm, crossover operations are performed on each of the first chromosome individuals to obtain a preset number of fourth chromosome individuals;

[0023] Using a preset mutation strategy, mutation operations are performed on each of the fourth chromosome individuals to obtain a preset number of fifth chromosome individuals;

[0024] Based on the first fitness score of the inventory management strategy corresponding to each of the fifth chromosome individuals and the inventory management constraints, determine the preset number of second chromosome individuals corresponding to the current iteration number.

[0025] According to the present invention, a method for inventory management of railway spare parts, wherein determining a target inventory management strategy based on the target chromosome individual includes:

[0026] Determine the first code and the second code corresponding to the target chromosome individual;

[0027] Based on the first code corresponding to the target chromosome individual, determine the second ordering point corresponding to the target chromosome individual;

[0028] Based on the second code corresponding to the target chromosome individual, determine the second order quantity corresponding to the target chromosome individual;

[0029] The target inventory management strategy is determined based on the second ordering point corresponding to the target chromosome individual and the second ordering quantity corresponding to the target chromosome individual.

[0030] According to the present invention, a method for inventory management of railway spare parts includes constructing an objective function, comprising:

[0031] Based on the procurement costs, ordering costs, storage costs, stockout loss costs, and consumption costs corresponding to the inventory management strategy, determine the total cost corresponding to the inventory management strategy;

[0032] The objective function is obtained by minimizing the total cost corresponding to the inventory management strategy.

[0033] Secondly, the present invention also provides an inventory management device for railway spare parts, the device comprising the following modules:

[0034] A determination module is used to construct an objective function; the objective function is used to calculate the minimum total cost corresponding to at least one inventory management strategy; and to determine the inventory management constraints for railway spare parts.

[0035] The inventory management module is used to iteratively update a preset number of first chromosome individuals according to the objective function, the inventory management constraints, and the preset iteration termination condition to obtain the target chromosome individual at the time of iteration termination; and to determine the target inventory management strategy based on the target chromosome individual.

[0036] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the inventory management method for railway spare parts as described above.

[0037] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the railway spare parts inventory management method as described above.

[0038] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the inventory management method for railway spare parts as described above.

[0039] The present invention provides a method, apparatus, equipment, and storage medium for railway spare parts inventory management. First, an objective function is constructed to calculate the minimum total cost corresponding to at least one inventory management strategy. Then, the inventory management constraints for railway spare parts are determined. Next, based on the objective function, the inventory management constraints, and a preset iteration termination condition, a preset number of first chromosome individuals are iteratively updated to obtain the target chromosome individual at the time of iteration termination. Finally, based on the target chromosome individual, a target inventory management strategy is determined.

[0040] In this invention, an objective function is first constructed and constraints are determined. Since the objective evaluation function is used to calculate the minimum total cost corresponding to at least one inventory management strategy, then, based on the objective function, inventory management constraints, and preset iteration termination conditions, a preset number of first chromosome individuals are iteratively updated to obtain the target chromosome individual at the time of iteration termination. The first chromosome individuals and the target chromosome individuals represent the corresponding inventory management strategies. Thus, the target inventory management strategy is determined based on the target chromosome individuals, thereby improving the reliability of railway spare parts inventory management. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1 This is one of the flowcharts illustrating the railway spare parts inventory management method provided by the present invention.

[0043] Figure 2 This is a schematic diagram of the coding of decision variables in the railway spare parts inventory management method provided by the present invention.

[0044] Figure 3 This is the second flowchart of the railway spare parts inventory management method provided by the present invention.

[0045] Figure 4 This is a schematic diagram illustrating the control of spare parts inventory changes in the railway spare parts inventory management method provided by the present invention.

[0046] Figure 5 This is the third flowchart of the railway spare parts inventory management method provided by the present invention.

[0047] Figure 6 This is a schematic diagram of the railway spare parts inventory management device provided by the present invention.

[0048] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0050] The following is combined with Figures 1-7 The present invention describes a method, apparatus, equipment, and storage medium for managing the inventory of railway spare parts.

[0051] Figure 1 This is one of the flowcharts illustrating the railway spare parts inventory management method provided by the present invention, such as... Figure 1 As shown, the method includes the following:

[0052] Step 101: Construct the objective function; the objective function is used to calculate the minimum total cost corresponding to at least one inventory management strategy;

[0053] Specifically, the subject of this invention is an electronic device, and this invention is used to improve the reliability of railway spare parts inventory management.

[0054] Rail transit systems are complex systems integrating multiple disciplines. The equipment, i.e., railway spare parts, involves a wide range of specialties, including track, bridges, tunnels, electrical engineering, and power supply. Due to the diversity and complexity of these railway spare parts, rail transit equipment spare parts are characterized by a large variety of types and models. The high uncertainty of demand for railway spare parts, along with the small demand for some parts, makes inventory management of rail transit equipment spare parts extremely difficult.

[0055] Inventory management primarily focuses on ordering time and quantity. Basic inventory control strategies can be categorized into (r, S) and (r, Q) strategies that consider the reorder point, and (T, S) and (T, Q) strategies that consider the inspection cycle. The reorder point-based strategy requires continuous monitoring of inventory levels. When inventory falls below the reorder point r, the (r, Q) strategy orders Q spare parts (a fixed order quantity), while the (r, S) strategy replenishes inventory to a pre-set quantity S (the order quantity is not fixed). The inspection cycle-based strategy fixes the time between two inspections (i.e., the order cycle) at T. The (T, S) strategy replenishes inventory to a pre-set quantity S, while the (T, Q) strategy replenishes inventory at a fixed quantity Q.

[0056] In this embodiment, the objective function is first constructed. It is understood that the costs associated with the inventory management strategy include, for example, procurement costs, ordering costs, storage costs, spare parts shortage costs, and consumption costs. Procurement costs are fixed costs within each ordering cycle; ordering costs are proportional to the number and quantity of spare parts ordered; storage costs are related to the quantity and duration of spare parts held; shortage costs refer to the costs incurred when the supply of spare parts is interrupted, causing the railway to malfunction, and are proportional to the shortage duration; consumption costs refer to the costs incurred during railway maintenance due to the consumption of spare parts, including preventative maintenance replacement costs and fault repair replacement costs in this invention. Consumption costs are related to the reliability and malfunction of the spare parts.

[0057] Furthermore, after determining the cost type of the inventory management strategy, an objective function for calculating inventory management can be constructed based on various cost calculation methods. This objective function is used to calculate the minimum total cost corresponding to at least one inventory management strategy. It can be understood that the lower the total cost of an inventory management strategy, the better the strategy; conversely, the higher the total cost, the worse the strategy. The decision variables for the objective function are determined based on the selected inventory strategy type. For example, based on the (r, Q) strategy, the decision variables are the reorder point and the order quantity. The process of finding the optimal inventory management is also the process of determining the optimal decision variables.

[0058] Step 102: Determine the inventory management constraints for railway spare parts;

[0059] Specifically, after determining the objective function for railway spare parts inventory management, the constraints for railway spare parts inventory management can be further determined, that is, the constraints corresponding to the objective function. The more comprehensive the constraints, the better the reliability of the target inventory management strategy found.

[0060] Among them, the constraints are, for example, the constraints corresponding to the ordering cycle, the constraints corresponding to the ordering point, the constraints corresponding to the ordering quantity, and the constraints corresponding to availability. Availability is represented, for example, by the ratio of the spare parts running time within an ordering cycle to the duration of an ordering cycle. The larger the value of this ratio, the higher the availability corresponding to the inventory management strategy.

[0061] Furthermore, after determining the objective function and constraints, the objective of this embodiment is to minimize the value of the objective function while satisfying the constraints, and to take the inventory management strategy corresponding to the minimum value of the objective function as the optimal inventory management strategy, that is, the target inventory management strategy.

[0062] Step 103: Based on the objective function, inventory management constraints, and preset iteration termination conditions, iteratively update a preset number of first chromosome individuals to obtain the target chromosome individual at the time of iteration termination; the first chromosome individual and the target chromosome individual represent the corresponding inventory management strategy.

[0063] Specifically, after determining the objective function and constraints, this embodiment can transform the inventory management problem into a multi-objective optimization problem. The decision variables of the objective function are determined based on the selected inventory strategy type. For example, based on the (r, Q) strategy, the decision variables are the reorder point and the order quantity. The process of finding the optimal inventory management is also the process of finding the optimal decision variables. For example, optimization algorithms can be used to solve this problem, such as genetic algorithms, particle swarm optimization, etc.

[0064] In the optimization process using genetic algorithms, different chromosome individuals correspond to different decision variables, i.e., different inventory management strategies. Based on the objective function, inventory management constraints, and a preset iteration termination condition, a preset number of first chromosome individuals can be iteratively updated to obtain the target chromosome individual at the time of iteration termination. For example, the objective function (including constraints) can be used as the basis for evaluating the quality of the first chromosome individuals to iteratively update them. If the iteration termination condition is met, the best first chromosome individual at the time of iteration termination is determined as the optimal chromosome individual, i.e., the target chromosome individual.

[0065] Step 104: Determine the target inventory management strategy based on the target chromosome individual.

[0066] Specifically, after the target chromosome individual is obtained through optimization, since different chromosome individuals correspond to different decision variables, that is, different inventory management strategies, the target inventory management strategy can be further determined by determining the decision variables corresponding to the target chromosome individual.

[0067] In the method provided in this embodiment, firstly, an objective function is constructed to calculate the minimum total cost corresponding to at least one inventory management strategy; the inventory management constraints for railway spare parts are determined; then, based on the objective function, the inventory management constraints, and the preset iteration termination condition, a preset number of first chromosome individuals are iteratively updated to obtain the target chromosome individual at the time of iteration termination; furthermore, based on the target chromosome individual, the target inventory management strategy is determined.

[0068] In this invention, an objective function is first constructed and constraints are determined. Since the objective evaluation function is used to calculate the minimum total cost corresponding to at least one inventory management strategy, then, based on the objective function, inventory management constraints, and preset iteration termination conditions, a preset number of first chromosome individuals are iteratively updated to obtain the target chromosome individual at the time of iteration termination. The first chromosome individuals and the target chromosome individuals represent the corresponding inventory management strategies. Thus, the target inventory management strategy is determined based on the target chromosome individuals, thereby improving the reliability of railway spare parts inventory management.

[0069] According to the present invention, a method for inventory management of railway spare parts is provided. The inventory management strategy is determined based on a first ordering point and a first order quantity of spare parts within an ordering cycle. The inventory management strategy characterizes ordering a first order quantity of spare parts when the spare parts inventory quantity is less than or equal to the first ordering point. Based on the objective function, inventory management constraints, and a preset iteration termination condition, a preset number of first chromosome individuals are iteratively updated to obtain the target chromosome individual at the time of iteration termination, including:

[0070] Initialize a preset number of first chromosome individuals; the inventory management strategy is represented by a first code and a second code, whereby the first code is used to characterize the first order point corresponding to the inventory management strategy, and the second code is used to characterize the first order quantity corresponding to the inventory management strategy.

[0071] Using the objective function, the first fitness score of the inventory management strategy corresponding to each individual of the first chromosome is calculated;

[0072] Based on the first fitness score of the inventory management strategy corresponding to each first chromosome individual and the inventory management constraints, determine the preset number of second chromosome individuals corresponding to the current iteration number; the inventory management constraints include at least one of the following: the ordering cycle duration is greater than or equal to the order lead time, the first reorder point is greater than or equal to 0, the first order quantity is greater than or equal to the number of spare parts failures, and the spare parts availability rate within the ordering cycle is greater than the preset availability rate threshold.

[0073] For any given iteration, a predetermined number of second chromosome individuals corresponding to the current iteration are determined as the third chromosome individuals corresponding to the next iteration.

[0074] Based on the preset iteration termination condition and the third chromosome individual corresponding to the next iteration, determine the preset number of second chromosome individuals corresponding to the iteration termination.

[0075] The target chromosome individual is determined based on the preset number of second chromosome individuals and the objective function at the end of the iteration.

[0076] Specifically, in some embodiments, the inventory management strategy is determined based on the first ordering point and the first order quantity of spare parts within the ordering cycle. That is, the inventory management strategy is a (ordering point, order quantity) strategy type. Correspondingly, the inventory management strategy is used to characterize ordering a first order quantity of y spare parts when the spare parts inventory quantity is less than or equal to the first ordering point x.

[0077] Correspondingly, the process of iteratively updating the optimal target chromosome (the individual target chromosome at the end of the iteration) using an optimization algorithm in step 103 can be transformed into finding the optimal values ​​of the decision variables (first order point x, first order quantity y). An example of this process is shown below, including the following steps.

[0078] First, the population is initialized, i.e., a predetermined number of individuals with the first chromosome. Biological evolution typically occurs in groups; therefore, genetic algorithms require multiple individuals for manipulation, necessitating population initialization. Experiments have shown that using chaotic mapping to generate random numbers significantly improves the fitness function value, and replacing conventional uniformly distributed random number generators with chaotic mapping yields better results. Therefore, this study uses the commonly used Gaussian mapping to initialize the population. The chaotic mapping expression is as follows:

[0079]

[0080] in, Indicates according to The random number obtained from the mapping expression, when i=0, This represents the initial random number.

[0081] It is understandable that different chromosomes represent different values ​​of decision variables (first order point x, first order quantity y), which correspond to different inventory management strategies. Inventory management strategies can be represented using a first code and a second code. The first code represents the first order point corresponding to the inventory management strategy, and the second code represents the first order quantity. For example, a binary encoding method can be used to encode the values ​​of the decision variables. An example of this encoding method is shown below:

[0082] Figure 2 This is a schematic diagram of the coding of decision variables in the railway spare parts inventory management method provided by the present invention, such as... Figure 2 As shown, the total coding length of a chromosome is B+D, where the length of the first code is the coding length of the first order point x, and the length of the second code is the coding length of the first order quantity y. It can be understood that the precision is set to, for example, 0.1, the minimum order point and minimum order quantity can be 0, and the historical maximum order point is... The maximum order quantity is ,again , Therefore, the encoding length is .

[0083] Furthermore, using the objective function, the first fitness score of the inventory management strategy corresponding to each first chromosome individual is calculated. The objective function characterizes the minimum total inventory management cost of each inventory management strategy. In this embodiment, the smaller the minimum total inventory management cost of each strategy, the higher the fitness score of the chromosome individual, and the more easily it is inherited by the next generation. Similarly, the first fitness score corresponding to each first chromosome can be calculated, and the first fitness score can be used to characterize the quality of the individual.

[0084] Furthermore, it can be understood that inventory management constraints include at least one of the following: order cycle length is greater than or equal to order lead time, first reorder point is greater than or equal to 0, first order quantity is greater than or equal to the number of spare parts failures, and spare parts availability within the order cycle is greater than a preset availability threshold. Inventory management constraints are expressed as follows:

[0085] (1) Inventory management constraint one: The ordering cycle should be greater than or equal to the order lead time. An ordering cycle includes the preventive maintenance time, the time for consumed spare parts to reach the ordering point, and the order lead time. Considering the special case where the initial spare parts inventory of an ordering cycle is the ordering point, the constraint of the ordering cycle is expressed as follows:

[0086]

[0087] in, Indicates the duration of the order cycle. This indicates the duration of the order lead time.

[0088] (2) Inventory management constraint two: The first reorder point is greater than or equal to 0, represented as x .

[0089] (3) Inventory Management Constraint Three: The first order quantity is greater than or equal to the number of times spare parts fail, expressed as ,in, This indicates the first order quantity, and N represents the number of spare parts. This indicates the failure rate of spare parts. This indicates the duration of the order cycle.

[0090] (4) Inventory Management Constraint Four: Availability is greater than the threshold, that is, the first order quantity is greater than or equal to the number of spare parts failures and the spare parts availability rate within the ordering cycle is greater than the preset availability rate threshold, expressed as:

[0091] .

[0092] in, Indicates availability, Indicates the duration of the order cycle. This indicates the time during which preventative maintenance does not consume spare parts; PT represents the preventative maintenance time PT' and the time from normal operation to the occurrence of a fault. the sum of Indicates the order quantity. This indicates the total time from when spare parts are consumed to when they are ordered. Indicates the out-of-stock period. This indicates the preset availability threshold.

[0093] Furthermore, based on the first fitness score corresponding to each first chromosome individual and the inventory management constraints, a predetermined number of second chromosome individuals corresponding to the current iteration number can be determined. For example, the first fitness scores corresponding to each first chromosome individual that satisfies the inventory management constraints can be sorted from smallest to largest, and then the top predetermined number of first chromosome individuals can be selected as second chromosome individuals. It is understandable that the process of determining the predetermined number of second chromosome individuals corresponding to the current iteration number is similar for each iteration.

[0094] Furthermore, for any given iteration, a predetermined number of second chromosome individuals corresponding to the current iteration can be identified as the third chromosome individuals corresponding to the next iteration.

[0095] Furthermore, based on the preset iteration termination condition and the third chromosome individual corresponding to the next iteration, a preset number of second chromosome individuals are determined when the iteration terminates. The preset iteration termination condition is used to determine the termination time of the algorithm iteration. The iteration termination condition can be preset, for example, a maximum number of iterations of 100. When the number of operations reaches 100, the optimal solution is returned, and no further iteration update operations are performed.

[0096] Furthermore, based on the preset number of second chromosome individuals at the time of iteration termination and the objective function, the target chromosome individual is determined. For example, the second fitness score corresponding to the preset number of second chromosome individuals at the time of iteration termination is calculated using the objective function, and the target chromosome individual, i.e., the population-optimal individual, is determined based on the second fitness score corresponding to the preset number of second chromosome individuals at the time of iteration termination.

[0097] In the method provided in this embodiment, a binary decoding method is designed for the decision variables of the first ordering point and the second ordering quantity in railway spare parts inventory management. Based on the objective function and constraints, an efficient and fast genetic algorithm is used to solve for the target chromosome individual. Thus, the optimal target inventory management strategy can be determined based on the target chromosome individual, thereby improving the reliability of railway spare parts inventory management.

[0098] According to the present invention, a method for inventory management of railway spare parts, a target chromosome individual is determined based on a preset number of second chromosome individuals and an objective function at the end of the iteration, including:

[0099] Determine the inventory management strategies corresponding to each individual with the second chromosome;

[0100] Using the objective function, calculate the second fitness score corresponding to each inventory management strategy for each individual on the second chromosome;

[0101] Sort the second fitness scores from largest to smallest, and determine the second chromosome individual with the largest second fitness score as the target chromosome individual.

[0102] Specifically, in some embodiments, the process of determining the target chromosome individual based on a preset number of second chromosome individuals and the objective function at the end of the iteration can be implemented through the following steps:

[0103] First, determine the inventory management strategy corresponding to each individual on the second chromosome. Since different chromosomes represent different values ​​of decision variables, the different values ​​of decision variables can be used to represent the corresponding inventory management strategy. For example, decode each individual on the second chromosome to obtain the values ​​of the corresponding decision variables.

[0104] Furthermore, after obtaining the values ​​of the decision variables corresponding to each second chromosome individual, the objective function is used to calculate the second fitness score corresponding to each inventory management strategy, that is, to calculate the value of the objective function corresponding to each inventory management strategy, which is also the minimum value of the total inventory management cost corresponding to each inventory management strategy.

[0105] Furthermore, the second chromosome scores are sorted from largest to smallest, and the individual with the highest second fitness score is identified as the target chromosome. It can be understood that the objective function corresponding to the inventory management strategy is the minimum total inventory management cost for each strategy. The total inventory management cost is a core indicator of the reliability of the inventory management strategy; a lower total cost indicates higher reliability. Therefore, identifying the individual with the highest second fitness score as the target chromosome means finding the optimal chromosome, and the target inventory management strategy corresponding to the target chromosome is the optimal inventory management strategy.

[0106] In the method provided in this embodiment, the inventory management strategies corresponding to each second chromosome individual are first determined. Using the objective function, the second fitness score corresponding to each inventory management strategy for each second chromosome individual is calculated. The second fitness scores are sorted from largest to smallest. The second chromosome individual with the largest second fitness score is determined as the target chromosome individual, which facilitates finding the optimal chromosome individual. The target inventory management strategy corresponding to the target chromosome individual is the optimal inventory management strategy, and the higher the reliability of spare parts inventory management, the better.

[0107] According to the present invention, a method for inventory management of railway spare parts, based on the first fitness score of the inventory management strategy corresponding to each first chromosome individual and the inventory management constraints, determines a preset number of second chromosome individuals corresponding to the current iteration number, including:

[0108] Using a preset crossover algorithm, crossover operations are performed on each first chromosome individual to obtain a preset number of fourth chromosome individuals;

[0109] Using a preset mutation strategy, mutation operations are performed on each individual of the fourth chromosome to obtain a preset number of individuals of the fifth chromosome;

[0110] Based on the first fitness score of the inventory management strategy corresponding to each fifth chromosome individual and the inventory management constraints, determine the preset number of second chromosome individuals corresponding to the current iteration number.

[0111] Specifically, in some embodiments, after a certain number of iterations during the population iteration process, the movement speed of each chromosome individual in the population gradually decreases, and there is a certain probability that it will "get stuck" in a local optimum. At this time, the crossover algorithm and mutation strategy can be selected as needed to enhance the possibility of a global optimum.

[0112] Correspondingly, the process of determining the preset number of second chromosome individuals corresponding to the current iteration number can be achieved through the following steps.

[0113] First, using a pre-defined crossover algorithm, crossover operations are performed on each individual with the first chromosome to obtain a predetermined number of individuals with the fourth chromosome. The crossover algorithm in genetic algorithms, also known as the crossover operator or recombination operator, simulates the mating process of organisms in nature and is used to generate new individuals in genetic algorithms. Here are some key points about the application of crossover algorithms in genetic algorithms: Single-point crossover: Only one crossover point is randomly set in the encoded individuals, and then parts of the chromosomes of the two paired individuals are exchanged at that point. This is the simplest crossover method. Multi-point crossover: Multiple crossover points are selected, and then parts of the chromosomes of the paired individuals are exchanged at these points. Multi-point crossover allows for more flexible recombination of genetic information. Uniform crossover: At each gene position, a gene is selected from two parent individuals with a certain probability and passed on to the offspring. This method can more evenly mix the genetic characteristics of the parents. Arithmetic crossover: Suitable for individuals encoded with real numbers, it generates the gene values ​​of the offspring by linearly interpolating between the corresponding gene values ​​of the two parent individuals. Sequence-based crossover: Particularly suitable for permutation encoding problems, such as the Traveling Salesman Problem, it generates new sequences by exchanging parts of the sequence in the parent individuals. Crossover Probability (Pc): Controls the frequency of crossover operations. A higher crossover probability can increase genetic diversity, but may also disrupt optimal genetic combinations. Crossover Operator Design: When designing the crossover operator, it's necessary to consider how to determine the crossover point and how to perform partial gene exchange. Adaptive Crossover Strategy: Dynamically adjusts the crossover probability based on the algorithm's performance to balance exploration and exploitation. It's understandable that crossover operations help increase population diversity and prevent the algorithm from prematurely converging to a local optimum.

[0114] For example, this embodiment selects an available and efficient single-point crossover algorithm, using the xovsh crossover function. Specifically, the crossover rate is set to 0.8, and a probability between 0 and 1 is randomly generated. If this random probability is greater than the set crossover rate, the parents are shuffled and crossed at a random crossover point.

[0115] Furthermore, using a pre-defined mutation strategy, mutation operations are performed on each individual of chromosome four to obtain a pre-defined number of individuals of chromosome five. The pre-defined crossover algorithm and mutation strategy are important means in optimization algorithms to explore new solution spaces and avoid local optima. The mutation strategy in the Genetic Algorithm (GA) simulates the mutation process in organisms in nature. Its purpose is to introduce new genetic information, increase population diversity, avoid the algorithm getting trapped in local optima, and thus help find the global optimum. Here are some key points about the application of mutation strategies in genetic algorithms: Bit Flip: In this strategy, a gene value at a random position is flipped, for example, from 0 to 1 or from 1 to 0. This strategy is simple and easy to implement and is often used in binary-encoded genetic algorithms. Crossover Mutation: In the crossover mutation strategy, two different solutions are selected, and crossover occurs at some positions between them to generate new solutions. This method can combine the characteristics of two solutions, which helps to explore the solution space. Insertion Mutation: A gene value is randomly selected from one solution and inserted into a position in another solution. This method can alter the structure of solutions, facilitating the exploration of new solution spaces. Replacement Mutation: Randomly selects a subset of gene values ​​from one solution and replaces them with a subset of gene values ​​from another solution. This helps introduce new genetic traits.

[0116] It should be noted that the specific mutation strategy selected in this embodiment is not limited and depends on the specific implementation of the electronic device. For example, this embodiment uses 0 and 1 mutations.

[0117] Furthermore, based on the first fitness score of the inventory management strategy corresponding to each fifth chromosome individual and the inventory management constraints, a preset number of second chromosome individuals corresponding to the current iteration number are determined. Further, after crossover and mutation of the chromosome individuals, the preset number of second chromosome individuals corresponding to the current iteration number can be determined based on the mutated fifth chromosome individuals. For example, firstly, it is determined whether each fifth chromosome individual satisfies the constraints; then, the first fitness scores of the inventory management strategy corresponding to each fifth chromosome individual that satisfies the constraints are sorted, thereby determining the preset number of fifth chromosome individuals before sorting as the second chromosome individuals, completing the current iteration.

[0118] In the method provided in this embodiment, during the process of obtaining the optimal target inventory management strategy using a genetic algorithm, crossover and mutation strategies can be selected as needed to enhance the possibility of a global optimal solution, and the final target inventory management strategy obtained has high reliability.

[0119] Figure 3 This is the second flowchart of the railway spare parts inventory management method provided by the present invention, showing a flowchart of obtaining the optimal target chromosome individual using a genetic algorithm, as shown below. Figure 3 As shown, the method includes:

[0120] Step 301: Determine the encoding method;

[0121] Step 302: Initialize the population;

[0122] Step 303: Calculate the fitness value of the chromosome individual;

[0123] Step 304: Selection, crossover, mutation;

[0124] Step 305: Determine whether the iteration termination condition is met;

[0125] Step 306: Decode the current chromosome individual;

[0126] Step 307: Output the optimal solution.

[0127] That is, output the values ​​of the decision variables corresponding to the optimal chromosome individual, which is also the optimal inventory management strategy.

[0128] According to the present invention, a method for managing railway spare parts inventory, based on a target chromosome individual, determines a target inventory management strategy, including:

[0129] Determine the first and second codes corresponding to the target chromosome individual;

[0130] Based on the first code corresponding to the target chromosome individual, determine the second ordering point corresponding to the target chromosome individual;

[0131] The second order quantity corresponding to the target chromosome individual is determined based on the second code corresponding to the target chromosome individual;

[0132] The target inventory management strategy is determined based on the second ordering point and the second ordering quantity corresponding to the target chromosome individual.

[0133] Specifically, in some embodiments, the process of determining the target inventory management strategy based on the target chromosome individual in step 104 can be implemented in the following ways:

[0134] First, the first and second codes corresponding to the target chromosome individual are determined. It is understood that the chromosomes in this invention can be represented using the first and second codes. The first and second codes are obtained by binary encoding the first order point and the first order quantity using convenient and easy-to-use binary encoding. The first code is used to characterize the first order point corresponding to the inventory management strategy, and the second code is used to characterize the first order quantity corresponding to the inventory management strategy.

[0135] Further, based on the first code corresponding to the target chromosome individual, the second ordering point corresponding to the target chromosome individual is determined, and based on the second code corresponding to the target chromosome individual, the second order quantity corresponding to the target chromosome individual is determined. Specifically, after determining the target chromosome, the first code and the second code corresponding to the target chromosome individual can also be determined. Further, the target chromosome is decoded, that is, the first code of the binary string representing the first ordering point and the second code of the binary string representing the first order quantity are represented back to decimal numbers to obtain the second ordering point and the second order quantity corresponding to the target chromosome. An example of the decoding method is as follows:

[0136] Step 1: Decoding the second order point:

[0137]

[0138] in, This indicates the second ordering point corresponding to the target chromosome individual. This indicates the length of the first code.

[0139] Step 2: Decoding the second order quantity:

[0140]

[0141] in, This indicates the second order quantity corresponding to the target chromosome. This indicates the encoding length of the second code.

[0142] Furthermore, based on the second ordering point and the second ordering quantity corresponding to the target chromosome individual, a target inventory management strategy is determined. This invention is based on a (ordering point, ordering quantity) strategy for inventory control of railway spare parts; therefore, determining the second ordering point and the second ordering quantity corresponding to the target chromosome also determines the optimal target inventory management strategy.

[0143] For example, if the second reorder point corresponding to the target inventory management strategy is x=1000 units and the second order quantity is y=200 units, then the target inventory management strategy means that when the inventory quantity is lower than the second reorder point of 1000 units, 200 spare parts will be ordered.

[0144] In the method provided in this embodiment, firstly, the first and second codes corresponding to the target chromosome individual at the time of iteration termination are determined, and after decoding, the second ordering point and the second ordering quantity corresponding to the target chromosome individual are obtained. Then, the target inventory management strategy is determined. The encoding method in this invention is simple and easy to implement, and the effect of finding the optimal target inventory management strategy is high.

[0145] According to the present invention, a method for inventory management of railway spare parts is provided, comprising constructing an objective function, including:

[0146] Based on the procurement costs, ordering costs, storage costs, stockout loss costs, and consumption costs corresponding to the inventory management strategy, determine the total cost corresponding to the inventory management strategy.

[0147] The objective function is obtained by minimizing the total cost corresponding to the inventory management strategy.

[0148] Specifically, in some embodiments, step 101, constructing the objective function, can be achieved through the following steps:

[0149] First, based on the procurement costs, ordering costs, storage costs, and shortage costs associated with the inventory management strategy, the total cost corresponding to the strategy is determined. Procurement costs are fixed costs within each ordering cycle, and are proportional to the number and quantity of spare parts ordered. Storage costs are related to the quantity and duration of spare parts held. Shortage costs refer to the costs incurred when the supply of spare parts is interrupted, causing the railway to malfunction, and are proportional to the duration of the shortage. Consumption costs refer to the costs incurred during railway maintenance when spare parts are consumed. In this invention, the total cost corresponding to the inventory management strategy includes the cost of preventative maintenance replacement of spare parts and the cost of fault repair replacement of spare parts, which are related to the reliability and malfunctions of components.

[0150] Then, the objective function is obtained by minimizing the total cost corresponding to the inventory management strategy. In other words, the objective function is defined as minimizing the total cost. For example, the constructed objective function can be expressed as follows:

[0151] ZC min =CF+y×CJ+SC×Am×ZT+YF+ y×GF+QC×QT

[0152] In the formula, ZC represents total cost, CF represents procurement cost, y represents the quantity of spare parts procured each time, CJ represents the price of a single spare part, SC represents the spare part storage cost per unit time, Am represents the average inventory level within a ordering cycle, ZT represents the length of an ordering cycle, YF represents the cost of preventative maintenance, GF represents the cost of replacing a single spare part during fault repair, QC represents the cost of stockout per unit time, and QT represents the stockout duration.

[0153] Furthermore, the parameters such as procurement costs and individual spare parts prices in the objective function are constants. For other variables that need to be calculated, further explanation is provided below:

[0154] (1) Average inventory level

[0155] Let A represent the inventory level. Then, the average inventory level Am within a single ordering cycle is shown in the following formula:

[0156]

[0157] in, This represents the average inventory level over a single ordering period. This indicates the duration of a subscription cycle. It can be expressed by the following formula:

[0158]

[0159] Where y represents the quantity of spare parts purchased each time. Indicates the order point. Indicates the quantity of spare parts. Indicates the failure rate. This indicates the lead time for ordering.

[0160] (2) Ordering cycle

[0161] The duration of an ordering cycle, ZT, consists of the time during which preventative repairs do not consume spare parts, the total time for repairs to be completed from the point of purchase to the ordering point, and the lead time for ordering, as shown in the following formula:

[0162] ZT = PT + CT + LT

[0163] Where ZT represents the duration of a single ordering cycle, PT represents the time during which preventative maintenance and other repairs do not consume spare parts, and PT is the preventative maintenance time PT' plus the time from normal operation to the occurrence of a failure. the sum of The lead time is represented by CT, which is the total time from the point of purchase to the point of reordering. CT is the average of the exponents used to replace a spare part during a repair, as shown in the following formula:

[0164]

[0165] in, This indicates the time required to replace a spare part during maintenance.

[0166] (3) Out-of-stock period

[0167] The states of the N spare parts are independent of each other. Therefore, the probability of m component failures occurring within the lead time can be obtained using a binomial distribution, as shown in the following formula:

[0168]

[0169] in, Let N represent the probability of m component failures occurring within the order lead time, and N represent the quantity of spare parts. This indicates the lead time for ordering.

[0170] If, during the lead time period, the number of times spare parts need to be replaced due to malfunctions exceeds the ordering point x, and the spare parts have not yet arrived, the railway will be unable to operate normally due to a shortage of spare parts. The downtime can be expressed as follows:

[0171]

[0172] in, Indicates the downtime. Indicates the ordering point. This indicates the lead time for ordering.

[0173] Therefore, the spare parts out-of-stock time within a single ordering cycle is

[0174]

[0175] in, Indicates the time when spare parts are out of stock. This represents the probability of m component failures occurring within the order lead time. Indicates the downtime.

[0176] In the method provided in this embodiment, the total cost corresponding to the inventory management strategy is determined based on the procurement cost, ordering cost, storage cost, stockout loss cost, and consumption cost corresponding to the inventory management strategy. Then, the minimum value of the total cost corresponding to the inventory management strategy is taken to obtain the objective function. In this embodiment, the total cost corresponding to the inventory management strategy includes the cost of spare parts replacement for preventive repair and the cost of spare parts replacement for fault repair. This is related to the reliability and failure of the components, which improves the reliability of railway spare parts inventory management.

[0177] Figure 4 This is a schematic diagram illustrating the control of spare parts inventory changes in the railway spare parts inventory management method provided by this invention. The changes in spare parts inventory quantity are as follows: Figure 4As shown in the diagram, when the quantity of spare parts is reduced to only reorder point x, an order is placed for a quantity of y. The interval from the start of the order to delivery, LT as shown in the diagram, is called the lead time. If the spare parts are not used during this period, the quantity of spare parts upon delivery is x + y, which is the maximum inventory quantity. After the spare parts arrive, preventive maintenance is performed. The preventive maintenance and subsequent normal operation do not consume spare parts until a fault occurs, at which point the spare parts are replaced. This time interval during which spare parts are not consumed is represented by PT in the diagram. CT refers to the time interval from when a fault occurs and the spare parts are replaced until only the quantity of spare parts remaining at reorder point x. The time interval between two spare parts deliveries is one ordering cycle, ZT in the diagram. Because spare parts consumption depends on the reliability of the spare parts, the times shown in the diagram are not necessarily fixed values, and these times are affected by reorder point x and order quantity y. Therefore, this patent constructs an inventory management method for railway spare parts based on reorder point x and order quantity y.

[0178] Figure 5 This is the third flowchart of the railway spare parts inventory management method provided by the present invention, as shown below. Figure 5 As shown, the method includes:

[0179] Step 501: Based on the reliability status of railway spare parts, establish the objective function of the railway spare parts management method; wherein, the objective function aims to minimize the total inventory management cost of railway spare parts, and is constrained by order cycle constraints, order point constraints, order quantity constraints, and availability constraints; the total inventory management cost includes at least one of the following: procurement costs, ordering costs, storage costs, spare parts shortage loss costs, and consumption costs.

[0180] Step 502: Encode the two decision variables, order point and order quantity, into binary form, and use a genetic algorithm to solve the problem in order to determine the optimal inventory strategy.

[0181] The following describes the railway spare parts inventory management device provided by the present invention. The railway spare parts inventory management device described below and the railway spare parts inventory management method described above can be referred to in correspondence.

[0182] Figure 6 This is a schematic diagram of the railway spare parts inventory management device provided by the present invention, as shown below. Figure 6 As shown, the railway spare parts inventory management device 600 includes the following modules:

[0183] The determination module 610 is used to construct an objective function; the objective function is used to calculate the minimum total cost corresponding to at least one inventory management strategy; and to determine the inventory management constraints for railway spare parts.

[0184] The inventory management module 620 is used to iteratively update a preset number of first chromosome individuals according to the objective function, the inventory management constraints, and the preset iteration termination condition to obtain the target chromosome individual at the time of iteration termination; the first chromosome individuals and the target chromosome individuals represent the corresponding inventory management strategies; and the target inventory management strategy is determined based on the target chromosome individual.

[0185] In the device provided in this embodiment, the first step is to determine the objective function by the module 610, which is used to calculate the minimum total cost corresponding to at least one inventory management strategy; determine the inventory management constraints for railway spare parts; then, the inventory management module 620 iteratively updates a preset number of first chromosome individuals according to the objective function, the inventory management constraints, and the preset iteration termination condition to obtain the target chromosome individual at the time of iteration termination; and then, based on the target chromosome individual, determine the target inventory management strategy.

[0186] In this invention, an objective function is first constructed and constraints are determined. Since the objective evaluation function is used to calculate the minimum total cost corresponding to at least one inventory management strategy, then, based on the objective function, inventory management constraints, and preset iteration termination conditions, a preset number of first chromosome individuals are iteratively updated to obtain the target chromosome individual at the time of iteration termination. The first chromosome individuals and the target chromosome individuals represent the corresponding inventory management strategies. Thus, the target inventory management strategy is determined based on the target chromosome individuals, thereby improving the reliability of railway spare parts inventory management.

[0187] According to the present invention, an inventory management device 600 for railway spare parts is provided, wherein the inventory management strategy is determined based on a first ordering point and a first ordering quantity of spare parts within an ordering cycle; the inventory management strategy is used to characterize ordering the first ordering quantity of spare parts when the spare parts inventory quantity is less than or equal to the first ordering point.

[0188] The inventory management module 620 is specifically used for:

[0189] Initialize the preset number of first chromosome individuals; the inventory management strategy is represented by a first code and a second code, the first code being used to characterize the first order point corresponding to the inventory management strategy, and the second code being used to characterize the first order quantity corresponding to the inventory management strategy.

[0190] Using the objective function, the first fitness score of the inventory management strategy corresponding to each individual of the first chromosome is calculated;

[0191] Based on the first fitness score of the inventory management strategy corresponding to each first chromosome individual and the inventory management constraints, determine the preset number of second chromosome individuals corresponding to the current iteration number; the inventory management constraints include at least one of the following: the ordering cycle duration is greater than or equal to the order lead time duration, the first reorder point is greater than or equal to 0, the first order quantity is greater than or equal to the number of spare parts failures, and the spare parts availability rate within the ordering cycle is greater than a preset availability rate threshold.

[0192] For any given iteration, the preset number of second chromosome individuals corresponding to the current iteration are determined as the third chromosome individuals corresponding to the next iteration.

[0193] Based on the preset iteration termination condition and the third chromosome individual corresponding to the next iteration, determine the preset number of second chromosome individuals corresponding to the iteration termination;

[0194] The target chromosome individual is determined based on the preset number of second chromosome individuals corresponding to the termination of the iteration and the target function.

[0195] According to the present invention, a railway spare parts inventory management device 600 is provided, wherein the inventory management module 620 is further used for:

[0196] Determine the respective inventory management strategies corresponding to each individual of the second chromosome;

[0197] Using the objective function, calculate the second fitness score corresponding to each of the inventory management strategies for each individual of the second chromosome;

[0198] The individuals with the highest second fitness scores are sorted from largest to smallest, and the individual with the highest second fitness score is identified as the target chromosome individual.

[0199] According to the present invention, a railway spare parts inventory management device 600 is provided, wherein the inventory management module 620 is further used for:

[0200] Using a preset crossover algorithm, crossover operations are performed on each of the first chromosome individuals to obtain a preset number of fourth chromosome individuals;

[0201] Using a preset mutation strategy, mutation operations are performed on each of the fourth chromosome individuals to obtain a preset number of fifth chromosome individuals;

[0202] Based on the first fitness score of the inventory management strategy corresponding to each of the fifth chromosome individuals and the inventory management constraints, determine the preset number of second chromosome individuals corresponding to the current iteration number.

[0203] According to the present invention, a railway spare parts inventory management device 600 is provided, wherein the inventory management module 620 is further used for:

[0204] Determine the first code and the second code corresponding to the target chromosome individual;

[0205] Based on the first code corresponding to the target chromosome individual, determine the second ordering point corresponding to the target chromosome individual;

[0206] Based on the second code corresponding to the target chromosome individual, determine the second order quantity corresponding to the target chromosome individual;

[0207] The target inventory management strategy is determined based on the second ordering point corresponding to the target chromosome individual and the second ordering quantity corresponding to the target chromosome individual.

[0208] According to the present invention, a railway spare parts inventory management device 600 is provided, wherein the determining module 610 is specifically used for:

[0209] Based on the procurement costs, ordering costs, storage costs, stockout loss costs, and consumption costs corresponding to the inventory management strategy, determine the total cost corresponding to the inventory management strategy;

[0210] The objective function is obtained by minimizing the total cost corresponding to the inventory management strategy.

[0211] Figure 7 An example of a physical structure diagram of an electronic device is shown below. Figure 7 As shown, the electronic device may include: a processor 710, a communications interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communications interface 720, and the memory 730 communicate with each other via the communication bus 740. The processor 710 can call logical instructions in the memory 730 to execute a railway spare parts inventory management method, which includes:

[0212] Construct an objective function; the objective function is used to calculate the minimum total cost corresponding to at least one inventory management strategy.

[0213] Determine the constraints for railway spare parts inventory management;

[0214] Based on the objective function, the inventory management constraints, and the preset iteration termination condition, a preset number of first chromosome individuals are iteratively updated to obtain the target chromosome individual at the time of iteration termination.

[0215] Based on the target chromosome individuals, a target inventory management strategy is determined.

[0216] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0217] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is able to execute the railway spare parts inventory management method provided by the above methods, the method comprising:

[0218] Construct an objective function; the objective function is used to calculate the minimum total cost corresponding to at least one inventory management strategy.

[0219] Determine the constraints for railway spare parts inventory management;

[0220] Based on the objective function, the inventory management constraints, and the preset iteration termination condition, a preset number of first chromosome individuals are iteratively updated to obtain the target chromosome individual at the time of iteration termination.

[0221] Based on the target chromosome individuals, a target inventory management strategy is determined.

[0222] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the railway spare parts inventory management method provided by the methods described above, the method comprising:

[0223] Construct an objective function; the objective function is used to calculate the minimum total cost corresponding to at least one inventory management strategy.

[0224] Determine the constraints for railway spare parts inventory management;

[0225] Based on the objective function, the inventory management constraints, and the preset iteration termination condition, a preset number of first chromosome individuals are iteratively updated to obtain the target chromosome individual at the time of iteration termination.

[0226] Based on the target chromosome individuals, a target inventory management strategy is determined.

[0227] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0228] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0229] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for inventory management of railway spare parts, characterized in that, include: Construct the objective function; The objective function is used to calculate the minimum total cost corresponding to at least one inventory management strategy; The inventory management strategy is determined based on the first order point and the first order quantity of spare parts within the ordering cycle; The inventory management strategy is used to represent ordering the first order quantity of spare parts when the spare parts inventory quantity is less than or equal to the first ordering point. The lower the total cost of the inventory management strategy, the better the inventory management strategy; the higher the total cost of the inventory management strategy, the worse the inventory management strategy. The construction of the objective function includes: determining the total cost corresponding to the inventory management strategy based on the procurement cost, ordering cost, storage cost, stockout loss cost, and consumption cost corresponding to the inventory management strategy; and taking the minimum value of the total cost corresponding to the inventory management strategy to obtain the objective function. Determine the inventory management constraints for railway spare parts; the inventory management constraints include at least one of the following: the ordering cycle length is greater than or equal to the order lead time, the first ordering point is greater than or equal to 0, the first order quantity is greater than or equal to the number of spare parts failures, and the spare parts availability rate within the ordering cycle is greater than a preset availability rate threshold. Based on the objective function, the inventory management constraints, and the preset iteration termination condition, a preset number of first chromosome individuals are iteratively updated to obtain the target chromosome individual at the time of iteration termination; the first chromosome individuals and the target chromosome individuals are used to characterize the corresponding inventory management strategy. Based on the target chromosome individuals, determine the target inventory management strategy; The step of iteratively updating a preset number of first chromosome individuals based on the objective function, the inventory management constraints, and a preset iteration termination condition to obtain the target chromosome individual at the time of iteration termination includes: Initialize the preset number of first chromosome individuals; the inventory management strategy is represented by a first code and a second code, the first code being used to characterize the first order point corresponding to the inventory management strategy, and the second code being used to characterize the first order quantity corresponding to the inventory management strategy. Using the objective function, the first fitness score of the inventory management strategy corresponding to each individual of the first chromosome is calculated; Based on the first fitness score of the inventory management strategy corresponding to each first chromosome individual and the inventory management constraints, determine the preset number of second chromosome individuals corresponding to the current iteration number; For any given iteration, the preset number of second chromosome individuals corresponding to the current iteration are determined as the third chromosome individuals corresponding to the next iteration. Based on the preset iteration termination condition and the third chromosome individual corresponding to the next iteration, determine the preset number of second chromosome individuals corresponding to the iteration termination; The target chromosome individual is determined based on the preset number of second chromosome individuals corresponding to the termination of the iteration and the target function.

2. The method for inventory management of railway spare parts according to claim 1, characterized in that, The step of determining the target chromosome individual based on the preset number of second chromosome individuals corresponding to the iteration termination and the target function includes: Determine the inventory management strategy corresponding to each second chromosome individual at the time of iteration termination; Using the objective function, calculate the second fitness score corresponding to each of the inventory management strategies for each individual of the second chromosome; The individuals with the highest second fitness scores are sorted from largest to smallest, and the individual with the highest second fitness score is identified as the target chromosome individual.

3. The method for managing the inventory of railway spare parts according to claim 1, characterized in that, The step of determining the preset number of second chromosome individuals corresponding to the current iteration number based on the first fitness score of the inventory management strategy corresponding to each first chromosome individual and the inventory management constraints includes: Using a preset crossover algorithm, crossover operations are performed on each of the first chromosome individuals to obtain a preset number of fourth chromosome individuals; Using a preset mutation strategy, mutation operations are performed on each of the fourth chromosome individuals to obtain a preset number of fifth chromosome individuals; Based on the first fitness score of the inventory management strategy corresponding to each of the fifth chromosome individuals and the inventory management constraints, determine the preset number of second chromosome individuals corresponding to the current iteration number.

4. The method for inventory management of railway spare parts according to claim 1, characterized in that, The determination of the target inventory management strategy based on the target chromosome individual includes: Determine the first code and the second code corresponding to the target chromosome individual; Based on the first code corresponding to the target chromosome individual, determine the second ordering point corresponding to the target chromosome individual; Based on the second code corresponding to the target chromosome individual, determine the second order quantity corresponding to the target chromosome individual; The target inventory management strategy is determined based on the second ordering point corresponding to the target chromosome individual and the second ordering quantity corresponding to the target chromosome individual.

5. A railway spare parts inventory management device, characterized in that, include: The module is defined to construct the objective function; The objective function is used to calculate the minimum total cost corresponding to at least one inventory management strategy; The inventory management strategy is determined based on the first order point and the first order quantity of spare parts within the ordering cycle; The inventory management strategy is used to represent ordering the first order quantity of spare parts when the spare parts inventory quantity is less than or equal to the first ordering point. The lower the total cost of the inventory management strategy, the better the inventory management strategy; the higher the total cost of the inventory management strategy, the worse the inventory management strategy. The determining module is specifically used to determine the total cost corresponding to the inventory management strategy based on the procurement cost, ordering cost, storage cost, stockout loss cost, and consumption cost corresponding to the inventory management strategy. The objective function is obtained by minimizing the total cost corresponding to the inventory management strategy. Determine the constraints for railway spare parts inventory management; The inventory management constraints include at least one of the following: the ordering cycle duration is greater than or equal to the order lead time, the first ordering point is greater than or equal to 0, the first order quantity is greater than or equal to the number of spare parts failures, and the spare parts availability rate within the ordering cycle is greater than a preset availability rate threshold. The inventory management module is used to iteratively update a preset number of first chromosome individuals according to the objective function, the inventory management constraints, and the preset iteration termination condition, so as to obtain the target chromosome individual at the time of iteration termination. Based on the target chromosome individuals, determine the target inventory management strategy; The inventory management module is specifically used to initialize the preset number of first chromosome individuals; The inventory management strategy is represented by a first code and a second code. The first code is used to characterize the first order point corresponding to the inventory management strategy, and the second code is used to characterize the first order quantity corresponding to the inventory management strategy. Using the objective function, the first fitness score of the inventory management strategy corresponding to each individual of the first chromosome is calculated; Based on the first fitness score of the inventory management strategy corresponding to each first chromosome individual and the inventory management constraints, determine the preset number of second chromosome individuals corresponding to the current iteration number; For any given iteration, the preset number of second chromosome individuals corresponding to the current iteration are determined as the third chromosome individuals corresponding to the next iteration. Based on the preset iteration termination condition and the third chromosome individual corresponding to the next iteration, determine the preset number of second chromosome individuals corresponding to the iteration termination; The target chromosome individual is determined based on the preset number of second chromosome individuals corresponding to the termination of the iteration and the target function.

6. An electronic 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 program, it implements the railway spare parts inventory management method as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the railway spare parts inventory management method as described in any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the railway spare parts inventory management method as described in any one of claims 1 to 4.

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