A spare part management method, device, equipment, medium and product

Abstract

A spare part management method, device, equipment, medium and product are disclosed. Spare part management related parameters of a spare part to be managed are acquired, and the spare part management related parameters at least include spare part characteristic parameters, equipment state parameters, production plan parameters, supply chain association parameters and inventory parameters; an improved weed optimization algorithm is used to generate an initial weed individual based on the spare part management related parameters, and the initial weed individual is encoded as an initial decision vector formed by an initial purchase quantity, an initial dynamic safety stock and an initial reorder point; a dynamic breeding quantity and a dynamic mutation step length are determined based on the spare part management related parameters, breeding and mutation of the initial weed individual are executed, and a child weed individual is generated; a total cost of a whole cycle corresponding to the spare part management related parameters is taken as an objective function, adaptive constraint processing and competitive elimination are executed on the initial weed individual and the child weed individual, and a target decision vector is output. The scheme can adapt to the spare part management demand under a dynamic production scene.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of industrial management technology, and in particular to a spare parts management method, apparatus, equipment, medium and product. Background Technology

[0002] Against the backdrop of the intelligent and refined development of industrial production, the importance of spare parts management is increasing day by day. Taking cigarette production as an example, the equipment on its production line, such as cigarette making machines, packaging machines, and tobacco processing equipment, has an increasingly sophisticated structure, involving a wide variety of parts with different specifications, and integrating intelligent functions such as automatic fault diagnosis and remote operation and maintenance interfaces. Therefore, it is urgent to adapt to production needs through reasonable spare parts management methods.

[0003] In existing technologies, spare parts management relies heavily on manual statistics and experience-based judgment, which suffers from problems such as delayed information updates, opaque inventory status, and inaccurate demand forecasts, making it difficult to adapt to the spare parts management needs of efficient production. Spare parts management can also be carried out through spare parts management models, but these models rely on existing and historical inventory data for analysis and decision-making. Given the high mobility of spare parts procurement and usage, static models are also difficult to meet the actual spare parts management needs. Summary of the Invention

[0004] This invention provides a spare parts management method, apparatus, equipment, medium, and product that can adapt to the spare parts management needs in dynamic production scenarios.

[0005] In a first aspect, embodiments of the present invention provide a spare parts management method, including: Obtain the spare parts management related parameters for the spare parts to be managed. The spare parts management related parameters include at least spare parts characteristic parameters, equipment status parameters, production plan parameters, supply chain related parameters, and inventory parameters. Using an improved weed optimization algorithm, initial weed individuals are generated based on the spare parts management-related parameters. The initial weed individuals are encoded as an initial decision vector formed by the initial purchase quantity, initial dynamic safety stock, and initial reorder point. Based on the spare parts management parameters, determine the dynamic reproduction quantity and dynamic variation length, execute the reproduction and mutation of the initial weed individuals, and generate offspring weed individuals; Using the minimum total cost throughout the entire lifecycle corresponding to the spare parts management parameters as the objective function, adaptive constraint processing and competitive elimination are performed on the initial weed individuals and the offspring weed individuals, and the target decision vector is output.

[0006] Secondly, embodiments of the present invention provide a spare parts management device, comprising: The acquisition module is used to acquire spare parts management-related parameters of the spare parts to be managed. The spare parts management-related parameters include at least spare parts characteristic parameters, equipment status parameters, production plan parameters, supply chain association parameters, and inventory parameters. An initialization module is used to generate initial weed individuals based on the spare parts management-related parameters using an improved weed optimization algorithm. The initial weed individuals are encoded as an initial decision vector formed by the initial purchase quantity, the initial dynamic safety stock, and the initial reorder point. The reproduction and mutation module is used to determine the dynamic reproduction quantity and dynamic variation length based on the spare parts management related parameters, execute the reproduction and mutation of the initial weed individuals, and generate offspring weed individuals; The output module is used to perform adaptive constraint processing and competitive elimination on the initial weed individuals and the offspring weed individuals with the objective function of minimizing the total cost of the entire life cycle corresponding to the spare parts management-related parameters, and output the objective decision vector.

[0007] Thirdly, embodiments of the present invention provide an electronic device, including: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the method as described in the first aspect.

[0008] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that cause a processor to execute the method described in the first aspect.

[0009] Fifthly, embodiments of the present invention provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0010] The technical solution of this invention involves obtaining spare parts management-related parameters for the spare parts to be managed. These parameters include at least spare parts characteristic parameters, equipment status parameters, production plan parameters, supply chain correlation parameters, and inventory parameters. An improved weed optimization algorithm is used to generate initial weed individuals based on these parameters. Each initial weed individual is encoded as an initial decision vector formed by the initial purchase quantity, initial dynamic safety stock, and initial reorder point. Based on the spare parts management-related parameters, the dynamic reproduction quantity and dynamic variation time are determined, and the initial weed individuals are allowed to reproduce and mutate, generating offspring weed individuals. Using the minimum total lifecycle cost corresponding to the spare parts management-related parameters as the objective function, adaptive constraint processing and competitive elimination are performed on the initial weed individuals and the offspring weed individuals, outputting the target decision vector. This solution constructs a multi-dimensional parameter system encompassing equipment, spare parts, inventory, suppliers, and production. Combined with the adaptive decision-making capability of the improved weed optimization algorithm, it achieves intelligent decision-making regarding spare parts purchase quantity, dynamic safety stock, and reorder point. This minimizes the total lifecycle cost while ensuring production continuity, and is adaptable to spare parts management needs in dynamic production scenarios.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a spare parts management method provided according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of a spare parts management device according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device that implements an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Example 1 Figure 1 This is a flowchart of a spare parts management method according to Embodiment 1 of the present invention. This embodiment is applicable to situations involving spare parts management. The method can be executed by a spare parts management device, which can be implemented in software and / or hardware and integrated into an electronic device. Furthermore, the electronic device includes, but is not limited to, computers, laptops, servers, etc.

[0017] like Figure 1 As shown, the method includes: S110. Obtain the spare parts management related parameters of the spare parts to be managed. The spare parts management related parameters include at least spare parts characteristic parameters, equipment status parameters, production plan parameters, supply chain association parameters, and inventory parameters.

[0018] Spare parts to be managed can be understood as spare parts of a certain type that are to be managed; no specific limitation is made here.

[0019] Spare part characteristic parameters can be parameters that characterize the characteristics of the spare part itself, such as including but not limited to the following parameters: The spare parts importance coefficient, with a value range of [0,1], is a parameter used to distinguish the importance of spare parts. Important spare parts account for a larger proportion. This parameter can be obtained from the equipment operation and maintenance manual. Storage cost refers to the cost required to store spare parts, in yuan / piece. This parameter can be obtained from spare parts technical documents. Spare parts lifecycle is a parameter used to record the lifecycle of spare parts, measured in months. This parameter can be obtained from spare parts technical documents. Disposal cost refers to the cost of disposing of scrapped spare parts, in yuan / piece. This parameter can be obtained from scrapping records.

[0020] Equipment status parameters can be parameters that characterize the status of equipment or spare parts within equipment, and may include, but are not limited to, the following parameters: Spare parts usage time in equipment indicates the duration of use of spare parts in equipment, in months. This parameter can be obtained from equipment operation records, maintenance records, etc. Equipment load rate represents the ratio of actual equipment capacity to rated capacity. The higher the equipment load rate, the greater the operating intensity of the equipment. Average monthly consumption indicates the average monthly consumption of spare parts in the equipment, in units of pieces; The standard deviation of monthly average consumption reflects the fluctuations in monthly average consumption demand.

[0021] It should be noted that equipment status parameters can be obtained from the equipment sensing system. The equipment sensing system may include industrial sensors (such as vibration sensors, temperature sensors, speed sensors, etc.) installed on core components of key equipment such as winding and sealing units and packaging units, used to collect data related to the operating status of the equipment in real time; it may also include equipment operation recorders, used to record basic data such as changes in equipment load; and it may also include other components used for intelligent sensing, which are not limited here.

[0022] Production planning parameters can be parameters used to guide production, and may include, but are not limited to, the following parameters: Annual order quantity represents the annual order quantity of spare parts, in units of pieces. This parameter can be obtained from the production scheduling system. The production adjustment coefficient represents the adjustment range of the short-term production plan. For example, it is 1.2 when increasing production and 0.8 when reducing production. This parameter can be obtained from the production scheduling system. The seasonal production factor is related to seasonal changes; for example, it is taken as 1.3 during the peak season and 0.7 during the off-season. Predicted demand, such as the demand for spare parts in the next 7 days, in units of pieces, can be obtained by forecasting equipment status parameters and production plans.

[0023] Supply chain related parameters can be parameters related to the supply chain, such as, but not limited to, the following parameters: Fixed cost per order represents the fixed cost incurred when ordering spare parts. Unit procurement cost represents the cost required to procure a single spare part. The raw material fluctuation coefficient represents the fluctuation of raw material costs. When costs increase, the value is greater than 1, such as 1.2, and when costs are stable, the value is 1. Supplier reliability score, which characterizes the degree of supplier reliability, ranges from 0 to 1; Supplier capacity utilization rate represents the current proportion of a supplier's capacity being used, with a value ranging from 0.5 to 1; The supply channel risk coefficient represents the risk brought about by the supplier's supply channel, and its value ranges from 0 to 1. The logistics delay risk coefficient represents the risk caused by the supplier's logistics delays, and its value ranges from 0 to 1. Emergency procurement premium rate represents the ratio of emergency procurement cost to regular cost, with a value ranging from 1 to 2. Uncontrollable downtime losses represent losses caused by uncontrollable downtime due to emergency situations.

[0024] It should be noted that supply chain correlation parameters can be obtained from the supply chain information system by accessing the data stored in the system through its data interface.

[0025] Inventory parameters can be inventory-related parameters, such as, but not limited to, the following parameters: Current inventory represents the real-time inventory quantity of spare parts, in units of pieces; In-transit inventory represents the quantity of spare parts that have been ordered but not yet received into the warehouse, in units of pieces; Effective inventory, calculated as current inventory plus in-transit inventory minus projected demand, in units of pieces; Warehouse space utilization rate, actual occupied volume / available volume, with a value ranging from 0 to 1.

[0026] It should be noted that inventory parameters can be obtained from the intelligent inventory monitoring system. This system features real-time location and status monitoring capabilities. Each spare part is assigned a unique RFID tag, and readers within the warehouse identify the spare part's location and inbound / outbound records in real time. The system also includes intelligent shelving for the storage management of precision spare parts (such as circuit boards and precision gears). The shelving integrates weight sensors (for real-time monitoring of inventory quantities) and environmental sensors (for collecting temperature and humidity data to ensure compliant storage conditions).

[0027] S120. Using an improved weed optimization algorithm, an initial weed individual is generated based on the spare parts management related parameters. The initial weed individual is encoded as an initial decision vector formed by the initial purchase quantity, the initial dynamic safety stock, and the initial reorder point.

[0028] The weed optimization algorithm can be understood as an intelligent optimization algorithm that simulates the growth, reproduction, and competition processes of weeds in nature. Its core is to find the optimal solution through the reproduction, mutation, and competitive iteration of individual weeds (solutions). In this embodiment of the invention, the improved weed optimization algorithm is an improvement upon the traditional weed optimization algorithm, incorporating improvements adapted to component management scenarios in aspects such as initial solution generation, reproduction number adjustment, variable asynchronous length adjustment, and competition radius adjustment.

[0029] In this step, the initial purchase quantity can be determined by incorporating raw material fluctuation coefficients, which are related parameters in the supply chain, as a correction term on the basis of the traditional purchase quantity model; the initial minimum safety stock can be determined based on equipment status parameters under equipment load and spare parts demand fluctuations, and the importance of spare parts can be introduced to adjust the initial dynamic safety stock based on the initial minimum safety stock; the initial reorder point can be obtained from the intelligent inventory monitoring system; the three-dimensional vector formed by the initial purchase quantity, the initial dynamic safety stock, and the initial reorder point is used as the initial decision vector, i.e., the initial weed individual, for subsequent reproduction and mutation of the weed individual.

[0030] S130. Based on the spare parts management related parameters, determine the dynamic reproduction quantity and dynamic variation time, execute the reproduction and mutation of the initial weed individual, and generate offspring weed individuals.

[0031] In this step, the reproduction probability can be dynamically determined based on supply chain parameters and production plan parameters, taking into account supplier reliability, supplier capacity, production demand, etc., so that high-quality weed individuals can be preferentially spread. Based on the dynamically determined reproduction probability, the reproduction probability is converted into a specific reproduction quantity, that is, the dynamic reproduction quantity is determined. The dynamic variable length can be determined based on the raw material fluctuation coefficient included in the supply chain parameters, taking into account raw material fluctuations.

[0032] The process involves reproducing and mutating the initial weed individuals to generate offspring weed individuals. Reproduction refers to generating offspring based on a dynamic reproduction rate; mutation refers to generating offspring through Gaussian mutation based on a dynamically varying growth rate, such as using a Gaussian mutation with a mean of 0 and a variance of 0, based on the initial purchase quantity. Gaussian distributed random numbers are mutated by adding random perturbations.

[0033] It should be noted that during the reproduction and mutation process, the following constraints must be met for each dimension of the weed individual: the purchase quantity must be between 0 and the maximum purchase quantity limit; the dynamic safety stock must be between the minimum safety stock and twice the minimum safety stock; and the reorder point must be between 0.5 times the dynamic safety stock and the dynamic safety stock.

[0034] S140. Using the minimum total cost throughout the entire lifecycle corresponding to the spare parts management parameters as the objective function, perform adaptive constraint processing and competitive elimination on the initial weed individuals and the offspring weed individuals, and output the objective decision vector.

[0035] Total lifecycle cost corresponding to spare parts management parameters It can be expressed by the following formula: .

[0036] Among them, inventory costs Represented as: ; For the first Spare parts; For dynamic safety stock; For storage costs; Importance coefficient for spare parts; This refers to the utilization rate of warehouse space.

[0037] Procurement costs Represented as: ; For the first Spare parts; For purchase quantity; Unit procurement cost; Fixed cost per order; For supplier capacity utilization; This represents the risk coefficient for logistics delays.

[0038] Downtime costs Represented as: ; For the first Spare parts; Losses due to uncontrollable downtime caused by emergency spare parts availability; This is an indicator function used to determine whether to trigger downtime cost accounting. Its value is determined by the following rule: when the effective inventory... The function value is 1 when the value is less than 0, otherwise the function value is 0. This refers to the equipment load rate.

[0039] disposal costs Represented as: ; For the first Spare parts; This is an indicator function used to determine whether to trigger disposal cost accounting. Its value is determined by the following rule: when the spare part's lifecycle... - If the spare parts have a service life of less than 3 months, meaning the spare parts are nearing the end of their service life or are about to be scrapped, the function value is 1; otherwise, the function value is 0. For the first The disposal cost of such spare parts; For the first Current inventory of spare parts.

[0040] Emergency premium costs Represented as: ; For the first Spare parts; For emergency procurement; As an indicator function, its value selection rules are as follows: The value is 1 if the condition is met, and 0 otherwise. Unit procurement cost; Premium rate for emergency procurement.

[0041] With the goal of minimizing the total cost over the entire lifecycle, adaptive constraint processing and competitive elimination are applied to all weed individuals, including the initial weed individuals and their offspring. Specifically, the adaptive constraint processing can include attenuating the individual fitness, represented by the reciprocal of the total cost over the entire lifecycle, based on a penalty mechanism, and can also include repairing the variables involved in the penalty based on a repair mechanism. The penalty mechanism and the repair mechanism are not limited. The competitive elimination can be based on determining the competition radius according to the importance of spare parts and the risk of the supply channel, and then competitively eliminating weed individuals according to the determined competition radius.

[0042] When the results of adaptive constraint processing and competitive elimination meet the iteration termination condition, such as the individual fitness obtained from multiple consecutive iterations tending to stabilize, the algorithm converges and outputs the target decision vector, that is, the optimal combination of purchase quantity, dynamic safety stock and reorder point, so as to minimize the total cost of the whole cycle.

[0043] The technical solution of this invention involves obtaining spare parts management-related parameters for the spare parts to be managed. These parameters include at least spare parts characteristic parameters, equipment status parameters, production plan parameters, supply chain correlation parameters, and inventory parameters. An improved weed optimization algorithm is used to generate initial weed individuals based on these parameters. Each initial weed individual is encoded as an initial decision vector formed by the initial purchase quantity, initial dynamic safety stock, and initial reorder point. Based on the spare parts management-related parameters, the dynamic reproduction quantity and dynamic variation time are determined, and the initial weed individuals are allowed to reproduce and mutate, generating offspring weed individuals. Using the minimum total lifecycle cost corresponding to the spare parts management-related parameters as the objective function, adaptive constraint processing and competitive elimination are performed on the initial weed individuals and the offspring weed individuals, outputting the target decision vector. This solution constructs a multi-dimensional parameter system encompassing equipment, spare parts, inventory, suppliers, and production. Combined with the adaptive decision-making capability of the improved weed optimization algorithm, it achieves intelligent decision-making regarding spare parts purchase quantity, dynamic safety stock, and reorder point. This minimizes the total lifecycle cost while ensuring production continuity, and is adaptable to spare parts management needs in dynamic production scenarios.

[0044] In one embodiment, generating initial weed individuals based on the spare parts management related parameters includes: The initial purchase quantity is determined based on the annual order quantity included in the production planning parameters, the storage cost included in the spare parts characteristic parameters, and the fixed cost per order and raw material fluctuation coefficient included in the supply chain correlation parameters. The initial minimum safety stock is determined based on the equipment status parameters, including the average monthly consumption, equipment load rate, and standard deviation of average monthly consumption. The initial dynamic safety stock is then determined by combining the initial minimum safety stock with the spare parts importance coefficient, which is included in the spare parts characteristic parameters. The initial reorder point is obtained from the inventory intelligent monitoring system.

[0045] Initial purchase quantity It can be determined using the following formula: ; in, Annual order volume; Fixed cost per order; For storage costs; This represents the raw material fluctuation coefficient.

[0046] Initial dynamic safety stock It can be determined using the following formula: ; ; in, This represents the average monthly consumption. Equipment load rate; This represents the standard deviation of the average monthly consumption. To The coefficients used for adjustment; The initial minimum safety stock; This represents the importance coefficient for spare parts.

[0047] The initial reorder point can be obtained from the intelligent inventory monitoring system based on the spare parts type.

[0048] The above method can generate multiple initial weed individuals, such as 100.

[0049] In one embodiment, determining the dynamic reproduction quantity and dynamic variable duration based on the spare parts management related parameters includes: Based on the supplier reliability score and supplier capacity utilization rate included in the supply chain correlation parameters, and the production adjustment coefficient included in the production planning parameters, the dynamic reproduction probability is determined. Multiply the dynamic reproduction probability by the set number and then round down to obtain the dynamic reproduction number; The product of the set scaling factor and the raw material fluctuation factor, which is included in the supply chain-related parameters, is used as the dynamic variable length.

[0050] Dynamic reproduction probability It can be determined using the following formula: ; in, Rate the reliability of suppliers; This is a production adjustment factor; This refers to the supplier's capacity utilization rate.

[0051] Dynamic breeding quantity It can be determined using the following formula: This is to ensure that 1-5 offspring are produced during reproduction, thus guaranteeing population diversity.

[0052] Dynamic variable asynchronous length It can be represented as , This represents the raw material fluctuation coefficient.

[0053] In one embodiment, the adaptive constraint processing includes a penalty mechanism and a repair mechanism; The execution logic of the penalty mechanism includes: when a penalty condition is triggered, the individual fitness is reduced by the penalty coefficient associated with the penalty condition; the penalty condition is at least one of the following: the warehouse space utilization rate included in the inventory parameters exceeds a set utilization rate threshold, or there are expired spare parts; the individual fitness is the reciprocal of the total cost over the entire cycle. The execution logic of the repair mechanism includes: when the warehouse space utilization rate exceeds the set utilization rate threshold, reducing the purchase quantity by setting a reduction coefficient; and removing expired spare parts from the current inventory included in the inventory parameters when expired spare parts exist.

[0054] The punishment mechanism can be represented as: ; ; in, The total cost over the entire lifecycle, its reciprocal being the individual fitness. , The individual fitness after decay; This represents the warehouse space utilization rate, with 0.8 serving as the set utilization threshold. This is an indicator function; it takes the value 1 when the spare part expires and 0 otherwise. The penalty coefficient associated with the penalty condition is... .

[0055] The repair mechanism can be represented as: like By setting a reduction coefficient, such as 0.8, the purchase quantity is reduced, and the purchase quantity is multiplied by 0.8 to obtain the new purchase quantity; If expired spare parts exist, the quantity of expired spare parts is subtracted from the current inventory included in the inventory parameters to obtain the new current inventory.

[0056] In one embodiment, the execution logic for the competition elimination includes: In each iteration, the competition region is divided based on the competition radius. Within the same competition region, the individual with the highest fitness is retained, and the population size is kept constant. The competition radius is determined based on the spare part importance coefficient included in the spare part characteristic parameters and the supply channel risk coefficient included in the supply chain association parameters. If the individual fitness fluctuation is lower than the set fluctuation threshold in multiple consecutive iterations, the target decision vector is output.

[0057] Competition radius It can be represented as: ; in, Importance coefficient for spare parts; This refers to the risk factor of the supply chain.

[0058] Competition rules can be understood as, according to Divide the population into competitive regions. Within each competitive region, retain the individual with the highest fitness and eliminate the rest to maintain a fixed population size of 100.

[0059] If the fitness fluctuation of an individual is lower than a set fluctuation threshold of 1% for multiple consecutive iterations, such as three consecutive generations, the algorithm converges and outputs the optimal individual, which is the target decision vector; otherwise, it returns to continue executing the reproduction and mutation process and subsequent processes.

[0060] In one embodiment, the method further includes: If the current inventory, which is included in the inventory parameters, is lower than the product of the warning buffer coefficient and the predicted demand and seasonal production factor, which are included in the production planning parameters, the spare parts to be managed will be added to the inventory warning list. Purchase orders are generated based on the target decision vector.

[0061] That is, in Add the spare parts to be managed to the inventory warning list at that time. The current inventory is 0.7, and the early warning buffer coefficient is 0.7. To predict demand, It is a seasonal production factor.

[0062] Purchase orders are generated based on the target decision vector. This means that available inventory is continuously monitored based on the reorder point in the target decision vector. When available inventory drops to the reorder point, the system automatically triggers the procurement process and generates a purchase order. In this purchase order, the purchase quantity in the target decision vector is specified as the purchase quantity for this order, and dynamic safety stock is used as the lower limit of inventory to be maintained for this purchase, ensuring that the inventory can cover the risk of fluctuations after the order is executed.

[0063] Optionally, algorithm iteration optimization can be introduced, such as fine-tuning the hyperparameters of the improved weed optimization algorithm weekly with new data; comparing the cost difference between the improved weed optimization algorithm and historical decisions quarterly, and triggering algorithm reconstruction if the cost difference exceeds a set difference threshold, such as adjusting the competition radius formula.

[0064] Optionally, a default value setting can be set when the data source system fails. For example, when the supply chain information system fails, the supplier reliability score can be set to the average of the past three months, and the production adjustment coefficient can be set to 0.8.

[0065] Optionally, when the supply channel risk coefficient exceeds 0.5, the dynamic safety stock of core spare parts (i.e., spare parts with an importance coefficient exceeding 0.8) is increased by 50%, and backup suppliers are activated; when the warehouse space utilization rate exceeds 0.9, the procurement of non-emergency spare parts (i.e., spare parts with an importance coefficient below 0.5) is suspended, and priority is given to using near-expiration spare parts (i.e., spare parts with a remaining life of less than 3 months).

[0066] This invention achieves intelligent management of dynamic spare parts inventory by constructing a complete parameter system and an improved weed optimization algorithm. In practical applications, it can effectively reduce the shortage rate of core spare parts, and is especially suitable for industrial scenarios such as cigarette production where equipment continuity requirements are high and spare parts are complex, thus having significant technical value.

[0067] Example 2 Figure 2 This is a schematic diagram of a spare parts management device according to Embodiment 2 of the present invention. This embodiment can be applied to situations where spare parts management is implemented, such as... Figure 2 As shown, the specific structure of the device includes: The acquisition module 21 is used to acquire spare parts management related parameters of the spare parts to be managed. The spare parts management related parameters include at least spare parts characteristic parameters, equipment status parameters, production plan parameters, supply chain association parameters, and inventory parameters. Initialization module 22 is used to generate initial weed individuals based on the spare parts management related parameters using an improved weed optimization algorithm. The initial weed individuals are encoded as an initial decision vector formed by the initial purchase quantity, the initial dynamic safety stock, and the initial reorder point. The reproduction and mutation module 23 is used to determine the dynamic reproduction quantity and dynamic variation length based on the spare parts management related parameters, execute the reproduction and mutation of the initial weed individual, and generate offspring weed individuals; Output module 24 is used to perform adaptive constraint processing and competitive elimination on the initial weed individuals and the offspring weed individuals with the objective function of minimizing the total cost of the entire life cycle corresponding to the spare parts management related parameters, and output the objective decision vector.

[0068] The spare parts management device provided in this embodiment acquires spare parts management-related parameters of the spare parts to be managed through an acquisition module. These parameters include at least spare parts characteristic parameters, equipment status parameters, production plan parameters, supply chain correlation parameters, and inventory parameters. An initialization module uses an improved weed optimization algorithm to generate initial weed individuals based on these spare parts management-related parameters. Each initial weed individual is encoded as an initial decision vector formed by the initial purchase quantity, initial dynamic safety stock, and initial reorder point. A reproduction and mutation module determines the dynamic reproduction quantity and dynamic variation time based on the spare parts management-related parameters, and performs reproduction and mutation on the initial weed individuals to generate offspring weed individuals. An output module uses the minimum total cost corresponding to the spare parts management-related parameters throughout the entire lifecycle as the objective function, performs adaptive constraint processing and competitive elimination on the initial weed individuals and the offspring weed individuals, and outputs the target decision vector. This solution constructs a multi-dimensional parameter system encompassing equipment, spare parts, inventory, suppliers, and production. Combined with the adaptive decision-making capabilities of an improved weed optimization algorithm, it enables intelligent decision-making regarding spare parts procurement quantities, dynamic safety stock, and reorder points. This minimizes total lifecycle costs while ensuring production continuity, and is adaptable to spare parts management needs in dynamic production scenarios. Furthermore, initialization module 22 is specifically used for: The initial purchase quantity is determined based on the annual order quantity included in the production planning parameters, the storage cost included in the spare parts characteristic parameters, and the fixed cost per order and raw material fluctuation coefficient included in the supply chain correlation parameters. The initial minimum safety stock is determined based on the equipment status parameters, including the average monthly consumption, equipment load rate, and standard deviation of average monthly consumption. The initial dynamic safety stock is then determined by combining the initial minimum safety stock with the spare parts importance coefficient, which is included in the spare parts characteristic parameters. The initial reorder point is obtained from the inventory intelligent monitoring system.

[0069] Furthermore, the reproductive mutation module 23 is specifically used for: Based on the supplier reliability score and supplier capacity utilization rate included in the supply chain correlation parameters, and the production adjustment coefficient included in the production planning parameters, the dynamic reproduction probability is determined. Multiply the dynamic reproduction probability by the set number and then round down to obtain the dynamic reproduction number; The product of the set scaling factor and the raw material fluctuation factor, which is included in the supply chain-related parameters, is used as the dynamic variable length.

[0070] Furthermore, the adaptive constraint processing includes a penalty mechanism and a repair mechanism; The execution logic of the penalty mechanism includes: when a penalty condition is triggered, the individual fitness is reduced by the penalty coefficient associated with the penalty condition; the penalty condition is at least one of the following: the warehouse space utilization rate included in the inventory parameters exceeds a set utilization rate threshold, or there are expired spare parts; the individual fitness is the reciprocal of the total cost over the entire cycle. The execution logic of the repair mechanism includes: when the warehouse space utilization rate exceeds the set utilization rate threshold, reducing the purchase quantity by setting a reduction coefficient; and removing expired spare parts from the current inventory included in the inventory parameters when expired spare parts exist.

[0071] Furthermore, the execution logic for the competitive elimination process includes: In each iteration, the competition region is divided based on the competition radius. Within the same competition region, the individual with the highest fitness is retained, and the population size is kept constant. The competition radius is determined based on the spare part importance coefficient included in the spare part characteristic parameters and the supply channel risk coefficient included in the supply chain association parameters. If the individual fitness fluctuation is lower than the set fluctuation threshold in multiple consecutive iterations, the target decision vector is output.

[0072] Furthermore, the device also includes: The list addition module is used to add the spare parts to be managed to the inventory warning list when the current inventory, which is included in the inventory parameters, is lower than the product of the warning buffer coefficient and the predicted demand and seasonal production factor, which are included in the production plan parameters. The order generation module is used to generate purchase orders based on the target decision vector.

[0073] The spare parts management device provided in the embodiments of the present invention can execute the spare parts management method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0074] Example 3 Figure 3 This is a schematic diagram of the structure of an electronic device implementing embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0075] like Figure 3As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 performs various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0076] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0077] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as spare parts management methods.

[0078] In some embodiments, the spare parts management method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the spare parts management method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the spare parts management method by any other suitable means (e.g., by means of firmware).

[0079] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0080] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0081] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0082] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0083] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0084] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0085] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0086] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A spare parts management method, characterized in that, include: Obtain the spare parts management related parameters for the spare parts to be managed. The spare parts management related parameters include at least spare parts characteristic parameters, equipment status parameters, production plan parameters, supply chain related parameters, and inventory parameters. Using an improved weed optimization algorithm, initial weed individuals are generated based on the spare parts management-related parameters. The initial weed individuals are encoded as an initial decision vector formed by the initial purchase quantity, initial dynamic safety stock, and initial reorder point. Based on the spare parts management parameters, determine the dynamic reproduction quantity and dynamic variation time, execute the reproduction and mutation of the initial weed individuals, and generate offspring weed individuals; Using the minimum total cost throughout the entire lifecycle corresponding to the spare parts management parameters as the objective function, adaptive constraint processing and competitive elimination are performed on the initial weed individuals and the offspring weed individuals, and the target decision vector is output.

2. The method according to claim 1, characterized in that, Initial weed individuals are generated based on the aforementioned spare parts management parameters, including: The initial purchase quantity is determined based on the annual order quantity included in the production planning parameters, the storage cost included in the spare parts characteristic parameters, and the fixed cost per order and raw material fluctuation coefficient included in the supply chain correlation parameters. The initial minimum safety stock is determined based on the equipment status parameters, including the average monthly consumption, equipment load rate, and standard deviation of average monthly consumption. The initial dynamic safety stock is then determined by combining the initial minimum safety stock with the spare parts importance coefficient, which is included in the spare parts characteristic parameters. The initial reorder point is obtained from the inventory intelligent monitoring system.

3. The method according to claim 1, characterized in that, Based on the aforementioned spare parts management parameters, the dynamic reproduction quantity and dynamic variable time are determined, including: Based on the supplier reliability score and supplier capacity utilization rate included in the supply chain correlation parameters, and the production adjustment coefficient included in the production planning parameters, the dynamic reproduction probability is determined. Multiply the dynamic reproduction probability by the set number and round down to obtain the dynamic reproduction number; The product of the scaling factor and the raw material fluctuation factor, which is included in the supply chain-related parameters, is used as the dynamic variable length.

4. The method according to claim 1, characterized in that, The adaptive constraint processing includes a penalty mechanism and a repair mechanism; The execution logic of the penalty mechanism includes: when a penalty condition is triggered, the individual fitness is reduced by the penalty coefficient associated with the penalty condition; the penalty condition is at least one of the following: the warehouse space utilization rate included in the inventory parameters exceeds a set utilization rate threshold, or there are expired spare parts; the individual fitness is the reciprocal of the total cost over the entire cycle. The execution logic of the repair mechanism includes: when the warehouse space utilization rate exceeds the set utilization rate threshold, reducing the purchase quantity by setting a reduction coefficient; and removing expired spare parts from the current inventory included in the inventory parameters when expired spare parts exist.

5. The method according to claim 1, characterized in that, The execution logic for the competitive elimination includes: In each iteration, the competition region is divided based on the competition radius. Within the same competition region, the individual with the highest fitness is retained, and the population size is kept constant. The competition radius is determined based on the spare part importance coefficient included in the spare part characteristic parameters and the supply channel risk coefficient included in the supply chain association parameters. If the individual fitness fluctuation is lower than the set fluctuation threshold in multiple consecutive iterations, the target decision vector is output.

6. The method according to claim 1, characterized in that, Also includes: If the current inventory, which is included in the inventory parameters, is lower than the product of the warning buffer coefficient and the predicted demand and seasonal production factor, which are included in the production planning parameters, the spare parts to be managed will be added to the inventory warning list. Purchase orders are generated based on the target decision vector.

7. A spare parts management device, characterized in that, include: The acquisition module is used to acquire spare parts management-related parameters of the spare parts to be managed. The spare parts management-related parameters include at least spare parts characteristic parameters, equipment status parameters, production plan parameters, supply chain association parameters, and inventory parameters. An initialization module is used to generate initial weed individuals based on the spare parts management-related parameters using an improved weed optimization algorithm. The initial weed individuals are encoded as an initial decision vector formed by the initial purchase quantity, the initial dynamic safety stock, and the initial reorder point. The reproduction and mutation module is used to determine the dynamic reproduction quantity and dynamic variation length based on the spare parts management related parameters, execute the reproduction and mutation of the initial weed individuals, and generate offspring weed individuals; The output module is used to perform adaptive constraint processing and competitive elimination on the initial weed individuals and the offspring weed individuals with the objective function of minimizing the total cost of the entire life cycle corresponding to the spare parts management-related parameters, and output the objective decision vector.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.

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