Equipment support resource scheduling system and method based on multi-objective optimization

Abstract

The invention discloses an equipment support resource scheduling system and method based on multi-objective optimization. The method comprises the following steps: reading support resource information, equipment work content and equipment information participating in work from a database; generating equipment work requirements and importance grades; generating a guarantee resource optimization list according to the guarantee resource information and the equipment working content in combination with a multi-target resource scheduling model; extracting life guarantee resource data and guarantee resource fault and equipment maintenance data of the corresponding single equipment; according to the generated equipment work demand, the importance grade and the extracted data, generating an equipment guarantee resource replacement demand and work association analysis; carrying out comparative analysis on the resource replacement demand, the work association analysis and the guarantee resource optimization list, and judging whether a quantity demand is met or not; if yes, a guarantee resource candidate list is generated, matching sorting and benefit analysis are carried out, and a guarantee resource scheduling scheme is generated. The practical application difficulty and the operation cost of the algorithm can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of target optimization, and in particular to an equipment support resource scheduling system and method based on multi-objective optimization. Background Art

[0002] Currently, research methods for resource scheduling are mainly divided into two categories: the first type of research method is to develop a resource scheduling plan by establishing a reasonable mathematical programming model for computer simulation. The main representative methods include Petri net model, queuing theory, DEVS, UML, etc.; the second type is based on a pre-set resource allocation plan, and then uses algorithms for optimization analysis to obtain a more scientific and reasonable resource scheduling plan. The main research methods include genetic algorithms, ant colony algorithms, neural networks, etc. The above research focuses on theoretical research and is not very practical in practical application, which is mainly reflected in the following aspects:

[0003] 1. The algorithm is difficult to apply in practice. Computer simulation algorithms require continuous adjustment of simulation parameters based on analysis of simulation results and influencing factors to achieve a relatively reasonable resource scheduling solution. Genetic algorithms and ant colony algorithms are more complex to adjust parameters, placing higher demands on users and making them more difficult to use.

[0004] 2. High data dependency. Existing simulation algorithms require users to provide a large amount of accurate equipment data and support resource data when building simulation models. Otherwise, the simulation results of support resource scheduling decisions will be significantly affected.

[0005] 3. The algorithm has high operating costs. Computer simulation and swarm intelligence algorithms require a large amount of computing resources and high requirements for supporting hardware equipment, which increases the cost of algorithm implementation and application. Summary of the Invention

[0006] In response to the technical defects mentioned in the background technology, the purpose of the embodiments of the present invention is to provide an equipment support resource scheduling system and method based on multi-objective optimization, aiming to solve one of the technical problems in the related technology at least to a certain extent.

[0007] To achieve the above objectives, in a first aspect, an embodiment of the present invention provides an equipment support resource scheduling system based on multi-objective optimization, the system comprising:

[0008] The acquisition module is used to import data information from the database; wherein the data information includes read support resource information, equipment work content and equipment information participating in the work;

[0009] A decomposition module, configured to generate equipment work requirements and importance levels according to the equipment work content;

[0010] The first processing module is configured to:

[0011] Based on the support resource information and equipment work content and in combination with a pre-built multi-objective resource scheduling model, a preferred list of support resources based on remaining life is generated; wherein the multi-objective resource scheduling model is solved by converting the multi-objective problem into a single-objective planning problem and solving it based on the priority order of each objective;

[0012] An extraction module is used to extract the information of the equipment involved in the work to obtain the life support resource data and support resource failure and equipment maintenance data of the corresponding single equipment;

[0013] A second processing module is used to generate equipment support resource replacement requirements and work correlation analysis based on the generated equipment work requirements and importance classification and the extracted data;

[0014] A judgment module is used to compare and analyze the equipment support resource replacement demand and work correlation analysis with the support resource optimization list based on remaining life to determine whether the quantity requirement is met;

[0015] Scheduling module, used to:

[0016] If not, transfer requirements and purchase requirements will be generated;

[0017] If so, a list of security resources to be selected will be generated;

[0018] Based on the list of candidate security resources, matching sorting and benefit analysis are performed to generate a security resource scheduling plan.

[0019] As a specific implementation of the present application, the matching rule between guarantee resources and equipment is based on the life similarity criterion, so as to maximize the benefits.

[0020] As a specific implementation method of the present application, the selection principles of input basis variables, output basis variables and pivot elements in the solution process are the same as those of the simplex method of linear programming. The difference is that the achievement value of the lower-level goal is selected on the premise of not affecting the achievement value of the higher-level goal, and this iteration is repeated until the achievement function of the lowest-level goal is optimized.

[0021] As a specific implementation method of this application, when considering the optimal value of the next-level goal, the corresponding constraints of the previous-level goal must be considered at the same time, and the achievement value is added as a constraint to ensure that when optimizing the lower-level goal, the optimal value of the higher-level goal will not be degraded or destroyed.

[0022] As a specific implementation of the present application, when solving the model, a sequence method or a simplex method is used to obtain the corresponding optimal solution.

[0023] In a second aspect, an embodiment of the present invention further provides an equipment support resource scheduling method based on multi-objective optimization, which is applied to the equipment support resource scheduling system based on multi-objective optimization described in the first aspect, and the method includes the following steps:

[0024] Importing data information from a database; wherein the data information includes read support resource information, equipment work content, and equipment information participating in the work;

[0025] Generating equipment work requirements and importance levels according to the equipment work content;

[0026] Based on the support resource information and equipment work content and in combination with a pre-built multi-objective resource scheduling model, a preferred list of support resources based on remaining life is generated; wherein the multi-objective resource scheduling model is solved by converting the multi-objective problem into a single-objective planning problem and solving it based on the priority order of each objective;

[0027] Extracting the equipment information involved in the work to obtain life support resource data, support resource failure data, and equipment maintenance data of the corresponding individual equipment;

[0028] Generating equipment support resource replacement requirements and work correlation analysis based on the generated equipment work requirements and importance classification and the extracted data;

[0029] Compare and analyze the equipment support resource replacement requirements and work correlation analysis with the support resource optimization list based on remaining life to determine whether the quantity requirements are met;

[0030] If not, transfer requirements and purchase requirements will be generated;

[0031] If so, a list of security resources to be selected will be generated;

[0032] Based on the list of candidate security resources, matching sorting and benefit analysis are performed to generate a security resource scheduling plan.

[0033] The technical solution provided by the embodiment of the present invention decomposes the security decision-making objectives to determine the importance of the objectives through a resource scheduling model based on multi-objective optimization, optimizes and adjusts the allocation under limited resources, converts the multi-objective problem into a single-objective planning problem, and solves it based on the priority order of each objective to achieve the goal of maximizing resource utilization efficiency.

[0034] The entire solution reduces the complexity of implementing resource scheduling decision-making algorithms, simplifies the decision-making process such as simulation modeling and algorithm parameter configuration, and makes it more valuable for application in actual projects.

[0035] It also reduces the demand for algorithm data, and the data requirements for resource scheduling decisions can be met without complex and accurate data support; and reduces the operating costs of resource scheduling decisions. Fewer computing resources are required, and the requirements for supporting hardware equipment are lower. Resource scheduling plans can be quickly obtained, improving operational efficiency; and engineering experience such as resource failures and equipment maintenance that ensure resource scheduling is incorporated, so that the generated scheduling decision plan is more in line with the actual situation, improving decision-making efficiency while meeting user needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the specific implementation of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the specific implementation or the description of the prior art.

[0037] Figure 1 This is a principle block diagram of an equipment support resource scheduling system based on multi-objective optimization provided by an embodiment of the present invention;

[0038] Figure 2 This is a flow chart of an equipment support resource scheduling method based on multi-objective optimization provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0040] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0041] Please refer to Figure 1 An embodiment of the present invention provides an equipment support resource scheduling system based on multi-objective optimization, the system comprising:

[0042] The acquisition module is used to import data information from the database; wherein the data information includes read support resource information, equipment work content and equipment information participating in the work;

[0043] A decomposition module, configured to generate equipment work requirements and importance levels according to the equipment work content;

[0044] During implementation, the equipment work can be broken down into multiple sub-tasks. The required equipment models and quantities can be determined based on the content of each sub-task. Each sub-task has a different level of importance, and those with higher importance will be prioritized for resources.

[0045] The first processing module is configured to:

[0046] Based on the support resource information and equipment work content and in combination with a pre-built multi-objective resource scheduling model, a preferred list of support resources based on remaining life is generated; wherein the multi-objective resource scheduling model is solved by converting the multi-objective problem into a single-objective planning problem and solving it based on the priority order of each objective;

[0047] In this embodiment, after the equipment importance is graded, the required support resource list of the first-level important equipment is first extracted, and the life requirement of the equipment's support resources is calculated according to the equipment's working intensity. If the remaining life of the support resources is greater than the working time, it is listed in the preferred list; then the equipment of the next level of importance is extracted, and the preferred list of support resources for the equipment of this level is continued to be generated. And so on, the total preferred list of support resources can be obtained.

[0048] An extraction module is used to extract the information of the equipment involved in the work to obtain the life support resource data and support resource failure and equipment maintenance data of the corresponding single equipment;

[0049] A second processing module is used to generate equipment support resource replacement requirements and work correlation analysis based on the generated equipment work requirements and importance classification and the extracted data;

[0050] A judgment module is used to compare and analyze the equipment support resource replacement demand and work correlation analysis with the support resource optimization list based on remaining life to determine whether the quantity requirement is met;

[0051] Scheduling module, used to:

[0052] If not, transfer requirements and purchase requirements will be generated;

[0053] If so, a list of security resources to be selected will be generated;

[0054] Based on the list of candidate security resources, matching sorting and benefit analysis are performed to generate a security resource scheduling plan.

[0055] The data information in the database is shown in the following tables.

[0056] Table 1: Data structure of the security resource information model

[0057] Serial number Field Description 1 Securing resource keys 2 Guaranteed resource model 3 Guaranteed resource name 4 Warehouse time 5 Production time 6 Security resource classification 7 Total remaining life 8 Remaining life of the stage 9 Total lifespan 10 Total life of the stage 11 Technical status 12 Mean Time Between Repairs 13 Warehouse (location)

[0058] Table 2 Equipment information model data structure

[0059]

[0060]

[0061] Table 3 Work information model data structure

[0062]

[0063] Table 4 Equipment requirement model data structure

[0064]

[0065] Table 5 Maintenance consumption standard configuration model data structure

[0066]

[0067] It should be noted that the demand for replacement parts of the support resources of a single piece of equipment is related to the equipment's life consumption, failures and scheduled maintenance. As the working time of a single piece of equipment increases, the demand for replacement parts includes three parts: first, the support resources need to be replaced when they reach the end of their life cycle; second, the faulty parts need to be replaced according to the probability of failure; third, some support resources need to be replaced when the equipment repair time limit is reached.

[0068] The matching of support resources takes into account two aspects of benefits: one is work efficiency, that is, ensuring that equipment with high security importance can be satisfied to the maximum extent; the other is economic benefit, that is, trying to keep the life of support resources slightly longer than or equal to the life of equipment, to avoid the situation where the life of support resources and equipment are similar, but the support resources expire first, causing equipment maintenance and affecting work use.

[0069] The above is just an example and is not intended to be limiting.

[0070] The matching rule between support resources and equipment is based on the life similarity criterion to maximize benefits; that is, based on the remaining life or repair cycle of the equipment, support resources for equipment with a slightly longer remaining life or a slightly later repair cycle are prioritized.

[0071] In this embodiment, the multi-objective resource scheduling model design is to perform a trade-off analysis based on the objectives of guaranteeing mission importance, guaranteeing benefits, guaranteeing time, and guaranteeing matching, and to generate a guarantee resource scheduling plan by constructing a model.

[0072] Constructing a multi-objective resource scheduling model

[0073] Assuming that there are n types of support resources of a certain type, and the number of support resources required for equipment support is m, then There are a set of optional security resources S, and a reasonable security resource allocation plan needs to be obtained. If the equipment importance is divided into l levels, the multi-objective resource scheduling model for equipment of any level is as follows:

[0074]

[0075] Where, l i It is the target priority, which is divided into 2 levels. is the target positive and negative deviation, H iys To ensure the remaining life of resources, T iys is the remaining life of the equipment, D0 is the start time of equipment operation, D e is the end time of equipment work, and t0 is the average working intensity of a single piece of equipment.

[0076] Algorithm Implementation

[0077] When solving a multi-objective resource scheduling problem, it is usually difficult to meet multiple objective requirements. Therefore, a non-inferior solution is found according to the weight of the objectives, and the multi-objective problem is converted into a single-objective programming problem. The problem can be solved by adopting methods such as efficiency optimization and goal achievement. The basic idea of ​​the goal planning sequence (sequencer) algorithm is to decompose the goal planning model into a series of single linear programming models according to the priority of each goal in the achievement function. The traditional simplex method can be used to complete the solution process one by one. The selection principles of the input and output basis variables and pivot elements in the solution process are the same as those of the simplex method of linear programming. The difference is that the achievement value of the lower-level goal should be selected without affecting the achievement value of the higher-level goal. This iteration is repeated until the achievement function of the lowest-level goal is optimized.

[0078] Step 1: Let i = 1 (l i Represents the priority level of the target being considered, and there are k levels in total), and establishes a resource scheduling model containing only level l1 targets:

[0079]

[0080] Here, i∈l1 refers to the constraints only considering the l1-level objectives, and f1 refers to the objective function only considering the l1-level objectives; e i represents the expected target value of the i-th goal; st represents the constraint; x represents the decision variable.

[0081] Step 2: Solve this model using the sequence method, simplex method, etc., and get the problem f1=f1(d + ,d - )of That is the optimal value that can be achieved by the l1-level goal in the original goal planning.

[0082] Step 3: Set i=i+1. If i>k, go to step 5, otherwise go to the next step.

[0083] Step 4: Create the next priority level i Single-objective resource scheduling model:

[0084]

[0085]

[0086] Here, i∈l1∪l2...∪l i It means that when considering the optimal value of the next level goal, the corresponding constraints of the previous level goal must be considered at the same time, and additional constraints must also be considered. This ensures that the optimal value of the higher-level objective will not be degraded or destroyed when optimizing the lower-level objective.

[0087]

[0088] In this embodiment, the matching rule between support resources and equipment is based on the life similarity criterion to maximize benefits; that is, based on the remaining life or repair cycle of the equipment, support resources for equipment with a slightly longer remaining life or a slightly later repair cycle are prioritized for matching.

[0089] Step 5: The solution of the last single-objective linear programming model is the solution of the original objective programming model, and is the following vector: It also reflects the degree of achievement of each goal, also known as the achievement vector.

[0090] The above solution uses a resource scheduling model based on multi-objective optimization to decompose the security decision-making objectives to determine their importance. It then optimizes and adjusts the allocation within the context of limited resources, transforming the multi-objective problem into a single-objective planning problem and solving it based on the priority order of each objective to maximize resource utilization efficiency.

[0091] The entire solution reduces the complexity of implementing resource scheduling decision-making algorithms, simplifies the decision-making process such as simulation modeling and algorithm parameter configuration, and makes it more valuable for application in actual projects.

[0092] It also reduces the demand for algorithm data, and the data requirements for resource scheduling decisions can be met without complex and accurate data support; and reduces the operating costs of resource scheduling decisions. Fewer computing resources are required, and the requirements for supporting hardware equipment are lower. Resource scheduling plans can be quickly obtained, improving operational efficiency; and engineering experience such as resource failures and equipment maintenance that ensure resource scheduling is incorporated, so that the generated scheduling decision plan is more in line with the actual situation, improving decision-making efficiency while meeting user needs.

[0093] Reference Figure 2 Based on the same inventive concept, an embodiment of the present invention further provides an equipment support resource scheduling method based on multi-objective optimization, which is applied to the equipment support resource scheduling system based on multi-objective optimization described in the first aspect. The method includes the following steps:

[0094] Importing data information from a database; wherein the data information includes read support resource information, equipment work content, and equipment information participating in the work;

[0095] Generating equipment work requirements and importance levels according to the equipment work content;

[0096] Based on the support resource information and equipment work content and in combination with a pre-built multi-objective resource scheduling model, a preferred list of support resources based on remaining life is generated; wherein the multi-objective resource scheduling model is solved by converting the multi-objective problem into a single-objective planning problem and solving it based on the priority order of each objective;

[0097] Extracting the equipment information involved in the work to obtain life support resource data, support resource failure data, and equipment maintenance data of the corresponding individual equipment;

[0098] Generating equipment support resource replacement requirements and work correlation analysis based on the generated equipment work requirements and importance classification and the extracted data;

[0099] Compare and analyze the equipment support resource replacement requirements and work correlation analysis with the support resource optimization list based on remaining life to determine whether the quantity requirements are met;

[0100] If not, transfer requirements and purchase requirements will be generated;

[0101] If so, a list of security resources to be selected will be generated;

[0102] Based on the list of candidate security resources, matching sorting and benefit analysis are performed to generate a security resource scheduling plan.

[0103] Furthermore, the matching rules between support resources and equipment are based on the lifespan similarity criterion to maximize benefits.

[0104] The principles for selecting input and output basis variables and pivot elements in the solution process are the same as those of the simplex method of linear programming. The difference is that the achievement value of the lower-level goal must be selected without affecting the achievement value of the higher-level goal. This process is repeated until the achievement function of the lowest-level goal is optimized.

[0105] When considering the optimal value of the next-level goal, the corresponding constraints of the previous-level goal must be considered at the same time, and the achievement value must be added as a constraint to ensure that the optimal value of the higher-level goal obtained will not be degraded or destroyed when optimizing the lower-level goal.

[0106] It should be noted that for a more specific description of the workflow of the method embodiment, please refer to the aforementioned system embodiment section, which will not be repeated here.

[0107] The entire solution reduces the complexity of implementing resource scheduling decision-making algorithms, simplifies the decision-making process such as simulation modeling and algorithm parameter configuration, and makes it more valuable for application in actual projects.

[0108] It also reduces the demand for algorithm data, and the data requirements for resource scheduling decisions can be met without complex and accurate data support; and reduces the operating costs of resource scheduling decisions. Fewer computing resources are required, and the requirements for supporting hardware equipment are lower. Resource scheduling plans can be quickly obtained, improving operational efficiency; and engineering experience such as resource failures and equipment maintenance that ensure resource scheduling is incorporated, so that the generated scheduling decision plan is more in line with the actual situation, improving decision-making efficiency while meeting user needs.

[0109] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. An equipment support resource scheduling system based on multi-objective optimization, characterized in that: The system comprises: The acquisition module is used to import data information from the database; wherein the data information includes read support resource information, equipment work content and equipment information participating in the work; A decomposition module, configured to generate equipment work requirements and importance levels according to the equipment work content; The first processing module is configured to: Based on the support resource information and equipment work content and in combination with a pre-built multi-objective resource scheduling model, a preferred list of support resources based on remaining life is generated; wherein the multi-objective resource scheduling model is solved by converting the multi-objective problem into a single-objective planning problem and solving it based on the priority order of each objective; An extraction module is used to extract the information of the equipment involved in the work to obtain the life support resource data and support resource failure and equipment maintenance data of the corresponding single equipment; A second processing module is used to generate equipment support resource replacement requirements and work correlation analysis based on the generated equipment work requirements and importance classification and the extracted data; A judgment module is used to compare and analyze the equipment support resource replacement demand and work correlation analysis with the support resource optimization list based on remaining life to determine whether the quantity requirement is met; Scheduling module, used to: If not, transfer requirements and purchase requirements will be generated; If so, a list of security resources to be selected will be generated; Based on the list of candidate security resources, matching sorting and benefit analysis are performed to generate a security resource scheduling plan.

2. The equipment support resource scheduling system based on multi-objective optimization according to claim 1, characterized in that: The matching rules between support resources and equipment are based on the life similarity criterion to maximize benefits.

3. The equipment support resource scheduling system based on multi-objective optimization according to claim 2, characterized in that: The principles for selecting input and output basis variables and pivot elements in the solution process are the same as those of the simplex method of linear programming. The difference is that the achievement value of the lower-level goal must be selected without affecting the achievement value of the higher-level goal. This process is repeated until the achievement function of the lowest-level goal is optimized.

4. The equipment support resource scheduling system based on multi-objective optimization according to claim 3, characterized in that: When considering the optimal value of the next-level goal, the corresponding constraints of the previous-level goal must be considered at the same time, and the achievement value must be added as a constraint to ensure that the optimal value of the higher-level goal obtained will not be degraded or destroyed when optimizing the lower-level goal.

5. The equipment support resource scheduling system based on multi-objective optimization according to any one of claims 1 to 4, characterized in that: When solving the model, the sequence method or simplex method is used to obtain the corresponding optimal solution.

6. A method for scheduling equipment support resources based on multi-objective optimization, characterized in that: Applied to the equipment support resource scheduling system based on multi-objective optimization described in claim 1, the method comprises the following steps: Importing data information from a database; wherein the data information includes read support resource information, equipment work content, and equipment information participating in the work; Generating equipment work requirements and importance levels according to the equipment work content; Based on the support resource information and equipment work content and in combination with a pre-built multi-objective resource scheduling model, a preferred list of support resources based on remaining life is generated; wherein the multi-objective resource scheduling model is solved by converting the multi-objective problem into a single-objective planning problem and solving it based on the priority order of each objective; Extracting the equipment information involved in the work to obtain life support resource data, support resource failure data, and equipment maintenance data of the corresponding individual equipment; Generating equipment support resource replacement requirements and work correlation analysis based on the generated equipment work requirements and importance classification and the extracted data; Compare and analyze the equipment support resource replacement requirements and work correlation analysis with the support resource optimization list based on remaining life to determine whether the quantity requirements are met; If not, transfer requirements and purchase requirements will be generated; If so, a list of security resources to be selected will be generated; Based on the list of candidate security resources, matching sorting and benefit analysis are performed to generate a security resource scheduling plan.

7. The equipment support resource scheduling method based on multi-objective optimization according to claim 6, characterized in that: The matching rules between support resources and equipment are based on the life similarity criterion to maximize benefits.

8. The equipment support resource scheduling method based on multi-objective optimization according to claim 7, characterized in that: The principles for selecting input and output basis variables and pivot elements in the solution process are the same as those of the simplex method of linear programming. The difference is that the achievement value of the lower-level goal must be selected without affecting the achievement value of the higher-level goal. This process is repeated until the achievement function of the lowest-level goal is optimized.

9. The equipment support resource scheduling method based on multi-objective optimization according to claim 8, characterized in that: When considering the optimal value of the next-level goal, the corresponding constraints of the previous-level goal must be considered at the same time, and the achievement value must be added as a constraint to ensure that the optimal value of the higher-level goal obtained will not be degraded or destroyed when optimizing the lower-level goal.

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