CN117172441A - Special material guarantee decision planning method and device - Google Patents
Special material guarantee decision planning method and device Download PDFInfo
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Abstract
The invention belongs to the technical field of special material guarantee, and particularly relates to a special material guarantee decision-making planning method and device. The implementation process of the method comprises the following steps: constructing a mathematical model of a special material guarantee process, and solving the model by utilizing an improved genetic algorithm according to a decision-making planning target to obtain a planning result; the crossover operator adopted by the improved genetic algorithm is a single-body crossover operator, wherein the single-body crossover operator refers to turning over part of genes of one individual to generate a new individual, and the turning over refers to carrying out position exchange on the genes. According to the invention, a genetic algorithm is used for resolving the mathematical model of the special material guarantee process, and the crossover operator in the genetic algorithm performs position exchange for one chromosome (individual), so that the gene correction process in the conventional crossover operator is omitted, the optimal calculation of the mathematical model of the special material guarantee process is realized with high efficiency, and the calculation time is greatly reduced on the premise of meeting the special material guarantee requirement.
Description
Technical Field
The invention belongs to the technical field of special material guarantee, and particularly relates to a special material guarantee decision-making planning method and device.
Background
The special material guarantee mainly refers to the work contents of storage, unpacking/boxing, in-warehouse transfer, assembly/disassembly, vertical transfer, horizontal transfer, hanging and unloading and the like of guarantee objects such as missiles, bombs, torpedoes, pods, hanging beams, hanging frames, gas cylinders, fuzes, initiating explosive devices and the like, and transfers the guarantee objects with specified quantity and specification to specified positions in a specified time window, and completes specified guarantee operation contents. The special material guarantee is characterized in that: the security requirements of the guaranteed objects are higher, and the security requirements of different guaranteed objects are different; the timeliness requirement of the guarantee process is strong, if the required guarantee content cannot be completed in the appointed window time, the failure of the previous system task can be caused; the weight, the outline dimension, the external interface and other physical characteristics of the guarantee objects are large in difference, the specification of the guarantee objects is large, and the demand difference is large; when the types of the guarantee objects are various and different combinations of different guarantee links, guarantee equipment, guarantee channel areas and the like are considered, the number of possible task planning schemes to be faced by the special material guarantee system increases exponentially.
In summary, task planning of a special material support system is a typical combination optimization problem, and when the system scale is large, an optimal task planning scheme cannot be obtained by manual calculation. To solve this problem, genetic algorithms are often used in the prior art to solve this problem. The genetic algorithm is a method for searching an optimal solution through simulating a natural evolution process, and the included genetic operators comprise a crossover operator, a mutation operator and a selection operator. The cross matching flow of the cross operator commonly used in the prior art is shown in fig. 1-1 to 1-3, and the determination of the gene position of the cross operator is shown in fig. 1-1; 1-2, repeated genes exist in the chromosome after the crossover operation is finished, repeated genes '3', '5' exist in the Child1 chromosome, repeated genes '8', '11' exist in the Child2 chromosome, child1 and Child2 cannot be directly resolved from a coding space to a solution space, gene correction is needed to be carried out on Child1 and Child2 respectively, so that the effectiveness of crossover operators is ensured, the crossover parts do not participate in correction, and random correction is carried out on gene positions outside the crossover parts; 1-3, child1 and Child2 have completed correction, both chromosomes are legal, and one-to-one mapping of coding space to solution space can be achieved. According to the description of the process, the process is easy to generate pseudo solutions, and then the pseudo solution correction process is needed to be implemented to realize one-to-one mapping from the coding space to the solution space, so that the calculation consumption is obviously increased, the solution efficiency is low, and the requirement of a special material guarantee system for rapidly carrying out guarantee task planning cannot be met.
Disclosure of Invention
The invention aims to provide a special material guarantee decision-making planning method and device, which are used for solving the problem that genetic calculation in the prior art cannot meet the requirement of a special material guarantee system for rapidly carrying out guarantee task planning due to low solving efficiency.
In order to solve the technical problems, the invention provides a special material guarantee decision-making planning method, which comprises the steps of constructing a mathematical model of a special material guarantee process, and solving the model by using an improved genetic algorithm according to a decision-making planning target to obtain a planning result; the crossover operator adopted by the improved genetic algorithm is a single-body crossover operator, wherein the single-body crossover operator refers to turning over part of genes of one individual to generate a new individual, and the turning over refers to carrying out position exchange on the genes.
The beneficial effects of the technical scheme are as follows: according to the invention, a genetic algorithm is used for resolving a mathematical model of a special material guarantee process, and a crossover operator in the genetic algorithm is used for exchanging positions of two chromosomes instead of exchanging positions of the crossover region of the two chromosomes, so that the mode is based on guaranteeing individual diversity, a gene correction process in a conventional crossover operator is omitted, the optimal calculation of the special material guarantee process mathematical model is realized at high efficiency, the calculation time is greatly reduced on the premise of meeting the special material guarantee requirement, an effective and reliable optimal guarantee task planning scheme can be rapidly proposed, a basis is provided for smoothly implementing the special material guarantee, and a foundation is laid for improving the efficiency of a special material guarantee system.
Further, the partial genes include at least three genes.
The beneficial effects of the technical scheme are as follows: when part of genes are selected more, child of offspring has larger difference with Parent part, and the problem of falling into 'early ripening' (namely falling into local optimum) can be well avoided.
Further, the mathematical model of the special material guarantee process is as follows:
c max ＝c(j n ,m)
wherein c (j) i K) represents at j i The operation time when the kth operation link of the special materials in batches is completed, i=1, 2,3, …, n, k=1, 2,3, …, m, n represent the total batch times of the special materials, m represents the total number of operation links included in each batch of special materials, and each operation link included in each batch of special materials is the same; c max The maximum operation time of special material guarantee is represented.
The beneficial effects of the technical scheme are as follows: the mathematical model ensures the integrity of special material guarantee data and provides command decision basis for different levels of commanders.
Further, the decision plan is aimed at determining a dispatch sequence { j for a particular material lot 1 ,j 2 ,…,j n } to make c max Minimum.
In order to solve the technical problem, the invention also provides a special material guarantee decision-making planning device, which comprises a memory and a processor, wherein the processor is used for executing computer program instructions stored in the memory to realize the following method: constructing a mathematical model of a special material guarantee process, and solving the model by utilizing an improved genetic algorithm according to a decision-making planning target to obtain a planning result; the intersection operators adopted by the improved genetic algorithm are single-body intersection operators, and the single-body intersection operators are as follows: the inversion of a portion of the gene locus of an individual to create a new individual refers to the positional exchange of the gene locus.
The beneficial effects of the technical scheme are as follows: the device ensures the effective and reliable execution of the special material guarantee decision-making planning method. In addition, the method uses a genetic algorithm to calculate a mathematical model of a special material guarantee process, the crossover operator in the genetic algorithm does not exchange the crossover region of two chromosomes, but exchanges the positions of one chromosome (individual), the method omits the gene correction process in the conventional crossover operator on the basis of guaranteeing individual diversity, and the gene codes of all gene positions are legal, so that the optimization calculation of the mathematical model of the special material guarantee process is realized with high efficiency, the calculation time is greatly reduced on the premise of meeting the special material guarantee requirement, an effective and reliable optimal guarantee task planning scheme can be rapidly proposed, the basis is provided for smoothly implementing the special material guarantee, and the foundation is laid for improving the efficiency of the special material guarantee system.
Further, the partial loci include at least three loci.
The beneficial effects of the technical scheme are as follows: when part of genes are selected more, child of offspring has larger difference with Parent part, and the problem of falling into 'early ripening' (namely falling into local optimum) can be well avoided.
Further, the mathematical model of the special material guarantee process is as follows:
c max ＝c(j n ,m)
wherein c (j) i K) represents the j i The operation time from the special material of the batch to the kth link is i=1, 2,3, …, n, k=1, 2,3, …, m, n represents the total batch number of the special material, m represents the total number of operation links included in each batch of the special material, and each operation link included in each batch of the special material is the same; c max The maximum operation time of special material guarantee is represented.
The beneficial effects of the technical scheme are as follows: the mathematical model ensures the integrity of special material guarantee data and provides command decision basis for different levels of commanders.
Further, the decision plan is aimed at determining a special material lot sequence { j } 1 ,j 2 ,…,j n } to make c max Minimum.
Drawings
FIG. 1-1 is a schematic diagram of determining crossover operator gene sites during matched crossover using crossover operators in the prior art;
FIGS. 1-2 are schematic diagrams of genetic correction in matched crossover using crossover operators in the prior art;
FIGS. 1-3 are schematic diagrams of corrections made during matched crossover using crossover operators in the prior art;
FIG. 2-1 is a tree structure diagram of the special material guarantee of the present invention;
FIG. 2-2 is a data structure diagram of a special material assurance model of the present invention;
FIG. 3 is a schematic diagram of the individual crossover operator process of the present invention.
Detailed Description
The invention mainly aims at solving the problem of low solving efficiency of special material guarantee task planning by solving the constructed special material guarantee process mathematical model based on the genetic algorithm of the single body crossover operator, and can realize the special material guarantee decision planning method and the special material guarantee decision planning device based on the concept. The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent.
An embodiment of a special material guarantee decision-making planning method comprises the following steps:
the implementation process of the special material guarantee decision planning method is as follows:
1) And constructing a mathematical model of the special material guarantee process, and determining a decision planning target.
According to the special material guarantee flow, the special material guarantee flow can be divided into operation links such as storage position storage, in-warehouse transfer, unpacking/boxing, vehicle loading, transfer, guarantee object assembly, guarantee object hanging and unloading and the like, the operation time difference of different guarantee objects in each guarantee operation link is large, and the special material guarantee process can be described as follows: let c (j) i K) represents at j i The time of operation when the kth link of the batch of special materials is completed, { j 1 ,j 2 ,…,j n And the scheduling sequence of the special material batches is shown, the finishing time of the special material guarantee operation of m operation links for the total n batches of special materials can be shown as follows:
it should be noted that each batch of special materials includes the same operation links. The maximum operation time of the special material guarantee is as follows:
c max ＝c(j n ,m) (5)
the goal of decision planning is to determine { j } 1 ,j 2 ,…,j n } to make c max Minimum.
Specifically, each special material guarantee Task is expressed as Task, each Batch of special materials is expressed as Batch, and the Task is composed of a plurality of batches, describing smaller values of the maximum transport quantity and the actual demand quantity of certain special materials. Each Batch consists of several operations, which can be generally described as "storage site retrieval/storage," "in-warehouse transfer," "unpacking/boxing," and "vehicle loading," "transfer," "secure object assembly," "secure object mounting," etc. Each Operation may consist of several stages for describing several actions of each job link. The graph describing the relationship between Task, batch, operation and Stage through the tree structure is shown in fig. 2-1, and the dispatch director can pay attention to Task information, batch information, operation information or Stage information through the command hierarchy, respectively. Each Stage, operation and Batch consists of several identical data structures, including "start time", "end time", "start position" and "end position". c (j) i K) represents the time consuming Operation of each Batch, which refers only to the actual job time, excluding the latency of the relevant resources. The data structure is shown in fig. 2-2, which not only ensures the integrity of special material guarantee data, but also provides command decision basis for different levels of commanders. A plurality of Batch sets exist for each special material guarantee task according to c max ＝c(j n The solution requirement of m) determines c for the scheduling ordering of Batch max ＝c(j n M) the final effective solution.
2) And (3) solving the model constructed in the step (1) by utilizing an improved genetic algorithm according to the objective of decision planning, so as to obtain an optimal planning result meeting the objective. The crossover operator adopted by the improved genetic algorithm is a single-body crossover operator, wherein the single-body crossover operator refers to turning over part of genes of one individual to generate a new individual, and the turning over refers to carrying out position exchange on the genes.
As shown in FIG. 3, which shows a schematic diagram of single-body crossover, the Parent chromosome selects an appropriate crossover region, and uses a "flip" strategy to generate a new Child chromosome, specifically, the genes "9" and "10" are subjected to position exchange. Of course, the inversion strategy is not limited to the strategy used in fig. 3, and for example, the inverted genes may be plural, for example, the genes "1" and "13" may be further subjected to positional exchange in addition to positional exchange of the genes "9" and "10" to generate a new Child chromosome, and of course, these two genetic exchanges may be simultaneously performed, for example, after positional exchange of the genes "9" and "10" and further positional exchange of the genes "9" and "8" to generate a new Child chromosome, and so on. According to the method, the gene codes of all gene positions of the Child chromosome are legal, so that the partial matching cross gene correction process is omitted, the calculation efficiency can be improved by 40%, and meanwhile, the diversity of individuals is increased.
In summary, the invention provides a single-body crossover genetic algorithm, and the algorithm is applied to solving a mathematical model of a special material guarantee process, and the method can solve the problem of calculation resource consumption due to pseudo solution correction generated by a matched crossover genetic algorithm, and can better avoid the 'premature' problem of the traditional genetic algorithm due to controllable single-body crossover process. Therefore, the method greatly reduces the calculation time consumption on the premise of not reducing the special material guarantee planning quality, and provides a high-efficiency and orderly planning scheme for the special material guarantee implementation.
An embodiment of a special material guarantee decision-making planning device:
the embodiment of the special material guarantee decision-making planning device comprises a memory, a processor and an internal bus, wherein the processor and the memory are communicated with each other and data are interacted with each other through the internal bus. The processor is configured to execute computer program instructions stored in the memory to implement the methods described in the embodiments of the special material assurance decision making method of the present invention. The processor can be a microprocessor MCU, a programmable logic device FPGA and other processing devices; the memory may be various memories for storing information by using electric energy, or may be other memories.
Claims (8)
1. A special material guarantee decision-making planning method is characterized in that a special material guarantee process mathematical model is constructed, and the model is solved by utilizing an improved genetic algorithm according to a decision-making planning target to obtain a planning result; the crossover operator adopted by the improved genetic algorithm is a single-body crossover operator, wherein the single-body crossover operator refers to turning over part of genes of one individual to generate a new individual, and the turning over refers to carrying out position exchange on the genes.
2. The special material assurance decision-making method of claim 1, wherein the partial genes comprise at least three genes.
3. The special material guarantee decision-making method according to claim 1 or 2, wherein the special material guarantee process mathematical model is:
c max ＝c(j n ,m)
wherein c (j) i K) represents at j i The operation time when the kth operation link of the special materials in batches is completed, i=1, 2,3, …, n, k=1, 2,3, …, m, n represent the total batch times of the special materials, m represents the total number of operation links included in each batch of special materials, and each operation link included in each batch of special materials is the same; c max The maximum operation time of special material guarantee is represented.
4. A special material assurance decision-making method according to claim 3, wherein the decision-making is aimed at determining a dispatch sequence { j for a special material lot 1 ,j 2 ,…,j n } to make c max Minimum.
5. A special material assurance decision-making device comprising a memory and a processor, wherein the processor is configured to execute computer program instructions stored in the memory to implement the method of: constructing a mathematical model of a special material guarantee process, and solving the model by utilizing an improved genetic algorithm according to a decision-making planning target to obtain a planning result; the intersection operators adopted by the improved genetic algorithm are single-body intersection operators, and the single-body intersection operators are as follows: the inversion of a portion of the gene locus of an individual to create a new individual refers to the positional exchange of the gene locus.
6. The special material assurance decision-making device of claim 5, wherein the partial loci comprise at least three loci.
7. The special material assurance decision-making apparatus of claim 5 or 6, wherein the special material assurance process mathematical model is:
c max ＝c(j n ,m)
wherein c (j) i K) represents the j i The operation time from the special material of the batch to the kth link is i=1, 2,3, …, n, k=1, 2,3, …, m, n represents the total batch number of the special material, m represents the total number of operation links included in each batch of the special material, and each operation link included in each batch of the special material is the same; c max The maximum operation time of special material guarantee is represented.
8. The special material assurance decision-making apparatus of claim 7, wherein the decision-making is aimed at determining a special material lot sequence { j 1 ,j 2 ,…,j n } to make c max Minimum.
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| CN109255513A (en) * | 2018-07-18 | 2019-01-22 | 南瑞集团有限公司 | A kind of power telecom network scene work order dispatching method |
| CN111242454A (en) * | 2020-01-07 | 2020-06-05 | 杭州电子科技大学 | Chemical accident multi-target two-stage emergency rescue material scheduling method |
| CN115203631A (en) * | 2022-07-14 | 2022-10-18 | 安徽大学 | Multi-modal data analysis method and system based on improved genetic algorithm |
Non-Patent Citations (1)
| Title |
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| 张少辉 ，刘舜，李亚飞，金钊 ，靳远远，王少参， 赵建波，徐明亮: "航空母舰舰载机弹药保障作业调度优化算法", 《航空学报》, vol. 44, no. 20, 20 March 2023 (2023-03-20), pages 1 - 15 * |
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