CN113344343A - Equipment maintenance training personnel post optimization matching method based on capacity - Google Patents

Equipment maintenance training personnel post optimization matching method based on capacity Download PDF

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CN113344343A
CN113344343A CN202110551993.1A CN202110551993A CN113344343A CN 113344343 A CN113344343 A CN 113344343A CN 202110551993 A CN202110551993 A CN 202110551993A CN 113344343 A CN113344343 A CN 113344343A
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曹继平
曹小平
王涛
李锋
李亚奇
蔡艳平
刘建友
韩晓霞
李静
王新军
苏延召
姜柯
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Rocket Force University of Engineering of PLA
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Abstract

The invention discloses a capability-based method for optimizing and matching posts of equipment maintenance trainers, which comprises the following steps: step S1: constructing a capability evaluation model of equipment maintenance training personnel; step S2: constructing an optimized matching model of equipment maintenance training personnel; step S3: and performing human resource optimization configuration by using the equipment maintenance training personnel optimization matching model. Utilize equipment maintenance training personnel ability evaluation model to evaluate personnel maintenance training ability, through maintenance training evaluation objective maintenance personnel ability of maintaining, carry out reasonable personnel's structural adjustment to specific post, can avoid manpower resources's waste, effectively improve the maintenance benefit.

Description

Equipment maintenance training personnel post optimization matching method based on capacity
Technical Field
The invention belongs to the technical field of equipment maintenance, and particularly relates to a method for optimizing and matching posts of equipment maintenance trainers based on capacity.
Background
The maintenance training personnel refer to training personnel and serve as subjective active main bodies of equipment maintenance training activities, and the maintenance training personnel are important components of equipment maintenance training resources. Whether the ability, level and quantity of the training personnel meet the requirements or not directly influences the effect of equipment maintenance training. The main purpose of the maintenance training personnel evaluation is to research and evaluate whether the maintenance training personnel ability can adapt to the corresponding post requirement according to the organization training post requirement, and adjust the training plan in time, optimize the training scheme and improve the training benefit.
a) Basic characteristics of equipment maintenance trainers. The equipment maintenance training personnel are the most basic and important resources for training, and compared with material resources, the equipment maintenance training personnel have the characteristics of motility, plasticity, fluidity, continuity in the development process and the like. Equipment maintenance trainee's motility: the training device can stimulate the working potential of the training personnel and improve the training efficiency by improving the working capacity and the training motivation of the training personnel. Plasticity of equipment maintenance training personnel: the training personnel continuously improve the working capacity of the training personnel through repair and training to meet the requirements of posts, and the plasticity of manpower resources for equipment maintenance and training is particularly important as the equipment is updated and replaced more frequently. Mobility of equipment maintenance trainers: the frequent change of equipment maintenance trainers caused by activities such as movement, lifting and the like of trainers at different posts and different units is indicated. Persistence of the development process: the equipment maintenance training personnel also need to be continuously trained in the training process, and the requirements of maintenance training can be met by continuously developing manpower resources.
b) And (6) classifying. The equipment maintenance training personnel mainly comprise various personnel such as maintenance training commanders, management, theories, technologies, scientific researches and the like, and the dividing method comprises the following steps: the device can be divided into a primary level, a middle level and a high level according to the hierarchy; the method is divided according to knowledge structure and can be divided into theoretical type, technical type and management type, theoretical management type, technical management type, theoretical technical type, comprehensive type and the like. FIG. 1 is a diagram of equipment maintenance trainers divided according to knowledge structure.
The management type personnel are the core of the organization maintenance training, the theoretical type personnel are the foundation of the maintenance training organization, and the technical type personnel are the basic units for developing the maintenance training. The theoretical management personnel mainly undertake maintenance training management and scientific research work; the technical management personnel are mainly responsible for technical management work in the equipment maintenance process; the theoretical technical personnel have stronger capability of solving practical training problems and are experts in theory and technology; the comprehensive personnel are the complex personnel for maintenance and training.
Equipment maintenance trainers capability assessment is a complex dynamic system. The evaluation of the ability of maintenance training personnel is important content of the evaluation of the equipment maintenance training system, is the basis for carrying out optimized configuration on maintenance training human resources, and the evaluation result is used as the basis for the optimized configuration of personnel. And the personnel ability level can be qualified for the related work of equipment maintenance training only when the requirement is met, otherwise, the related ability training is required.
When describing the ability of equipment maintenance trainers, the description cannot be simply described as being able to meet the post or unable to meet the post requirements, and the description is too fuzzy to accurately and comprehensively reflect the ability of the personnel.
Disclosure of Invention
The invention aims to provide a method for optimizing and matching the posts of equipment maintenance trainers based on capacity aiming at the existing technical problems.
The technical scheme adopted by the invention is as follows:
a method for optimizing and matching posts of equipment maintenance trainers based on capacity comprises the following steps:
step S1: constructing a capability evaluation model of equipment maintenance training personnel:
s11: determining an evaluation index set, and constructing an equipment maintenance training personnel capability evaluation index system: determining a plurality of evaluation indexes by analyzing 3 factors of basic ability, time ability and innovation ability which restrict the ability of equipment maintenance trainers to form an index set, thereby constructing an equipment maintenance trainer ability evaluation index system;
s12: calculating the weights of the plurality of evaluation indexes determined in the step S11 so as to determine the weights;
s13: forming an equipment maintenance training personnel capability evaluation model by the constructed equipment maintenance training personnel capability evaluation index system and determining the weights of a plurality of evaluation indexes contained in the equipment maintenance training personnel capability evaluation index system;
step S2: constructing an equipment maintenance training personnel optimization matching model:
s21: evaluating the personnel maintenance training ability by utilizing an equipment maintenance training personnel ability evaluation model;
s22: determine the optimal matching model for the equipment maintenance trainee as
Figure BDA0003075841540000031
cijMatching the ith individual with the jth post comprehensive score; let M equal max { cijGet cij′＝M-cijThen the maximum value to be solved is converted into the minimum value, i.e.
Figure BDA0003075841540000032
Wherein xijThe decision variable represents whether the ith person is matched with the jth post, and the specific assignment mode is as follows: when the ith person matches the jth position, xij1, otherwise xij＝0；
Step S3: and performing human resource optimization configuration by using the equipment maintenance training personnel optimization matching model.
Preferably, in step S11, the evaluation indexes include a learning ability factor, a knowledge amount factor, a learning history factor, an operation and use ability factor, a maintenance skill factor, an analysis and troubleshooting ability factor, a teaching and group training ability factor, an ability factor for solving new problems, an exploration ability factor, and an innovation awareness factor.
Preferably, in step S12, the calculation process of the weight of the evaluation index specifically includes:
step S121: establishing a judgment matrix A ═ aij)m×n，
Figure BDA0003075841540000041
Wherein i is 1,2, …, m; j is 1,2, …, n, aijRepresenting the evaluation value of the ith expert on the jth index;
step S122: normalizing the judgment matrix to obtain a matrix R ═ (R)ij)m×nFor larger and more satisfactory indicators, there are:
Figure BDA0003075841540000042
for smaller, more satisfactory indicators, there are:
Figure BDA0003075841540000043
in the larger and more satisfactory index, amaxIs the most satisfactory of the same index, aminIs the least satisfied; smaller and more satisfactory index, aminIs the most satisfactory of the same index, amaxIs the least satisfied;
if all the indexes of the personnel ability are larger and better, normalization is carried out by using a formula I, and the result is as follows:
Figure BDA0003075841540000044
step S123: determining the entropy of each evaluation index as follows:
Figure BDA0003075841540000045
in the above formula, i is 1,2, …, m; j is 1,2, …, n;
pijis defined as:
Figure BDA0003075841540000046
the entropy value of each evaluation index is obtained by calculation as follows: h (H)1,H2,…,H10)＝(0.7421,0.5803,0.8734,0.8441,0.7737,0.7372,0.5888,0,0.8824,0.7435)；
Step S124: calculating the weight value W (W) of each evaluation index1,w2,…,wn)：
Figure BDA0003075841540000051
Obtaining W (W) by calculation1,w2,…,w10)＝(0.0797,0.1298,0.0391,0.0482,0.0700,0.0812,0.1271,0.3092,0.0364,0.0793)。
Preferably, in step S12, the calculation process of the weight of the evaluation index specifically includes:
step S121: constructing an evaluation index characteristic value matrix A ═ (b)ij)m×n；
Figure BDA0003075841540000052
Step S122: calculating the coefficient of variation of the jth evaluation index
Figure BDA0003075841540000053
Wherein D is the mean square error of the j-th evaluation index characteristic value,
Figure BDA0003075841540000054
Figure BDA0003075841540000055
the characteristic value mean value of the jth evaluation index is obtained; namely, it is
Figure BDA0003075841540000056
Step S123: calculating the weight w of the jth evaluation indexj′；
Figure BDA0003075841540000057
The weighted value of each evaluation index obtained by the variation coefficient method is as follows: w '(W'1,w′2,…,w′10)＝(0.0778,0.0799,0.1601,0.1826,0.0505,0.1355,0.0906,0.0913,0.0624,0.0693)。
Preferably, in step S12, the calculation process of the weight of the evaluation index specifically includes:
step S121: establishing a judgment matrix A ═ aij)m×n，
Figure BDA0003075841540000061
Wherein i is 1,2, …, m; j is 1,2, …, n, aijRepresenting the evaluation value of the ith expert on the jth index;
step S122: normalizing the judgment matrix to obtain a matrix R ═ (R)ij)m×nFor larger and more satisfactory indicators, there are:
Figure BDA0003075841540000062
for smaller, more satisfactory indicators, there are:
Figure BDA0003075841540000063
in the larger and more satisfactory index, amaxIs the most satisfactory of the same index, aminIs the least satisfied; smaller and more satisfactory index, aminIs the most satisfactory of the same index, amaxIs the least satisfied;
if all the indexes of the personnel ability are larger and better, normalization is carried out by using a formula I, and the result is as follows:
Figure BDA0003075841540000064
step S123: determining the entropy of each evaluation index as follows:
Figure BDA0003075841540000065
in the above formula, i is 1,2, …, m; j is 1,2, …, n;
pijis defined as:
Figure BDA0003075841540000066
the entropy value of each evaluation index is obtained by calculation as follows: h (H)1,H2,…,H10)＝(0.7421,0.5803,0.8734,0.8441,0.7737,0.7372,0.5888,0,0.8824,0.7435)；
Step S124:calculating the weight value W (W) of each evaluation index1,w2,…,wn)：
Figure BDA0003075841540000071
Obtaining W (W) by calculation1,w2,…,w10)＝(0.0797,0.1298,0.0391,0.0482,0.0700,0.0812,0.1271,0.3092,0.0364,0.0793)；
Step S125: constructing an evaluation index characteristic value matrix A ═ (b)ij)m×n；
Figure BDA0003075841540000072
Step S126: calculating the coefficient of variation of the jth evaluation index
Figure BDA0003075841540000073
Wherein D is the mean square error of the characteristic value of the jth evaluation index
Figure BDA0003075841540000074
Figure BDA0003075841540000075
The characteristic value mean value of the jth evaluation index is obtained; namely, it is
Figure BDA0003075841540000076
Step S127: calculating the weight w of the jth evaluation indexj′；
Figure BDA0003075841540000077
The weighted value of each evaluation index obtained by the variation coefficient method is as follows: w '(W'1,w′2,…,w′10)＝(0.0778,0.0799,0.1601,0.1826,0.0505,0.1355,0.0906,0.0913,0.0624,0.0693)；
Step S128: weighting by using the weighted value combination of each evaluation index calculated in step S124 and step S127:
vi＝λwj′+(1-λ)wj(formula seven)
Wherein, the preference coefficient λ is 0.5, and according to the formula seven, the final weight obtained is: v ═ V (V)1,v2,…,v10)＝(0.0788,0.1048,0.0996,0.1154,0.0602,0.1083,0.1089,0.2003,0.0494,0.0743)。
Compared with the prior art, the invention has the beneficial effects that: the invention relates to a capacity-based equipment maintenance trainer post optimization matching method, which determines a plurality of evaluation indexes to form an index set by analyzing 3 factors of basic capacity, time capacity and innovation capacity which restrict the capacity of equipment maintenance trainers, thereby constructing an equipment maintenance training personnel capability evaluation index system, calculating the weight of each evaluation index, forming an equipment maintenance training personnel capability evaluation model by the constructed equipment maintenance training personnel capability evaluation index system and determining the weight of each evaluation index contained in the equipment maintenance training personnel capability evaluation index system, evaluating the personnel maintenance training capability by using the equipment maintenance training personnel capability evaluation model, maintenance ability of maintenance personnel is objectively determined through maintenance training and evaluation, reasonable personnel structure adjustment is carried out for specific posts, waste of human resources can be avoided, and maintenance benefits are effectively improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a diagram of a division of a knowledge structure of a maintenance trainer;
FIG. 2 is a flow chart of a method for capability-based optimal matching of equipment maintenance trainers stations, according to an embodiment of the present invention;
FIG. 3 is a chart of equipment serviceman capability evaluation index system of FIG. 2;
fig. 4 is a flowchart of calculating the weight of each evaluation index in example 3.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 2 and 3, the invention specifically discloses a method for optimizing and matching stations of equipment maintenance trainers based on capabilities, which comprises the following steps:
step S1: constructing a capability evaluation model of equipment maintenance training personnel:
s11: determining an evaluation index set, and constructing an equipment maintenance training personnel capability evaluation index system: through analyzing 3 factors of basic ability, time ability and innovation ability restricting the ability of equipment maintenance training personnel, 10 evaluation indexes are determined, which are respectively as follows: learning ability factors, mastered knowledge quantity factors, academic factors, operation and use ability factors, maintenance skill factors, analysis and discharge failure ability factors, teaching and group training ability factors, ability factors for solving new problems, exploration ability factors and innovation consciousness factors to form an index set, thereby constructing an equipment maintenance training personnel ability evaluation index system;
s12: calculating the weights of the 10 evaluation indexes determined in the step S11, thereby determining the weights; the specific process comprises the following steps:
s121: establishing a judgment matrix A ═ aij)m×nWherein i is 1,2, …, m; j is 1,2, …, n, aijIndicating the ratio of the magnitudes of influence of index i and index j, i.e.
Figure BDA0003075841540000101
Is provided with 6 experts M (M)1,M2,…,M6) The 10-way capacity of the equipment maintenance trainers was scored separately. In the scoring process, the score ranges from 0 to 4, with higher scores indicating a greater ability of the person being evaluated in this regard and conversely weaker scores. The evaluation results are shown in table one:
table-training support personnel each index evaluation value
Figure BDA0003075841540000102
S122: normalizing the judgment matrix to obtain a matrix R ═ (R)ij)m×nFor larger and more satisfactory indicators, there are:
Figure BDA0003075841540000103
for smaller, more satisfactory indicators, there are:
Figure BDA0003075841540000104
in the larger and more satisfactory index, amaxIs the most satisfactory of the same index, aminIs the least satisfied; smaller and more satisfactory index, aminIs the most satisfactory of the same index, amaxIs the least satisfied;
if all the indexes of the personnel ability are larger and better, normalization is carried out by using a formula I, and the result is as follows:
Figure BDA0003075841540000111
step S123: determining the entropy of each evaluation index as follows:
Figure BDA0003075841540000112
in the above formula, i is 1,2, …, m; j is 1,2, …, n;
pijis defined as:
Figure BDA0003075841540000113
the entropy value of each evaluation index is obtained by calculation as follows: h (H)1,H2,…,H10)＝(0.7421,0.5803,0.8734,0.8441,0.7737,0.7372,0.5888,0,0.8824,0.7435)；
Step S124: calculating the weight value W (W) of each evaluation index1,w2,…,wn)：
Figure BDA0003075841540000114
Obtaining W (W) by calculation1,w2,…,w10)＝(0.0797,0.1298,0.0391,0.0482,0.0700,0.0812,0.1271,0.3092,0.0364,0.0793)；
S13: forming an equipment maintenance training personnel capability evaluation model by the constructed equipment maintenance training personnel capability evaluation index system and the weight of 10 evaluation indexes contained in the system;
step S2: constructing an equipment maintenance training personnel optimization matching model:
s21: evaluating the personnel maintenance training ability by utilizing an equipment maintenance training personnel ability evaluation model;
s22: determining the optimal matching model for equipment maintenance trainers as the greater the more optimal the model, i.e.
Figure BDA0003075841540000115
cijMatching the ith individual to the jth post composite score, which depends on the personnel maintenance training ability evaluation value determined in step S21; for the convenience of solution, let M be max { cijGet cij′＝M-cijThen solving for the maximum value is converted into solving for the minimum value, i.e.
Figure BDA0003075841540000121
Wherein xijThe decision variable represents whether the ith person is matched with the jth post, and the specific assignment mode is as follows: when the ith person matches the jth position, xij1, otherwise xij＝0。
Step S3: the method comprises the following steps of carrying out optimized configuration of human resources by utilizing an optimized matching model of equipment maintenance training personnel, wherein the process specifically comprises the following steps:
first, the unbalanced assignment problem is translated into a balanced assignment problem. If m individuals are allocated to n positions, wherein m is larger than n, m-n virtual working positions are set when the positions are matched, as shown in the table II.
Balanced matching between two watch positions
Figure BDA0003075841540000122
It should be noted that, since the smaller the calculation, the better the solution, the comprehensive evaluation value at the established virtual position is guaranteed to be larger than maxcij(i-1, 2, …, n; j-1, 2, …, m). In solving the assignment problem, it is clear that the person whose calculation result is assigned to the virtual position does not take any task.
Example 2
Referring to fig. 2 and 3, the invention specifically discloses a method for optimizing and matching stations of equipment maintenance trainers based on capabilities, which comprises the following steps:
step S1: constructing a capability evaluation model of equipment maintenance training personnel:
s11: determining an evaluation index set, and constructing an equipment maintenance training personnel capability evaluation index system: through analyzing 3 factors of basic ability, time ability and innovation ability restricting the ability of equipment maintenance training personnel, 10 evaluation indexes are determined, which are respectively as follows: learning ability factors, mastered knowledge quantity factors, academic factors, operation and use ability factors, maintenance skill factors, analysis and discharge failure ability factors, teaching and group training ability factors, ability factors for solving new problems, exploration ability factors and innovation consciousness factors to form an index set, thereby constructing an equipment maintenance training personnel ability evaluation index system;
s12: calculating the weights of the 10 evaluation indexes determined in the step S11, thereby determining the weights; the specific process comprises the following steps:
step S121: constructing an evaluation index characteristic value matrix A ═ (a)ij)m×n；
Figure BDA0003075841540000131
Step S122: calculating the coefficient of variation of the jth evaluation index
Figure BDA0003075841540000132
Wherein D is the mean square error of the j-th evaluation index characteristic value,
Figure BDA0003075841540000133
Figure BDA0003075841540000134
the characteristic value mean value of the jth evaluation index is obtained;
Figure BDA0003075841540000135
step S123: calculating the weight w of the jth evaluation indexj′；
Figure BDA0003075841540000136
The weighted value of each evaluation index obtained by the variation coefficient method is as follows: w '(W'1,w′2,…,w′10)＝(0.0778,0.0799,0.1601,0.1826,0.0505,0.1355,0.0906,0.0913,0.0624,0.0693)；
S13: forming an equipment maintenance training personnel capability evaluation model by the constructed equipment maintenance training personnel capability evaluation index system and the weight of 10 evaluation indexes contained in the system;
step S2: constructing an equipment maintenance training personnel optimization matching model:
s21: evaluating the personnel maintenance training ability by utilizing an equipment maintenance training personnel ability evaluation model;
s22: determining the optimal matching model for equipment maintenance trainers as the greater the more optimal the model, i.e.
Figure BDA0003075841540000141
cijMatching the ith individual to the jth post composite score, which depends on the personnel maintenance training ability evaluation value determined in step S21; for the convenience of solution, let M be max { cijGet cij′＝M-cijThen solving for the maximum value is converted into solving for the minimum value, i.e.
Figure BDA0003075841540000142
Wherein xijThe decision variable represents whether the ith person is matched with the jth post, and the specific assignment mode is as follows: when the ith person matches the jth position, xij1, otherwise xij＝0。
Step S3: the method comprises the following steps of carrying out optimized configuration of human resources by utilizing an optimized matching model of equipment maintenance training personnel, wherein the process specifically comprises the following steps:
first, the unbalanced assignment problem is translated into a balanced assignment problem. If m individuals are allocated to n positions, wherein m is larger than n, m-n virtual working positions are set when the positions are matched, as shown in table three.
Balanced matching between watch three posts
Figure BDA0003075841540000143
Figure BDA0003075841540000151
It should be noted that, since the smaller the calculation, the better the solution, the comprehensive evaluation value at the established virtual position is guaranteed to be larger than maxcij(i-1, 2, …, n; j-1, 2, …, m). In solving the assignment problem, it is clear that the person whose calculation result is assigned to the virtual position does not take any task.
Example 3
Referring to fig. 2, 3 and 4, the invention specifically discloses a method for optimizing and matching the stations of equipment maintenance trainers based on capabilities, which comprises the following steps:
step S1: constructing a capability evaluation model of equipment maintenance training personnel:
s11: determining an evaluation index set, and constructing an equipment maintenance training personnel capability evaluation index system: through analyzing 3 factors of basic ability, time ability and innovation ability restricting the ability of equipment maintenance training personnel, 10 evaluation indexes are determined, which are respectively as follows: learning ability factors, mastered knowledge quantity factors, academic factors, operation and use ability factors, maintenance skill factors, analysis and discharge failure ability factors, teaching and group training ability factors, ability factors for solving new problems, exploration ability factors and innovation consciousness factors to form an index set, thereby constructing an equipment maintenance training personnel ability evaluation index system;
s12: calculating the weights of the 10 evaluation indexes determined in the step S11, thereby determining the weights; the specific process comprises the following steps:
step S121: establishing a judgment matrix A ═ aij)m×nWherein i is 1,2, …, m; j is 1,2, …, n, aijIndicating the ratio of the magnitudes of influence of index i and index j, i.e.
Figure BDA0003075841540000152
Specifically, the method comprises the following steps: is provided with 6 experts M (M)1,M2,…,M6) The 10-way capacity of the equipment maintenance trainers was scored separately. An evaluated index set U (U) can be determined1,U2,U3,…,U10) In the scoring process, the score ranges from 0 to 4, with higher scores indicating a greater ability of the person being evaluated in this regard and conversely weaker scores. The evaluation results are shown in table four:
TABLE FOUR training support personnel each index evaluation value
Figure BDA0003075841540000161
Construction of an evaluation matrix
Figure BDA0003075841540000162
Step S122: normalizing the judgment matrix to obtain a matrix R ═ (R)ij)m×nFor larger and more satisfactory indicators, there are:
Figure BDA0003075841540000163
for smaller, more satisfactory indicators, there are:
Figure BDA0003075841540000164
in the larger and more satisfactory index, amaxIs the most satisfactory of the same index, aminIs the least satisfied; smaller and more satisfactory index, aminIs the most satisfactory of the same index, amaxIs the least satisfied;
if all the indexes of the personnel ability are larger and better, normalization is carried out by using a formula I, and the result is as follows:
Figure BDA0003075841540000171
step S123: determining the entropy of each evaluation index as follows:
Figure BDA0003075841540000172
in the above formula, i is 1,2, …, m; j is 1,2, …, n;
pijis defined as:
Figure BDA0003075841540000173
the entropy value of each evaluation index is obtained by calculation as follows: h (H)1,H2,…,H10)＝(0.7421,0.5803,0.8734,0.8441,0.7737,0.7372,0.5888,0,0.8824,0.7435)；
Step S124: calculating the weight value W (W) of each evaluation index1,w2,…,wn)：
Figure BDA0003075841540000174
Obtaining W (W) by calculation1,w2,…,w10)＝(0.0797,0.1298,0.0391,0.0482,0.0700,0.0812,0.1271,0.3092,0.0364,0.0793)；
Step S125: constructing an eigenvalue matrix A' ═ b of the evaluation indexij)m×n；
Figure BDA0003075841540000175
Step S126: calculating the coefficient of variation of the jth evaluation index
Figure BDA0003075841540000176
Wherein D is the mean square error of the characteristic value of the jth evaluation index
Figure BDA0003075841540000177
Figure BDA0003075841540000178
The characteristic value mean value of the jth evaluation index is obtained; namely, it is
Figure BDA0003075841540000181
Step S127: calculating the weight w of the jth evaluation indexj′；
Figure BDA0003075841540000182
The weighted value of each evaluation index obtained by the variation coefficient method is as follows: w '(W'1,w′2,…,w′10)＝(0.0778,0.0799,0.1601,0.1826,0.0505,0.1355,0.0906,0.0913,0.0624,0.0693)；
Step S128: weighting by using the weighted value combination of each evaluation index calculated in step S124 and step S127:
vi＝λwj′+(1-λ)wj(formula seven)
Wherein, the preference coefficient λ is 0.5, and according to the formula seven, the final weight obtained is: v ═ V (V)1,v2,…,v10)＝(0.0788,0.1048,0.0996,0.1154,0.0602,0.1083,0.1089,0.2003,0.0494,0.0743)。
S13: forming an equipment maintenance training personnel capability evaluation model by the constructed equipment maintenance training personnel capability evaluation index system and the weight of 10 evaluation indexes contained in the system;
step S2: constructing an equipment maintenance training personnel optimization matching model:
s21: evaluating the personnel maintenance training ability by utilizing an equipment maintenance training personnel ability evaluation model;
s22: determining the optimal matching model for equipment maintenance trainers as the greater the more optimal the model, i.e.
Figure BDA0003075841540000183
cijMatching the ith individual to the jth post composite score, which depends on the personnel maintenance training ability evaluation value determined in step S21; for the convenience of solution, let M be max { cijGet cij′＝M-cijThen solving for the maximum value is converted into solving for the minimum value, i.e.
Figure BDA0003075841540000184
Step S3: the method comprises the following steps of carrying out optimized configuration of human resources by utilizing an optimized matching model of equipment maintenance training personnel, wherein the process specifically comprises the following steps:
first, the unbalanced assignment problem is translated into a balanced assignment problem. If m individuals are allocated to n positions, wherein m is larger than n, m-n virtual working positions are set when the positions are matched, as shown in the table five.
Table five post balance matching
Figure BDA0003075841540000191
It should be noted that, since the smaller the calculation, the better the solution, the comprehensive evaluation value at the established virtual position is guaranteed to be larger than maxcij(i-1, 2, …, n; j-1, 2, …, m). In solving the assignment problem, it is clear that the person whose calculation result is assigned to the virtual position does not take any task.
It can be seen from the results that when performing the optimized configuration of human resources, it is not necessarily optimal to allocate positions simply according to the position capability of a certain person, and the overall optimal maintenance efficiency is the final goal.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way. Any simple modification, change and equivalent changes of the above embodiments according to the technical essence of the invention are still within the protection scope of the technical solution of the invention.

Claims (5)

1. A method for optimizing and matching posts of equipment maintenance trainers based on capabilities is characterized by comprising the following steps:
step S1: constructing a capability evaluation model of equipment maintenance training personnel:
s11: determining an evaluation index set, and constructing an equipment maintenance training personnel capability evaluation index system: determining a plurality of evaluation indexes by analyzing 3 factors of basic ability, time ability and innovation ability which restrict the ability of equipment maintenance trainers to form an index set, thereby constructing an equipment maintenance trainer ability evaluation index system;
s12: calculating the weights of the plurality of evaluation indexes determined in the step S11 so as to determine the weights;
s13: forming an equipment maintenance training personnel capability evaluation model by the constructed equipment maintenance training personnel capability evaluation index system and determining the weights of a plurality of evaluation indexes contained in the equipment maintenance training personnel capability evaluation index system;
step S2: constructing an equipment maintenance training personnel optimization matching model:
s21: evaluating the personnel maintenance training ability by utilizing an equipment maintenance training personnel ability evaluation model;
s22: determine the optimal matching model for the equipment maintenance trainee as
Figure FDA0003075841530000011
cijMatching the ith individual with the jth post comprehensive score; let M equal max { cijGet cij′＝M-cijThen the maximum value to be solved is converted into the minimum value, i.e.
Figure FDA0003075841530000012
Wherein xijThe decision variable represents whether the ith person is matched with the jth post, and the specific assignment mode is as follows: when the ith person matches the jth position, xij1, otherwise xij＝0；
Step S3: and performing human resource optimization configuration by using the equipment maintenance training personnel optimization matching model.
2. The method as claimed in claim 1, wherein the evaluation indexes include learning ability factor, knowledge amount factor, academic degree factor, operation and use ability factor, maintenance skill factor, analysis and troubleshooting ability factor, teaching and group training ability factor, ability factor for solving new problems, exploration ability factor and innovation consciousness factor in step S11.
3. The method for optimizing and matching the posts of the equipment maintenance training staff based on the capability of claim 2, wherein in the step S12, the calculation process of the weight of the evaluation index specifically comprises:
step S121: establishing a judgment matrix A ═ aij)m×n，
Figure FDA0003075841530000021
Wherein i is 1,2, …, m; j is 1,2, …, n, aijRepresenting the evaluation value of the ith expert on the jth index;
step S122: normalizing the judgment matrix to obtain a matrix R ═ (R)ij)m×nFor larger and more satisfactory indicators, there are:
Figure FDA0003075841530000022
for smaller, more satisfactory indicators, there are:
Figure FDA0003075841530000023
in the larger and more satisfactory index, amaxIs the most satisfactory of the same index, aminIs the least satisfied; smaller and more satisfactory index, aminIs the most satisfied of the same index，amaxIs the least satisfied;
if all the indexes of the personnel ability are larger and better, normalization is carried out by using a formula I, and the result is as follows:
Figure FDA0003075841530000031
step S123: determining the entropy of each evaluation index as follows:
Figure FDA0003075841530000032
in the above formula, i is 1,2, …, m; j is 1,2, …, n;
pijis defined as:
Figure FDA0003075841530000033
the entropy value of each evaluation index is obtained by calculation as follows: h (H)1,H2,…,H10)＝(0.7421,0.5803,0.8734,0.8441,0.7737,0.7372,0.5888,0,0.8824,0.7435)；
Step S124: calculating the weight value W (W) of each evaluation index1,w2,…,wn)：
Figure FDA0003075841530000034
Obtaining W (W) by calculation1,w2,…,w10)＝(0.0797,0.1298,0.0391,0.0482,0.0700,0.0812,0.1271,0.3092,0.0364,0.0793)。
4. The method for optimizing and matching the posts of the equipment maintenance training staff based on the capability of claim 2, wherein in the step S12, the calculation process of the weight of the evaluation index specifically comprises:
step S121: constructing an evaluation index characteristic value matrix A ═ (b)ij)m×n；
Figure FDA0003075841530000041
Step S122: calculating the coefficient of variation of the jth evaluation index
Figure FDA0003075841530000042
Wherein D is the mean square error of the j-th evaluation index characteristic value,
Figure FDA0003075841530000043
Figure FDA0003075841530000044
the characteristic value mean value of the jth evaluation index is obtained; namely, it is
Figure FDA0003075841530000045
Step S123: calculating the weight w of the jth evaluation indexj′；
Figure FDA0003075841530000046
The weighted value of each evaluation index obtained by the variation coefficient method is as follows: w '(W'1,w′2,…,w′10)＝(0.0778,0.0799,0.1601,0.1826,0.0505,0.1355,0.0906,0.0913,0.0624,0.0693)。
5. The method for optimizing and matching the posts of the equipment maintenance training staff based on the capability of claim 2, wherein in the step S12, the calculation process of the weight of the evaluation index specifically comprises:
step S121: establishing a judgment matrix A ═ aij)m×n，
Figure FDA0003075841530000047
Wherein i is 1,2, …, m; j is 1,2, …, n, aijRepresenting the evaluation value of the ith expert on the jth index;
step S122: normalizing the judgment matrix to obtain a matrix R ═ (R)ij)m×nFor larger and more satisfactory indicators, there are:
Figure FDA0003075841530000051
for smaller, more satisfactory indicators, there are:
Figure FDA0003075841530000052
in the larger and more satisfactory index, amaxIs the most satisfactory of the same index, aminIs the least satisfied; smaller and more satisfactory index, aminIs the most satisfactory of the same index, amaxIs the least satisfied;
if all the indexes of the personnel ability are larger and better, normalization is carried out by using a formula I, and the result is as follows:
Figure FDA0003075841530000053
step S123: determining the entropy of each evaluation index as follows:
Figure FDA0003075841530000054
in the above formula, i is 1,2, …, m; j is 1,2, …, n;
pijis defined as:
Figure FDA0003075841530000055
the entropy value of each evaluation index is obtained by calculation as follows: h (H)1,H2,…,H10)＝(0.7421,0.5803,0.8734,0.8441,0.7737,0.7372,0.5888,0,0.8824,0.7435)；
Step S124: calculating the weight value W (W) of each evaluation index1,w2,…,wn)：
Figure FDA0003075841530000061
Obtaining W (W) by calculation1,w2,…,w10)＝(0.0797,0.1298,0.0391,0.0482,0.0700,0.0812,0.1271,0.3092,0.0364,0.0793)；
Step S125: constructing an evaluation index characteristic value matrix A ═ (b)ij)m×n；
Figure FDA0003075841530000062
Step S126: calculating the coefficient of variation of the jth evaluation index
Figure FDA0003075841530000063
Wherein D is the mean square error of the characteristic value of the jth evaluation index
Figure FDA0003075841530000064
Figure FDA0003075841530000065
The characteristic value mean value of the jth evaluation index is obtained; namely, it is
Figure FDA0003075841530000066
Step S127: calculating the weight w of the jth evaluation indexj′；
Figure FDA0003075841530000067
The weighted value of each evaluation index obtained by the variation coefficient method is as follows: w '(W'1,w′2,…,w′10)＝(0.0778,0.0799,0.1601,0.1826,0.0505,0.1355,0.0906,0.0913,0.0624,0.0693)；
Step S128: weighting by using the weighted value combination of each evaluation index calculated in step S124 and step S127:
vi＝λwj′+(1-λ)wj(formula seven)
Wherein, the preference coefficient λ is 0.5, and according to the formula seven, the final weight obtained is: v ═ V (V)1,v2,…,v10)＝(0.0788,0.1048,0.0996,0.1154,0.0602,0.1083,0.1089,0.2003,0.0494,0.0743)。
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