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ЭЛЕКТРОННЫЙ НАУЧНЫЙ ЖУРНАЛ «APRIORI. CЕРИЯ: ЕСТЕСТВЕННЫЕ И ТЕХНИЧЕСКИЕ НАУКИ»
№ 3 2015
УДК 331
PERSONNEL SELECTION USING A FUZZY DELPHI METHOD
Zuka Abduljabbar *
student
Higher School of Economics, Moscow author@apriori-journal. ru
Abstract. An important phase of human resources management is personnel selection. When candidates apply for specific jobs or nominate to take higher position in an organization, the basic purpose of personnel selection operations is to determine who has the necessary knowledge, skills, and ability to satisfy the requirement of the job successfully. Some multi criteria decision making techniques have been used for this paper, such as MMPI test, Analytic Hierarchy Process and Fuzzy Delphi method, which consists of experts answers to questionnaires in two or more rounds. After each round, a facilitator provides an anonymous summary of the experts' forecasts from the previous round as well as the reasons they provided for their judgments. Thus, experts are encouraged to revise their earlier answers in light of the replies of other members of their panel. It is believed that during this process the range of the answers will decrease and the group will converge towards the «correct» answer. Finally, the process is stopped after a pre-defined stop criterion.
Key words: Fuzzy Delphi method; personal selection; human resource management.
* Author's supervisor professor - Zykov S.V., Higher School of Economics, Moscow.
УПРАВЛЕНИЕ ЧЕЛОВЕЧЕСКИМИ РЕСУРСАМИ В ОРГАНИЗАЦИЯХ С ПРИМЕНЕМИЕМ НЕЧЕТКИХ МЕТОДОВ
Зука Абдулджаббар
студент
Высшая школа экономики, Москва
Аннотация. Важным этапом в управлении человеческими ресурсами является отбор персонала. Когда кандидаты проходят отбор на узкоспециальные должности или на руководящие посты организации, основная цель деятельности по подбору персонала состоит в том, чтобы определить, кто обладает необходимыми знаниями, навыками и возможностями, чтобы успешно удовлетворить требованиям вакансии. В данной статье были использованы некоторые многокритериальные техники принятия решений, например, стандартизированный многофакторный метод исследования личности (ММР1), метод анализа иерархий и нечеткий метод Дельфи, который состоит из ответов экспертов на вопросники на двух или более этапах. После каждого этапа координатор предоставляет анонимное заключение мнений экспертов из предыдущего этапа, а также аргументы, которыми они обосновывают свое мнение. Таким образом, экспертам предлагается пересмотреть свои прежние ответы с учетом ответов других членов группы. Считается, что во время этого процесса количество ответов будет уменьшаться, а группа будет стремиться к «правильным» ответам. В конце концов, процесс прекращается после заранее определенного критерия остановки.
Ключевые слова: нечеткий метод Дельфи; личный выбор; управление человеческими ресурсами.
INTRODUCTION
Personnel selection is the process of selecting individuals who meet the requirement to perform a particular job or role. Personnel selection determines the input quality of personnel and plays a significant role in human resource management. It is a very important decision making process for an organization's success. The Fuzzy Delphi Method, a multi criteria decision making technique, was used in this study to assist a group of decision makers in selecting one person from a number of applicants. The feedbacks show that this model is quite reliable in Personnel selection. and can ameliorate the efficiency of decision making process .This situation was chosen because it is a part of my master degree graduation project (Human Resource DBMS).
THE BUSINESS CASE
The head of the IT department was posted to Moscow, so the organization has to choose a new Manager for this section, these decisions usually made by General Director with advice of two persons the first one is his assistant (deputy of director) and the second one is an expert from the department itself. Sometimes the decision is not optimal especially when a big competition occurred among several employees that are qualified to have this position. In this case using a systematic solution is better in order to reach the most acceptable choice for everyone.
EXPERT DEFINITION
According to the rules in my organization for the above mentioned situation, the decision is made by three experts who are considered the most significant in this case. They are:
1. The First Expert: The general director, the head of the department and the responsible of several sections, who has first priority in decision making.
2. The Second Expert: The previous Manager of the section, who has the second priority in decision making. He is considered as an important source for presenting reports to the first expert about each candidate due to his experience and knowledge during his service as a manager.
3. The Third Expert: The deputy of the general director, who has the third priority in decision making. He has more experience and contact with candidate that the first expert and less than the second. He is responsible for giving recommendations about the reports presented by the second expert.
CRITERIA DEFINITION
According to the user requirements, we determine ten evaluation criteria. These criteria are a set of requirements for the most proper employee to be chosen as a Manger for IT section. The set of chosen criteria is based on the aggregation of my organization. As it is previously mentioned that the set consists of ten of the most important criteria. They are listed in order of importance. The numbers shown in the figure1 present the priority for each criteria which is allocated using MMPI test Minnesota Multiphase Personality Inventory (Smriti Chand, Job Selection Test).
Figure 1. Criteria priority according to MMPI test
Then We used these priorities to find the «weights» by applying Analytic Hierarchy Process (AHP) as it shown in figure 2.
Construct comparing criteria matrix toFinding the wieght for each Criteria according to AHP
CI C2 C3 C4 C5 C6 C7 C8 C9 C10
CI 1.00 2.00 1.00 2.00 1.00 0.50 0.33 2.00 2.00 1.00
C2 0.50 1.00 1.00 1.00 0.50 0.50 0.50 3.00 2.00 0.33
C3 1.00 1.00 1.00 0.50 0.33 0.50 0.33 0.50 1.00 1.00
C4 0.50 1.00 2.00 1.00 0.33 0.50 0.33 0.50 0.50 0.50
C5 1.00 2.00 3.00 3.00 1.00 3.00 1.00 2.00 1.00 1.00
C6 2.00 2.00 2.00 2.00 0.33 1.00 0.50 2.00 2.00 1.00
C7 3.00 2.00 3.00 3.00 1.00 2.00 1.00 2.00 2.00 1.00
C8 0.50 0.33 2.00 2.00 0.50 0.50 0.50 1.00 2.00 1.00
C9 0.50 0.50 1.00 2.00 1.00 0.50 0.50 0.50 1.00 1.00
CIO 1.00 3.00 1.00 2.00 1.00 1.00 1.00 1.00 1.00 1.00
Lamda_max=15.50, C.R.= 0.07. lmportance_weight wj are:wi=o.3,w2=o.2,w3=o.i
W4=0.09 , W5=0.08 , W6=0.07 , W7=0.06 ,W8=0.05 , W9=0.04 , W10=0.01.
Figure 2. Weights for each criteria by applying Analytic Hierarchy Process
Following are the definitions and scores of weights for each criteria according to the table mentioned above.
• Leadership: leadership has been described as «a process of social influence in which one person can enlist the aid and support of others in the accomplishment of a common task». This is considered to be an essential quality for a manager. The weight score for this criteria is 0.3.
• Motivation: employees are motivated either by promotion or financial reword. A manager should be someone who has already been promoted at least once due to his experience in the job. The weight score for this criteria is 0.2.
• Communication skills: communication skills can help all aspects of person's life, from your professional life to social gatherings and everything
5
in between. The ability to communicate information accurately, clearly and as intended, is a vital life skill and something that should not be overlooked. Hence the weight score is 0.1.
• Work Experience: experience in every field gives the ability and wisdom to manage a situation that may happen during your work. This is considered to be an essential quality for a manager. The weight score for this criteria is 0.09.
• Creativity: is a phenomenon whereby something new and valuable is created (if the employee wants to be something he has to do something). Each successful organization aims to use creative managers in its department. The weight score is 0.08 .
• Graduate Degree: a manager should have a qualified higher than bachelor's degree, which is related to their specialist field. The weight score is 0.07.
• Technical Knowledge: the manager must have technical competence in criteria aspects of the work. However there is considerable disagreement between experts on the issue of how much technical knowledge is required. The more technically aware one is, the better they will be able to understand risks, potential roadblocks, and impacts of delays to the schedule. The manager requires technical knowledge to deal with this position successfully. The weight score is 0.06.
• Proficiency: to be a manager one must be a professional in his job and have advancement in knowledge or skill . The weight score is 0.05.
• Foreign Language: its important for every employee in this organization to have at least 2 languages, the head of each section may deal with foreign firm or embassies. The weight score for this criteria is 0.04.
• Age: the oldest candidate is usually considered to be the most experienced. However this organization prefers to have a middle-aged manager. They think this age-group are more able to manage the group than others. The weight is 0.01.
THE COMPUTING PROCESS
At the first step we have collected the opinions of all the experts concerning every criterion on a particular alternative. we then systematized their answers, converted the verbal opinions into the digits according to Saaty scale from 0 to 9:
Figure3 shows the linguistic table on Saaty scale.
Figure 3.Representation of linguistic table on Saaty scale
- Very good: will take scores from 6 to 9;
- Good: will take scores from 4 to 8;
- Medium: will take scores from 2 to 5;
- Bad: will take scores from 0 to 3.
Table1 represents experts opinion for each candidate according to each criteria in the first and second rounds (x1,x2,x3 mean candidate 1, candidate 2, candidate 3 and crit 1 means criteria № 1 which is mentioned above as «leadership»). VG = very good, G = good, M = medium, B = bad. EXP. 1 = expert № 1 who represents the General director in our case and EXP. 2 = expert № 2 who represents the previous Manager for the section, finally EXP. 3 = Deputy Director.
Experts Opinion in
the
Table 1
First and Second rounds
First Round
Second Round
Criteria Candidates Exp. 1 Exp.2 Exp.3
crt.l VG VG VG
AC M VG G
JO B G G
crt.2 X> G VG VG
-C M vc; G
AO VG G VC!
crt.3 VG M VG
AO B M G
AO G B G
crt.4 AU VG G VG
AO VG G G
AO G M VG
At! VG G B
crt.5 AO M G VG
AO M VG G
ATI VG M G
crt.6 AO M VG VG
AS B M M
crt.7 A3 G VG VG
AG VG VG G
AO VCi G VCi
ACl VG G M
crt.8 AO M G G
AO G M VG
AC| VG M G
crt.9 AO VG VG VG
AO B M M
crt.io VG M VG
AO B M G
AO G M VG
Criteria Candidates Exp. 1 Exp.2 Exp.3
crt.l AU AO VG M €> VG VG G
AO B G G
crt.2 AO G VG VG
AO M VCi G
AO VG G VG
crt.3 ACl AO VCi B M M VG G
AO G B G
crt.4 Atl AO VG VG G G O G
AO G M VG
.«1 VG G B
crt.5 AO AO & G VG VG G
Atl VCi M G
crt.6 AO M VG VG
AO B M M
crt.7 AQ G VG VG
AO VG VCi G
AO VG G VG
crt.8 ACl AO Ö G G M G
AO G M VG
-VI VCi M G
crt.9 AO VG VG VG
AO B M M
crt.l 0 JO AO VG B M M VG G
AO G M VG
The data collected from the first Delphi round may not be accurate enough. After proper analysis of the outcomes, the first round is typically followed by more rounds that address more specific questions or provide more information or ideas, in order to achieve consensus between opinions (Hartman and Baldwin, 1995). Round two typically focuses on addressing major areas of concerns, whereas some additional information may be provided by the investigator in order to identify areas for improvement and attain consensus. During round two, each expert is allowed to review other experts' opinions and make comments or modify his other original responses until consensus is reached at the end of round two. After the second Delphi round, responses are grouped together for analysis.
We have to mention that Delphi rounds could be two, three or more in this example we used two rounds but in the implementation we make the comparison between the current year around and the two previous year rounds, that means we used three rounds in each year selection process .
For the second round of expert opinion one can notice the changing in opinion in the table 1. We did all the numerical calculations in Excel sheet (see appendix) . In the excel sheet we calculate all the deviations between the opinions in the first and second round which did not effect the final result even though decrease of candidate1 values made him so close to candidate 3 values.
The result of fuzzy Delphi is a fuzzy number. Therefore, it is necessary that the defuzzition method is applied. This stage is dedicated to the summarizing of all the results and choosing the best candidate from all mentioned.
After the second iteration has passed, we converted these words to fuzzy numbers and calculated the deviation between 1st and 2nd round , and found the average for the second iteration.
r =round2 av. of expert opinion about candidate according to each cretirea
Figure 4. Fuzzy equation
Then we created three final matrices demonstrating the average results for every alternative according to the converted linguistic representations. The resulting matrices are demonstrated in table 2.
Table 2
Candidates final results after applying fuzzy method
RESULT FOR THE 1st CANDIDATE 4.93 5.93 7.05 8.29
RESULT FOR THE 2nd CANDIDATE 4.50 5. 43 6.50 7.74
RESULT FOR THE 3rd CANDIDATE 3.15 3.91 5.50 6.54
DEFUZZIFICATION STAGE
There are many kind of defuzzification method so we chose the set with the highest membership (Mean of maximum). In our case this is a set of the first candidate , we decided to choose this kind of defuzufication method because the result function is symmetrical and Mean of Max is the must suitable method for such case , The results of averages from every candidate can be seen on figure 5 which is so describe clearly that our function is symmetrical.
1.2
0.00 2.00 4.00 6.00 8.00 10.00
Figure 5. Showing the result of the Candidates X1 > X2 > X3
This defuzzification method calculates the most plausible result. Rather than averaging the degrees of membership of the output linguistic terms, the Mean of Max defuzzification method selects the typical value of the most valid output linguistic term. To apply MOM defuzification methoed we have to Finds the mean of the values that correspond to the maximum fuzzy values.
In our case we want to use the best of possible solutions, that collect the most powerful features and highly correspond to the criteria stated.
After applying this method for all the candidate we found that «candidate 1» has the Largest values as the first candidate.
CONCLUSION
The main goal of this study to help decision makers to reach a consensus on personnel selection for a specific job position. In this paper, all the calculations were performed in MS Excel package and were counted automatically via applying cell formulas according to the essential counting of the Fuzzy Delphi method.
A systematic model for personal selection was presented. The goal of the methodology is to form the panel of experts and determine the criteria for personnel selection. In this method, the fuzzy Delphi technique was used to seek best ideas from experts to select the most suitable criteria for this position holder and assign the weight for each criteria, give opinions, feedback and discuss .
When we start see the Expert opinions we were confused between candidate 1 and candidate 2 their values are very close specially in work experience, technical knowledge and graduation degree and these doubts became bigger during the second round when the expert decrease 2 criteria values from candidates However after using the fuzzy Delphi method with all formulas and calculation it became clear that candidate 1 has the maximum result which qualifies him to occupy the position of Manager for IT section. All these formulas have been converted into a program «manager selection» which is sub system from a human resource database system customized specially for my organization in Iraq .
According to previous studies in literature, there is no systematic method that can help organization in preparing and choosing personnel selection criteria. However, this research has showed that the Fuzzy Delphi method can be used as guidelines for organizations and that can enable each organization to determine the essential criteria of each job position and the most suitable person for this job position. This study selected the criteria according to MMPI test, Review of pertinent literature , discussion with experts, using Delphi method and using fuzzy method all this three together increase the efficiency of criteria selection stage, and give reliable result. The proposed mod-
el can also be applied to problems such as project selection, material selection and many other areas of management decision problems or strategy selection problems.
As a future steps to personnel selection problem:
1. Comparison of the proposed approach to other MADM methods such as Fuzzy Integral, VIKOR.
2. Situations should be studied, in which a group of decision makers, each one of different importance, are involved in the decision making process.
3. Developing hybrid methodology based on fuzzy linguistic, solving dependency and hierarchical structure for criteria.
4. Applying purposed methods to real world group decision making problems in diverse disciplines containing both crisp and fuzzy data together.
Appendix 1
The calculation of expert opinion in each criteria for each candidate
B7 » I U \ 1
— E F G H 1 J K L M N O P Q R s T U V W X Y
4 round 1 round 2 round 1 round2
5 Candidate!. Cretreial Candidate 2 Cretirea 1
6
7 Exp 1 6 7 8 9 6 7 8 9 Exp 1 2 3 4 5 6 7 8 9
8 Exp 2 6 7 8 9 4 5 7 8 Exp 2 6 7 8 9 6 7 8 9
9 Exp 3 6 7 8 9 6 7 8 9 Exp3 4 5 6 8 6 7 8 9
10 11 12 13 14 avareg Can1.Crt2 6 7 8 9 5.333333 6.333333 7 666667 8 666667 avareg Can2.Crt2 4 5 6 7 333333 6 7 8 9
0 0 0 0
2 2 1 1
0 0 0 0
15 Exp 1 4 5 6 8 4 5 6 8 Exp1 6 7 8 9 6 7 8 9
16 Exp 2 6 7 8 9 6 7 8 9 Exp 2 2 3 4 5 2 3 4 5
17 Exp3 6 7 8 9 6 7| 8 9 Exp3 4 5 6 8 4 5 6 8
18 avareg 5 333333 6333333 7333333 8 666667 5 333333 6 333333 7 333333 8 666667 avareg 4 5 6 7333333 4 5 6 7 333333
19
20
21 Can1.Crt3 Can2.Crt3
22
23 Exp 1 6 7 8 9 6 7 8 9 Exp 1 0 0 2 3 0 0 2 3
24 Exp 2 2 3 4 5 2 3 4 5 Exp 2 2 3 4 5 2 3 4 5
25 Exp 3 6 7 8 9 6 7 8 9 Exp3 4 5 6 8 4 5 6 8
26 avareg 4 666667 5 666667 6666667 7666667 4 666667 5 666667 6 666667 7 666667 avareg 2 2666667 4 5.333333 2 2 666667 4 5 333333
27
28
29 Can1.Crt4 Can2.Crt4
30
31 Exp 1 6 7 8 9 6 7 8 9 Exp 1 6 7 8 9 6 7 8 9
32 Exp 2 4 5 6 8 4 5 6 8 Exp 2 4 5 6 8 4 5 6 8
33 Exp 3 6 7 8 9 4 5 6 8 Exp 3 4 5 6 8 4 5 6 8
34 15 16 17 18 avareg Can1.Crt5 5 333333 6 333333 7 333333 8 666667 4 666667 5 666667 6 666667 8 333333 avareg Can2.Crt5 4 666667 5 666667 6 666667 8333333 4666667 5 666667 6 666667 8 333333
-0 66667 -0 66667 -0 66667 -0.33333
1.333333 1 333333 1.333333 0666667
1.333333 1.333333 1.333333 0.666667
19 Exp 1 6 7 8 9 6 7 8 9 Exp1 2 3 4 5 2 3 4 5
.0 Exp 2 4 5 6 8 4 5 6 8 Exp 2 4 5 6 8 4 5 6 8
■1 Exp 3 4 5 6 8 4 6 6 8 Exp3 6 7 8 9 6 7 8 9
2 4666667 5 666667 6 666667 8 333333 4 666667 5 666667 6 666667 8 333333 avareg 4 5 6 7333333 4 5 6 7 333333
■3
4
5 Can1.Crt6 Can2.Crt6
6
■7 Exp 1 6 7 8 9 6 7 8 9 Exp 1 2 3 4 5 2 3 4 5
■8 Exp 2 2 3 4 5 2 3 4 5 Exp 2 6 7 8 9 6 7 8 9
■9 Exp 3 4 5 6 8 4 5 6 8 Exp3 6 7 8 9 6 7 8 9
>0 avareg 4 5 6 7333333 4 5 6 7.333333 avareg 4 666667 5666667 6 666667 7 666667 4 666667 5 666667 6 666667 7 666667
i1
¡2
53 Can1.Crt7 Can2.Crt7
54
55 Exp 1 4 5 7 8 4 5 7 8 Exp 1 6 7 8 9 6 7 8 9
56 Exp 2 6 7 8 9 6 7 8 9 Exp 2 6 7 8 9 6 7 8 9
57 Exp 3 avareg 6 7 8 9 6 7 8 9 Exp3 4 5 6 8 4 5 6 8
58 5.333333 6 333333 7 666667 8 666667 5.333333 6333333 7 666667 8 666667 avareg 5 333333 6333333 7 333333 8.666667 5.333333 6.333333 7.333333 8 666667
59
60
61 Can1.Crt8 Can2.Crt8
62
63 Exp 1 6 7 8 9 6 7 8 9 Exp 1 2 3 4 5 0 0 2 3
64 Exp 2 4 5 6 6 4 5 6 8 Exp 2 4 5 6 8 4 5 6 8
65 66 67 68 69 Exp 3 avareg Can1.Crt9 2 3 4 5 2 3 4 5 Exp3 4 3 333333 5 4.333333 6 5 333333 8 7 4 5 6 8
4 5 6 7333333 4 5 6 7.333333 avareg 3.333333 4.333333 3 333333 4
Can2.Crt9 -0.66667 -0.66667 -0.66667 -1
-0.66667 -0.66667 -0 66667 -1
2 666667 3.333333 4666667 6.333333
70
71 Exp 1 6 7 8 9 6 7 8 9 Exp 1 6 7 8 9 6 7 8 9
72 Exp 2 2 3 4 5 2 3 4 5 Exp 2 0 0 2 3 0 0 2 3
73 Exp3 4 5 6 8 4 5 6 8 Exp3 6 7 8 9 6 7 8 9
74 avareg 4 5 6 7.333333 4 5 6 7.333333 avareg 4 4 666667 6 7 4 4.666667 6 7
75 76
77 Can1.Crt1 0 Can2.Crt1 0
78
79 Exp 1 6 7 8 9 6 7 8 9 Exp 1 0 0 2 3 0 0 2 3
80 Exp 2 2 3 4 5 2 3 4 5 Exp 2 2 3 4 5 2 3 4 5
81 Exp 3 6 7 8 9 6 7 8 9 Exp3 4 5 6 8 4 5 6 8
82 avareg 4666667 5 666667 6666667 7.666667 4 666667 5 666667 6 666667 7 666667 avarege 2 2666667 4 5.333333 2 2 666667 4 5.333333
m AA AB AC AD AE AF AG AH AI AJ AK AL AM AN AO AP AQ AR AS AT AU AV
3
4 round 1 round 2
5 6 7 Candidate Exp 1 3 Cretirea 1
0 0 2 3 0 0 2 3
8 Exp2 4 5 7 8 4 5 7 8
9 Exp 3 4 5 7 8 4 5 7 8
10 avareg 2.66667 3.33333 5.33333 6.33333 2.66667 3.33333 5.33333 6.33333
11 12
13 14 15 16 17 Can3.Crt2 Exp 1 Exp 2 Exp 3
6 9 6 7 8 9
4 5 7 8 4 5 7 8
6 7 8 9 6 7 8 9
18 19 20 21 22 23 24 25 26 27 28 avareg Can3.Cr13 Exp 1 Exp 2 Exp3 avareg 5.33333 6.33333 7.66667 8.66667 5.33333 6.33333 7.66667 8.66667
2 3 4 5 2 3 4 5 IstCand 5.93 7.051 8.29 1 1 0
4 5 7 8 4 5 7 8 3rd.Cand 3.91 5.50 6.54 —
2 3 4 5 2 3 4 5 2ndCand 5.43 6.501 7.74
266667 3.66667 5 6 2.66667 3.66667 5 6
29 Can3.Crt4
30 t n \
31 32 Exp 1 Exp 2 0 0 2 3 0 0 2 3 r \\
4 5 7 8 4 5 7 8 k \
33 34 Exp3 avareg 0 0 2 3 0 0 2 3 \ \ I
1.33333 1.66667 3.66667 4 66667 1.33333 1 66667 3.66667 4,66667 \ \ \ -B-CAN-2
35 36 / \ \ -A-CAN-3
I / \ \
37 Can3.Cr15 / / \
38 39 40 41 42 43 44 45 Exp 1 Exp 2 Exp 3 avareg Can3.Crt6 / I
2 3 4 5 0 0 2 3
4 5 7 8 4 5 7 8
0 0 2 3 0 0 2 3
2 2.66667 4.33333 5.33333 2 2.66667 2.33333 2.33333
-2 -2.33333 -2.66667 -2.66667
2 2.66667 2.33333 2.33333
1.33333 1.66667 3.66667 4.66667
46 47 48 49 50 Exp 1 Exp 2 Exp3 avareg
0 0 2 3 0 0 2 3
2 3 4 5 2 3 4 5
2 3 4 5 2 3 4 5
1.33333 2 3.33333 433333 1.33333 2 3.33333 4.33333
51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 Can3.Crt7 Exp 1 Exp 2 Exp3 avareg
6
7 8 9 6 7 8 9
4 5 6 8 4 5 6 8
6 5.33333 7 8 7.33333 9 6 7 633333 8 9
6.33333 8.66667 5.33333 7.33333 8.66667
Can3.Cr18 Exp 1 Exp 2 Exp3 avareg
4 5 6 8 4 5 6 8
2 3 4 5 2 3 4 5
6 7 8 9 6 7 8 9
4 5 6 7.33333 4 5 6 7.33333
Can3.Cr19
70 9
71 72 73 74 75 76 77 78 79 80 81 82 Exp 1 Exp 2 Exp3 avareg Can4.Cr11 Exp 1 Exp 2 Exp3 avareg 6 7 8 6 7 8 9
2 3 4 5 2 3 4 5
2 3 4 5 2 3 4 5
3.33333 4.33333 5.33333 6.33333 3.33333 433333 5.33333 6.33333
0
4 5 6 8 4 5 6 8
2 3 4 5 2 3 4 5
6 7 8 9 7.33333 6 7 8 9 7.33333
6 4 5 6
Список использованных источников
1. Chen L.S., Cheng C.H. Selecting IS personnel use fuzzy GDSS based on metric distance method // Euro. J. of Operational Research. 2005. V. 160. P. 803-820.
2. A group MADM method for personnel selection problem using Delphi technique based on intuitionist fuzzy sets [Электронный ресурс]. Режим доступа: http://www.jmisci.com/index.php/JMIS/article/view/Bali2013
3. An application of Delphi method for eliciting criteria inpersonnel selection problem [Электронный ресурс]. Режим доступа: https://www.acade mia.edu/843929/An_Application_of_Delphi_method_for_eliciting_criteria _in_Personnel_Selection_problem
4. Open source presentation: Defuzzification [Электронный ресурс]. Режим доступа: http://www.csee.wvu.edu/classes/cpe521/presentations/ DEFUZZ.pdf
5. A fuzzy ANP-based approach to R&D project selection // Int. J. of Production Research. 2005. V. 43. Is. 24. [Электронный ресурс]. Режим доступа: http://www.tandfonline.com/doi/abs/10.1080/00207540500219 031#.UzVgOvmSwTM
6. How to use mean of max defuzzification method [Электронный ресурс]. Режим доступа: http://zone.ni.com/reference/en-XX/help/371361L-01/ lvpidmain/io_meanmax
7. Smriti Chand, Job Selection Test (MMPI test) [Электронный ресурс]. Режим доступа: http://www.yourarticlelibrary.com/hrm/job-selection-test-purpose-types-ability-and-developing-a-test-programme/35278
