دانلود ترجمه مقاله کاربرد مدل تصمیم گیری چند معیاره فازی در استخدام کارکنان

بخشی از ترجمه:

این تحقیق به معنای بهبود فقدان از پردازش توان گیری کارکنان و هم چنین کاهش حس منحصر به فرد و تکی درسطح نظارتی توسط فاز منطقی و روشهای پردازش تحلیلی مراتبی می باشد. در این تحقیق سعی میشود که ویژگی ها و خصوصیات مناسب با مهارتهای حرفه ای و کلیدی را با اطلاعات کافی از طریق آمار بدست آورند و آنالیزی از پردازش مراتب تحلیلی برای پیش بینی پردازش نیروی تازه ( توان گیری ) منطقی صورت گیرد گه بر اساس نتیجه معیار چند گانه فازی صورت می گیرد . هدف از این مقاله انتخاب مدل درست و مبنی بر شایستگی می باشد . نتایج بدست آمده نشانگر ایجاد مدل معیار چندگانه فازی می باشد . راه حل درست و واقعی برای از بین بردن کاستی های توان گیری و سازمانها با اطلاعات بیشتر در تصمیم گیری می باشد .
١- مقدمه:
منبع انسانی یکی از اجزای مهم در برنامه های کاربردی چند منظوره و رقابت از آن در مقایسه با استعدادهای متفاوت بین آنها می باشد. بیشتر مدیران سازمانها امیدوار به پیدا کردن داوطلبان مناسب هستند. این امر به معنای آن است که کارکنان توان گیر یکی امر مهمو لازم برای آنها محسوب می شود . به هر حال این یک فعالیت خیلی گران به شمار می آید که نیازمند زمان و بها زیادی است.

بخشی از مقاله انگلیسی:

Summary This study is intended to improve the lack of recruitment processes as well as reduce individual senses of supervisory level by fuzzy logic and Analytic Hierarchy Process methods. This study tries to identify appropriate personality traits and key professional skills through the information statistics and analysis of Analytic Hierarchy Process in order to expect the recruitment process be more reasonable based on the fuzzy multiple criteria decision making model to achieve the goal of merit-based selection. The results showed that the fuzzy multiple criteria model constructed in this study could indeed solve the shortcomings in existing enterprises’ recruitment, and provide more information for decision-making reference. Key words: AHP, fuzzy logic, multiple criteria, decision making, employee recruitment 1. Introduction Human resources are important assets of enterprises, and competition in the enterprise has become a competition for talent. Most enterprise managers are hoping to find appropriate candidates. It means that employee recruitment has become one of the most important and indispensable activities [1]. However, it is a very expensive activity which requires a large number of cost and time. Enterprises’ demand should not be considered unilaterally as well as candidates’ individual needs and expectations should also be taken into account in order to avoid recruiting unsuitable personnel and low effectiveness. Therefore, how to design the approach becomes a prerequisite for a company to recruit qualified and suitable employees [2]. If a set of personnel database is integrated and equipped to assist the recruitment, most problems can be solved, including: how to attract talents to enter the business, the unification of the recruitment processes, strengthening service quality, preservation, management and backup of biographical data [3]. Therefore, the productivity of human resource unit can be improved as well as its strategic role and status can be strengthened. It would not only allow enterprises being easy to understand the human resource context, facilitating control and scheduling, but also allow them realizing job vacancies to recruit and train new employees [4]. Today’s information services industry is highly knowledge-intensive, which embraces a wide range. With the advances in science and technology improvement, one can say that its applications cover all the industries. The professional threshold is no longer limited to information personnel. It is more and more common to cooperate with professional talents in other areas. In order to grasp and organize personnel as well as to identify the appropriate staff, employee recruitment will contribute to the operation and development of enterprises [5]. This study is focused on the recruitment of mid-level supervisory staff for information industry. It is intended to improve the lack of recruitment processes as well as reduce individual senses of supervisory level by fuzzy logic and Analytic Hierarchy Process methods. This study tries to identify appropriate personality traits and key professional skills through the information statistics and analysis of Analytic Hierarchy Process in order to expect the recruitment process be more reasonable based on the fuzzy multiple criteria decision making model to achieve the goal of merit-based selection. 2. Literature Review 2.1 Analytic Hierarchy Process (AHP) Analytic Hierarchy Process, a multi-objective decisionmaking method, was developed by Saaty in 1971 [6]. It is mainly used in the uncertainty of decision-making issues with many assessment criteria. The interactive hierarchies are intended to make effective decision-making for complex issues or uncertainty in risk, or to search for consistency when determining divergence. After constant application, modification and validation, the entire theory of AHP was completed in 1980. The theory is simple and 114 IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.7, July 2009 easy to operate. It can capture the majority of experts and policy makers’ opinions at the same time, which is very practical in practice [7]. Application of AHP method to deal with complex issues can be broadly divided into the following six steps: (1) Problem definition The system of problem should be maximized to cover all the possible factors. At the same time, the planning group should be established to define the scope of the problem. (2) Construction of hierarchy Members in the planning group identify the criteria and Sub-criteria of action affecting the problem, as well as the nature of alternatives and alternative programs with brainstorming; secondly, report this preliminary structure to decision-makers or decision-making groups for amendment; then, the planning group or members in decision-making group check all the elements affecting the problem, determining the Binary Relation between them; if the decision is made by the planning group, it is required to report to decision-makers or decision-making group; finally, construct the hierarchy of the whole problem with Interpretive Structural Modeling or Hierarchical Structural Analysis. (3) Questionnaire design and survey Elements of each hierarchy should be compared in pairs taking one element in the previous hierarchy as a benchmark. Therefore, each paired comparison needs a questionnaire design, decision-makers or members in decision-making group fill out the 1-9 scale. The questionnaire has to clearly describe the questions for each paired comparison with detailed notes. (4) Consistency testing According to survey results, establish pairwise comparison matrix, get the Eigen-values and eigenvectors with a calculator or computer, and then test the consistency. If the consistency of matrix does not meet the requirements, it shows inconsistencies in decision-makers to determine, for that reason, planners should explain it clearly to decision-makers. (5) Hierarchy consistency testing If the consistency in each pairwise comparison matrix is consistent with the requirements, it is necessary to test the consistency of the whole hierarchy. If it does not meet the requirements, it means the correlations between elements have problems, which should be analyzed again. (6) The choice of alternative If the whole hierarchy passes the consistency testing, the priority vector of alternative can be obtained. If there is only one decision-maker, it is only required to calculate the priority level of alternative; if there is a decisionmaking group, it is required to calculate the alternatives for each decision-making member. Finally the weighted integrated comment is obtained with weighted average method, which is used to determine the priority of alternatives [6][7]. 2.2 Fuzzy Analytic Hierarchy Process (FAHP) Since AHP cannot overcome the ambiguity in decisionmaking, Laarhoved & Pedrycz (1983) evolved Saaty’s traditional AHP, and developed it into FAHP, they substituted triangular fuzzy numbers directly into the pairwise comparison matrix, so as to deal with the ambiguity in criteria measuring and decision-making [8]. The traditional AHP had some shortcomings in its application. It was unable to cope with the subjective, vague and imprecise properties in people’s decisionmaking. Buckley (1985) modified Saaty’s AHP paired comparison values, and then he used trapezoidal fuzzy numbers to express the relative important criteria, and then obtained fuzzy weights with geometric mean [9]. In assessing customers’ demand for service quality, usually traditional ways are not easy to express. But in fuzzy theory, the “linguistic variable” is used to overcome this barrier to express customers’ importance. “Linguistic variable” refers to the variable is natural or artificial word or phase, which is primarily used to deal with complex or ill-defined circumstances. Traditional way of AHP assessment takes researcher’s vague ideas as a specific value to output, thus neglecting the characteristics of internumerical and linguistic “plausibility”. In order to make up for the inadequacy of traditional methods, the calculation of fuzzy theory is used to express the meaning of linguistic variable. As FAHP uses fuzzy concept, there will not have overall disparity due to researcher’s preferences for certain factors, which is far more objective than the traditional AHP [10]. 3. Research Methodology 3.1 Design the expert questionnaire The first step of the research methodology in this study is to design the expert questionnaire. After referring to relevant literatures and interviews with business experts, the questionnaire is classified into two parts: one is personal traits, such as “ability traits, personality traits, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.7, July 2009 115 motivation traits”, the other is management skills, such as “technical skills, interpersonal skills, conceptual skills”. The effectiveness of the questionnaire is tested with a validity check. The content of the questionnaire is adjusted according to pre-test results, and at last the final questionnaire is defined by the fuzzy theory. After recovering all expert questionnaires, the dimensions of information are reduced with factor analysis, supplemented by the concepts of fuzzy logic method, the values, maximum value and geometric mean of importance are identified. Because the geometric mean can present all the experts’ opinions best, it is used as the basis to set the threshold value. The factors above the threshold value are selected and retained, that is, it is intended to select the influencing factors with higher importance. 3.2 Construct AHP framework (1) Construct the hierarchy framework This study used AHP to break down complicated decisionmaking issues into several specific items. Generally, the framework of AHP can be divided into Tri-Tiered, the first Tier is Goal, which can be regarded as the ultimate goal of selection; the second Tier is Aspects / Objectives, which are the objectives to be achieved based on the Goal; the third Tier is sub-Criteria [6]. The purpose of the framework is to systematize the complexity of the problem. Through the decomposition in different hierarchies, decision-making problems and objectives are clearer. Individual hierarchy can be grouped to get different combinations, the elements or criteria affecting the system are grouped into a number of hierarchies, interaction and impact of the whole system should be a consideration. Therefore, when constructing the framework there are two issues to be solved: one is how to establish the hierarchical relationship, and two is how to assess the impact of properties in each hierarchy. The hierarchy structure is started from the overall goal, and then spread up to aspects and objectives, after that, the relevant criteria or attributes are defined.