Expertise-based decision makers’ importance weights for solving group decision making problems under fuzzy preference relations

The quality of a decision is influenced by the level of expertise of the Decision Makers (DMs). In Group Decision Making, alternatives’ scores are obtained by integrating the DMs opinions and the importance weights of the DMs greatly affect the resulted value. Expertise level is defined as the ability to differentiate consistently and expressed as the CWS-Index, a ratio between the Discrimination and Inconsistency. The DMs give their evaluations in pairwise comparison of Fuzzy Preference Relations (FPR) and the additivity property of FPR generates the estimators needed to get the CWS-Indexes and the expertise-based ranking of DMs. The weights of the DMs are obtained by using Induced Ordered Weighted Averaging (IOWA) operator and Basic Unit Monotonic Increasing functions and the resulted weights are used to evaluate the available alternatives to get the best one based on Fuzzy Majority and IOWA operators. This paper proposed an expertise-based weight allocation method for DMs and a numerical example is discussed to illustrate this expertise-based model to get the best alternative and it concluded that the higher the DMs’ expertise level, the higher his/her weight, and these weights affect the alternatives’ score and the rank of the alternatives

Expertise-based ranking of experts: An assessment level approach

The quality of a formal decision is influenced by the level of expertise of the decision makers (DMs). The composition of a team of DMs can change when new members join or old members leave, based on their ranking. In order to improve the quality of decisions, this ranking should be based on their demonstrated expertise. This paper proposes using the experts’ expertise levels, in terms of ‘the ability to differentiate consistently’, to determine their ranking, according to the level at which they assess alternatives. The expertise level is expressed using the CWS-Index (Cochran-Weiss-Shanteau), a ratio between Discrimination and Inconsistency. The experts give their evaluations using pairwise comparisons of Fuzzy Preference Relations with an Additive Consistency property. This property can be used to generate estimators, and replaces the repetition needed to obtain the CWS-Index. Finally, a numerical example is discussed to illustrate the model for producing expertise-based ranking of experts

The Assessment Model to Rank Applicants for Research and Development Job Position in PT ABC

Staff turnover has a negative impact on an organization's progress and development. Unfortunately, companies commonly experience difficulty replacing departing employees with qualified applicants that fit their job specifications. Staff turnover is influenced by the recruitment and selection process, and effective recruitment and selection reduce employee turnover, which boosts an organization's profitability. Therefore, organizations must consider getting competent people that fit the company's job specifications from the beginning of the recruitment process, demonstrating the importance of a well-organized and methodical hiring process. This article presents an assessment model to rank the applicants for research and development job positions in a company. The methods used in this model are the Fuzzy Analytical Hierarchy Process (F-AHP), Alfares' weighting method, and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS). The selection criteria for research and development job positions were classified into subjective, objective, and absolute factors and customized for PT ABC. The expert provided his judgments on the importance of the criteria in fuzzy pairwise comparisons, and the criterion weights were determined using F-AHP. The objective criteria weights were: education 0.039, working experience 0.083, analysis ability 0.274, research ability 0.290, and planning ability 0.312. At the same time, the subjective criteria weights were: interpersonal skills 0.267, software mastery 0.229, problem-solving ability 0.212, English fluency 0.053, and the weight of project management ability 0.239. The Alfares method will be used to weigh the sub-criteria. The criteria/sub-criteria and their weights will be used in the assessment model for ranking the candidates in the research and development job to rank potential applicants using the F-TOPSIS method

The Integration of AHP and SAW Methods with Multiple Decision-Makers for Supplier Selection. A Case Study of UD BSA, Surabaya

Supplier selection is a decision-making process that considers many criteria. The industry has developed and implemented many Multi-criteria Decision-Making (MCDM) methods for supplier selection. The Analytic Hierarchy Process (AHP) method is one of the most widely used methods, however, the AHP method requires a lot of pairwise comparisons between criteria and alternatives, which will be more complex when there is interdependence between criteria and alternatives. In order to produce better decisions, the AHP method is often implemented with other methods like the Simple Additive Weighting (SAW) method. The SAW method applies simple calculations and does not require complicated computer programs. In previous studies, a single decision-maker carried out AHP and SAW together in the manufacturing industry. In this study, a decision-making method was developed by several decision- makers (DMs) by integrating the AHP and SAW methods. The results of integrating AHP and SAW with multiple decision-makers were tested at a wood trading company called UD BSA for supplier selection. In this study, 3 alternative suppliers were considered. The literature results and brainstorming with the company resulted in 5 criteria and 8 sub-criteria that were considered in selecting suppliers at UD BSA with 2 DMs: the owner as DM1 and experienced purchasing staff as DM 2. The DM weighting was determined by considering each DM’s expertise in assessing the criteria and sub-criteria. The integration of the AHP and SAW methods with 2 DM produces the output weight of each DM and the calculation results of the supplier's assessment with the highest ranking. Based on the calculation results, DM2 has a higher weight of 56.13%, which means that DM 2 is more expert in assessing criteria and sub-criteria. In addition, the best-selected supplier is PP, with the highest overall score of 0.8354. From this case study, UD BSA designed a supplier ranking system, which the company can use if there are new suppliers or future supplier performance change

Assessment Model for Ranking Prospective Candidates for the Position of Production Supervisor in a Manufacturing Company

Human resources have a substantial influence on the success and progress of an organisation, and finding qualified candidates who meet the employment requirements is a common challenge for businesses. However, the average unemployment rate in any nation is typically very high, and together with the difficulty experienced by companies in terms of finding dependable personnel, this highlights the importance of an objective, organised approach to hiring new personnel. This paper proposes a multiple-criteria assessment model that allows a company to rank the fitness level of potential workers to production supervisor position in a manufacturing company. The methods used to evaluate prospective workers are multi-expert.analytic.hierarchy.process (multi-expert AHP) and preference. ranking. organization. method for enrichment. evaluation (PROMETHEE). The criteria for selecting production supervisors were categorised into subjective and objective types, and were customised for use by company XYZ. The criteria weights were determined using Multi-expert AHP. The experts provided their scores for the significance of the criteria via pairwise comparisons; these scores were then aggregated based on their level of expertise in evaluating criteria, and the criteria weights were established. Finally, PROMETHEE was used to determine the ranking of the prospective candidates for the position of production supervisor in company XYZ

EXPERTISE-BASED EXPERTS IMPORTANCE WEIGHTS IN ADVERSE JUDGMENT

The objective of this research was to propose the use of expertise levels of experts to determine the experts’ importance weights since there has been no research that determines the 'importance weight' using the expertise level as a whole. The significance of this research was the integration of three concepts, namely: the expert’s expertise level, FPR’s Additive Consistency and the Induced-OWA operator to obtain the expert’s importance weight in adverse judgment situation. The Expertise level of an expert in adverse judgment situation is determined by his/her own assessment on a set of alternatives and defined as ‘the ability to differentiate consistently’ and expressed as the ratio between Discrimination and Inconsistency. The experts provided their preferences using FPR (Fuzzy Preference Relations) since FPR has Additive Consistency property to replicate each element of FPR matrix. Experts were sorted according to their expertise level and the experts’ importance weights followed the OWA (Ordered Weighted Averaging) operator’s weights which were determined by parameterization using Basic Unit-Interval Increasing Monotonic functions. The experts’ importance weights model illustrated by a numerical example, and it concluded that the higher the expert’s expertise level, the higher his/her importance weight

MCDM-Based Instrument for Fresh Graduate’s Competencies Conformity in Banking Service Field

Competency mismatch is a mismatch between the qualifications expected by the company and the competencies possessed by individuals. This mismatch arises because the individual does not know the competencies needed to be able to work in the position which is being applied for. In addition, the company as a recruiter has high expectations of individual competencies. There are several consequences that arise due to the competency mismatch. From individual’s perspective, one will have difficulty developing and will have an impact on salary dissatisfaction. From the company’s point of view, the company will not get the expected output from the workers. As for the state, this mismatch causes an increase in the number of unemployment which has an impact on investors’ interest. The absence of certain measurement causes difficulties for both individuals and companies in measuring the degree of competency conformity. This research was conducted to minimize the possibility of a competency mismatch by creating conformity assessment model to measure the competency conformity from the job seeker’s point of view and job qualifications from the company's point of view by assigning 3 positions namely, Management Development Program (MDP), Relationship Officer (RO), and IT Trainee in BCA Bank as a recruiting company and the view of prospective graduates of Industrial Engineering from the University of Surabaya as the prospective workers. The Pareto diagram method was used to determine the criteria that were considered the most important in recruiting employees in the bank and resulting 7 subjective criteria, namely, communication skills, target-oriented, honest, tenacious, have good analytical skills, able to work in any condition, active and disciplined, attractive, and willingness to be placed anywhere, as well as 5 objective criteria namely, age, education level, GPA, software mastery, and height. One of the MCDM methods, namely the Analytical Hierarchy Process (AHP) was used to create a suitability assessment model for 3 positions at BCA bank. The MDP’s most important criteria were analytical skills with a weight of 0.27 and education level, GPA, and mastery of software with a weight of 0.27. In the RO position, the most important criteria were attractive appearance with a weight of 0.32 and mastery of software with a weight of 0.34. In the IT Trainee position, the most important criteria were analytical skills with a weight of 0.22 and education level with a weight of 0.27. The assessment model was then implemented on one of the prospective graduates and obtained results of 86 for MDP, 80.9 for RO, and 81.85 for IT Trainees, meaning that in this case the prospective graduate is most suitable to work in an MDP position

A Self-Assessment Model for Measuring the Fitness Level of Industrial Engineering Graduates Competence to a Quality Control Job Position

Competence mismatch is a mismatch between the company’s job specification and employee competences. Competence mismatch has to be reduced in order to increase employee’s satisfaction and motivation to improve company performance which leads to great benefits for both the company and their employees. A measurement tool is required in order to reduce the potential for competence mismatches at an early stage. This paper proposes a self-assessment model to measure the fitness level of Ubaya Industrial Engineering alumni competences. The criteria were collected form job vacancies from 8 companies and categorized as subjective and objective criteria. Criteria reduction was performed using pareto principle and yield 4 objective criteria and 4 subjective criteria. The criteria weights were determined based on the evaluation from 3 experts who provided their scores in preference ordering dan utility values. The experts’ evaluation scores need to be unified by transforming to Fuzzy Preference Relations and then aggregated to get the criteria weights. The criteria and their weights will be used in this self-assessment model for measuring the fitness level of candidates in terms of the fitness percentage for the Quality Control job

Assessing Individual Fitness for Research and Development Position using Fuzzy AHP and Pareto: Case Study in Manufacturing Industry

Research and development function play significant role in the success of company’s venture and this function has a strict set of recruitment criteria to ensure company can find a good candidate among applicants. The strict recruitment criteria can be time and money consuming while still prone to wrong recruitment which can lead to a high turnover for the company. To help companies in selecting competent candidates for the workforce, there is a potential workforce self-assessment model made for industrial engineering students or graduates. The assessment model is created in advance by identifying the criteria for research and development job positions required by the manufacturing industry. The criteria that have been identified are grouped based on categories and based on the same understanding. Furthermore, Pareto 80/20 method is used to find out the most influential criteria and Fuzzy Analytical Hierarchy Process (FAHP) method is using expert considerations whose consistency was tested using the Analytical Hierarchy Process (AHP) consistency test. The expert used in this research is a professional from a manufacturing company in Indonesia. The research identified 5 objective criteria where analytical capabilities has the most weight and 4 subjective criteria where problem solving skill has the most weight, to be considered. The model provides fitness in terms of suitability percentage for the R&D job