Fuzzy Technique For Order Performance Research Articles

Managing risk in emergency supply chains – An empirical study

ABSTRACT Relief organisations face significant logistical challenges in the aftermath of natural disasters, characterised by volatile environments. Effective risk management in these contexts hinges on the identification, evaluation, and mitigation of potential risk events. This study addresses this critical need by aiming to identify and prioritise the most critical strategies to improve resilience. To achieve this, a novel framework, the Fuzzy Analytical Hierarchy Process - A fuzzy Technique for Order Performance by Similarity to Ideal Solution (FAHP-FTOPSIS), is proposed. An empirical application demonstrates that this framework effectively and systematically prioritises strategies for mitigating risks. This study examined existing mitigation strategies and identified eight that are particularly significant. Among these, collaboration and coordination, flexible transportation capabilities, and flexible supply bases emerged as the three most critical mitigation strategies for emergency supply chains. This finding emphasises the importance of prioritising these critical strategies in the development of strategic emergency supply chain plans. The practical implementation of these strategies, substantiated by empirical data from credible sources, would significantly enhance the preparedness of stakeholders and relief actors. This translates to a proactive ability to anticipate and respond to potential risk factors, ultimately leading to a more effective response to natural disasters.

Evaluating lecturer performance in Vietnam: An application of fuzzy AHP and fuzzy TOPSIS methods

Higher education is regarded as being of paramount importance in Vietnam and as being essential to raising the level of the country's labor force and promoting economic progress. Evaluation of lecturers is one of the institution's activities and a crucial component of managing human resources in higher education institutions. How to evaluate faculty members' overall performance using a range of criteria is one of the key evaluation-related challenges. This study presents a method that uses fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) to assess and rank the performance of lecturers. Specifically, the evaluation framework is developed by identifying criteria and sub-criteria based on a comprehensive review of existing literature. Following that, the fuzzy AHP approach is used to determine the weights of the criteria and sub-criteria using the pairwise comparisons. The Fuzzy TOPSIS approach is employed to assess and prioritize lecturers identified through expert evaluation. When applied in group decision-making, utilizing fuzzy AHP and fuzzy TOPSIS promotes agreement among decision-makers and diminishes uncertainty in decision-making processes. The utilization of the multiple criterion measurement approach can then be used to carry out the evaluation. The suggested framework is also demonstrated via a case study. The use of this framework can improve the evaluation's objectivity, accuracy, and scientific methodology. It is believed that this work will assist managers of higher education institutions improve their standards for educational quality.

Performance measurement of factors affecting consumer experience in healthcare services using fuzzy and grey techniques

PurposeThe importance of consumer experience in service industries, particularly healthcare, is widely acknowledged as it captures the intricacies of quality management. In tandem with the emerging research trends that evaluate service excellence through user experience, this study renders a performance analysis of the dimensions of consumer experience that individually or collectively shape healthcare consumers’ perceptions of service quality.Design/methodology/approachA cross-sectional study was conducted across 13 mid-tier corporate hospitals to collect data from 438 patients. The data was processed through factor analysis in SPSS to confirm sample adequacy and factor extractability. Further, two independent multi-criteria decision-making (MCDM) tools, Fuzzy Technique for Order Performance by Similarity to Ideal Solution (F-TOPSIS) and Grey Relational Analysis (GRA), were executed to render performance analysis of identified factors.FindingsUsing F-TOPSIS, factors such as “information” and “hospital environment” received higher performance ratings, while items related to “communication with doctors” and “humanistic care” received lower rankings. Minor yet anticipated deviations were observed while verifying performance scores using GRA. Nonetheless, both outcomes exhibited a strong correlation coefficient of 97.14%, confirming analytical consistency.Originality/valueHitherto, such usages of hybrid MCDM techniques have rarely been executed to convey a clear understanding of consumers’ experiences in healthcare services. Moreover, the findings provide a clear insight into consumers’ key response areas, which can further be translated to maximize consumer gratification, thus assisting healthcare managers in improving service performance and clinical decision-making.

Adaptive Multi-Criteria-Based Load Balancing Technique for Resource Allocation in Fog-Cloud Environments

Recently, to deliver services directly to the network edge, fog computing, an emerging and developing technology, acts as a layer between the cloud and the IoT worlds. The cloud or fog computing nodes could be selected by IoTs applications to meet their resource needs. Due to the scarce resources of fog devices that are available, as well as the need to meet user demands for low latency and quick reaction times, resource allocation in the fog-cloud environment becomes a difficult problem. In this problem, the load balancing between several fog devices is the most important element in achieving resource efficiency and preventing overload on fog devices. In this paper, a new adaptive resource allocation technique for load balancing in a fog-cloud environment is proposed. The proposed technique ranks each fog device using hybrid multi-criteria decision- making approaches Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS), then selects the most effective fog device based on the resulting ranking set. The simulation results show that the proposed technique outperforms existing techniques in terms of load balancing, response time, resource utilization, and energy consumption. The proposed technique decreases the number of fog nodes by 11%, load balancing variance by 69% and increases resource utilization to 90% which is comparatively higher than the comparable methods.

Temporary safety zone site selection during haze Pollution: An integrated approach with FAHP and FTOPSIS

Haze pollution poses significant health risks, driving organizations to mitigate its impact. Temporary safety zones are essential for protecting people during haze pollution incidents. However, current strategies may fall short in meeting demand under certain conditions. To address this challenge and aid decision-making, we propose a methodology for selecting temporary safety zone sites during haze pollution. The methodology integrates the Fuzzy Analytic Hierarchy Process (FAHP), the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS), and a fuzzy mathematical model. Criteria for site selection are established through literature reviews and expert interviews and the FAHP assigns weights to each criterion. FTOPSIS evaluates candidate locations based on the criteria, generating scores for each site. Finally, a multi-objective fuzzy mathematical model identifies optimal locations, considering budget limits, capacity restrictions, and other constraints. We validated the approach in the Sarapee district in Thailand. The results demonstrate the model’s ability to generate optimal solutions, identifying suitable locations for temporary safety zones during haze pollution crises. This research is significant for decision-makers and governments who need to strategically place temporary safety zones and safeguard communities from the hazards of haze pollution. By adopting this integrated approach, organizations can enhance their response to haze pollution incidents, protecting public health and well-being.

Modeling barriers to social responsibility accounting (SRA) and ranking its implementation strategies to support sustainable performance – a study in an emerging market

PurposeToday, with the increasing involvement of the environment and human beings business units, paying attention to fulfilling social responsibility obligations while making a profit has become increasingly necessary for achieving sustainable development goals. Attention to profit by organizations should not be without regard to their social and environmental performance. Social responsibility accounting (SRA) is an approach that can pay more attention to the social and environmental performance of companies, but it has many barriers. Therefore, the purpose of this study is to identify barriers to SRA implementation and provide strategies to overcome these barriers.Design/methodology/approachIn this study, the authors identify barriers to social responsibility accounting implementation and provide strategies to overcome these barriers. By literature review, 12 barriers and seven strategies were identified and approved using the opinions of six academic experts. Interpretive structural modeling (ISM) has been used to identify significant barriers and find textual relationships between them. The fuzzy technique for order performance by similarity to ideal solution (TOPSIS) method has been used to identify and rank strategies for overcoming these barriers. This study was undertaken in Iran (an emerging market). The data has been gathered from 18 experts selected using purposive sampling and included CEOs of the organization, senior accountants and active researchers well familiar with the field of social responsibility accounting.FindingsBased on the results of this study, the cultural differences barrier was introduced as the primary and underlying barrier of the social responsibility accounting barriers model. At the next level, barriers such as “lack of public awareness of the importance of social responsibility accounting, lack of social responsibility accounting implementation regulations and organization size” are significant barriers to social responsibility accounting implementation. Removing these barriers will help remove other barriers in this direction. In addition, the results of the TOPSIS method showed that “mandatory regulations, the introduction of guidelines and social responsibility accounting standards,” “regulatory developments and government incentive schemes to implement social responsibility accounting,” as well as “increasing public awareness of the benefits of social responsibility accounting” are some of the essential social responsibility accounting implementation strategies.Practical implicationsThe findings of the study have implications for both professional accounting bodies for developing the necessary standards and for policymakers for adopting policies that facilitate the implementation of social responsibility accounting to achieve sustainability.Social implicationsThis paper creates a new perspective on the practical implementation of social responsibility accounting, closely related to improving environmental performance and increasing social welfare through improving sustainability.Originality/valueExperts believe that the strategies mentioned above will be very effective and helpful in removing the barriers of the lower level of the model. To the best of the authors’ knowledge, for the first time, this study develops a model of social responsibility accounting barriers and ranks the most critical implementation strategies.

A novel fuzzy SECA model based on fuzzy standard deviation and correlation coefficients for resilient-sustainable supplier selection

Selecting the best out of a pool of potential suppliers is one of the most significant challenges in supply chain management. Although numerous works have studied supplier selection (SS) as a function of individual criterion (e.g., sustainability or resiliency), few studies have explored it as an all-in-one. To address this gap, this study proposes a novel fuzzy multi-criteria decision-making (MCDM) framework for a real-world color and glaze case study. First, resilient-sustainable criteria are identified for evaluating the performance of the suppliers. Expert opinions and literature review are then utilized to establish a set of criteria in two different categories: sustainability and resiliency. This two-pronged approach ensured that important criteria are considered in the evaluation process. Next, a new fuzzy simultaneous evaluation of criteria and alternatives (FSECA) model is developed to highlight the most crucial criteria and the best supplier. The proposed model utilizes a predetermined set of criteria reference weights as benchmarks to calculate the final weights that closely align with these reference points. Moreover, the consideration of fuzzy standard deviation and correlation coefficients of fuzzy numbers are incorporated to take into account the inherent variability and uncertainty in the data, resulting in a more accurate and robust analysis of the data. To validate the proposed method, the findings are compared to the state-of-the-art fuzzy approaches for SECA method, and other MCDM techniques such as fuzzy weighted aggregated sum product assessment (FWASPAS), fuzzy technique for order performance by similarity to ideal solution (FTOPSIS), fuzzy Multi-Objective Optimization on the basis of Ratio Analysis (FMOORA), fuzzy simple additive weighting (FSAW), and fuzzy complex proportional assessment (FCOPRAS). Moreover, by applying the geometrical analysis for interactive aid (GAIA), a visualization approach, the results demonstrate that the performance of the proposed framework is satisfactory and highlight the most crucial criteria, such as the supplier responsiveness in resiliency and delivery in economic impact of sustainability categories. Since choosing supplier is a strategic decision, the proposed work can help managers to choose the right suppliers.

Telemedicine implementation framework for Malaysia: An integrated SWOT-MCDM approach

ObjectivesDespite the increase in the utilization of telemedicine worldwide, especially during the pandemic, its implementation in Malaysia's healthcare industry remains scarce. Hence, this study aims to understand the current state of telemedicine utilization by identifying the constraints and establishing the optimum telemedicine implementation strategy. MethodsThe study proposes an integrated methodology based on strengths, weaknesses, opportunities, and threats (SWOT) analysis, analytical hierarchy process entropy (AHPE), and fuzzy technique for order performance by similarity to the ideal solution (FTOPSIS). The SWOT analysis is performed for the situational assessment of telemedicine technology in Malaysia. Each element of SWOT was assessed using AHPE to establish priorities, followed by the FTOPSIS approach to provide strategies for its successful adoption. ResultsThe findings show that continuous government support and encouragement for market acquisition, cost-cutting, profit-maximizing, and Internet of Things (IoT)-based adoption to establish a strong telemedicine network is the most important strategy for telemedicine technology implementation. While using a structured method to improve service quality, the implementation of a knowledge-sharing management program for telemedicine technology is ranked as the least preferred telemedicine technology implementation strategy. ConclusionsThe study suggests a systematic way of developing and evaluating telemedicine technology implementation strategies for further improving Malaysian healthcare patient experiences and policy endeavours. This study proposed alternative strategies to expand telemedicine technology implementation. It is hoped that this study could further enhance knowledge and serve as a guide for stakeholders in the healthcare industry to understand their business environment better.

A fuzzy TOPSIS model for selecting digital technologies in circular supply chains

Several digital technologies are available to facilitate the transition toward a circular supply chain infrastructure. Small-Medium Enterprises (SMEs) should assess their readiness and measure their performance to select the most appropriate digital technology. This study explores how well-established digital technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), Cloud Manufacturing (CM), and Big Data Analytics (BDA) impact circular supply chain infrastructure in SMEs. Questionnaires have been distributed to collect employees’ preferences concerning the circular supply chain management criteria (profit, innovation, sustainability, and optimization). The responses have been organized into three clusters using Principal Component Analysis (PCA). A fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) technique is adopted to evaluate these technologies since it constitutes a reliable managerial tool when vagueness impacts the smooth operation of the supply chain. Results indicate the ranking order of the investigated digital technologies (CPS>IoT>CM>BDA) as well as the circular benefits and the supply chain attributes imparted upon implementing these technologies. Such benefits and attributes are provided to assess the impact of these digital technologies on a circular economy. Lastly, the perspective of the selection process affected by other factors, such as the enterprise’s extroversion level and its internal structure, are discussed.

Using Fuzzy TOPSIS and Balanced Scorecard for Kaizen Evaluation

Abstract Background Kaizen is a very important continuous improvement technique; however, measuring kaizen results/benefits have not been clearly and comprehensively addressed by the literature. Objectives This paper aims to propose a kaizen measuring system by integrating a Balanced Scorecard (BSC) and a Fuzzy Technique for Order Performance by Similarity to Ideal Solution (Fuzzy TOPSIS). Methods/Approach Three research instruments were distributed to kaizen experts to allocate kaizen benefits into the four BSC perspectives. The best measures of kaizen benefits were determined by employing the Fuzzy TOPSIS technique. Results The results present a kaizen performance evaluation system where the benefits were allocated into the four BSC perspectives, and the best measure for each kaizen benefit was chosen using fuzzy TOPSIS. Conclusions The research contributes to the literature by proposing a kaizen measurement system that will pair each benefit of using kaizen with BSC perspectives and measures, thus expanding the advantages of adopting kaizen to any sector or industry.

Ranking Strategic Goals with Fuzzy Entropy Weighting and Fuzzy TOPSIS Methods: A Case of the Scientific and Technological Research Council of Türkiye

This study involves an integrated approach consisting of the Fuzzy AEW Method which considers all relevant criteria and involves the contribution of all members of a strategic planning team in the determination of strategic goals as well as the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. The Fuzzy AEW method has been used in weighing the criteria whereas the Fuzzy TOPSIS method has been used in determining the order of the alternatives. This article presents a real-world case using this model in setting targets for improving the institutional capacity of the Scientific and Technological Research Council of Türkiye (TÜBITAK). In this study, strategic goals have been set forth considering the opinion of strategic planning experts and previous strategic plans, and then the said method has been applied. The model could easily be applied both in the public and private sectors. This new model involves the effective planning of scarce resources and ensures digitalization in planning as well as the determination of goals through analytical methods. Ineffective meetings and workshops of the past will be replaced by a participatory and transparent structure.

Evaluation of barriers and solutions to renewable energy acceleration in Malawi, Africa, using AHP and fuzzy TOPSIS approach

Malawi's access to electricity remains worrisome (15 %), forcing the government to rely on fossil fuels. Thus, renewable energy resources may be one of the best options. However, several barriers are impeding the development of renewable energy technologies. Therefore, this study identifies the six main barriers and nineteen sub-barriers to renewable energy acceleration in Malawi. The study also suggested solutions to these barriers. Analytic hierarchy process (AHP) and fuzzy Technique for Order Performance by Similarity to Ideal Solution (fuzzy TOPSIS) approaches were utilized for prioritization. The results indicated that the top two barriers include economic and political-governmental. Furthermore, the top two sub-barriers include lack of funding and high investment cost. Corruption and political commitment have also shown serious contributions to low uptake. Financial incentives, institutional coordination, and research and knowledge sharing are among the highly recommended solutions. This study could help policymakers incorporate necessary strategies when developing energy-related policies.

FUZZY TOPSIS IN SELECTING LOGISTIC HANDLING OPERATOR: CASE STUDY FROM POLAND

Reliable and effective selection of logistic handling operator is a particularly demanding process due to the short reaction time or high level of accompanying stress. Moreover, diversification of transported cargo makes use of classical indicators and methods of carrier selection highly unsatisfactory for decision-makers. To solve this problem, managers are seeking multi-criteria decision methods that improve the decision-making process related to the selection of the carrier and reduce the risk indicator related to the incorrect implementation of the transport order. Thus, in this paper, we present a Multi-Criteria Decision-Making (MCDM) approach for selecting logistic handling operators under partial or incomplete information (uncertainty) and taking into account the different type of transported cargo. The proposed approach comprises 2 main steps. In the 1st step, we identify the input parameters, mainly connected with criteria for carrier selection depending on the type of transported cargo. In the 2nd step, experts provide linguistic ratings to the potential alternatives against the selected criteria and the best alternative is chosen. At this stage, the fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) approach is used. Later, the applicability of the developed method is presented based on the chosen case company. The comparison of classical and fuzzy approaches to decision-making process is also given.

A systematic assessment of multi‐dimensional risk factors for sustainable development in food grain supply chains: A business strategic prospective analysis

AbstractThe food grain supply chain (FGSC) is composed of several links, stretching from the point of production to the point of consumption. A broken connection might produce a food catastrophe. The structural imbalance of India's FGSC is an obstacle to achieving sustainability; this has to be addressed if the country is to preserve national food security. This present study aims to develop a systematic assessment of the risks and the priority of risk‐mitigating solutions in attaining sustainability in the Indian FGSC. Multiple groups of individuals and businesses involved in the FGSC have been surveyed and interviewed, with their responses analyzed. A total of 31 risk factors and 11 risk‐reduction strategies are identified. Further, the identified risk factors are classified into five‐dimensional sustainability criteria (environmental, economic, institutional, technical, and social) by using exploratory factor analysis (EFA). Then, a fuzzy analytical hierarchy process (FAHP), combined with the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS) method, is adopted to find the most critical risk factors and choose the best course of strategies for risk mitigation. The study finds that inability to incorporate advanced technology imposes the highest risk to sustainability followed by natural disasters. Ensuring end‐to‐end computerization using advanced technology like agri 4.0 is the need of the hour in intercepting the range of FGSC risks. The results may help policymakers create a comprehensive risk mitigation plan and taxonomy to increase supply chain resilience.

Multi-criteria Handoff Decision making Algorithm for Seamless Mobility in Heterogenous Wireless Networks

Wireless networks have been through many transformations to provide Quality of Service (QoS) and seamless mobility. Handoff management plays a vital role in maintaining quality of service while roaming. The focus of the paper is to develop a handoff technique for optimized handoff decision making. Multi-Criteria Decision Making (MCDM) techniques are utilized for making decision where more than one contradicting parameter are involved. Fuzzy Analytic Hierarchy Process (FAHP) is used for weight calculation and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) decides the rank of available networks. The proposed technique utilizes six network parameters i.e., received signal strength, bandwidth, signal to interference & noise ratio, delay, packet loss, and bit error rate for weights calculation under different traffic classes and three decision makers i.e., network, mobile node, and user preference for optimized handoff. The results show that the proposed technique effectively classify the best available network in terms of their rank and reduces the number of unnecessary handoffs.

Environmentally friendly strategy for hybrid flow shop scheduling with the unrelated parallel machines

With the increasing environmental pressure, reducing the environmental impact on hybrid flow shops (HFS) has attracted extensive attention because of its broad industrial applications. The selection of machines in the implementation of HFS is a complex decision-making process that hinders the optimization efficiency in shops. However, this issue has not been addressed thoroughly. In light of this, a multi-objective mathematical model is formulated for the minimization of makespan and energy consumption of the hybrid flow shop scheduling problem (HFSP). The environmental performance of machines is calculated by the proposed evaluation index and is ranked on the basis of the integrated entropy and fuzzy technique for order performance by similarity to ideal solution method. Moreover, to solve the multi-objective model of HFSP, an improved differential evolution algorithm with a heuristic active decoding rule that incorporates the ranking of environmental performance of machines into the iterative process of the algorithm is presented. Finally, a case study is presented to evaluate the effectiveness of the proposed method and to prove the feasibility of the ranking-based differential evolution algorithm (RBDE). The result shows that the proposed RBDE outperforms RBNSGA-II and RBPSO in searching for non-dominated solutions that can solve the environmental production of HFSP effectively.

An Integrated Approach with CLFPR-Based ANP and Fuzzy TOPSIS for Evaluating Business Performance of Rural Homestays: Study from China

Given the high importance of the performance of rural homestays to the local economy, this study aims to fill the gap in homestay performance research and make rural homestays more competitive and sustainable after the coronavirus disease epidemic (COVID-19). Integrating a consistent linguistic fuzzy preference relations-based analytic network process (CLFPR-based ANP) and a fuzzy technique for order performance by similarity to ideal solution (TOPSIS), this study constructs a comprehensive evaluation model of the performance of rural homestays and empirically analyzes homestay performance in Zhejiang. The results show the following: (1) Among the criteria’s weights by CLFPR-based ANP, homestay operation and management, service quality, and homestay geist and community co-prosperity should be given much more attention; the importance of factors regarding the environment and building of rural homestays is gradually weakening. (2) In light of ranking alternatives based on performance evaluation by fuzzy TOPSIS, the homestay with the optimal performance has been found, which practitioners can use as a benchmark. Therefore, the priorities of these criteria further deepen the understanding of the performance of rural homestays and underline the development direction for practitioners. Simultaneously, in terms of feasibility and reliability, the integrated approach comprises a beneficial attempt and becomes an effective evaluation tool for practitioners to improve effectiveness.

Role of Industry 4.0 in agile manufacturing to achieve sustainable development

AbstractThe business world is becoming increasingly complex, disrupted, and uncertain. As Industry 4.0 (I4.0) takes shape, to endure this change and focus on growth, industries must learn to embrace change and move quickly; this is particularly so for the automotive industry, which is a major sector of any economy. Agility is needed to embrace this change and promote sustainability across every industry. To face these contingencies, it is essential to identify and understand the strategies that can aid industries in sustaining real‐time scenarios. In this paper, we aim to assess the agility indicators that bring resilience to the inclusion of I4.0 and sustainability in the automotive sector. The unexpected changes in the environment related to sustainability and technological disruptions of I4.0 require the identification of sustainable agile mitigation strategies to manage such situations. The fuzzy Delphi methodology assesses agility indicators based on a questionnaire for prioritization. Further, the fuzzy technique for order performance by similarity to ideal solution (TOPSIS) is utilized to prioritize these agility indicators with the agility strategies that manufacturing industries can deploy to enhance the sustainability of their operations. The fuzzy extension to the methodologies helps in incorporating vagueness and ambiguity in the decision‐making process. A fuzzy approach to prioritizing agile strategies as the industries are changing is helpful. The decision makers have only a few clear ideas about how best to integrate them. Among the agile strategies, “resilience,” “technological capabilities,” and “customer‐centric innovation” are the top strategies. By executing the strategies suggested in the study and understanding the agility indicators, practitioners can ensure resilience in the work systems and minimize the impact of the disruptions caused by the industrial revolution. Thus, this research has been underpinned by the organizational change theory and discusses all mitigation strategies concerning the rapid changes organizations are facing with the fourth industrial revolution.

A Hybrid Model Based on SEM and Fuzzy TOPSIS for Supplier Selection

How to select the right suppliers has become a crucial strategic decision for manufacturing companies facing various performance measures. This study proposed a hybrid selection model integrating factor analysis, structural equation modeling (SEM), and the fuzzy Technique for Order Performance by Similarity to Ideal Solution to (TOPSIS) design a supplier selection model. The study distilled 70 original selection criteria from the relevant literature and identified eight main influencing factors based on factor analysis. The relevance of factors was evaluated by SEM and the performance of suppliers assessed by fuzzy TOPSIS. The model was applied in a manufacturing company in China to verify its practical significance. By combining different methods, the model can select relevant and important supplier selection criteria and include unquantifiable, incomplete, and vague information in the decision model. The hybrid model merges the advantages of each method and takes into account of uncertainty associated with supplier selection process.

A Fuzzy Multi-Criteria Decision Making Methodology for Job Evaluation

Aims: In this study, the integrated methods Hesitant Fuzzy Analytic Hierarchy Process (HF-AHP), Fuzzy COmplex Proportional Assessment (F-COPRAS) and Fuzzy Technique for Order Performance to Ideal Solution (F-TOPSIS) were used for job evaluation studies in a food company. Background: There has been a decline in employee performance in the company. Unfair wages and unequal workload were identified as the reasons for the failure. Therefore, it has been observed that the staff turnover rate in the company is quite high. Objective: The objective is to determine a fair wage policy that will increase employee satisfaction by stratifying with job evaluation analysis between positions. Methods: The experts of Human Resources Department determined eight competency evaluation criteria for job evaluation studies in the proposed approach. Based on their judgments on these criteria, the competencies were rated using a linguistic scale and the weighting values were calculated using HF-AHP method. These values are inputs for the next stage. Employees were ranked using F-COPRAS and F-TOPSIS methods. Results: This study showed that the integrated method can be an effective alternative solution approach for calculating the weighting values and ranking of competencies in job evaluation studies. Conclusion: It has been shown that the use of the strata created as a result of this study is a great facilitator in determining employee pay policies.