10 research outputs found

    Firmalar─▒n ─░┼č Sa─čl─▒─č─▒ ve G├╝venli─či Performans─▒n─▒n ├çok Kriterli Karar Verme Y├Ântemleri Yard─▒m─▒yla ├ľl├ž├╝lmesi

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    ─░┼č sa─čl─▒─č─▒ ve g├╝venli─či y├Ânetim sistemleri, i┼č s├╝re├žlerinde olu┼čan kaza ve hastal─▒klar─▒n say─▒s─▒n─▒n azalt─▒lmas─▒ amac─▒yla kullan─▒lan temel yakla┼č─▒mlardan biridir. S├╝rekli geli┼čme ba─člam─▒nda in┼ča edilen bu sistemlerin hedeflenen sonu├žlar─▒ do─čurmas─▒, sistem kapsam─▒nda geli┼čtirilen performans ├Âl├ž├╝m ara├žlar─▒n─▒n olu┼čturulmas─▒ ile m├╝mk├╝nd├╝r. Kapsaml─▒ bir performans ├Âl├ž├╝m arac─▒, birden fazla g├Âstergeyi i├žermeli, say─▒sal analiz imkan─▒ sunmal─▒ ve kar┼č─▒la┼čt─▒rma yapabilmeye elveri┼čli olmal─▒d─▒r. Bu ara┼čt─▒rmada, etkin bir i┼č sa─čl─▒─č─▒ ve g├╝venli─či performans ├Âl├ž├╝m├╝ arac─▒n─▒n geli┼čtirilmesi amac─▒yla; performans g├Âsterge havuzu olu┼čturulmu┼č ve uygun g├Âstergeler taranarak belirlenmi┼č, Entropy ve TOPSIS tabanl─▒ ├žok kriterli bir karar verme modeli geli┼čtirilmi┼č, T├╝rkiyeÔÇÖde demir-├želik sekt├Âr├╝nde faaliyet g├Âsteren bir firmadan elde edilen veriler kullan─▒larak Entropy tabanl─▒ bir g├Âsterge a─č─▒rl─▒─č─▒ belirleme ├žal─▒┼čmas─▒ yap─▒lm─▒┼č, firman─▒n i┼č sa─čl─▒─č─▒ ve g├╝venli─či performans─▒, y─▒llar itibariyle TOPSIS kullan─▒larak kar┼č─▒la┼čt─▒rmal─▒ olarak hesaplanm─▒┼č ve ara┼čt─▒rman─▒n sonu├žlar─▒, zay─▒f yanlar─▒ ve yeni ara┼čt─▒rma imkanlar─▒ tart─▒┼č─▒lm─▒┼čt─▒r. Ara┼čt─▒rman─▒n bulgular─▒na g├Âre, ÔÇťg├╝venli olmayan faaliyetler nedeniyle meydana gelen kazalar─▒n say─▒s─▒ÔÇŁ, 0,2971 a─č─▒rl─▒─č─▒yla en ├Ânemli g├Âstergedir. Buna ek olarak, TOPSIS tabanl─▒ g├Âreceli yak─▒nl─▒k de─čerleri, firman─▒n i┼č sa─čl─▒─č─▒ ve g├╝venli─či performans s─▒ralamas─▒n─▒n 2018, 2017, 2016, 2015 ve 2014 ┼čeklinde ger├žekle┼čti─čini ortaya koymaktad─▒r. Bu bulgulara g├Âre, firman─▒n i┼č sa─čl─▒─č─▒ ve g├╝venli─či performans─▒nda y─▒llar itibari ile bir geli┼čme sa─čland─▒─č─▒ sonucuna ula┼č─▒lm─▒┼čt─▒r. Bu ara┼čt─▒rmada geli┼čtirilen ├žok kriterli karar verme modelinin karar vericilerin karar alma s├╝re├žlerine belirtilen katk─▒lar─▒ sunabilece─či ├Âng├Âr├╝lmektedir: i┼č sa─čl─▒─č─▒ ve g├╝venli─či performanslar─▒n─▒n kar┼č─▒la┼čt─▒rmal─▒ analizlerinin yap─▒lmas─▒, i┼č sa─čl─▒─č─▒ ve g├╝venli─či alan─▒ndaki farkl─▒ g├Âstergelerin dikkate al─▒nmas─▒, bu analizlerin y─▒ll─▒k raporlarda kullan─▒lmas─▒yla payda┼člar─▒n firma hakk─▒nda yapacaklar─▒ yat─▒r─▒m kararlar─▒n─▒n sa─čl─▒kl─▒ bir bi├žimde al─▒nabilmesi ve sezgilere d├Ân├╝k karar verme s├╝recinin bilimsel olarak desteklenmesi

    Integrated Fuzzy AHP-TOPSIS Method to Analyze Green Management Practice in Hospitality Industry in the Sultanate of Oman

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    Climate change is the most serious threat that the modern world has ever faced. This has led to increasing attention from the government, industries, researchers, and practitioners on the theme of green practice. Due to the heightened awareness of climate change, the hospitality industry is under pressure to implement green practices and reduce the environmental impact of their operation. The research aims at understanding the indicators that define green practice in the hospitality industry and then developing a model that can be used to measure the green score. The research identifies twenty-six indicators of green practice in the hotel industry. These indicators were clustered into six different criteria. Based on the identified indicators and criteria, an integrated fuzzy AHP-TOPSIS method is proposed to calculate the green score. The fuzzy AHP method is used to calculate the weight of the criteria and indicators, while the fuzzy TOPSIS method is used to calculate the green score and rank hotels. The fuzzy AHP result shows that the criterion ÔÇťRecycling and ReuseÔÇŁ has the highest weight among the identified criteria, while ÔÇťGreen Training and IncentivesÔÇŁ has the lowest weight. The application of the proposed method is demonstrated by using a case study of hotels situated in the Sultanate of Oman. The result shows that the 4-star and 5-star hotels in the Sultanate have green scores between 0.56 and 0.641 out of 1.0 at a 95% confidence interval. The results further show that having a high star ranking hotel does not necessarily mean that the hotel is better in terms of green practice. The developed model helps the hotel industry to understand the indicator and criteria, as identified in this research work, they need to improve in order to improve their overall green management practice.┬ę 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

    Green Closed-Loop Supply Chain NetworksÔÇÖ Response to Various Carbon Policies during COVID-19

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    As concerns about the environment continue to increase and restrictions become tougher, professionals in business and legislators are being compelled to investigate the environmental effects of the activities associated with their supply chains. The control of carbon emissions by governments all over the world has involved the adoption of a variety of strategies to lower such emissions. This research optimizes COVID-19 pandemic logistics management as well as a green closed-loop supply chain design (GCLSCD) by basing it on carbon regulatory rules. This research looks at three of the most common types of normal CO2 restrictions. In the models that have been proposed, both costs and emissions are optimized. When it comes to supply chain (SC) activities, there is a delicate balance to strike between location selection, the many shipment alternatives, and the fees and releases. The models illustrate these tensions between competing priorities. Based on the numerical experiment, we illustrate the impact that a variety of policies have on costs in addition to the efficiency with which they reduce emissions. By analyzing the results of the models, managers can make predictions concerning how regulatory changes may affect overall emissions from SC operations

    Fuzzy AHP Approach to Prioritizing the Critical Success Factors of Organizational Culture

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    WOS: 000454643100010The world that we live in where the social and organizational life rapid social and cultural transformations are experienced, and the intensities of change and competition are intensely perceived has a dynamism. In this dynamism, every human being is a part of an organization. In these organizations, there is a culture which is defined in various ways by various thinkers and it is a rather complex concept. However, all definitions have reached the conclusion that the culture is a common entity shared with a community. In the 1980s, the concept of organizational culture has emerged. While many formal definitions exist, organizational culture is basically a term used to describe the environment where people work and the influence it has on how they think, act, and experience work. Therefore, organizational culture is stated as a system of values, behaviors, habits, norms, beliefs and that direct the behaviors of individuals in an organization. As each individual has a unique personality, every organization has its own personality that distinguishes it from other organizations. Hence, organizational culture consists of several abstract and complementary factors. Literature has shown that there are many factors affecting the success of organizational culture. The prioritization of these factors for the organizations and the effective use of the available resources are gaining importance at this stage. At this stage, different approaches are taken in the literature to prioritize and sort the criteria. MCDM approach which is one of the most prominent approaches was used in this study. The proposed approach was tested based on the opinions of the decision makers and the results were shared. (C) AIMI Journal

    An Interval-Valued Pythagorean Fuzzy AHP and COPRAS Hybrid Methods for the Supplier Selection Problem

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    Abstract Companies must be able to identify their suppliers appropriately and effectively in order to survive in the competitive market conditions. In order to fulfill and surpass the expectations of the consumers and clients, companies need to interact with the relevant suppliers. It is a tough manner for companies to select the best supplier from a large number of relevant alternatives. The selection process of the appropriate supplier involves multiple interacting and competing factors. Generally, the selection process and its results cause a waste of time and money. For this purpose, MCDM methodologies are utilized to manage this complex process efficiently. MCDMs allows for consistent and accurate decision-making as well as the selection of the most appropriate supplier. MCDM is one the most preferred tool to select the best alternative under the conflicting and competitive criteria when the evaluations are made in crisp numbers. Therefore, MCDM methods are preferred in various applications in academia and real life. However, the evaluations could not be always possible with crisp numbers, especially in vague environments or evaluations needs qualitative data. This study is one of the first to combine the AHP and COPRAS supplier selection methods with interval-valued Pythagorean fuzzy (IPF) logic. The effectiveness of these IPF-AHP and IPF-COPRAS evaluations for the supplier selection problem is compared and examined. The experimental results of case scenarios show that IPF is an effective way to apply in decision-making applications. In addition, sensitivity analysis is conducted to evaluate the proposed methodologies. According to sensitivity analysis, the IPF-AHP and IPF-COPRAS be able to illustrate the effects of small changings in criteria weights. Therefore, companies can use the IPF-AHP and IPF-COPRAS to assist their decision-makers in identifying and selecting the best suppliers

    Q-ROF Fuzzy TOPSIS and VIKOR Methods for the Selection of Sustainable Private Health Insurance Policies

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    As a result of the inability of people to meet their demands in the face of increasing demands, people tend to have private health insurance in addition to the general health insurance offered as a public service. Due to the increasing trend of taking out private sustainable health insurance, the number of private sustainable health insurance plans in the health insurance market has increased significantly. Therefore, people may be confronted by a wide range of private health insurance plan options. However, there is limited information about how people analyze private health insurance policies to protect their health in terms of benefit payouts as a result of illness or accident. Thus, the objective of this study is to provide a model to aid people in evaluating various plans and selecting the most appropriate one to provide the best healthcare environment. In this study, a hybrid fuzzy Multiple Criteria Decision Making (MCDM) method is suggested for the selection of health insurance plans. Because of the variety of insurance firms and the uncertainties associated with the various coverages they provide, q-level fuzzy set-based decision-making techniques have been chosen. In this study, the problem of choosing private health insurance was handled by considering a case study of evaluations of five alternative insurance companies made by expert decision makers in line with the determined criteria. After assessments by expert decision makers, policy choices were compared using the Q-Rung Orthopair Fuzzy (Q-ROF) sets Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Q-ROF VIšeKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods. This is one of the first attempts to solve private health policy selection under imprecise information by applying Q-ROF TOPSIS and Q-ROF VIKOR methods. At the end of the case study, the experimental results are evaluated by sensitivity analysis to determine the robustness and reliability of the obtained results
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