Fuzzy Technique For Order Preference Research Articles

Application of an MCDM model with data mining techniques for green supplier evaluation and selection

The evaluation and selection of green suppliers are some of the most important tasks in green supply chain management (GSCM). The purpose of this study is to develop an effective green supplier evaluation model. Currently the evaluation criteria are determined through a literature review combined with decision-maker opinions. A few studies have used data mining techniques to screen the core criteria. This study proposes a novel hybrid MCDM model, that integrates the support vector machine (SVM), the fuzzy best worst method (FBWM) and, the fuzzy technique for order preference by similarity to an ideal solution (FTOPSIS) approaches to select the most suitable green suppliers. A case study, using data from a multinational electronics manufacturer, is carried out for illustration. First, the SVM is used to extract the core criteria from the historical data. The original 25 criteria are reduced to 13 criteria. Then, the FBWM is used to obtain the weights of the core criteria. Finally, the FTOPSIS is used to integrate the performance and prioritize the green suppliers. Finally, practical management implications and suggestions for improvement for decision-makers and green suppliers are provided

Non-oil economic transition for economic and environmental sustainability in Saudi Arabia: a multi-factor analysis under fuzzy environment.

In this study, an analysis of Saudi Arabia’s economic diversification, particularly non-oil transition, is conducted. Initially, key success factors and sub-factors that provide basis for the analysis are identified through literature survey. Three key factors (economic, labor, and market), twenty-one sub-factors (seven under each factor) are identified. To obtain the relative importance of factors and sub-factors, fuzzy analytical hierarchy process (FAHP) has been used. The economic criterion obtained the highest weight followed respectively by fiscal and labor criteria. The “proportion of Saudis in the workforce” sub-criterion received the highest weight under labor criterion, the “investment-intensive business models” sub-criterion obtained the highest weight under economic criterion, and the “increase non-oil revenue” sub-criterion got the highest weight under fiscal criterion. Overall, increase non-oil revenue sub-criterion (under fiscal criterion) received the highest weight. Later, eight major non-oil sectors are prioritized with respect to criteria and sub-criteria using fuzzy technique for order preference by similarity to the ideal solution. Petrochemicals sector ranked topped in the contribution to achieving non-oil transition. The findings of the study shall enable the government and policymakers to specifically design policies for respective sectors knowing their importance in the transition and subsequently bring a new cycle of prosperity to the Kingdom of Saudi Arabia.

Multi-Objective Optimal Placement and Sizing of DGs by Hybrid Fuzzy TOPSIS and Taguchi Desirability Function Analysis Approach

Multi-objective optimal placement and sizing of Distributed Generation in a distribution system is a challenging endeavor. It is seen that in most cases DG placement is required for multiple criteria, however, placement is done using a single criterion. It is imperative to take into account all criteria and their importance for DG placement. Optimal placement of DG is portrayed as a multi-criteria decision-making (MCDM) problem in this paper. An optimal place is found by applying the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). Voltage stability has been studied in context with DG sizing, however, power stability has not been given due consideration. In this paper, maintaining power stability is one of the objectives for finding optimal DG size. It is observed that the addition of this objective restricts DG size. A robust Taguchi Desirability Function Analysis (TDFA) technique is applied for estimating the optimal size of DG. TDFA can optimize almost a thousand objectives simultaneously. Each objective can be added effortlessly. Moreover, the proposed approach offers a set of near-optimal solutions. IEEE 33-bus radial distribution system and restructured IEEE 118-bus radial distribution system are used for testing.

Multi-criteria decision making approaches to select appropriate enhanced oil recovery techniques in petroleum industries

To select the optimum methods, such criteria like reservoir heterogeneity, reservoir pressure, reservoir temperature, crude oil type API (American Petroleum Institute), and brine salinity. EOR methods contained water flooding, chemical flooding, nanofluids injection, CO2 (carbon dioxide) injection, SAGD/WAG (Surfactant assisted gas drainage/water alternative gas injection), and hybrid injection. Multi-criteria decision-making methods of FTOPSIS (a fuzzy technique for order preference by similarity to ideal solution) and FAHP (fuzzy analytic hierarchy process) were used to select the optimum in the EOR method. The hybrid injection is considered the preferable oil recovery enhancement method in both FTOPSIS and FAHP methods. It has a normalized weight of 74% and 23% for FTOPSIS and FAHP, respectively. In the FTOPSIS method, SAGD/WAG and CO2 methods are the second and third preferred enhanced oil recovery methods with the normalized weight of 65% and 56%, respectively. In the FAHP method, CO2 and SAGD/WAG methods are the second and third preferred enhanced oil recovery methods with the normalized weight of 19% and 14%.

A FAHP-FTOPSIS approach for choosing mid-term occupational healthcare measures amid the COVID-19 pandemic

BackgroundThe COVID-19 pandemic has severely affected factories all over the world. Occupational health care in factories has become a challenging task. In order to accomplish this task, it is necessary to change the production environment of a factory by adopting mid-term (to long-term) occupational healthcare measures is necessary. However, most of the guidelines and checklists provided by governments and international health organizations focus on short-tern responses to the pandemic. There are very few suggestions on what a factory should do in the medium to long term. This research aims to establish a systematic procedure for selecting suitable mid-term occupational healthcare measures for a factory amid the COVID-19 pandemic. MethodA fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) approach is proposed. In the proposed FAHP-FTOPSIS approach, FAHP is applied to derive the fuzzy priorities of factors that are critical to the suitability of a mid-term occupational healthcare measure based on the subjective judgments of a decision maker. Subsequently, the derived fuzzy priorities are input into FTOPSIS to evaluate the overall performance of a mid-term occupational healthcare measure. After considering five criteria, the best-performing mid-term occupational healthcare measure is the most suitable measure and will be selected by the factory. ResultsThe FAHP-FTOPSIS approach has been applied to compare the overall performances of four mid-term occupational healthcare measures for a factory based on the subjective judgment of a decision maker. According to the experimental results, the most suitable mid-term occupational healthcare measure for the factory was “isolating machines”, followed by “further automation”. ConclusionsA suitable mid-term occupation healthcare measure needs to effectively prevent the spread of COVID-19 and be accepted by workers, and should have low estimated total costs, low interference with existing operations, and seamless integration with other functional activities. The priorities of these critical factors depend on the subjective judgment of a decision maker. Therefore, different decision makers will come to different conclusions.

A novel distance learning ergonomics checklist and risk evaluation methodology: A case of Covid-19 pandemic.

Many governments decided to cancel face‐to‐face teaching and learning activities in schools and universities. They replaced them with online teaching and distance learning activities to prevent the spread of Coronavirus disease 2019 (COVID‐19). Due to this sudden change, students experienced some anthropometric, environmental, and psychosocial difficulties at home during the distance learning process. This study focuses on determining the importance of anthropometric, environmental, and psychosocial factors in the distance learning process during the COVID‐19 pandemic. This study presents main factors and their subfactors affecting ergonomic conditions of university students during distance learning. A novel distance learning ergonomics checklist is proposed based on the Occupational Safety and Health Administration checklists. The data are collected via a questionnaire filled by 100 university students who attend the Ergonomics course online. Then, the integrated methodology includes Voting Analytic Hierarchy Process integrated Pythagorean Fuzzy Technique for Order Preference by Similarity to An Ideal Solution method is adopted to prioritize the factors determined. Thirty‐nine different subfactors are evaluated under five titles, and the most important factors are determined using the proposed methodology. With the results achieved, it is seen that the suggested checklist and proposed methodology can be used by public and private education organizations as a guide for improving their distance learning strategies.

Decision Support Model for Prioritization of Cotton Plant Diseases using Integrated FAHP-TOPSIS approach

Cotton, the essential cash crop of India plays a predominant part in the agricultural and industrial enlargement of the world. The evolution of cotton plant begins with the germination of seed and its growth depends on the accessibility of temperature, soil moisture and oxygen. The desirable characteristics combined in cotton make no other fiber to duplicates its value. There are beyond 75 critical diseases leads to the substantial destruction and economic losses in cotton crop. Premature analysis of the cotton plant diminishes the disease, results in the significant enhancement in the superiority of the product. Massive yield of cotton crop is vanished each year due to fast incursion by insects and pests. Verticilium wilt, grey mildew, leaf spot and leaf blight are some of major cotton disease in cotton plant which extremely affects the productivity. This research discusses the multi criteria decision making evaluation tool to identify the major disease causing factors of cotton crop. Fuzzy AHP, a Multi Criteria Decision making method (MCDM) is applied to impact the disease causing risk factors by determining the weights of the criteria. Moreover, a Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a ranking MCDM methodology has been applied to rank the alternatives. The outcomes and methods described in this research based on the derived criteria and sub criteria of risk factors will be a noble orientation in producing more perfect, active and efficient decision support tool for the farmers to identify and diagnose the risk factors during the cultivation of the cotton crop

Agile Supplier Evaluation Using Hierarchical TOPSIS Method

Increased competitiveness of the market enforces companies to respond quickly and appropriately to sudden changes in the market in order to adapt to continuously updated conditions of business environment and keep their survivals. Agility concept rises at this level due to necessity of coping with unpredictable changes and uncertainty. Agility enables the firms responsiveness in a quick and an effective way to the set of interdependent changes required in design, production, marketing and organization of the companies. This study addresses agile supplier selection problem. Hierarchical fuzzy technique for order preference by similarity to ideal solution approach (TOPSIS) is proposed for agile supplier selection problem in an airline company.

Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective

This paper presents a strategic roadmap to handle the issue of resource allocation among the green supply chain management (GSCM) practices. This complex issue for supply chain stakeholders highlights the need for the application of supply chain finance (SCF). This paper proposes the five Vs of big data (value, volume, velocity, variety, and veracity) as a platform for determining the role of GSCM practices in improving SCF implementation. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their roles in SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was then applied to evaluate GSCM practices on the basis of the five Vs. In addition, interpretive structural modeling (ISM) was used to visualize the optimum implementation of the GSCM practices. The outcome is a hybrid self-assessment model that measures the environmental maturity of SCF by the coherent application of three multicriteria decision-making techniques. The development of the Basic Readiness Index (BRI), Relative Readiness Index (RRI), and Strategic Matrix Tool (SMT) creates the potential for further improvements through the integration of the RRI scores and ISM results. This hybrid model presents a practical tool for decision-makers.

Identifying the Strategic Priorities of the Technical Factors for the Sustainable Low Carbon Industry Based on Macroeconomic Conditions

This study aims to find out the strategic priorities of the technical factors to have sustainable low carbon industry. For this purpose, a hybrid multi-criteria decision-making (MCDM) is proposed that contains three different stages. First, the economic criteria required for sustainable development and the technical needs of the low carbon industry are defined by making a comprehensive literature review. After that, economic criteria are weighted by using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) methodology. Finally, technical factors are ranked with the help of the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach. Moreover, another analysis is also performed by considering fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to evaluate the consistency of the analysis results. The main motivation of this study is to define the primary technical factors to minimize carbon emission problem by proposing a hybrid MCDM model. The findings indicate that research and development for renewable sources has the greatest importance for low-carbon industry. In addition, the analysis results of fuzzy TOPSIS and fuzzy VIKOR are quite similar. This situation demonstrates the consistency and coherency of the ranking results. Hence, it is recommended that countries should mainly give importance to the research and development investments so that the costs of renewable energy problems can be minimized. This situation can attract the attentions of the companies to invest in these projects. In this way, it will be possible to use a cleaner energy in industrial production.

A decision support system for the selection of sustainable biomass resources for bioenergy production

Bioenergy production from biomass is a multicriteria decision-making (MCDM) problem involving multidimensional criteria due to the vast range of resources available for consideration. In this paper, we propose a hybrid fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) and analytical hierarchy process (AHP) that is able to tackle vagueness and ambiguity in data and produce weights for all criteria in the MCDM problem. By utilizing this methodology, different aspects of experts’ data evaluation including fuzzy values can be converted into crisp values. Accuracy in each decision matrix is ensured by determining the weights of the subcriteria. We then apply the model to sustainable biomass resource selection in Ghana. Specifically, we comprehensively review the sustainable criteria within the bioenergy domain namely social, environmental, and economic aspects together with thirteen subcriteria. The proposed model was applied to biomass resource alternatives including crop residue, animal manure, forest residue, energy crops, and municipal solid waste. Results show the weights of thirteen subcriteria to be within the range of 0.10340 and 0.05263. The FTOPSIS shows that animal manure may be the most desirable resource for bioenergy production in Ghana, followed by energy crop, municipal solid waste, forest residue, and crop residue, respectively. Engineering and energy planning problems with conflicting multicriteria data can be easily solved by using this proposed hybrid AHP/FTOPSIS framework.

Outsourcing Reverse Logistics for E-Commerce Retailers: A Two-Stage Fuzzy Optimization Approach

On the heels of the online shopping boom during the Covid-19 pandemic, the electronic commerce (e-commerce) surge has many businesses facing an influx in product returns. Thus, relevant companies must implement robust reverse logistics strategies to reflect the increased importance of the capability. Reverse logistics also plays a radical role in any business’s sustainable development as a process of reusing, remanufacturing, and redistributing products. Within this context, outsourcing to a third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies for today’s organizations, especially e-commerce players. The objective of this study is to develop a decision support system to assist businesses in the selection and evaluation of different 3PRLPs by a hybrid fuzzy multicriteria decision-making (MCDM) approach. Relevant criteria concerning the economic, environmental, social, and risk factors are incorporated and taken into the models. For obtaining more scientific and accurate ranking results, linguistic terms are adopted to reduce fuzziness and uncertainties of criteria weights in the natural decision-making process. The fuzzy analytic hierarchy process (FAHP) is applied to measure the criteria’s relative significance over the evaluation process. The fuzzy technique for order preference by similarity to an ideal solution (FTOPSIS) is then used to rank the alternatives. The prescribed method was adopted for solving a case study on the 3PRLP selection for an online merchant in Vietnam. As a result, the most compatible 3PRLP was determined. The study also indicated that “lead time,” “customer’s voice,” “cost,” “delivery and service,” and “quality” are the most dominant drivers when selecting 3PLRLs. This study aims to provide a more complete and robust evaluation process to e-commerce businesses and any organization that deals with supply chain management in determining the optimized reverse logistics partners.

Distance measures on intuitionistic hesitant fuzzy set and its application in decision-making

Intuitionistic hesitant fuzzy set (IHFS) provides a valid mean for dealing with the uncertainty of complex problems. Information measures on IHFS can measure the uncertain information, so that we shall introduce a method to construct a class of distance measures for IHFS in this paper. For constructing more objective distance measures to reflect the actual situation, we consider the information content and information clarity of IHFS simultaneously and utilize different functions to adjust their contribution. Its superiority is evidenced by an example of pattern recognition that the proposed distance measure improves numerically results obtained with existing distance measures. In particular, we investigate the connection between distance measure, similarity measure, and entropy measure of IHFS, and prove that they can be constructed mutually under this axiomatic framework. On this basis, we apply the proposed entropy measure to determine criteria weights in multi-criteria decision-making problems and design an extended intuitionistic hesitant fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) method, which effectiveness is presented by a practical application.

A Framework to Evaluate Project Complexity Using the Fuzzy TOPSIS Method

This work aims to help managers anticipate, detect, and keep under control complex situations before facing negative consequences. This article explores complexity modeling theory and develops a framework and associated score sheet to measure project complexity. A framework comprising ninety factors is presented and divided into seven categories: stakeholders, project team, project governance, product, project characteristics, resources, and environment. For the project complexity assessment grid, the project manager prioritizes and weighs its factors using linguistic variables. The score sheet is customizable in its handling of the factors and their weights. A critical state of the art on multi-criteria methodologies is presented, as well as reasons for using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. This method provides early-warning signs with the possibility of comparing multiple projects. It also enables one to measure and prioritize areas and domains where complexity may have the highest impact. Practical applications on three projects within an automotive manufacturer highlight the benefits of such an approach for managers. Project managers could use both a project complexity rating system and a measure of risk criticality to decide on the level of proactive actions needed. This research work differs from traditional approaches that have linked proactive actions to risk criticality but not project complexity.

Identification and ordering of safety performance indicators using fuzzy TOPSIS: a case study in Indian construction company

PurposeThe purpose of this paper is to propose a practical framework to measure the safety performance of workers in the Indian construction industry. The key safety performance indicators are identified and ordered on the premise that the higher order assignment of an indicator implies a strong indication of an effective safety performance.Design/methodology/approachVarious indicators of safety performance in the construction industry were identified from extant literature review combined with author's personal viewpoint. The identified variables were inquired for appropriateness for the Indian construction scenario by consultation with experts. Fuzzy Technique for order preference by similarity to ideal solution (TOPSIS) technique was considered for the ranking of the indicators from most to least important.FindingsThe most important highlight of the study was the importance of the role of management by participating in informing workers about the safety rules and compliance toward safety measures. Proper and timely safety training to the workers and equipping them with sophisticated safety equipment for daily activities is perceived to be highly important in ensuring a safe and healthy workplace environment. Controlling the absenteeism rate reduces the burden of extra work on the employees, thereby, encouraging safe work-related behavior.Originality/valueSenior management should make safety induction programs compulsory at the time of joining of the employees. The guidelines for safety practices, rules and information about the safety equipment should be properly documented and arranged in safety manuals. Periodical drills involving visual demonstration of the safety practices should be followed to ensure safety at workplace.

Application of MICMAC, Fuzzy AHP, and Fuzzy TOPSIS for Evaluation of the Maintenance Factors Affecting Sustainable Manufacturing

This paper presents an empirical study on the impact of maintenance function on more sustainable manufacturing processes. The work methodology comprises four stages. At first, ten factors of maintenance activities from a sustainable manufacturing point of view were identified. Then, in the second stage, the matrix of crossed impact multiplications applied to a classification (MICMAC) was carried out to categorize these factors based on their influence and dependence values. In the third stage, the criteria for evaluation of maintenance factors were defined, then the fuzzy analytic hierarchy process (F-AHP) was applied to determine their relative weights. In the last stage, the results of MICMAC and F-AHP analyses were used as inputs for the fuzzy technique for order preference by similarity to ideal solution (F-TOPIS) to generate aggregate scores and selection of the most important maintenance factors that have an impact on sustainable manufacturing processes. A numerical example is provided to demonstrate the applicability of the approach. It was observed that factors “Implementation of preventive and prognostic service strategies”, “The usage of M&O data collection and processing systems”, and “Modernization of machines and devices” are the major factors that support the realization of sustainable manufacturing process challenges.

A decision model for blockchain applicability into knowledge-based conversation system

Conversation systems usually suffer from the challenge of knowledge management from multiple human experts. The current mechanism used in knowledge-based conversation system is always based on centralized servers, which may be problematic in terms of transparency and security. Blockchain solutions are currently being proposed improve the security and efficiency in different domains. However, there are various blockchain platforms with different characteristics, and conversation system implemented using blockchain platform is not in place yet. In this paper, we clearly identified the requirement analysis of knowledge-based conversation system and present a decision model for identify the best fitting blockchain platform for knowledge-based conversation system. In the proposed method, multiple measurements including Analytical Hierarchy Process (AHP), Fuzzy analytical hierarchy process (FAHP), and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) are utilized to analyze and create consistent result together, which can be used for the selection of blockchain platforms and improve the efficiency of the decision-making process.

Evaluation of logistics providers for sustainable service quality: Analytics based decision making framework

In the present era of the circular economy, sustainable service quality has become an order winning criteria for all logistics provider across the world. The selection of appropriate logistics service providers (LSPs) greatly influences the performance of supply chains in terms of sustainability indicators. Most of the previous studies have not considered sustainability measures in the evaluation of logistics service providers. Therefore, the uniqueness of this study is that we are proposing a framework for selecting the best logistics provider based on sustainable service quality. Total seventeen attributes related to sustainable service quality are finalized based on literature review and subsequent focused group discussions. Through a questionnaire-based survey, data from 150 customers of LSPs are collected. Data is analysed through factor analysis and seventeen attributes of sustainable service quality are categorized into five factors namely Commitment, Competence, Communication, Creativity & Customization, and Coordination and Collaboration. It is named as a 5C framework. This framework is further used to illustrate the selection of best LSP based on sustainable service quality. Listed attributes are evaluated through hybrid Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) techniques. This decision-making framework is illustrated with the help of a real-life case study. Sensitivity analysis is also done to validate the robustness of the proposed framework. From this study, it has been observed that the development of competencies for the adoption of sustainable practices should be the thrust area for logistics service providers. Findings imply that the logistics providers should give more focus on sustainable network optimization, response time reduction, reliable green services, flexibility in green processes, and development of mutual trust with all stakeholders to become the first choice of their customers. Insights from the study will help LSPs to develop their strategies for ensuring sustainable service quality to customers.

Multi-Criteria Decision Analysis for user satisfaction-induced demand-side load management for an institutional building

The scope of this study is to present a suitable demand-side management technique to manage the load of a department in an educational institution located in Kanchipuram (12.9868° N, 79.9719° E), Tamil Nadu, India. The comfortable satisfaction level of the department is investigated on the basis of time and device preferences, and the absolute satisfaction of the user is evaluated. Demand-side management technique is incorporated to reduce the peak load. The optimized use of renewable energy with and without demand-side management strategy in grid-connected mode is analyzed. The feasibility study is accomplished by using HOMER simulation software for various configurations of renewable energy resources, such as solar photovoltaic, hydrogen fuel cell and backup facilities. The software simulation results indicated that the grid-connected solar and diesel generator system with demand-side management strategy is the suitable configuration in the view of economic perspective alone. However, when considering economic and environmental aspects, solar photovoltaic and grid configuration with demand-side management strategy proves to be effective. The Fuzzy-Analytical Hierarchy Process and Fuzzy-Technique for Order Preference by Similarity to Ideal Situation approaches are used to evaluate the best suitable configuration based on technical, economic and environmental perspectives. These approaches also inferred that a grid-connected photovoltaic system with demand-side management strategy is most suitable in all aspects of this case study. A sensitivity analysis is performed to examine the cost variation of the designed system concerning uncertain parameters which indicates that the obtained results are consistent.

A Hybrid Fuzzy Risk Assessment Framework for Determining Building Demolition Safety Index

Risk assessment is one of the most effective actions in the safety management of demolition projects. This paper provides a framework to determine building demolition safety index (BDSI), which shows the safety level of a building being demolished. Two phases are involved in this study. In the first phase, 11 potential risks in building demolition and their influencing factors were identified, evaluated, and classified using a hybrid approach consisting of the Delphi method, Fine-Kinney method, fuzzy fault tree analysis (FTA), fuzzy technique for order preference by similarity to ideal solution (TOPSIS), and fuzzy inference system (FIS). In the second phase, a checklist of the most important safety factors and sub-factors in the demolition operation was provided, and the equations needed to calculate BDSI were presented. The first phase of the study was validated by comparing the study’s results with available demographic data from Tehran Construction Engineering Organization, Iran. The second phase was validated by calculating the BDSI for two buildings and evaluating the relationship between BDSI and safety level. BDSI is useful for building demolition projects because it allows project managers to have a more realistic view of the risk level of the project and accordingly take the necessary measures to prevent accidents.