Multi‐criteria supplier selection using fuzzy AHP

How to choose a design thinking method for teaching the design of localization: A two-dimension linguistic fuzzy model with two-tuples

There are many different types of scientific design thinking methods, but it is necessary to evaluate the applicability of the methods to the components of the design teaching curriculum in universities. Therefore, this study assesses the applicability of design thinking in terms of "design practice" and "locality" based on the local design education philosophy and the characteristics of the students and courses. A two-dimensional linguistic fuzzy model with two-tuples was proposed, and the assessment values of 36 experts were statistically analysed using the Delphi, triangular fuzzy number, Euclidean distance, two-dimension linguistic label (2DLL), and two-dimensional linguistic weighted arithmetic aggregation (2DLWAA) methods. The results highlighted the 12 categories of design thinking methods that are most applicable to teaching and learning, indicating the basic views of university design faculty on the application of design thinking methods. Finally, the new design teaching methods have been validated and constructed through years of teaching practice, and have some reference value for teaching design courses in universities.

Determination of the most appropriate fertilizing method for apple trees using multi-criteria decision-making (MCDM) approaches

Appropriate tree fertilization with essential nutrients is considered as one of the major factors in enhancing the quality and quantity of horticultural crops. The most efficient way to fertilize trees is to dig holes around the trunks and fill them with appropriate chemical and organic fertilizer. Doing this operation with mechanized methods reduces costs and increases productivity compared to traditional methods. In the present study, multi-criteria decision-making (MCDM) methods, including deterministic analytical hierarchy process (AHP) and fuzzy analytical hierarchy process (FAHP), technique for order of preference by similarity to ideal solution (TOPSIS), fuzzy TOPSIS (FTOPSIS), and analytic network process (ANP), were used to score and select the appropriate fertilizing method for apple trees based on the growers and expert's perspectives. The criteria, including fertilizing operation cost, crop yield, the percentage of tree damages, ease of entering and moving fertilizing equipment in tree rows, field capacity (with or without machinery), comfort and safety of fertilizing operations, after-sales service, access to the required machinery and implements, crop selling price, and crop quality, were used in the above-mentioned methods. The fertilization methods (Hole digging) considered in the present study were traditional fertilization (Shovel), orchard Trencher, motor hole digger, fixed centerline tractor-mounted hole digger, and off-set tractor-mounted hole digger. Based on the results, the priority of mechanized fertilizing methods was determined as tractor-mounted hole diggers (AHP weight of 0.286, FAHP weight of 0.285, TOPSIS relative proximity of 0.65, and FTOPSIS relative proximity of 0.64), fixed centerline tractor-mounted hole diggers (AHP weight of 0.219, FAHP weight of 0.158, TOPSIS relative proximity of 0.56, and FTOPSIS relative proximity of 0.62), motor hole diggers (AHP weight of 0.171, FAHP weight of 0.079, TOPSIS relative proximity of 0.46, and FTOPSIS relative proximity of 0.31), and orchard trenchers (AHP weight of 0.12, FAHP weight of 0.057, TOPSIS relative proximity of 0.19, and FTOPSIS relative proximity of 0.20), respectively. Based on the ANP method, off-set and fixed centerline tractor-mounted hole diggers had the highest priority (weights of 0.43 and 0.27), followed by trencher (weight of 0.16), motor hole diggers (weight of 0.09), and the traditional method (weight of 0.04). Results showed that applying orchard tractors equipped with mounted diggers, especially off-set types, can play an important role in enhancing the quantity and quality of apples produced, as well as reducing the costs of fertilizing operations.

An information system success model for e-learning postadoption using the fuzzy analytic network process

The underutilization of e-learning among university lecturers is an important issue that needs to be resolved. This study aimed to formulate an e-learning postadoption model for Malaysian universities. Data were collected using self-administered questionnaires involving 36 e-learning experts who from lecturers in public and private universities in Malaysia. The data collected was then analyzed using the extent analysis method proposed by Chang (European Journal of Operational Research, 95(3), 649-655, 1996) to examine the weights and rankings of the factors and subfactors. This study showed that for e-learning postadoption, the most important factor is institution service quality, followed by system quality, content quality, instructors' characteristics, and learners' characteristics. This study extends the information systems success model into the e-learning postadoption context. In particular, this study offered insights concerning the dependencies among the factors in the model within the Malaysian university context. The findings are useful for the long-range strategic management of university administrators, and the model can be adopted as a reference to form a rating system to analyze e-learning postadoption. University administrators can analyze critical factors that increase e-learning's post adoption and lead to more efficient resource allocation and management of e-learning.

Modeling the effect of emergency response on domino effects in the coal gasification process by fuzzy hierarchical analysis and Bayesian network

Emergency response has an important impact on the mitigation of the domino effects. However, consequence analysis of the domino effect often ignores emergency response due to its complexity and uncertainty. In this paper, Fuzzy Analytic Hierarchy Process (FAHP) is adopted to evaluate the reliability of emergency response process. On this basis, using Bayesian Network (BN) model, the domino effect under the influence of emergency response is modeled. Based on the total duration of the above-mentioned emergency response process to determine the consequences of domino effect under the action of safety barriers. The application of the approach has been demonstrated by an illustrative case study. The results show that the probability of domino effect is reduced by an order of magnitude when active barriers, passive barriers and emergency response are considered together. This work can provide relevant basis for formulating safety protection measures in chemical process industry.

Appraisal of groundwater pollution risk by combining the fuzzy AHP and DRASTIC method in the Burdur Saline Lake Basin, SW Turkey

To ensure sustainable groundwater management, water resources must be protected in terms of quantity and quality. In this context, it is important to reveal the potential of existing groundwater resources to be affected by environmental and/or geogenic pollutants. In the present study, groundwater vulnerability assessment was performed using DRASTIC model by fuzzy AHP and GIS integration for Burdur Saline Lake basin, SW Turkey. In addition, validation and sensitivity analyses were performed in the study. Weight and rating values assigned for DRASTIC parameters and sub-parameters were determined by fuzzy AHP method, and these values were used in GIS analyses. In the validation analysis of the obtained groundwater vulnerability map, NO₃ ion was considered since the agricultural activities are intense in the study area. In addition, sensitivity analyses were performed to determine the most effective parameter. According to the obtained results, 26.43% of the study area was determined as the highly vulnerable areas, while 11.44 and 62.13% were moderate vulnerable and low vulnerable, respectively. Impact of the vadose zone, depth to water table, and net recharge parameters were determined as more effective than other parameters. The nitrate concentrations of the groundwater in the region confirm the vulnerability map obtained by the study. Therefore, it is recommended to take realistic and urgent protection measures aimed at sustainable use of groundwater in highly vulnerable areas.

Integrating Fuzzy Multiobjective Programming and System Dynamics to Develop an Approach for Talent Retention Policy Selection: Case on Health-Care Industry

The demand for medical services has been increasing yearly in aging countries. Medical institutions must hire a large number of staff members to provide efficient and effective health-care services. Because of high workload and pressure, high turnover rates exist among health-care staff members, especially those in nonurban areas, which are characterized by limited resources and a predominance of elderly people. Turnover in health-care institutions is influenced by complex factors, and high turnover rates result in considerable direct and indirect costs for such institutions (Lo and Tseng 2019). Therefore, health-care institutions must adopt appropriate strategies for talent retention. Because institutions cannot determine the most effective talent retention strategy, many of them simply passively adopt a single human resource (HR) policy and make minor adjustments to the selected policy. In the present study, system dynamics modeling was combined with fuzzy multiobjective programming to develop a method for simulating HR planning systems and evaluating the suitability of different HR policies in an institution. We also considered the external insurance policy to be the parameter for the developed multiobjective decision-making model. The simulation results indicated that reducing the turnover rate of new employees in their trial period is the most effective policy for talent retention. The developed procedure is more efficient, effective, and cheaper than the traditional trial-and-error approaches for HR policy selection.

An evaluation model for automobile intelligent cockpit comfort based on improved combination weighting-cloud model

Aiming at the comfort evaluation of automobile intelligent cockpit, an evaluation model based on improved combination weighting-cloud model is established. By consulting relevant literature, 4 first-class indexes and 15 second-class indexes, including noise and vibration, light environment, thermal environment and human-computer interaction, are selected to establish a comfort evaluation system. Later the subjective and objective weights obtained by improved Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) are combined by Game Theory. Considering the fuzziness and randomness of the index system, the combination weights obtained by Game Theory are combined with the cloud model. The floating cloud algorithms is used to determine the first-class and second-class index clouds and the comprehensive evaluation cloud parameters. Improvements were made in two commonly used similarity calculation methods, the expectation curve method (ECM) and the maximum boundary curve method (MCM). A new similarity calculation method is defined to optimize the evaluation results and determine the final comfort evaluation grade. Lastly, a 2021 Audi intelligent car under a certain working condition was selected to verify the correctness and rationality of the model using the fuzzy evaluation method. The results show that the cockpit comfort evaluation model based on the improved combination weighting-cloud model can better reflect the comprehensive comfort of automobile cockpit.

An intuitionistic dense fuzzy AHP- TOPSIS method for military robot selection

Land, marine and airborne are the three types of military robots used in the War-field. Land robots are the most crucially considered robots. Selecting a military land robot for a specific purpose is one of the challenging problems for a decision-maker to find the most preferred alternative when it includes fuzziness. Intangible factors are used while selecting the appropriate robotic system as it effectively deals with fuzziness. Intuitionistic dense fuzzy set, which is the combination of intuitionistic fuzzy set and dense fuzzy set, is capable of dealing with intangible factors. This study aims to design the integrated model on intuitionistic dense fuzzy AHP-TOPSIS to choose the most preferable military land robots under various circumstances. Robots for different types of situations, namely bomb disposal, search and rescue, surveillance and reconnaissance and war-fighter are considered. Moreover, the intuitionistic dense fuzzy AHP is utilized to calculate the subjective weights of the criteria and intuitionistic dense fuzzy TOPSIS is used to rank the alternatives. Further, a sensitivity analysis is examined to demonstrate the quality of the outcome and the results are compared with the fuzzy set, intuitionistic fuzzy set, and dense fuzzy set to show the efficiency of the proposed methodology.

An integrated multi-criteria decision-making approach to optimize the number of leagile-sustainable suppliers in supply chains

Global supply chains are increasingly becoming complex by having numerous suppliers around the world. To manage this complexity, organizations must identify the optimum number of suppliers. There have been many examples in the literature that used different approaches to solve this problem. Despite the importance of this issue, less attention has been paid to it and managers of the companies do not know how, and based on which approach and criteria, they should determine the optimal number of suppliers which leads to lower cost and higher reliability of the production line. Therefore, in this study, a hybrid methodology is proposed to expose the process of this problem which helps managers to learn how they can determine the optimal number of suppliers. We address this gap by developing an integrated approach based on multi-criteria decision-making (MCDM) comprising best-worst method (BWM), simple additive weighting (SAW), and technique for order preference by similarity to ideal solution (TOPSIS), and simulation to determine the optimal number of suppliers. This study utilizes a comprehensive approach based on leagile and environmentally sustainable criteria to determine the optimal number of suppliers. To examine the efficiency of the proposed approach, an empirical case study is conducted in an Iranian oil company. The final results represent that the scenario with a 1-1-1 arrangement (one supplier for each type of equipment) is the best possible scenario to determine the optimal number of leagile-sustainable suppliers. To examine the reliability and robustness of the obtained results, a sensitivity analysis based on the three most important criteria is conducted. Finally, discussions on the findings as well as theoretical and managerial implications are presented.

Modelling perceived risk in blockchain enabled supply chain utilizing fuzzy-AHP

Purpose

Blockchain has been considered a disrupting technology that can add value in various supply chains differently. The provenance framework matches the four blockchain capabilities of traceability, certifiability, trackability and verifiability to the five generic risks, namely, the financial risk, psychological risk, social risk, physical risk and performance risk. This will help in uncording which specific risk gets mitigated by the use of blockchain in a specific supply chain.

Design/methodology/approach

This study illustrates four supply chains, namely, pharmaceutical industry, fast moving consumer goods industry, precious metal and automotive industry, and maps the risks associated with them to the provenance framework wherein the applicability of blockchain is mapped. Fuzzy analytical hierarchical processing (F-AHP) is used to rank the risks in the supply chain.

Findings

Blockchain capabilities can elevate the provenance knowledge leading to assurance in terms of origin, authenticity, custody and integrity to mitigate the supply chain risks. Present work highlights the thrust areas across various supply chains and identifies the risk priority tasks aligning the contextual supply chain risks. This study has covered five major risk perceptions. This study contributes to the literature on blockchain, customer perceived risk, provenance and supply chain.

Practical implications

This methodology can be adopted to understand and market the application of blockchain in a supply chain. It brings the marketers and marketing perspective to the supply chain. Exhaustive risk perception can be included to get more comprehensive data on mapping the risks along different supply chains. Vertical extensions of this work can be consideration of other supply chains including dairy, fruits and vegetables, electronics and component assemblies to derive the comprehensive framework for mapping risk perceptions and thereby supply chain risk mitigation through blockchain technology.

Originality/value

This linkage between blockchain, perceived risk, applications in the supply chain and a tool to convince the customers about the blockchain applicability has not been discussed in the literature. Adopting the multi-criteria decision-making F-AHP approach, this study attempt to rank the risks and stimulate conversations around a common framework for multiple sectors.

Triage and priority-based healthcare diagnosis using artificial intelligence for autism spectrum disorder and gene contribution: A systematic review

The exact nature, harmful effects and aetiology of autism spectrum disorder (ASD) have caused widespread confusion. Artificial intelligence (AI) science helps solve challenging diagnostic problems in the medical field through extensive experiments. Disease severity is closely related to triage decisions and prioritisation contexts in medicine because both have been widely used to diagnose various diseases via AI, machine learning and automated decision-making techniques. Recently, taking advantage of high-performance AI algorithms has achieved accessible success in diagnosing and predicting risks from clinical and biological data. In contrast, less progress has been made with ASD because of obscure reasons. According to academic literature, ASD diagnosis works from a specific perspective, and much of the confusion arises from the fact that how AI techniques are currently integrated with the diagnosis of ASD concerning the triage and priority strategies and gene contributions. To this end, this study sought to describe a systematic review of the literature to assess the respective AI methods using the available datasets, highlight the tools and strategies used for diagnosing ASD and investigate how AI trends contribute in distinguishing triage and priority for ASD and gene contributions. Accordingly, this study checked the Science Direct, IEEE Xplore Digital Library, Web of Science (WoS), PubMed, and Scopus databases. A set of 363 articles from 2017 to 2022 is collected to reveal a clear picture and a better understanding of all the academic literature through a final set of 18 articles. The retrieved articles were filtered according to the defined inclusion and exclusion criteria and classified into three categories. The first category includes 'Triage patients based on diagnosis methods' which accounts for 16.66% (n = 3/18). The second category includes 'Prioritisation for Risky Genes' which accounts for 66.6% (n = 12/18) and is classified into two subcategories: 'Mutations observation based', 'Biomarkers and toxic chemical observations'. The third category includes 'E-triage using telehealth' which accounts for 16.66% (n = 3/18). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations and challenges of ASD research that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and discusses the open issues that help perform and improve the recommended solution of ASD research direction. In addition, this study critically reviews the literature and attempts to address the current research gaps in knowledge and highlights weaknesses that require further research. Finally, a new developed methodology has been suggested as future work for triaging and prioritising ASD patients according to their severity levels by using decision-making techniques.

Benchmarking Canadian solid waste management system integrating fuzzy analytic hierarchy process (FAHP) with efficacy methods

Solid waste management is a recent challenge for both developed and developing countries because of urbanization and population growth. This research aims to identify and compare the economic efficiency of Canadian waste management systems integrating the fuzzy analytic hierarchy process (FAHP) with the efficacy method. Six economic indicators-diversion gross domestic product (DGDP) ratio, gross domestic product (GDP) of all industries, GDP of Sector 562, diversion rate, waste management output indicator, and diversion size indicator-are considered in this study. Initially, the FAHP method was used to calculate the indicator weights, and the efficacy method then ranked the DGDP ratio as the most influential factor for the GDP-related indicators. The DGDP ratio and diversion rate were determined to be most critical in the assessment of the economic efficiency of a solid waste management system in Canada. The result also revealed that the economic performance of the waste management systems of Nova Scotia, British Columbia, and Ontario are better compared to those of other provinces. The outcome of this study will aid the government and provincial organizations in establishing an effective solid waste management plan to improve their overall performance.

Multi-Criteria Decision-Making System for Wind Farm Site-Selection Using Geographic Information System (GIS): Case Study of Semnan Province, Iran

Selecting the best place for constructing a renewable power plant is a vital issue that can be considered a site-selection problem. Various factors are involved in selecting the best location for a renewable power plant. Therefore, it categorizes as a multi-criteria decision-making (MCDM) problem. In this study, the site selection of a wind power plant is investigated in a central province of Iran, Semnan. The main criteria for classifying various parts of the province were selected and pairwise compared using experts’ opinions in this field. Furthermore, multiple restrictions were applied according to local and constitutional rules and regulations. The Analytic Hierarchy Process (AHP) was used to weigh the criteria, and according to obtained weights, wind speed, and slope were the essential criteria. Moreover, a geographic information system (GIS) is used to apply the weighted criteria and restrictions. The province’s area is classified into nine classes according to the results. Based on the restrictions, 36.2% of the total area was unsuitable, mainly located in the north part of the province. Furthermore, 2.68% (2618 km2) and 4.98% (4857 km2) of the total area are the ninth and eightieth classes, respectively, which are the best locations for constructing a wind farm. The results show that, although the wind speed and slope are the most essential criteria, the distance from power facilities and communication routes has an extreme impact on the initial costs and final results. The results of this study are reliable and can help to develop the wind farm industry in the central part of Iran.

Structural transformation of fuzzy analytical hierarchy process: a relevant case for Covid-19

Covid-19 has posed difficult and challenging situations to the supply chains and companies are in fix how to choose the vendors under the uncertainty and complexity in recent years. Therefore, this research aims to incorporate structural transformation of the fuzzy analytical hierarchy process (FAHP) that is most appropriate for the uncertainty and disruption caused by Covid-19 like situation for ensuring supplies from vendors. The conventional approaches for vendor selection and evaluation use numerous multi-criteria decision-making tools that may not ensure reliability in a dynamic situation caused due to Covid-19. In this research, Fleiss’ Kappa method ensures the reliability of responses from eight respondents by using pairwise comparisons and assigning weights as envisaged in FAHP. In addition to determine the reliability of responses, a step under FAHP has been altered. This alteration is demonstrated in the vendor selection case in the Covid-19 scenario. The research suggests a plausible system required to address the uncertainties associated with Covid-19 to select and evaluate vendors by modifying a FAHP. The proposed altered mechanism can be incorporated in a similar type of other decision-making circumstances such as Covid-19, where the decision-makers are more than one, and the situation is very dynamic. The study is likely to facilitate information management, algorithmic development in decision making, or machine-driven decisions in uncertain conditions. The study offers managerial implications to purchase managers to accommodate and combine multiple factors and responses concerning the vendor performances for their evaluation, thus making a process more reliable.

Supplier performance and selection from sustainable supply chain performance perspective

Purpose

This paper aims to address the supplier selection problem based on a developed framework capturing the essence of the supply chain operations reference (SCOR) model, sustainability and providing services to customers. Specifically, the authors consider planning, manufacturing, delivery, sustainability and customer service attributes to evaluate and select suppliers.

Design/methodology/approach

Relevant literature is reviewed, a framework capturing the essence of major supply chain functions was developed and suitable measurement attributes were identified. An integrated fuzzy analytic hierarchy process and fuzzy technique for order performance by similarity to ideal solution method are employed to obtain the final ranking of the attributes and suppliers. The proposed methodology is illustrated through a real case of an Indian automobile company.

Findings

The authors observed that planning, manufacturing, customer service, sustainability and delivery are preferred in decreasing order to select component suppliers for an automotive company. The impact of suppliers on planning and manufacturing is most important to consider while assessing suppliers. Interestingly, concerns about sustainability and delivery are the least cared factors when selecting suppliers. The top five criteria contain measures of operational efficiency rather than purchasing cost.

Originality/value

This paper proposes and demonstrates a supplier selection framework harmonizing supply chain functions of the SCOR model, sustainability and customers service that adds a valuable wing to literature that expounds on the connection of purchasing strategy to corporate strategy. A case study in an automotive company throws unique and valuable managerial implications for purchasing and supply chain performance.

Research themes in machine learning applications in supply chain management using bibliometric analysis tools

Purpose

This paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.

Design/methodology/approach

Unlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.

Findings

Using contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.

Originality/value

Modern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.

Prioritising crowdfunding benefits: a fuzzy-AHP approach

The current research aims to explore and prioritize the key benefits that influence the acceptance of crowdfunding as a fund-raising tool, either directly or indirectly. The study utilized a multidisciplinary approach to find out the varied benefits of crowdfunding. The study also prioritized the benefits by applying the buckley fuzzy-AHP technique. The results indicate the various benefits of crowdfunding. The study's results suggest fund-raising (FR), venture viability (VV), cost structure (CS), customer relationships (CR), demand building (DB), general strategy (GS), market intelligence (MI), personal sphere (PS), business ecosystem (BE), team development (TD) and product lifecycle (PL) are the benefits associated with the crowdfunding process. The findings also suggest that the topmost benefit of crowdfunding is FR. But our study has not categorized the benefits according to different types of crowdfunding, and the study's findings cannot be generalized as the study was conducted in India. The study highlighted the critical financial and non-financial benefits of crowdfunding which can help the entrepreneurs to have more insightful knowledge of the potential benefits of crowdfunding. Studying the financial and non-financial benefits of crowdfunding can further help the entrepreneurs utilize crowdfunding platforms, depending on the need, to provide the right solution to their requirements. This research is the first study to apply the buckley fuzzy-AHP technique to prioritize the multidisciplinary benefits of crowdfunding, thereby widening the knowledge base of academicians and entrepreneurs.

Evaluation of Social Responsibility of Major Municipal Road Infrastructure—Case Study of Zhengzhou 107 Auxiliary Road Project

Social responsibility plays an important role in the sustainable development of major municipal road infrastructure. In this study, a major municipal road infrastructure social responsibility (MMRISR) evaluation indicator system is developed for the comprehensive evaluation of social responsibility. Questionnaires and expert interviews were used to screen the initial indicators of the proposed system. Then, 24 indicators were selected from four dimensions to establish an MMRISR evaluation indicator system. The fuzzy analytic hierarchy process was employed to calculate the weights of each indicator. Finally, the Zhengzhou 107 Auxiliary Road Project was adopted as a case study to test the reliability of the proposed evaluation system. The contribution of this study lies in the provision of a novel indicator system for the social responsibility evaluation of major municipal road infrastructures, thus improving the science of project establishment and decision-making. The proposed social responsibility system can provide an efficient decision-making tool for social responsibility governance, fundamentally promoting the sustainable development of major municipal road infrastructures and the achievement of certain sustainable development goals.

Priorities in the Prevention Strategies for Medication Error Using the Analytical Hierarchy Process Method

As medication error is inherently “preventable”, we should try to minimize errors to improve patient safety and quality of care. The aim of this study was to prioritize strategies to prevent medication errors using the analytic hierarchy process (AHP) method. The hierarchy structure consisted of three stages: goal of the decision, decision criteria, and alternatives. Ten experts of patient safety research or clinical pharmacology compared each pair of criteria and alternatives and assigned a nine-point numerical scale. We used the eigenvector method to aggregate the pairwise comparisons obtained from experts and to estimate the weights of each criterion and alternative. Among the decision criteria, system improvement in reporting was the most preferred criterion, followed by cultural improvement and system improvement in the counterplan. The preferred alternative was a counterplan by healthcare institutions, followed by a change from a blame culture to safety culture and the building of a reporting system. A sensitivity analysis indicated that priorities were generally robust in the methods used for calculating the integrated matrices. We have suggested the priority of preventive strategies against medication errors using the AHP method. The prioritization of preventive strategies could help policymakers understand current needs and therefore develop evidence-based policies on patient safety.

Evaluating the impacts of small cascade hydropower from a perspective of stream health that integrates eco-environmental and hydrological values

With small cascade hydropower projects (SCHPs) increasingly employed in small and medium rivers, methods to assess changes in health status within the stream system have become essential to river ecological environment management. In this study, we used a cloud based fuzzy evaluation method to synthetically diagnose the health status of a stream, both as a whole and its parts (hydrological regime, riparian landscape, aquatic community, water quality, and social demand), under the impacts of SCHPs. The results indicated that: (1) average maximum and minimum flows decreased by 20% and 10% respectively, since SCHPs were implemented. Furthermore, the 38% increase in low flow frequency indicated that SCHPs might amplify droughts, the opposite of large hydropower projects which have been shown to alleviate drought; (2) implementation of SCHPs enhanced heterogeneity and fragmentation in riparian landscapes and decreased diversity of riparian vegetation, and dominant species were more likely to emerge on the upstream side of dam; (3) diversity of phytoplankton, zooplankton, and benthic animals decreased by 14%, 4%, and 16%, respectively, during high-impact period (HIP); and fish species decreased by 26% with a shift from rapid flow adapted to lentic and slow flow adapted species; and (4) the stream still exhibited a healthy state during HIP, but the degree of certainty belonging to "healthy" decreased from 0.279 to 0.192, indicating that the stream health was nearing a deteriorated state. This evaluation model clarified imperceptible and fuzzy changes in stream health which will be helpful in follow-up management decisions.

Service supplier selection under fuzzy and stochastic uncertain environments

Owing to the heterogeneity and inherent uncertainty of services, the selection of service suppliers is a complicated multi-attribute group decision-making (MAGDM) problem in which fuzzy criteria and stochastic criteria coexist. During the past few decades, many real-world supplier selection problems have been resolved using MAGDM methods. Nevertheless, extant research on supplier selection considers either fuzzy criteria or stochastic criteria, and hence most of these methods cannot address the complex and unstructured nature of contemporary service supplier selection problems. In this study, a novel technique for order preference by similarity to the ideal solution (TOPSIS) approach, integrating both fuzzy criteria and stochastic criteria, is developed; in this approach, the interval-valued intuitionistic fuzzy (IVIF) cross-entropy for fuzzy criteria and the Euclidean distance for stochastic criteria are used to acquire the rankings of alternatives. Moreover, a sensitivity analysis is conducted for a case study of hoisting service supplier selection, and a comparative analysis with other existing methods is performed to confirm the effectiveness and efficiency of the proposed approach.

Green supplier selection for textile industry: a case study using BWM-TODIM integration under interval type-2 fuzzy sets

Although environmental awareness has reached a high level, enterprises-regardless of their working domains-follow the concept of greenness for their practices. This awareness among the stakeholders and supply chain experts has a positive impact on the purchasing departments of enterprises in various sectors to consider greenness in their procurement processes. The critical decision that must be made in green supply chain management (GSCM) is supplier selection. In the textile industry, a highly competitive market in recent years, suppliers for this industry have crucial roles in business activities considering environmental issues. Therefore, green supplier selection (GSS) in the textile industry is considered a must-be process for the stakeholders. In this study, a GSS problem is tackled as a multi-criteria decision process. Best worst method (BWM) and TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) methods are merged under an improved fuzzy concept of interval type-2 fuzzy sets (IT2FSs). In determining green suppliers' evaluation criteria, BWM with interval type-2 fuzzy numbers (IT2F-BWM) is used. In selecting green suppliers, an interval type-2 fuzzy TODIM (IT2F-TODIM) is applied. Considering the characteristics of IT2FSs, BWM, and TODIM methods either individually and in integrated style, the proposed approach can handle uncertainty in the decision-making of GSS. To demonstrate the applicability of the approach, a case study in the Turkish textile industry is performed. Three green supplier alternatives (S1, S2, and S3) are assessed under forty-two sub-criteria. The study shows the most significant sub-criteria are recognized as dye and print quality, product design and pattern suitability, profit on the product, variation in price, and purchase cost. S2 green supplier has been selected as the most appropriate one. A sensitivity analysis is also fulfilled to check variation in the ranking of green suppliers.

A GIS-Cellular Automata-Based Model for Coupling Urban Sprawl and Flood Susceptibility Assessment

In Urban Planning (UP), it is necessary to take under serious consideration the inhibitors of the spread of a settlement in a specific direction. This means that all those parameters for which serious problems may arise in the future should be considered. Among these parameters are geo-hazards, such as floods, landslides, mud movement, etc. This study deals with UP taking into account the possibility of widespread flooding in settlement expansion areas. There is a large flooding history in Greece, which is accompanied by a significant number of disasters in different types of land use/land cover, with a large financial cost of compensation and/or rehabilitation. The study area is the drainage basin of Erasinos River in the Attica Region, where many and frequent flood events have been recorded. The main goal of this study is to determine the flood susceptibility of the study area, taking into account possible factors that are decisive in flood occurrence. Furthermore, the flood susceptibility is also determined, taking into account the scenarios of precipitation and the urban sprawl scenario in the area of reference. The study of flood events uses the Analytic Hierarchy Process (AHP) model and the urban sprawl model SLEUTH, which calibrates historical urban growth, using open and cost-free data and software. Eventually, flood susceptibility maps were overlaid with future urban areas to find the vulnerable areas. Following, three scenarios of flood susceptibility with the corresponding susceptibility maps and vulnerability maps, which measure the flood susceptibility of the current and future urban space of the study area, are presented. The results have shown significant peaks in the moderate class of flood susceptibility, while, in the third scenario, high values of flood susceptibility seem to appear. The proposed methodology and specifically the output maps can serve as a decision support tool to assist urban planners and hazard managers in making informed decisions towards sustainable urban planning.

Assessment of distribution center locations using a multi-expert subjective-objective decision-making approach

Distribution is a strategic function of logistics in different companies. Establishing distribution centers (DCs) in appropriate locations helps companies to reach long-term goals and have better relations with their customers. Assessment of possible locations for opening new DCs can be considered as an MCDM (Multi-Criteria Decision-Making) problem. In this study, a decision-making approach is proposed to assess DC locations. The proposed approach is based on Stepwise Weight Assessment Ratio Analysis II (SWARA II), Method based on the Removal Effects of Criteria (MEREC), Weighted Aggregated Sum Product Assessment (WASPAS), simulation, and the assignment model. The assessment process is performed using the subjective and objective criteria weights determined based on multiple experts' judgments. The decision matrix, subjective weights and objective weights are modeled based on the triangular probability distribution to assess the possible alternatives. Then, using simulation and the assignment model, the final aggregated results are determined. A case of DC locations assessment is addressed to show the applicability of the proposed approach. A comparative analysis is also made to verify the results. The analyses of this study show that the proposed approach is efficient in dealing with the assessment of DC locations, and the final results are congruent with those of existing MCDM methods.

GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study-Nature Park Golija, Serbia

The territory of the Republic of Serbia is vulnerable to various natural disasters, among which forest fires stand out. In relation with climate changes, the number of forest fires in Serbia has been increasing from year to year. Protected natural areas are especially endangered by wildfires. For Nature Park Golija, as the second largest in Serbia, with an area of 75,183 ha, and with MaB Reserve Golija-Studenica on part of its territory (53,804 ha), more attention should be paid in terms of forest fire mitigation. GIS and multi-criteria decision analysis are indispensable when it comes to spatial analysis for the purpose of natural disaster risk management. Index-based and fuzzy AHP methods were used, together with TOPSIS method for forest fire susceptibility zonation. Very high and high forest fire susceptibility zone were recorded on 26.85% (Forest Fire Susceptibility Index) and 25.75% (fuzzy AHP). The additional support for forest fire prevention is realized through an additional Internet of Thing (IoT)-based sensor network that enables the continuous collection of local meteorological and environmental data, which enables low-cost and reliable real-time fire risk assessment and detection and the improved long-term and short-term forest fire susceptibility assessment. Obtained results can be applied for adequate forest fire risk management, improvement of the monitoring, and early warning systems in the Republic of Serbia, but are also important for relevant authorities at national, regional, and local level, which will be able to coordinate and intervene in a case of emergency events.

Competitive strategy selection in retail E-Commerce using IF AHP & TOPSIS methodology

Retail e-commerce has been growing over the years by attracting entrepreneurs’ attention. Increasing interest in retail e-commerce has affected competition and made it necessary to choose the right competition strategy. This study aims at selecting the right competition strategy to be successful in retail e-commerce under vague and imprecise conditions. AHP and TOPSIS methods are used under intuitionistic fuzzy environment, which allows decision-makers to reflect their hesitation in their judgements. After a literature review on retail, e-commerce, and competition strategy, the main criteria and sub-criteria of multicriteria retail e-commerce alternative selection are presented. Cost leadership, differentiation, and focusing strategies are evaluated as competitive strategies, based on the opinions of experts collected through a questionnaire. The results of the study show that the strategies are ranked as differentiation, focus, and cost leadership strategies, respectively.

Comprehensive analysis and investigation of accident/occupational disease responsibility rates: A case study for accidental CO poisoning

One of the major problems of courts and insurance companies is the lack of a proper technique to determine the rate of responsibility of the parties involved in the accident. The aim of this study was to determine the Accident/Occupational Diseases Responsibility Rates (AOD RR) for complex events. Accordingly, a case study of a complex accident of carbon monoxide (CO) poisoning was selected and Occupational Accident Tree Analysis (OATA) and Occupational Accident Component Analysis (OACA) techniques, which were solely used to investigate occupational accidents, were used with the new names Accident/Occupational Disease Tree Analysis (AOD TA) and Accident/Occupational Disease Component Analysis (AOD CA) to evaluate their applicability to investigate non-occupational accidents and occupational diseases. For this purpose, causes of CO penetration to victims' room was assessed using gas tracking methods. Finally, Fuzzy Accident/Occupational Disease Tree/Component Analysis (FAOD TA and FAOD CA) techniques were developed and used for determining AOD RR in fuzzy environment. The results showed that the AOD RR obtained by AOD TA and AOD CA based on the average of experts' personal opinions and consensus between experts, and AOD TA and AOD CA in a fuzzy environment were close to each other, with the power terminal and the power cable crossing route being the main routes of transmission and penetration of CO to the victims' room. Also, the owner, contractor, tenant, and serviceman were responsible for the CO poisoning of victims. It can be concluded that any hole, crack, or fission in the building can result in CO penetration to the individuals' living rooms, and gas tracking in early winter, especially in older buildings, plays a very significant role in preventing residents' poisoning. Further, due to the uncertainty of AOD TA and AOD CA techniques, it is recommended to use FAOD TA and FAOD CA techniques for increasing the accuracy of the results. This will enhance the court and insurance companies' trust in the opinions of accident investigation experts, decrease delays in the proceedings, and prevent any violation of the individual rights.

An Integrated Kano Model, Fuzzy Analytical Hierarchy Process, and Decision Matrix for Sustainable Supplier Selection in Palm Oil Industries Indonesia, a Case Study

Industries have to integrate environmental, social, and economic aspects into their supply chain management to achieve sustainability. Hence, the industry needs to take appropriate actions in choosing the right suppliers. The aim of this study is to develop a framework for selecting sustainable suppliers by integrating quality management tools using the Kano model, Fuzzy Analytical Hierarchy Process, and Decision Matrix Method. To identify the critical sustainability criteria, the Kano model by the clustering the criteria for sustainable selection supplier was used. We then used the Fuzzy Analytical Hierarchy Process to determine the weight of each criterion and applied the Decision Matrix Method to select the most sustainable supplier. Afterward, the appropriate proposed framework was implemented in one of the palm oil industries in Indonesia to validate that the framework is applicable and useful. The study shows that the environmental dimension is the most sustainable supplier criteria followed by economy and social dimension. Quality, pollution control, and information disclosures were found to be important sub-dimensions for sustainable supplier selection.

An improved OWA-Fuzzy AHP decision model for multi-attribute decision making problem

Multi-attribute decision-making (MADM) is an important part of modern decision-making science. Fuzzy Analytic Hierarchy Process (Fuzzy AHP) is a popular model to deal with the issue of MADM for its flexible and effective advantages. However, The traditional Fuzzy AHP with some limitations does not consider the preference (attitude) of decision makers (DMs). In addition, some ideas of combining Ordered Weighted Average (OWA) and Fuzzy AHP don’t investigated the MADM well. Some programs are only applicable to a few examples, and more general cases do not result in effective decision making. Considering these shortcomings, an OWA-Fuzzy AHP decision model using OWA weights and Fuzzy AHP is proposed in this paper. Our contribution is that the proposed method can handle situations where the degree of fuzzy synthesis is not intersected. Moreover, the loss of information can be reduced in the process of applying the proposed method, so that the decision result is more reasonable than the previous methods. Several examples and comparative experimental simulation are given to illustrate the effectiveness and superiority of the proposed model.

A diversified AHP-tree approach for multiple-criteria supplier selection

A decision maker usually holds various viewpoints regarding the priorities of criteria, which complicates the decision making process. To overcome this concern, in this study, a diversified AHP-tree approach was proposed. In the proposed diversified AHP-tree approach, the judgement matrix of a decision maker is decomposed into several subjudgement matrices, which are more consistent than the original judgement matrix and represent diverse viewpoints on the relative priorities of criteria. Thus, a nonlinear programming model was established and optimized, for which a genetic algorithm is designed. To assess the effectiveness of the proposed diversified AHP-tree approach, it was applied to a supplier selection problem. The experimental results showed that the application of the diversified AHP-tree approach enabled the selection of multiple diversified suppliers from a single judgement matrix. Furthermore, all suppliers selected using the diversified AHP-tree approach were somewhat ideal.

An Investigation of Human Errors in Medication Adverse Event Improvement Priority Using a Hybrid Approach

The aim of this study was to analyze and provide an in-depth improvement priority for medication adverse events. Thus, the Human Factor Analysis and Classification System with subfactors was used in this study to analyze the adverse events. Subsequently, the improvement priority for the subfactors was determined using the hybrid approach in terms of the Analytical Hierarchy Process and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution. In Of the 157 medical adverse events selected from the Taiwan Patient-safety Reporting system, 25 cases were identified as medication adverse events. The Human Factor Analysis and Classification System and root cause analysis were used to analyze the error factors and subfactors that existed in the medication adverse events. Following the analysis, the Analytical Hierarchy Process and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution were used to determine the improvement priority for subfactors. The results showed that the decision errors, crew resource management, inadequate supervision, and organizational climate contained more types of subfactors than other error factors in each category. In the current study, 16 improvement priorities were identified. According to the results, the improvement priorities can assist medical staff, researchers, and decisionmakers in improving medication process deficiencies efficiently.

Integrated Framework for Detecting the Areas Prone to Flooding Generated by Flash-Floods in Small River Catchments

In the present study, the susceptibility to flash-floods and flooding was studied across the Izvorul Dorului River basin in Romania. In the first phase, three ensemble models were used to determine the susceptibility to flash-floods. These models were generated by a combination of three statistical bivariate methods, namely frequency ratio (FR), weights of evidence (WOE), and statistical index (SI), with fuzzy analytical hierarchy process (FAHP). The result obtained from the application of the FAHP-WOE model had the best performance highlighted by an Area Under Curve—Receiver Operating Characteristics Curve (AUC-ROC) value of 0.837 for the training sample and another of 0.79 for the validation sample. Furthermore, the results offered by FAHP-WOE were weighted on the river network level using the flow accumulation method, through which the valleys with a medium, high, and very high torrential susceptibility were identified. Based on these valleys’ locations, the susceptibility to floods was estimated. Thus, in the first stage, a buffer zone of 200 m was delimited around the identified valleys along which the floods could occur. Once the buffer zone was established, ten flood conditioning factors were used to determine the flood susceptibility through the analytical hierarchy process model. Approximately 25% of the total delimited area had a high and very high flood susceptibility.

Measuring the association of environmental, corporate, financial, and social CSR: evidence from fuzzy TOPSIS nexus in emerging economies

The purpose of this research is to measure the combing impact of corporate social responsibility on company performance and to conduct a comparative analysis among local and foreign companies in this context. This research aims to conduct an empirical analysis about how corporate social responsibility contributes to company performance. The study utilizes AHP and fuzzy TOPSIS theory to conduct research. The results revealed that environmental corporate social responsibility has a vital role in the development of organizational reputation and employee commitment. It can be observed from the results that the weights of environmental CSR, corporate CSR, financial CSR, and social CSR are 0.30, 0.25, 0.24, and 0.21, respectively. The preference of these four criteria is environmental CSR > corporate CSR > financial CSR > social CSR. The corporate CSR criterion got the maximum weight of 0.30, whereas the social CSR criteria received the lowest weight of 0.21. The financial CSR get weights of criteria 0.25, and the commercial potential obtained 0.24 weights, while the financial CSR got the 2nd highest criteria weight of 0.25, and the social CSR get weights of criteria 0.21 lowest weighted. The research provides valuable information for decision-makers. The study provides a valuable information for policy makers.

Municipal Solid Waste Landfill Site Selection Based on Fuzzy-AHP and Geoinformation Techniques in Asir Region Saudi Arabia

One of the main issues with solid waste management is finding appropriate sites for landfill. Non-scientific and inappropriate disposal practices have a negative impact on the environment which affects the quality of life. The study provides an integrated framework with a focus on structuring the decision-making process for the landfill suitability site map. This could be determined by the use of proper data collection, criterion weighting and normalization. In order to understand the procedures that affect the suitability of landfill sites, the integrated GIS-based fuzzy-AHP-MCDA method was implemented to appropriate landfill site for Abha-Khamis-Mushyet located in Aseer region Following the extensive literature review and expert opinion, 10 themes were selected for this study such as drainage density, land use/land cover (LULC), slope, elevation, lineament density, normalized difference vegetation index (NDVI), rainfall, distance from the airport, distance from road, and geology. These themes have been developed through RS (remote sensing) and conventional data. Subsequently, potential landfill sites were identified and divided into five classes: very low suitable (fuzzy value 0.20–0.45), low suitable (0.46–0.55), moderately suitable (0.56–0.65), high suitable (0.66–0.75), and very high suitable (0.76–0.92). According to the statistical analysis, 23.91% and 3.67% of the total area were within a very good and good landfill area, while 38.14% and 22.84% accounted for the moderate and poor suitable zone, respectively. As a quality-based site, the existing two landfill sites were located over a very low suitable and low suitable potential area while one landfill site was located over the high suitable∙ The spatial variance of high and very high potential landfill site zones found in the north-eastern, east-central and south-eastern parts of the watershed. The sensitivity analysis was performed to determine the efficacy of each parameter and reveals that the effective weights for each theme differ slightly from the theoretical weight assigned to the landfill site suitability zone. This technique and its findings can provide an appropriate guideline to assist hydrogeologists, engineers, regional planners, and decision-makers in selecting an optimal landfill site in the future.

Cyber-Attacks Risk Analysis Method for Different Levels of Automation of Mining Processes in Mines Based on Fuzzy Theory Use

The rising automation level and development of the Industry 4.0 concept in the mining sector increase the risk of cyber-attacks. As a result, this article focuses on developing a risk analysis method that integrates Kaplan's and Garrick's approach and fuzzy theory. The proposed approach takes into account the level of automation of the operating mining processes. Moreover, it follows five main steps, including identifying the automation level in a selected mine, definition of cyber-attack targets, identification of cyber-attack techniques, definition of cyber-attack consequences, and risk ratio assessment. The proposed risk assessment procedure was performed according to three cyber-attack targets (databases, internal networks, machinery) and seven selected types of cyber-attack techniques. The fuzzy theory is implemented in risk parameter estimation for cyber-attack scenario occurrence in the mining industry. To illustrate the given method's applicability, seven scenarios for three levels of mine automation are analyzed. The proposed method may be used to reveal the current cybersecurity status of the mine. Moreover, it will be a valuable guide for mines in which automation is planned in the near future.

Using FAHP and CBR to evaluate the intention of adoption of internet banking service: The example of web ATM

This research combines the Fuzzy Analytic Hierarchy Process (FAHP) with Case-Based Reasoning (CBR) to evaluate the intention of adoption of web ATM services. Compared with physical ATM service, web ATM allows users to perform financial transactions over the internet conveniently. Based on literature and considering the characteristics of web ATM, this study constructs a model for web ATM adoption that comprises three dimensions: The knowledge, the potential value, and the security. 222 valid user questionnaires are collected, and factor analysis is used to verify the factor structure of the decision hierarchy. FAHP is then used to calculate the weights of criteria with six experts through pairwise comparisons. Finally, FAHP weights are integrated into a CBR prediction mechanism for evaluating a user’s adoption intention toward web ATM. The results are helpful for financial institutions to understand and to evaluate the user behavior toward internet banking service adoption.

Evaluation of hospital disaster preparedness by a multi-criteria decision making approach: The case of Turkish hospitals

Considering the unexpected emergence of natural and man-made disasters over the world and Turkey, the importance of preparedness of hospitals, which are the first reference points for people to get healthcare services, becomes clear. Determining the level of disaster preparedness of hospitals is an important and necessary issue. This is because identifying hospitals with low level of preparedness is crucial for disaster preparedness planning. In this study, a hybrid fuzzy decision making model was proposed to evaluate the disaster preparedness of hospitals. This model was developed using fuzzy analytic hierarchy process (FAHP)-fuzzy decision making trial and evaluation laboratory (FDEMATEL)-technique for order preference by similarity to ideal solutions (TOPSIS) techniques and aimed to determine a ranking for hospital disaster preparedness. FAHP is used to determine weights of six main criteria (including hospital buildings, equipment, communication, transportation, personnel, flexibility) and a total of thirty-six sub-criteria regarding disaster preparedness. At the same time, FDEMATEL is applied to uncover the interdependence between criteria and sub-criteria. Finally, TOPSIS is used to obtain ranking of hospitals. To provide inputs for TOPSIS implementation, some key performance indicators are established and related data is gathered by the aid of experts from the assessed hospitals. A case study considering 4 hospitals from the Turkish healthcare sector was used to demonstrate the proposed approach. The results evidenced that Personnel is the most important factor (global weight = 0.280) when evaluating the hospital preparedness while Flexibility has the greatest prominence (c + r = 23.09).

Multi-criteria decision-making using GIS-AHP for air pollution problem in Igdir Province/Turkey

Today, environmental problems are increasing and threatening nature and human health. Air pollution is at the top of this threat. Air, the main source of life, is indispensable for humans and living things. Therefore, air pollution causes mass results. Many air pollution studies and many solution techniques have been proposed in the literature to deal with the air pollution problem. In this study, it is called analytical hierarchy process (AHP) and geographic information systems (GIS), which is one of the multi-criteria decision making methods used in the investigation of air pollution in Iğdır city center and its four districts: Tuzluca, Iğdır Central, Karakoyunlu, and Aralik. In this study, spatial analysis of the pollutant parameters using the GIS-AHP technique was performed with the help of the data obtained from Iğdır Weather Monitoring Stations. By determining the pollutant parameters, pollution distribution maps were created, and station-based statistics were evaluated with dynamic mapping. For the first time, 15 parameters of weather and topographic features were used. Based on this result, it is obvious that Iğdır center is the worst place in the air pollution problem for GIS-AHP method. It varies for 15 parameters using GIS-AHP, compared with the review of locations on a daily basis. The aim of this study is primarily to investigate possible places of air pollution. Also, by comparing the results of two methods, GIS and AHP, more accurate results are given.

Efficiency of Polder Modernization for Flood Protection. Case Study of Golina Polder (Poland)

This paper presents an analysis of the effectiveness of variants of the reconstruction of a polder, which is part of the flood protection system of a large urban agglomeration. The Golina polder, located in the floodplain of the Warta River, was selected as the case study. The multi-criteria decision support methods AHP and Fuzzy AHP were used to assess the effectiveness of individual variants. Information on the floods from 1997 and 2010, data on land cover, land development, nature and historical objects were used to estimate the value of the coefficients for the multi-criteria decision methods. It was shown that the planned deep modernization of the hydrotechnical infrastructure and the purchase of land in the polder area is less effective than maintaining the current state of development of the polder.

Supportiveness of Low-Carbon Energy Technology Policy Using Fuzzy Multicriteria Decision-Making Methodologies

The deployment of low-carbon energy (LCE) technologies and management of installations represents an imperative to face climate change. LCE planning is an interminable process affected by a multitude of social, economic, environmental, and health factors. A major challenge for policy makers is to select a future clean energy strategy that maximizes sustainability. Thus, policy formulation and evaluation need to be addressed in an analytical manner including multidisciplinary knowledge emanating from diverse social stakeholders. In the current work, a comparative analysis of LCE planning is provided, evaluating different multicriteria decision-making (MCDM) methodologies. Initially, by applying strengths, weaknesses, opportunities, and threats (SWOT) analysis, the available energy alternative technologies are prioritized. A variety of stakeholders is surveyed for that reason. To deal with the ambiguity that occurred in their judgements, fuzzy goal programming (FGP) is used for the translation into fuzzy numbers. Then, the stochastic fuzzy analytic hierarchical process (SF-AHP) and fuzzy technique for order performance by similarity to ideal solution (F-TOPSIS) are applied to evaluate a repertoire of energy alternative forms including biofuel, solar, hydro, and wind power. The methodologies are estimated based on the same set of tangible and intangible criteria for the case study of Thessaly Region, Greece. The application of FGP ranked the four energy types in terms of feasibility and positioned solar-generated energy as first, with a membership function of 0.99. Among the criteria repertoire used by the stakeholders, the SF-AHP evaluated all the criteria categories separately and selected the most significant category representative. Finally, F-TOPSIS assessed these criteria ordering the energy forms, in terms of descending order of ideal solution, as follows: solar, biofuel, hydro, and wind.

Supplier selection using hybrid multicriteria decision-making methods

Purpose

The paper develops a decision-making model for supplier selection combining quality function deployment (QFD), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). The efficacy of the model was demonstrated by applying it for supplier selection of lithium ion batteries.

Design/methodology/approach

The proposed methodology involved identifying customer requirements for lithium ion batteries and translating them to requisite technical characteristics using QFD. Further, separate sourcing, safety and sustainability-related supplier parameters were proposed taking into account the manufacturer's point of view. The relative weight of each parameter was then calculated using AHP, and finally, TOPSIS was used to select the best supplier.

Findings

The proposed methodology was applied to six suppliers of lithium ion batteries, and the obtained results were used to select the most and least preferred suppliers.

Practical implications

The obtained results cannot be generalized and are valid to the case environment. However, the proposed approach can be used for any environment related to supplier selection after capturing the corresponding parameters. The proposed approach does not restrict the number of parameters to be considered.

Originality/value

Many researches related to supplier evaluation are reported in literature, but few studies are available related to supplier performance evaluation for lithium ion batteries using QFD, AHP and TOPSIS. The study will provide a guideline for comparing and selecting supplier on the basis of performance in general and its application to lithium ion batteries in specific.

A novel FMEA model based on fuzzy multiple-criteria decision-making methods for risk assessment

Purpose

Risk assessment is a very important step toward managing risks in various organizations and industries. One of the most extensively applied risk assessment techniques is failure mode and effects analysis (FMEA). In this paper, a novel fuzzy multiple-criteria decision-making (MCDM)-based FMEA model is proposed for assessing the risks of different failure modes more accurately.

Design/methodology/approach

In this model, the weight of each failure mode is considered instead of risk priority number (RPN). Additionally, three criteria of time, cost and profit are added to the three previous risk factors of occurrence (O), severity (S) and detection (D). Furthermore, the weights of the mentioned criteria and the priority weights of the decision-makers calculated by modified fuzzy AHP and fuzzy weighted MULTIMOORA methods, respectively, are considered in the proposed model. A new ranking method of fuzzy numbers is also utilized in both proposed fuzzy MCDM methods.

Findings

To show the capability and usefulness of the suggested fuzzy MCDM-based FMEA model, Kerman Steel Industries Factory is considered as a case study. Moreover, a sensitivity analysis is conducted for validating the achieved results. Findings indicate that the proposed model is a beneficial and applicable tool for risk assessment.

Originality/value

To the best of authors’ knowledge, no research has considered the weights of failure modes, the weights of risk factors and the priority weights of decision-makers simultaneously in the FMEA method.