Fuzzy Technique For Order Preference Research Articles

An integrated fuzzy AHP and fuzzy TOPSIS approach for screening backfill materials for contaminant containment in slurry trench cutoff walls

The inclusion of backfill material is crucial for the effective containment of contaminants in a slurry trench cutoff wall. However, selecting the optimal backfill material is complex and involves multiple criteria. This study aimed to develop a new approach to assist in the selection process by rationally dealing with the subjective judgment of decision makers. The approach employed fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methods. Initially, consistent judgment matrices were established based on a unique consulting questionnaire. The fuzzy AHP method used Buckley's method to calculate the fuzzy weights of pairwise comparisons. The fuzzy TOPSIS method was then employed to rank and evaluate four different backfill materials. The proposed methodology was successfully implemented at a real contaminated site in China to select the appropriate backfill material for construction of a slurry trench cutoff wall. Based on the proposed integrated methodology, fiber reinforced reactive magnesia-activated slag-soil-bentonite (FMSB) was determined as the most appropriate backfill materials for contaminant containment in slurry trench cutoff walls at the site reported in this study.

A comprehensive approach to selecting mine transportation system using AHP and FUZZY-TOPSIS

Mine transportation approximately accounts for more than half of operating costs. Therefore selection of the appropriate mine transportation system by considering the effective criteria has become an important issue in mining engineering. A hybrid decision support system based on Analytical Hierarchy Process (AHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) was proposed to assess criteria and alternatives for balancing haulage distance, continuity, flexibility and reliability, which could provide the suitable balance between different goals, such as costs and safety. An application of this approach is carried out through the Sungun copper mine as case study. For this purpose, the production capacity, medium and short-term production plan, conditions of mining benches and ramps and future development plans collected from chosen mine located at the East Azerbaijan Province, Iran. This research has been studied comprehensively in terms of technical, economic, environmental, society, site information, geological and geo-mechanically criteria. More than 170 criteria were identified and classified under six main categories. To select the mine transportation system, the importance of each criteria is first measured relative to each other, then the importance of each option is evaluated for each criteria and were filtered to 55 items. The proposed model is based on the combination of the AHP and fuzzy TOPSIS. The hybrid model has advantages such as the possibility of using paired comparison and considering uncertainty. Finally, the in-pit crushing and conveying is selected as the best alternative, the truck-front shovel and truck-backhoe system are in the next priorities, respectively 45.2, 28.4 and 26.4%. In summary, from the results of the research, it can be mentioned to present the effective criteria in the selection of the transport system of open pit mines, to calculate their weight, to provide a framework for the selection of the transport system of open pit mines by considering the uncertainties.

Integration of ABC Analysis with Fuzzy DEMATEL and Fuzzy TOPSIS in Supplier Selection and Ranking Problems

While performing Always Better Control (ABC) analysis in an automobile industry, A category items are given higher priority, neglecting B and C category items which comprise 80% of the total inventory. Little attention to tail spend account poses problems in managing suppliers, unscheduled deliveries, and environmental issues. To address this gap a Multi-criteria decision making approach is proposed for supplier selection in each inventory class by integrating ABC Analysis with Fuzzy Decision-making trial and evaluation laboratory and Fuzzy Technique for Order Preference by Similarity to Ideal Solution. The best suppliers are selected based on criteria weights and criteria interrelationships for each A, B, and C inventory items. The study shows that managers have to focus on key criteria rather than a whole set of criteria for A, B, and C inventory suppliers thus saving costs and effort. The results show that delivery and environmentally friendly products as common and important criteria for supplier selection in each inventory items. The research results will help the decision-makers and policymakers in solving the supplier selection problems in the automobile sector and other similar sectors.

A Life Cycle Risk Management Framework for Green Building Project Stakeholders

The construction industry compromises the sustainability of the built environment because of its adverse impacts on the environment, economy, and society. In response, green building (GB) projects advocate for targeted sustainability goals while some particular risks in GB projects could cause several unfavorable outcomes. This study aims to assess the life cycle risks of GB projects and emphasize the role of stakeholders in the management of corresponding risks in a four-step analysis procedure. In the first two steps, a comprehensive literature review and focus-group discussion were employed to construct the decision-making framework suitable to the GB life cycle. A fuzzy analytical hierarchy process (AHP) was used to prioritize the identified risks based on expert opinions. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was used to associate stakeholders’ role with the management of GB risks that might arise throughout GB projects. Findings indicate that lack of experienced green construction staff, limited availability and reliability of green subcontractors and suppliers for the specific requirements of GB, and inflation of green materials’ prices are ranked as the most significant risks. A generic life cycle risk management framework for green buildings was developed to analyze the most significant risks and corresponding stakeholders. By addressing the gap in the literature, this study is expected to engage practitioners and GB project stakeholders for more effective and robust risk management frameworks.

A selectively calibrated derivation technique and generalized fuzzy TOPSIS for semiconductor supply chain localization assessment

There is a localization wave in the semiconductor industry for pushing wafer foundries to transfer their production capacity to where chip designers are located. The main reasons for the localization of semiconductor supply chains include the US–China trade war, geopolitics, COVID-19, the Ukraine–Russia war, and rising environmental awareness. However, this is a challenging task since some of the issues considered in traditional facility location selection problems are no longer decisive factors. To overcome this challenge, this study proposes the selectively calibrated derivation technique (SCDT)-generalized fuzzy technique for order preference by similarity to ideal solution (GFTOPSIS) approach to help wafer foundries select suitable facility locations for customer-required capacity localization. The proposed SCDT-GFTOPSIS approach has been applied to a real case to validate its effectiveness. According to the experimental results, SCDT effectively improved the accuracy of approximating the fuzzy priorities of criteria by reducing the mean absolute deviation (MAD) by 81%. In addition, the most suitable location for the facility was in Singapore, where the support of the local government was higher, while requiring less total investment and subject to less political and war risk. However, the final selection was subject to further negotiations.

Selection of strategies to improve energy efficiency in industry: A hybrid approach using entropy weight method and fuzzy TOPSIS

Energy has now turned out to be one of the key factors for competitiveness and economic growth across the globe. In this line, the manufacturing industries have been acknowledged among the biggest drivers of economic development in all nations. Considering rapid industrial development and high rates of energy consumption, energy demand is multiplying faster than ever. Accordingly, it seems critical to improve energy efficiency (EF) and reduce energy consumption in various industries. Finding the common types of energy waste, the strategies to step up EF can consequently provide industrial owners, developers, and planners with solutions to deal with such issues. Selecting the implementation strategies for this purpose is thus assumed multi-criteria decision-making (MCDM) process. In the present study, the ceramic tile industry in Iran, as one of the energy-intensive industries (EIIs) due to its manufacturing process, was investigated. To this end, the strategies to improve EF and their evaluation criteria were initially established by reviewing the related literature and eliciting expert opinions. Then, the Entropy Weight Method (EWM) and the fuzzy Technique for Order Preference by Similarity to Ideal Solution (fTOPSIS) were operated to allocate weights to the criteria, and rank the given strategies, respectively. Based on the literature review, 22 EF strategies and 4 evaluation criteria were retrieved, and finally approved by the experts. The study results revealed that “cost of implementing strategies” received the highest weight among the evaluation criteria. With regard to these criteria, “Reducing leakage in the compressed air production/distribution system”, Reducing power consumption of the presses, ball mills, crushers, and glazing during peak hours by shifting the load to non-peak hours”, as well as “Sealing/filling the pores between the rail wall and wagon (sand seal) in tunnel kilns”, were given the highest priority as the implementation strategies at factory level. Also, according to the change in the weight of the criteria, sensitivity analysis was performed for the robustness and strength of the ranking.

New similarity measure of Pythagorean fuzzy sets based on the Jaccard index with its application to clustering

A similarity measure is being widely used as a tool to create the degree of similarity between two different objects or sets. Several similarity measures between two Pythagorean fuzzy sets (PFSs) are suggested in literature. As far as our knowledge is concerned no one has considered similarity measure between two Pythagorean fuzzy sets (PFSs) based on Jaccard index (JI) so far. Therefore, in this manuscript a novel similarity measure based on JI between two PFSs is suggested. The JI is statics used to compare similarity as well as variety of simple sets. The advantages of proposed similarity based on Jaccard index includes: (i) It can be utilized to compare any two sets and it can also be applied to text data, numerical data and even categorical data. (ii) It is robust to outliers, which means that it is not significantly affected by presence of rare or unusual items exist in the data. This makes it useful in variety of applications including clustering and anomaly detection (iii) It is simple and efficient in computation which make it right choice in many applications where fast processing time is important. Then we gave several numerical examples to show the reliability of suggested similarity measures. Our proposed similarity measure between PFSs based on JI give better properties with respect to numerical results. Based on the numerical analysis, our proposed method is reliable, reasonable and better than the existing ones. Furthermore, we utilized proposed similarity measure with Pythagorean Fuzzy Technique of Order Preference by Similarity to Ideal Solution (PF-TOPSIS) in handling complex daily life issues involving multicriteria decision-making (MCDM) processes. Furthermore, similarity measure between two PFSs is also very useful in clustering. Therefore, we also apply our proposed similarity measure in an application to clustering based on Pythagorean fuzzy data set. Final results revealed that our suggested methods are well suited and reasonable in multicriteria decision-making and clustering environment.

Enhancing the level of confidence in the safe fire evacuation operation employing a combination of BIM and fuzzy TOPSIS method

PurposeThis research aims to provide optimization and route safety planning employing the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique.Design/methodology/approachThis research combines the use of graphical, communication tools and simulated models based on building information modeling (BIM) technology and agent-based modeling (ABM) to identify a safe evacuation route. Adopting the multi-criteria decision-making (MCDM) approach, the proposed rescue plan can reduce potential hazards along the evacuation route by selecting a safe route for evacuating residents and entering firefighters to the scene of the incident.FindingsThe results show that the use of simulated models along with MCDM methods in the selection of safe routes improves the performance of safe evacuation operations for both relief groups and residents.Practical implicationsThe introduced model can improve the performance management of different groups at the time of the incident and reduce casualties and property losses using the information received from sensors at the scene. Moreover, the proposed rescue plan prevents group and individual reactivation at the time of the incident.Originality/valueDespite many advances in the architecture, engineering and construction (AEC) industry, the number of victims of fire incidents in buildings is increasing compared to other natural disasters. Improving decision management based on effective parameters at the time of incident reduces casualties of residents and rescue workers.

Evaluation of Green Marketing Strategies by Considering Sustainability Criteria

The purpose of this study is to identify and prioritize green marketing strategies in the food industry by taking sustainability goals into consideration since it is very difficult to prioritize and select green marketing strategies when considering sustainability criteria in their implementation. Therefore, it is necessary to use an appropriate solution to prioritize these strategies. For this purpose, sustainability criteria and green marketing strategies are recognized. Then, sustainability criteria are screened using the fuzzy Delphi method. Subsequently, using the fuzzy stepwise weight assessment ratio analysis method, the criteria are weighted according to expert recommendations. Next, green marketing strategies in the Iranian food industry are ranked using several fuzzy multiple criteria decision-making methods: fuzzy additive ratio assessment, fuzzy complex proportional assessment, fuzzy technique for order preference by similarity to the ideal solution, and fuzzy Viekriterijumsko Kompromisno Rangiranje methods. Finally, the results are integrated with the help of the Copeland method to choose the best strategies. The results indicated that the criteria of ‘minimizing waste’, ‘minimizing environmental impacts’, and ‘consumer participation’ had the highest weight. In addition, the strategies for the ‘development of waste reduction technology, ‘development of market research to detect green needs’, and ‘use of recycled or reusable materials in products’ received a higher rank.

Computational Study of Security Risk Evaluation in Energy Management and Control Systems Based on a Fuzzy MCDM Method

Numerous cyberattacks on connected control systems are being reported every day. Such control systems are subject to hostile external attacks due to their communication system. Network security is vital because it protects sensitive information from cyber threats and preserves network operations and trustworthiness. Multiple safety solutions are implemented in strong and reliable network security plans to safeguard users and companies from spyware and cyber attacks, such as distributed denial of service attacks. A crucial component that must be conducted prior to any security implementation is a security analysis. Because cyberattack encounters in power control networks are currently limited, a comprehensive security evaluation approach for power control technology in communication networks is required. According to previous studies, the challenges of security evaluation include a power control process security assessment as well as the security level of every control phase. To address such issues, the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) based on multiple criteria decision-making (MCDM) is presented for a security risk assessment of the communication networks of energy management and control systems (EMCS). The methodology focuses on quantifying the security extent in each control step; in order to value the security vulnerability variables derived by the protection analysis model, an MCDM strategy incorporated as a TOPSIS is presented. Ultimately, the example of six communication networks of a power management system is modelled to conduct the security evaluation. The outcome validates the utility of the security evaluation.

A Hybrid Fuzzy AHP-TOPSIS Approach for Implementation of Smart Sustainable Waste Management Strategies

The integration of smart city technologies into waste management is a challenging field for decision makers due to its multivariate, multi-limiting, and multi-stakeholder structure, despite its contribution to the ecological and economic sustainability understanding of cities. The success of smart sustainable waste management strategies depends on many environmental, technical, economic, and social variables, and many stakeholders are involved in these processes. Using fuzzy multi-criteria decision-making (MCDM) methods helps decision makers determine effective, affordable, and acceptable smart waste management strategies. Although MCDM methods are widely used in various environmental engineering applications, the determination of smart sustainable waste management strategies using these methods has not yet received enough attention in the literature. This study aims to contribute to this gap in the literature by evaluating four different smart waste management strategies using a hybrid fuzzy MCDM method. The performance of the proposed strategy alternatives according to fifteen sub-criteria (under four main criteria selected from the literature) was evaluated using a combined application of fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order preference by similarity to obtain the ideal solution (fuzzy TOPSIS). For this evaluation, the subjective opinions of ten different experts working in academia, in the private sector, or in the public sector were obtained using prepared questionnaires. As a result, the sub-criteria of fewer atmospheric emissions (0.42), operational feasibility (0.64), initial investment costs (0.56), and increased awareness of sustainable cities (0.53) had the highest weight values in their main criteria groups. The performance ranking of the alternatives according to the closeness coefficient (CCi) values was obtained as A2 (0.458) > A3 (0.453) > A4 (0.452) > A1 (0.440), with A3 being slightly ahead of A4 due only to a 0.001 higher CCi value. To test the reliability and stability of the obtained performance ranking results, a sensitivity analysis was also performed using eighteen different scenarios, in which the weights of the different sub-criteria were increased by 25% or decreased by 50%, or they were assumed to be 1 and 0, or all sub-criteria in the same group had equal weight values. Since the performance ranking of the alternatives did not change, the ranking obtained at the beginning was found to be robust against the sub-criterion weight changes.

Mining online reviews for ranking products: A novel method based on multiple classifiers and interval-valued intuitionistic fuzzy TOPSIS

Online reviews can assist customers in making purchasing decisions, however, when multiple alternative products need to be compared based on online reviews, a large number of reviews information cannot be processed manually. Therefore, this study develops a novel method for product ranking based on mining online reviews and an interval-valued intuitionistic fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Concretely, it contains three major steps: (1) preprocessing, (2) calculating sentiment scores, and (3) ranking alternative products. First, the results of preprocessing are vectorization representation of online reviews, which are obtained with the Bidirectional Encoder Representations from Transformers (BERT) model. Then, sentiment orientations of online reviews of alternative products regarding different attributes are determined by a multiple classifiers system, which consists of multi-class base classifiers constructed using Support Vector Machine (SVM) with One-Vs-One (OVO) strategy. Lastly, based on the sentiment scores, the ranking result is obtained by the interval-valued intuitionistic fuzzy TOPSIS method. A case study on real-world datasets is provided to illustrate the application of our proposed method, which indicates the validity of our proposal. Thus, the method proposed in this paper can effectively assist consumers in selecting products based on online reviews that meet their preferences.

A Hybrid Multi-Criteria Decision-Making Framework for Ship-Equipment Suitability Evaluation Using Improved ISM, AHP, and Fuzzy TOPSIS Methods

The inherent complexity of large ships makes it challenging to evaluate ship designs systematically and scientifically. Knowledge-based expert systems can be reasonable solutions. However, this problem needs more rationality and better operability, especially in complicated ship-equipment suitability evaluation problems with numerous indicators and complex structures. This paper presents a hybrid multi-criteria decision-making (MCDM) framework to extend the ship-equipment suitability evaluation to group decision-making settings, where individual consistency and group consensus are thoroughly investigated to improve rationality and operability. As a result, an improved Interpretive Structural Modeling (ISM) method is developed to construct the evaluation index systems. Furthermore, based on an applicability analysis of the selected MCDM methods, an improved Analytical Hierarchy Process (AHP) method is proposed to distribute the index weights, and an applicable Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) method is utilized to evaluate and select appropriate ship designs. Finally, a ship-equipment environmental suitability evaluation case is examined. The results indicate that the proposed framework improves the rationality and operability of the decision-making process and provides practical support to decision-makers for the systematic and scientific evaluation of ship designs. Therefore, it can also be applied to other ship design evaluation and selection problems.

Assessing educational methods for tomorrow's supply chain leaders with the integration of skill development priorities: a fuzzy decision-making approach

PurposeEffective educational methods are critical for successfully training future supply chain talent. The paper proposes a fuzzy multi-criteria decision-making model to evaluate and select the best educational method for tomorrow's supply chain leaders integrating skill development priorities in an uncertain environment.Design/methodology/approachThe Grounded theory scheme is used to identify SC leaders' skillsets criteria and educational method alternatives. Fuzzy step-wise weight assessment ratio analysis sets the priority and determines the weight of 17 criteria. Eight decision-makers evaluate 13 alternatives using fuzzy linguistic terms. Fuzzy technique for order preference by similarity to ideal solution ranks and shows the most effective educational method. Sensitivity analysis presents the applicability of this study.FindingsIts implementation in a university-industry collaboration case in Brazil, Mentored learning from industry experts is the best educational method. The skill development priorities are data analytics ability, end-to-end supply chain vision and problem-solving. Technical skills are the most important criteria that influence the selection of the optimal option and educational methods related to learning from others rank in the top teaching pool, including multidisciplinary cross-cultural training.Originality/valueThis paper is among the first to evaluate educational methods with skill development priorities integration for supply chain students using fuzzy SWARA–fuzzy TOPSIS. It provides actionable insights: a decision-making procedure for educational method selection, a broad skills profile for supply chain professional success and educational methods that professors can bring to in classroom/virtual environment.

A novel fuzzy group decision-making approach based on CCSD method for thermal insulation board selection problem: A case study

Due to buildings’ high energy consumption and environmental impacts, selecting an appropriate thermal insulation board is crucial in reducing energy consumption in an existing building. Therefore, thermal insulation board selection has become an important issue. This study presents a combined fuzzy group decision-making approach that consists of the integrated fuzzy analytic hierarchy process and fuzzy technique for order preference by similarity to ideal solution, the integrated intuitionistic fuzzy analytic hierarchy process and intuitionistic fuzzy technique for order preference by similarity to ideal solution, and intuitionistic fuzzy technique for order preference by similarity to ideal solution for effectively evaluating thermal insulation boards. However, since these three fuzzy decision-making methods can produce different rankings, decision-making is a complex and challenging process for decision-makers. In order to overcome this problem, correlation coefficient and standard deviation method is utilized to construct the final ranking by integrating these approaches using regression statistics, standard deviations, and correlation coefficients between the closeness coefficients of each alternative in the relevant approach. The proposed approach provides an accurate ranking of thermal insulation boards validated through a real-world case study. Furthermore, the sensitivity analysis indicates that the combinations of different levels of criteria and approaches affect the final ranking. According to the study’s findings, the gray expanded polystyrene foam is the most suitable thermal insulation board in the combined fuzzy group decision-making approach.

EVALUATION OF CARBON FIBRE REINFORCED POLYMERS USING FUZZY TOPSIS MCDM METHOD

Carbon Fiber Reinforced Polymer (CFRP) is Repair of reinforced concrete structures and both are used for reinforcement One of the composite materials. Polymer reinforced with carbon fibres an advanced non-metal made of resin compound, which is of high strength, Weight loss, itching and excessive fatigue It has many excellent performances like resistance. Carbon fiber reinforced polymer (CFRP) is the best specific machine has properties, so these products High such as automobile and aerospace sectors widely used in technical industries. Dimensional or assembly related requirements CFRP composites to complement Machining is often necessary. The approach is based on the Fuzzy TOPSIS technique (Fuzzy Technique for Order Preference by Similarity to Ideal Solution) this analysis using the Alternative Value Conventional twist drill, Double point angle twist drill, Brad & spur drill, Dagger drill, Core drill and Cutting force, Cutting torque, Tool wear, Cutting temperature is Evaluation parameters value Core drill is got the first rank whereas is the Dagger drill is having the lowest rank.

An integrated QFD and fuzzy TOPSIS approach for supplier evaluation and selection

PurposeSupplier evaluation and selection is an essential (multi-criteria decision-making) MCDM process that considers qualitative and quantitative factors. This research work attempts to use a MCDM technique based on merging fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) and Quality Function Deployment (QFD) ideas. The study attempts to find the supplier's attributes (HOWs) to accomplish its goals after determining the product's characteristics to suit the company's needs (WHATs).Design/methodology/approachThe proposed research methodology comprises the following four steps: Step 1: Determine the product purchase requirements (“WHATs”) and those pertinent to supplier evaluation (“HOWs”). In Step 2, the relative importance of the “WHAT-HOW” correlation scores is determined and also the resulting weights of “HOWs”. In Step 3, linguistic evaluations of possible suppliers in comparison to subjective criteria are given to the decision-makers. Step 4 combines the QFD and F-TOPSIS techniques to select suppliers.FindingsA fuzzy MCDM method based on fusing and integrating fuzzy information and QFD is presented to solve the drawbacks of conventional decision-making strategies used in supplier selection. Using the F-TOPSIS method, fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS), the relative closeness coefficient values for all alternatives are computed. The suppliers are ranked by relating the closeness of coefficient values. This method permits the combination of ambiguous and subjective data expressed as fuzzy-defined integers or linguistic variables.Originality/valueQFD and TOPSIS, two widely used approaches, are combined in this article to rank and evaluate suppliers based on the traits that the suppliers choose to prioritize. This study demonstrates that the method employed could address multiple-criteria decision-making scenarios in a computationally efficient manner. The effectiveness and applicability of the method are illustrated using an example.

Identifying Essential Driving Factors of Industry 4.0 Maturity Models Using Fuzzy MCDM Methods

Industry 4.0 is still the 'next big thing' for many companies, and they are currently trying to adopt Industry 4.0 in their strategy. While Industry 4.0 continues to evolve, understanding of its driving factors remains crucial for industrial development and economic growth for competitive advantage. Those driving factors are revealed not only by exploiting advanced technology such as automation, robotization, Internet of Things, AI, smart system, or big data analytics but also the human factors, including culture or willingness to change. This paper aims to analyze the essential driving factors for Industry 4.0 maturity models. All factors are identified from a structured literature review. Then, the weights expressing the importance of the factors are calculated by obtaining expert opinions. This study utilizes a linguistic fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and using Decision Making Trial and Evaluation Laboratory (DEMATEL) method for weighting and ranking the driving factors and mapping the relationship between them. The analyzed driving factors identified from 52 references containing maturity models for Industry 4.0 are categorized into six domains and 16 subdomains. The presented study findings in this paper can support academicians and practitioners in comparing, developing, and improving Industry 4.0 maturity models as part of their adoption strategy and development.

FMEA Based Fuzzy Topsis for Assessment of Quality Problems in Telescopic Platform Production

In order to increase process and product quality fast and least costly, it is very useful to focus on the process that causes the important quality problems. In this study, quality problems and related processes in a company that produces on-vehicle equipment in the automotive industry are examined. The purpose of this study is to provide a quality improvement perspective to reduce process and product defects. Fault Mode and Effects Analysis (FMEA) is applied to detect faults, determine their causes and prioritize them. Prioritization of critical quality errors is revealed using traditional FMEA and FMEA-based fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The FMEA-based fuzzy TOPSIS method has been proposed because of its ability to handle uncertainty arising from the subjective judgments of decision makers. The results are compared and the preventions are suggested to avoid from the most important process that causes the quality problem.

A hybrid novel fuzzy AHP-TOPSIS technique for selecting parameter-influencing testing in software development

Software testing is one of the most important phases in the software development life cycle. Software testing cannot ensure successful outcomes until and unless done perfectly. For enhanced testing, it is extremely important to identify and prioritize the parameters that influence the testing process. Prioritization of Parameter-Influencing Testing (PIT) to ensure the testing environment’s reliability has piqued industry practitioners’ interest. A structured framework for the prioritization of PIT will result in the timely and successful execution of the software testing process. A case study for optimal PIT selection and ranking is conducted to address this problem by devising a hybrid technique based on a Fuzzy-Analytical Hierarchy Process (AHP) and Fuzzy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In the proposed framework, the linguistic data obtained from the industry experts are represented as triangular fuzzy numbers used to quantify the expert’s uncertainty in the assessment of PITs with respect to selection criteria. Furthermore, the fuzzy set is integrated into the AHP to determine the selection criteria weights. Finally, a fuzzy TOPSIS is used for ranking PITs. The results reveal that ‘Automated Testing’, ‘Verification/Validation’and ‘Good Design Principles’ are the crucial PITs. The results of this study will aid industry experts in determining the applicability of PITs and demonstrating the efficacy of the proposed framework for the PITs evaluation and ranking.