Fuzzy Technique For Order Preference Research Articles

Effectiveness evaluation of Construction 4.0 technologies in improving built environment resilience

Purpose Built environments are highly vulnerable to climatic disasters such as extreme floods, droughts and storms. Inaccurate decisions in adopting emerging construction technologies can result in missed opportunities to improve the resilience of built environments. Therefore, understanding the effectiveness of emerging construction technologies in improving built environment resilience can help in making better strategic decisions at the national and organizational levels. This study aims to evaluate the effectiveness of Construction 4.0 technologies in improving built environment resilience. Design/methodology/approach A list of Construction 4.0 technologies was adopted from a national strategic plan. Then, the data were collected using the fuzzy technique for order preference by similarity to ideal solution technique from selected built environment experts to determine the relative effectiveness of Construction 4.0 technologies in improving built environment resilience. Findings Six Construction 4.0 technologies are critical in improving built environment resilience (in rank order): building information modeling, autonomous construction, advanced building materials, big data and predictive analytics, internet of Things and prefabrication and modular construction. In addition, adopting Construction 4.0 technologies collectively is crucial, as moderate to strong connections exist among the technologies in improving built environment resilience. Originality/value To the best of the authors’ knowledge, this is one of the first papers that evaluate the effectiveness of Construction 4.0 technologies in improving built environment resilience. Industry professionals, researchers and policymakers can use the study findings to make well-informed decisions on selecting Construction 4.0 technologies that improve built environment resilience to climatic disasters.

Analysis of asset management difficulties observed in Brazilian firms: a study based on expert survey and fuzzy TOPSIS

PurposeThe purpose of this paper was to understand the difficulties related to asset management observed by experts in Brazilian organizations in light of the requirements outlined in the ISO 55001:2014 standard.Design/methodology/approachA survey was performed with asset management experts. The collected data were analyzed using frequency analysis, hierarchical cluster analysis and fuzzy technique for order preference by similarity to deal solution (TOPSIS).FindingsBased on data analysis, the most critical difficulties observed were related to managing and controlling the impact of changes in the company that affect asset management objectives; to the committing to and supporting the asset management system by the top management of the organization; to manage the processes for dealing with risks and opportunities for the asset management system and plans, and correcting failures in asset performance; and to plan and conduct actions in an integrated manner to identify and minimize adverse impacts associated with the asset management system, and afterwards verifying their effectiveness.Originality/valueThe findings of this study have important theoretical and practical contributions, since they indicate the most critical points observed in asset management in Brazil, which can be used as a source for future research and by professionals to prioritize difficulties in future planning and develop action plans to overcome them. The step-by-step methodological approach presented in this study provides professionals and researchers with a replicable method of identifying potential asset management difficulties in a given specific reality.

Reprocolla: Requirements Prioritisation Model with Collaboration Perspectives Based on Cost-Value Approach

The process of ranking requirements in software development is made up of various criteria and numerous stakeholders, which are properly selected for the effective prioritisation of software requirements. These requirements have encountered several challenges, including lack of scalability, complexity of pairwise comparisons and biases due to the cognitive load on stakeholders. Therefore, this research aimed to investigate and find solutions to improve the software prioritisation process using the Reprocolla model. The model was built by weighing the criteria in terms of benefits, opportunities, costs, and risks (BOCR) classification, which were then calculated using the fuzzy analytic hierarchy process (FAHP) method. Furthermore, the selection of alternatives was computed using the fuzzy technique for order preference using the similarity to an ideal solution (FTOPSIS) method. The success of Reprocolla was evaluated using seven datasets based on real projects and compared with the two existing methods for prioritising requirements, FAHP and FTOPSIS. The experiment results used inferential and descriptive statistics approaches with three indicators: accuracy, time consumption, and ease of use. Based on the three indicators mentioned above, the inferential statistics showed no significant difference between the perspectives of clients and developers. Whereas, descriptive statistics found that Reprocolla is more accurate, consumes less time, and has the highest ease of use percentage. The result showed that as stakeholders’ satisfaction level increases, the software development process becomes more accurate, thereby leading to a decrease in time consumption and a rise in ease ofusage. The result also showed that the development of the Reprocolla tool, a collaboration between humans and machines, enhanced the effectiveness of the requirements prioritisation process.

PLOUGHSHARES MATERIAL SELECTION: SHANNON’S ENTROPY BASED FUZZY TOPSIS APPLICATION

Material selection in the engineering design process is a challenging process because there are many materials and many contradictory features that must be together. The plough is the most important and basic tool of tillage. Large abrasions, cracks and even sudden breaks can occur in the plough that cultivates the soil. For this reason, the material chosen for the production of the ploughshares is extremely important. In this study, first of all, the criteria that are important in the selection of ploughshares iron were determined, and the criteria were weighted with the Interval Shannon Entropy method. Material preference ranking was made using "the Fuzzy Technique for Order Preference by Similarity to Ideal Solution" (FTOPSIS) technique. According to the result, the order of the ploughshare’s material selection criteria is, respectively; hardness, wear percentage, toughness, tensile stress, thermal conductivity and cost. As a result of the selection made using the FTOPSIS method; It has been determined that 33MnCrB5(1.7185) should be preferred in the first place among the candidate materials, and then the order of preference should be 51CrV4(1.8159), 60SiMn5(SAE9262), 41CrMo4 (1.7225QT-4140), respectively. Sensitivity analysis was performed at α =0.1,0.5 and 0.9 levels. Three α -cutting levels were identical to the sequence of alternatives.

Comprehensive Evaluation of EGM and Fuzzy TOPSIS-IPA: A Case Study of the Aesthetic Preference of Landscape Paintings from the Taiwan Region

This paper presents a comprehensive evaluation of EGM and fuzzy TOPSIS-IPA. The Evaluation Grid Method (EGM) of Miryoku Engineering was applied to study the aesthetic cognitive preferences of landscape paintings from the Taiwan region. This paper also summarizes the different aspects of the aesthetic literacy of people from the Taiwan region and proposes the attractive factors of paintings. Besides, the golden ratio scale semantics, that is, the traditional subjective consciousness model was replaced with the equal-ratio aesthetic scale semantics, and fuzzy satisfaction and importance were used as the basis for evaluation. Fuzzy Importance-Performance Analysis (IPA) was used to understand the public’s views on landscape paintings from the Taiwan region. The fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to rank affordable objects according to their proximity to the ideal target, evaluate their relative merits, and analyze the public's preferences and evaluation criteria. This study aimed to better understand the Taiwan region’s people’s awareness and aesthetic literacy of paintings, while also providing a reference for subsequent researchers and popularizing artistic products.

Implications of Construction 4.0 technologies to enhancing well-being: a fuzzy TOPSIS evaluation

Purpose Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being. Design/methodology/approach A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation. Findings Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them. Originality/value The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

Effective waste management in service industry: Fuzzy-based modelling approach for strategic decision-making.

Hospitals need to identify issues of greater importance on waste management because the implementation of many different strategies may lead to an unconscious increase in costs. Accordingly, the purpose of this study is to define the most effective waste management strategies in the service industry. For this purpose, a novel fuzzy decision-making model is proposed that has two different stages. In this context, six JCI-based indicators are weighted by using sine trigonometric fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology. Additionally, a comparative evaluation has also been conducted with sine trigonometric fuzzy Criteria Importance Through Intercriteria Correlation (CRITIC) technique to check the reliability of the findings. On the other hand, five different strategy alternatives are selected by considering the principles of the integrated waste management hierarchy approach. These items are evaluated by considering sine trigonometric fuzzy Technique for Order Preference by Similarity (TOPSIS). On the other side, these factors are also ranked with the help of sine trigonometric fuzzy Additive Ratio Assessment (ARAS) to test the consistency of the results. The main contribution is that prior strategies can be presented to the hospitals to have appropriate waste management process by defining the most important factors. Criteria weighting and alternative ranking results are the same in all combinations. Therefore, it is seen that the proposed model creates coherent and consistent results. It is defined that efficient storage of waste is the key issue to have effective waste management process. Moreover, 'reduce' is found as the most critical stage of this process.

Assessing Supplier Disruption Risks Using a Modified Pythagorean Fuzzy SWARA–TOPSIS Approach

As the complexity and uncertainty of global supply chains escalate, disruptions have become an increasingly common challenge in supply chain management. Suppliers, who serve as essential connectors for the seamless movement of goods and materials critical to production and distribution, are often at the center of these disruptions, highlighting their significant impact on the overall stability of the supply chain. This study proposes an innovative approach to assessing supplier disruption risks by combining the Pythagorean Fuzzy Step-wise Weight Assessment Ratio Analysis (PF-SWARA) with the Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF-TOPSIS). By reviewing the literature and consulting with supply chain experts, eight key risk factors were identified. The PF-SWARA method then quantifies the significance of these risks, while a modified PF-TOPSIS technique calculates each supplier’s risk score, facilitating the prioritization of suppliers for targeted improvement. The findings of the study indicate that “natural disasters and geopolitical risks,” “financial instability,” and “delivery delays” emerge as the top three critical disruption risk factors. Suppliers facing higher disruption risks should, therefore, formulate improvement strategies that target these three areas.

Application of fuzzy failure mode and effect analysis to investigate lean wastes in the sewing section

PurposeThis study aims to quantify and prioritize the main causes of lean wastes and to apply reduction methods by employing better waste cause identification methodologies.Design/methodology/approachWe employed fuzzy techniques for order preference by similarity to the ideal solution (FTOPSIS), fuzzy analytical hierarchy process (FAHP), and failure mode effect analysis (FMEA) to determine the causes of defects. To determine the current defect cause identification procedures, time studies, checklists, and process flow charts were employed. The study focuses on the sewing department of a clothing industry in Addis Ababa, Ethiopia.FindingsThese techniques outperform conventional techniques and offer a better solution for challenging decision-making situations. Each lean waste’s FMEA criteria, such as severity, occurrence, and detectability, were examined. A pairwise comparison revealed that defect has a larger effect than other lean wastes. Defects were mostly caused by inadequate operator training. To minimize lean waste, prioritizing their causes is crucial.Research limitations/implicationsThe research focuses on a case company and the result could not be generalized for the whole industry.Practical implicationsThe study used quantitative approaches to quantify and prioritize the causes of lean waste in the garment industry and provides insight for industrialists to focus on the waste causes to improve their quality performance.Originality/valueThe methodology of integrating FMEA with FAHP and FTOPSIS was the new contribution to have a better solution to decision variables by considering the severity, occurrence, and detectability of the causes of wastes. The data collection approach was based on experts’ focus group discussion to rate the main causes of defects which could provide optimal values of defect cause prioritization.

Challenges in the Integration of E-Procurement Procedures into Construction Supply Chains

The growing interest in digitalization signals a need for technology-oriented supply chain operations in the construction industry. Electronic procurement (e-procurement) aims to convert traditional procurement approaches into web-based/online platforms. Even though e-procurement is an effective tool that may improve supply chain management, the extent of e-procurement implementation has been slow to date. This study investigates the barriers that hinder e-procurement implementation in construction supply chains with the aim of prioritizing solutions to the identified barriers relative to time, cost, quality, and construction owner satisfaction. A comprehensive literature survey was performed, and a focus group discussion was organized for the purpose of the study, which resulted in the identification of 28 barriers. Then, a total of 131 construction practitioners were contacted to evaluate the barriers through a questionnaire survey. The responses were analyzed using the fuzzy Technique for Order Preference by Similarities to Ideal Solution (fuzzy TOPSIS) for prioritization. Finally, 15 semi-structured interviews were conducted to gain a deeper insight into the transformation process from the conventional procurement route to the e-procurement solution. Findings highlight that issues related to unexpected order cancellations, large variations in material costs, and the uncertain nature of the industry that requires a large number of changes are ranked as the most significant barriers. Given the highly competitive environment and the high demand for advanced technologies in the construction industry, a new paradigm can enhance the efficiency of supply chain operations. Exploring and eliminating the potential difficulties of adopting e-procurement in the procurement process may be a good start. Overall, this research is expected to facilitate the transformation of the procurement process by addressing the critical barriers identified by practitioners.

A comprehensive assessment model for livelihood vulnerability using fuzzy TOPSIS technique: A case of 2022 flood in Pakistan

One of the most significant natural disasters that results in significant economic losses and human misery is flooding. Flooding can threaten people’s livelihoods because it damages homes, infrastructure, and other essential assets. This study’s objective is to evaluate the livelihood vulnerability (LHV) of those who have been relocated and displaced as a result of the recent flood in Pakistan’s Khyber Pakhtunkhwa (KP) province. The study was conducted in three districts of KP province, which are Charsadda, Nowshera, and Dera Ismail Khan (D.I. Khan). First, a comprehensive literature review was conducted to identify relevant criteria for LHV assessment. As a result, fourteen criteria were selected to measure LHV in the selected regions. Then, a group of experts was invited to prioritize the criteria and provide linguistic ratings for the alternatives in terms of the selected criteria. Finally, the fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) method was used to classify the targeted districts on the basis of their closeness coefficient (CC) values. To assess the robustness of the proposed model, a sensitivity analysis was also provided. The findings of the study revealed that communities residing in D.I. Khan district have the highest LHV compared to Charsadda and Nowshera districts. Besides, loss of harvested or stored crops, total workability, arable landholding, and medical and living expenditures were found to be the most dominant criteria for LHV evaluation. The study concluded that greater efforts are needed during the transition period to improve migrant workers’ employment skills and help them reestablish sustainable sources of income.

Combining fuzzy MCDM with Kano model and FMEA: a novel 3-phase MCDM method for reliable assessment

AbstractIn the circular economy era, this study addresses sustainable business management for high-investment and long-life cycle projects, where accurate and reliable assessments are crucial to ensuring successful outcomes. The objective is to elevate the reliability of assessments by introducing a novel decision-making method that, for the first time, integrates time-based satisfaction and risk factors simultaneously. We propose a 3-phase multi-criteria decision-making (MCDM) method, which combines fuzzy MCDM comprising fuzzy analytic hierarchy process and fuzzy technique for order preference by similarity to ideal situation (TOPSIS), Kano model, and failure mode and effects analysis (FMEA) techniques, to handle reliable assessments effectively. Our method is distinct in its incorporation of time-based satisfaction weights derived from Kano model, emphasising decision-makers’ criteria preferences in short, medium, and long terms. Furthermore, we introduce risk-discounted weights by using FMEA to tune criteria scores. The method is validated via a numerical example case, assessing and selecting the most appropriate hydrogen storage method for lightweight vehicles. The results suggest that cryo-compressed hydrogen tank with 250–350 bar and at cryogenic temperature is the most suitable storage method. Health & safety with a weight of 0.5318 emerges as the most important main criterion, and permeation & leakage with a weight of 0.4008 is the most important sub-criterion. To bridge the gap between theoretical research and practical application, we transform the new method into a user-friendly web application with graphical user interface (GUI). End-users can conduct reliable assessments and foster sustainable business management through informed decision-making.

An Integrated Decision Support System for Low-Disturbance Surface Mining

Low-disturbance mining in surface mining (LDM) can transform traditional surface mine production systems into a more sustainable model by reducing the disturbance of surface mining, minimizing pollutant emissions, and reducing ecological impacts. The purpose of this paper is to explore the LDM evaluation method by applying multi-criteria decision-making to provide technical support for LDM implementation. Therefore, an evaluation method based on the combination of the fuzzy analytical hierarchy process (F-AHP) and grey clustering was proposed. Analyzed in terms of the current status of the evaluation indicators (reality) and the significance of the development of the LDM (desirability). Determined the weights and low-disturbance (LD) levels of the evaluation indicators. Combined with the fuzzy technique for order preference by similarity to an ideal solution (F-TOPSIS), the low-disturbance open pit mining paths are ranked, and finally, the decision support system for low-disturbance mining in surface mining is constructed. This study not only enriches the existing literature on related technologies but also lays the foundation for further research on LDM and provides exploratory insights for deeper improvement of LD level in surface mining.

Evaluating Construction 4.0 technologies in enhancing safety and health: case study of a national strategic plan

Purpose Policymakers are developing national strategic plans to encourage organizations to adopt Construction 4.0 technologies. However, organizations often adopt the recommended technologies without aligning with organizational vision. Furthermore, there is no prioritization on which Construction 4.0 technology should be adopted, including the impact of the technologies on different criteria such as safety and health. Therefore, this study aims to evaluate Construction 4.0 technologies listed in a national strategic plan that targets the enhancement of safety and health. Design/methodology/approach A list of Construction 4.0 technologies from a national strategic plan is evaluated using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Then, the data are analyzed using reliability, fuzzy TOPSIS, normalization, Pareto, sensitivity, ranking and correlation analyses. Findings The analyses identified six Construction 4.0 technologies that are critical in enhancing safety and health: Internet of Things, autonomous construction, big data and predictive analytics, artificial Intelligence, building information modeling and augmented reality and virtualization. In addition, six pairs of Construction 4.0 technologies illustrate strong relationships. Originality/value This study contributes to the existing body of knowledge by ranking a list of Construction 4.0 technologies in a national strategic plan that targets the enhancement of safety and health. Decision-makers can use the study findings to prioritize the technologies during the adoption process. Also, to the best of the authors’ knowledge, this study is the first to evaluate the impact of Construction 4.0 technologies listed in a national strategic plan on a specific criterion.

Knowledge Management Systems Evaluation in Food Industry: A Multicriteria Decision-Making Approach

Knowledge is a valuable intangible asset for companies that should be acquired, stored, shared, and applied to support decision making processes. Knowledge management systems (KMSs) integrate technology-based systems and organizational practices that enable organizations to manage their knowledge and improve their performance. Nowadays, several KMSs emerged, and it is not possible for organizations to use all of them at the same time. In this article, a multicriteria decision-making approach including fuzzy Delphi method (FDM), best worst method (BWM), and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) was proposed to determine the most appropriate KMSs. A comprehensive literature review allowed us to identify a list of KMSs and related evaluation criteria and subcriteria. Using FDM, the initial list of KMSs was identified, then BWM was adopted to calculate the weights of each criterion and subcriterion. Successively, the KMSs were ranked using FTOPSIS. In order to investigate the robustness of the model, sensitivity analysis was performed. The findings highlight that social media are providing a fast, widespread, and convenient infrastructure to share organizational knowledge. Consequently, organizations should pay more attention to their social channels to gain competitive advantages.

A Decision Support System Based on Hybrid Approach With Copras And Interval Type-2 Fuzzy Topsis For Evaluation Of Renewable Energy Alternatives

Renewable energy (RE) is a vital source for the sustainable development of society and economy. It plays a significant role in meeting energy requirements of both developed and developing countries. Moreover, renewable energy creats multiple benefits such as environmental improvement, increases fuel diversity, reduction of energy price, volality effect on their economy, national economic security, and increases in economic productivity. Selection of the most appropriate RE alternatives for any country can provide guidelines to planners of regional, national and global energy systems. The issue of ranking renewable energy sources involves many conflicting criteria and is a complicated problem since it needs to simultaneously incorporate technical, economic, cost, social-political, and environmental criteria. In this study, an integrated multi criteria decision making (MCDM) aproach consisting of interval type-2 fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Compressed Proportional Assessment (COPRAS) method is conducted to prioritize RE alternatives in order to direct planning of the national RE investments. A real case application for Turkey has been presented via expert evaluations to demonstrate applicability of the proposed methodology.

Mining and fusing unstructured online reviews and structured public index data for hospital selection

In the era of big data, publicly available data from official hospital sources and data from online reviews are easy to influence a patient's decision in choosing a hospital. However, existing research has rarely comprehensively considered the combined influence of these two factors. This paper proposes a hospital selection approach based on a fuzzy multi-criterion decision-making method, which considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. First, online reviews of general and specialized hospitals are collected and processed to get evaluation attributes of hospital and attribute weights. Then, text reviews are taken to classify topics and sentiments using logistic regression, light gradient boosting machine, and bidirectional encoder representations from transformers. The results of sentiment analysis are quantified using triangular fuzzy numbers to express evaluation values of hospitals. Based on patients’ preferences for online reviews and structured data on publicly available attributes of hospitals, final preference scores of hospitals are obtained using the fuzzy technique for order preference by similarity to ideal solution method. A case study is performed to illustrate the applicability of the proposed method. Robustness analysis from patient perspective and hospital perspective are executed to validate the effectiveness of the method.

Analysis of strategic priorities of green building projects for the efficient energy consumption

Purpose This study aims to investigate the strategic priorities of green building projects and analyze energy consumption alternatives in green residence projects using two innovative methods. Design/methodology/approach This study uses two methods, decision-making trial and evaluation laboratory (DEMATEL) to measure strategic priorities and golden-cut quantum spherical fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) to analyze energy consumption alternatives. Findings The study reveals that sustainability and atmosphere are the most significant factors in determining the priorities of green residence projects, whereas innovation has a limited impact on addressing environmental challenges in the building sector. The ranking of energy use alternatives shows that sustainability issues and atmosphere quality of space heating and cooking are the top priorities, whereas other factors like white goods, water heating, lighting and space cooling are ranked lower. Originality/value This paper offers a significant contribution to the understanding of green buildings by introducing innovative methodological approaches. Theoretically, it uses the DEMATEL to enhance traditional analytical frameworks, marking a novel effort in understanding green residence projects. In addition, the golden-cut quantum spherical fuzzy TOPSIS method is introduced, offering a comprehensive decision-making framework for green projects, considering factors like energy consumption and economic feasibility. This combination of methodologies provides a holistic evaluation, emphasizing sustainability in green building construction. This study reveals untapped potential for environmental sustainability and energy efficiency, enriching the existing knowledge base.

Material Selection Framework for Lift-Based Wave Energy Converters Using the Fuzzy Technique for Order Preference by Similarity to Ideal Solution

Material selection is a crucial aspect in the design of reliable, efficient and long-lasting wave energy converters (WECs). However, to date, the development of tailored methodologies applied to the material selection of WECs remains vastly unexplored. In this paper, a material selection framework for the case of lift-based WECs is developed. The application of the methodology is demonstrated with the hydrofoils of the device. Offshore steel, high-strength offshore steel, aluminium alloys, and carbon- and glass-fibre-reinforced composites are considered and evaluated subject to relevant criteria for wave energy converters, namely structural reliability, hydrodynamic efficiency, offshore maintainability, total manufacturing cost and environmental impact. Candidate materials are assessed via fuzzy TOPSIS for three scenarios of the life cycle of the WEC: conceptual, commercial and future projection stages. Results show that the choice of optimal materials could change from present to future and that multi-criteria decision-making tools aided by a fuzzy approach are useful design tools for novel WECs when field data are scarce. Hence, methodologies such as the ones presented in this work can help in reducing the probability of mechanical failures of emerging WEC technology.

Security Evaluation of Software by Using Fuzzy-TOPSIS through Quantum Criteria

Abstract: Quantum computer development attracts security experts in software. Software developers need to pay attention to the development of quantum computers in terms of software security. The security of software is at risk with the computation speed of quantum mechanisms in computing. Background: Software security evaluation focuses on the fundamental security features of the soft-ware as well as the quantum enable security alternatives. The rapid development of a number of qubits in quantum computers makes the present security mechanism of software insecure. The soft-ware security evaluation is the most crucial part of surveying, controlling, and administering security in order to further improve the properties of safety. Objective: It's crucial to understand that performing a security assessment early on in the develop-ment process can help you find bugs, vulnerabilities, faults, and attacks. In this quantitative study, the definition and use of the quantum computing security approach in software security will be cov-ered. The cryptographic calculations had to secure our institutions based on computers and networks. Methods: The Fuzzy Technique for Order Preference by Similarity to Ideal Situation (Fuzzy-TOPSIS) to quantitatively assess the rank of the quantum enable security alternatives with security factors. Results: The Quantum Key Distribution [A2], the quantum technique of security approach, has got the top priority and quantum key distribution in GHz state [A6] got the least in the estimation of software security during the era of quantum computer by the neural network method of Fuzzy-TOPSIS. Conclusion: The quantum mechanism of computing makes classical computing insecure. The secu-rity estimation of software makes developers focus on the quantum mechanism of security. The quantum mechanism of quantum key distribution is to make software secure.