Multiple-attribute Decision-making Technique Research Articles

An interactive data-driven multiple-attribute decision-making technique via interval-valued intuitionistic fuzzy sets for teaching quality evaluation in higher education

Improving the quality of higher education teaching is a systematic project. The improvement and formulation of relevant laws, regulations, and measures at the macro level are the minimum and specific requirements for the overall private universities, and are the basic guarantee for controlling the healthy and orderly development of universities. At the micro level, school management needs to focus on two aspects: leadership level construction and teacher level construction. Only by scientifically controlling the above issues and comprehensively considering them can the persistent problem of low teaching quality be fundamentally and gradually solved. In short, the construction of the quality assurance and evaluation system for higher education teaching in China is still in the initial stage of development. Therefore, universities should start from the guarantee and evaluation system to promote the construction of teaching process monitoring and evaluation systems, and improve the level of education and teaching quality on the basis of highlighting higher education teaching reform and research. The teaching quality evaluation of higher education in the era of artificial intelligence is a MADM. In this study, in light with interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted geometric (IVIFHIHWG) technique and induced OWG (I-OWG) technique, the induced IVIFHIHWG (I-IVIFHIHWG) technique is administrated. Then, the I-IVIFHIHWG technique is exploited to manage the MADM under IVIFSs. Finally, the numerical example for teaching quality evaluation of higher education in the era of artificial intelligence is exploited to verify the I-IVIFHIHWG technique. Thus, the main research contributions are administrated: (1) the I-IVIFHIHWG technique is administrated in line with the IVIFHIHWG and I-OWG technique; (2) the I-IVIFHIHWG technique is exploited to manage the MADM under IVIFSs; (3) the numerical example for teaching quality evaluation of higher education in the era of artificial intelligence and some comparative studies were exploited to verify the I-IVIFHIHWG technique.

Sustainable Last-Mile Delivery Solution Evaluation in the Context of a Developing Country: A Novel OPA–Fuzzy MARCOS Approach

With the surge in e-commerce volumes during COVID-19, improving last-mile logistics is extremely challenging, specifically for developing economies, due to poor infrastructures, lack of stakeholders’ cooperation, and untapped resources. In the context of Vietnam, there are certain solutions that can bring more efficient and sustainable last-mile logistics. In this paper, to evaluate and rank these potentially sustainable last-mile solutions (LMSs), we propose a novel hybrid multiple attribute decision-making (MADM) model that combines the Ordinal Priority Approach (OPA) and fuzzy Measurement of Alternatives and Ranking according to the COmpromise Solution (fuzzy MARCOS). Twelve sustainability factors of technical, economic, social, and environmental aspects were determined through a literature review and experts’ opinions to employ the MADM approach. A case study evaluating five LMSs in Vietnam concerning their sustainable implementation is solved to exhibit the proposed framework’s applicability. From the OPA findings, “efficiency”, “costs of implementation and control”, “voice of customer”, “reliability”, and “flexibility” are the topmost criteria when considering a new LMS implementation in the context of Vietnam. Moreover, sensitivity analysis and comparative analysis were performed to test the robustness of the approach. The results illustrate that the applied methods reach consistent solution rankings, where LMS-03 (convenience store pickup), LMS-02 (parcel lockers), and LMS-01 (green vehicles) are the best solutions in Vietnam. The study holds novelty in evaluating last-mile initiatives for Vietnam by utilizing a unique approach in the form of two novel MADM techniques, thus providing significant insights for research and applications.

A novel MADM technique based on projection methods under q-rung orthopair fuzzy setting and its application to computer network security evaluation

Computer network security evaluation is a basic work to determine the security performance of the network system and implement the network security management. It involves organizational management, network technology, personnel psychology, social environment and other factors. In recent years, with the rapid development of information technology in China, the problem of computer network security has become increasingly prominent. Although domestic and foreign scholars have sought effective methods of network security evaluation from different aspects and using different methods, many factors involved in network security are difficult to quantify, so far, there is no relatively mature quantitative evaluation method of network security. The computer network security evaluation is classical multiple attribute decision making (MADM) problems. In this article, based on projection measure, we shall introduce the projection models with q-rung orthopair fuzzy information. First of all, the definition of q-rung orthopair fuzzy sets (q-ROFSs) is introduced. In addition, to fuse overall q-rung orthopair fuzzy evaluation information, two aggregation operators including q-ROFWA and q-ROFWG operators is introduced. Furthermore, combine projection with q-ROFSs, we develop the projection models with q-rung orthopair fuzzy information. Based on developed weighted projection models, the multiple attribute decision making model is established and all computing steps are simply depicted. Finally, a numerical example for computer network security evaluation is given to illustrate this new model and some comparisons between the new proposed models and q-ROFWA and q-ROFWG operators are also conducted to illustrate advantages of the new built method.

Factors influencing the consumers’ behavioural intention to use online food delivery service: Empirical evidence from Taiwan

Understanding consumer preferences is crucial in helping online food delivery services (OFDS) increase operating revenue and competitiveness while achieving sustainable development. Prior studies on OFDS employed qualitative research and methods for statistical analysis, but few researchers have discussed the importance of the primary factors influencing consumers' selection and intention of OFDS from multiple-attribute decision-making (MADM) perspective. Based on a consumer's perspective, the MADM techniques were utilised in this study to build an assessment model for consumers' selection of OFDS. First, to determine the relative weight of each evaluation criterion and dimension, the analytic hierarchy process (AHP) was utilised. Subsequently, the complex proportional assessment of alternatives (COPRAS) was utilised to analyse the feasible solutions. Lastly, the modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) was employed to discuss the performance gap between each evaluation dimension and criterion. The three methods helped identify the primary evaluation criteria and determine the rankings of feasible solutions and related performance gaps. The methods were used to conduct an empirical analysis of the data. Several conclusions and suggestions have been provided based on the findings. The findings are anticipated to serve as a reference for OFDS operators to learn from, allowing them to better understand consumers' demands and provide better service. Finally, theoretical and managerial implications are explored.

Performance of the decision-making trial and evaluation laboratory

<abstract> <p>Multiple attribute decision-making (MADM) techniques constitute a practical approach for solving complex problems involving multiple and often conflicting criteria. Decision-making trial and evaluation laboratory (DEMATEL) is a popular MADM technique with both admirers and critics. This study presents a comprehensive review of DEMATEL through bibliometric analysis using the Scopus database. This article examined 3,521 papers published in journals, conferences or books between 1981 and 2023. We examined a few parameters for commenting on the performance of the technique. Among them are research outputs, the network of DEMATEL users, implementation subject areas, research zones, financing opportunities and publication hosts and their impact trends. We conclude from the findings of this study that the DEMATEL is capable of dealing with modern problem-solving in future environments. Although the growth of new MADMs is obvious, based on the gathered data, we forecast that more than 776 documents will be published in 2025 using DEMATEL for problem-solving. This expanding tendency will continue in the future. As distinct MADMs have diverse constraints, foundations, computing complexity and standpoints, which result in different performances, outmoded low-performance MADM techniques must be reported by researchers to continue this paper's objective to minimize ambiguity among decision-makers and practitioners. To facilitate such a comparison in the future, a quantitative performance coefficient was also developed here.</p> </abstract>

Applications of Hesitant Interval Neutrosophic Linguistic Schweizer-Sklar Power Aggregation Operators to MADM

Hesitant interval neutrosophic linguistic sets (HINLSs) are one of the core generalization of various sets, such as neutrosophic set (NS), interval neutrosophic set (INS), and interval neutrosophic linguistic set (INLS). HINLS can represent the uncertainty, inconsistency, and reluctance of assessment specialists by expressing qualitative and quantitative information. The goal of this article is to introduce a novel MADM technique that can account for changes in the semantic environment as well as negative consequences of experts’ excessive evaluation values. First, several innovative operational rules based on Schweizer-Sklar (SS) t -norm and t -conorm and a novel comparison procedure for HINLS are established by integrating different linguistic scale functions. This allows for varied semantic settings to be handled. Then, various innovative HINL Schweizer-Sklar power aggregation operators (AOs) are suggested, containing hesitant interval neutrosophic SS power average (HINLSSPA) operator, weighted hesitant interval neutrosophic SS power average (WHINLSSPA) operator, hesitant interval neutrosophic SS power geometric average (HINLSSPGA) operator, weighted hesitant interval neutrosophic SS power geometric average (WHINLSSPGA) operator, some core properties, and various special cases of these AOs are examined. Additionally, based on the initiated AOs, a multiple attribute decision making (MADM) technique with HINL information is anticipated. Finally, a numerical example is illustrated to show the effectiveness and practicality of the anticipated MADM method. A comparison with existing approaches are also discussed.

Ranking and selection of optimum alternatives based on linear scale transformation for cylindrical roller bearing

Bearing is the most precautious element in rotating machinery. Defects produced in any part of the bearing may lead to a hazardous environment that increases the noise and vibration. It can be diminished with appropriate loading combinations. The selection of optimum loading conditions requires developing an algorithm. Multiple Attribute Decision Making (MADM) techniques are utilized to pick up a unique and ideal solution or ranking of choices from the available alternatives. The MADM techniques have not been used for bearing fault diagnosis so far. This paper offers a novel methodology for the Ranking and Selection of Optimum Alternative based on Linear Scale Transformation (RSOA-LST), having combined benefit and cost criteria that rank the choices and determine the percentage influence of various alternatives. This methodology has been performed with several experimental signals of cylindrical roller bearing, acquired from twenty-four bearing conditions having several speeds and loading combinations. The normalization of attributes (statistical features) considering combined benefit-cost criteria, mean of various parameters (speeds, attributes, and bearing conditions), and Percentage Influencing (PI) are intended simultaneously to obtain the ranking of alternatives (loading conditions). Local Defect Analysis (LDA) and Global Defect Analysis (GDA) are carried out in this research. In LDA, the ranking of alternatives based on the PI for each defect and the ranking of bearings based on fluctuation in PI of various alternatives are attained. In GDA, the sequence of alternatives is obtained globally. The effectiveness of the suggested methodology has been verified over other MADM techniques. The outcomes state that the suggested technique effectively bifurcates the influence of alternatives locally as well as globally over other techniques.

Novel Aczel–Alsina operations‐based interval‐valued intuitionistic fuzzy aggregation operators and their applications in multiple attribute decision‐making process

In the creation of better multiple attribute decision-making (MADM) patterns to address the ambiguity in the expanding sophisticated of expert systems, the hypothesis of interval-valued intuitionistic fuzzy sets has proven to be an effective and advantageous technique. We employ Aczel–Alsina operations to remedy the MADM issue, wherein all data supplied by decision-makers is conveyed as interval-valued intuitionistic fuzzy (IVIF) decision matrices with all components described by an IVIF number (IVIFN). This allows us to satisfy much more demands from fuzzy decision-making concerns (IVIFN). In the framework of IVIFNs, we primarily describe several novel Aczel–Alsina operations. On the basis of these operations, we construct several novel IVIF aggregation operators, such as the IVIF Aczel–Alsina weighted averaging operator, the IVIF Aczel–Alsina order weighted averaging operator, and IVIF Aczel–Alsina hybrid averaging operator. We built up several features of such operators. We recommend an MADM technique dependent on the advanced IVIF aggregation operators. To demonstrate the effectiveness of the developed technique, we present an overview of research scientist selection. The experimental results show the viability and benefits of the created strategy by contrasting it with the different strategies. This paper reveals that some existing IVIF aggregation operators are particular instances of the operators induced in this paper.

Multiple-Attribute Decision-Making Using Fermatean Fuzzy Hamacher Interactive Geometric Operators

Fermatean fuzzy set (FFS) is a more efficient, flexible, and generalized model to deal with uncertainty, as compared to intuitionistic and Pythagorean fuzzy models. This research article presents a novel multiple-attribute decision-making (MADM) technique based on FFS. Aggregation operators (AOs), for example, Dombi, Einstein, and Hamacher, are frequently being used in the MADM process and are considered useful tools for evaluating the given alternatives. Among these, one of the most effective is the Hamacher operator. The salient feature of this operator is that it reduces the impact of negative information and provides more accurate results. Motivated by the primary characteristics of the Hamacher operator, we apply Hamacher interactive aggregation operators based on FFSs to determine the best alternative. Using Hamacher’s norm operations, we introduce some new geometric operators, namely, Fermatean fuzzy Hamacher interactive weighted geometric (FFHIWG) operator, Fermatean fuzzy Hamacher interactive ordered weighted geometric (FFHIOWG) operator, and Fermatean fuzzy Hamacher interactive hybrid weighted geometric (FFHIHWG) operator. Some important results and properties of the proposed AOs are discussed, and to achieve the optimal alternative, the proposed MADM technique is carried out in a real-life application of the medical field. An algorithm of the proposed technique is also developed. The significance of the proposed method is that Fermatean fuzzy Hamacher interactive geometric (FFHIG) operators deal with the relationship among belongingness degree (BD) and nonbelongingness degree (NBD) of the objects, which perform a crucial role in decision-making (DM). At last, to show the exactness and validity of the proposed work, a comparative analysis of the proposed model and the existing models is presented.

Identification of Consumer’s Preference towards Store Image: Using Analytical Hierarchical Process Technique

This study aims to identify consumer preferences for store image using the Analytic Hierarchy Process. Analytic Hierarchy Process is a Multiple Attribute Decision-Making technique used to observe, rank, and select a set of options under contradictory characteristics. Consumer preference refers to the individual's behavior when purchasing, consuming, or disposing of any specific product or service. Multiple factors can affect these behaviors. The store's attractive features are the factor affecting store image, dimension, and sub-dimension of store image. Data were collected from 29 experts. Experts are selected based on their experience of shopping in a shopping mall in Pakistan and industry. A bipolar questionnaire was used to collect data. This study explored the main dimensions of store image: convenience, price, quality, atmosphere, and safety. Further, the study's findings indicate that these dimensions of store image are important for marketers, which provides awareness regarding the psychological elements of customers that influence their perception of store image and helps the marketer recognize and forecast the customer's demand.

Particle swarm optimization application for multiple attribute decision making in vertical handover in heterogenous wireless networks

In wireless heterogenous networks, mobile terminals are covered by different wireless networks with varying quality of services to ensure the delivery of different classes of services. In this paper, Particle swarm optimization (PSO) was applied to the distance to ideal alternative (DIA) technique in the framework of network selection in a heterogenous wireless network. The PSO was applied to overcome the subjectivity and bias in the weights’ assignment process used in multiple attribute decision making (MADM). The PSO was utilized to optimize the weights of the DIA method through the maximization of the absolute value of the summation of the ranking differences among candidate networks. In this regard, two different optimization functions were introduced and used to generate the optimum weights. The performance of the PSO-based handover for the DIA method was investigated in terms of ranking difference, ranking abnormalities, and network selection. The results show that the proposed PSO-based weights’ assignment technique increased the ranking difference and reduced the ranking abnormalities without degrading the network selection when compared to the conventional DIA technique. The results of this paper are expected to widen the application of the DIA method and other MADM techniques to the handover process in wireless networks and other decision-based challenges in other fields.

A novel approach to relative importance ratings of customer requirements in QFD based on probabilistic linguistic preferences

Quality function deployment (QFD) is a customer-oriented product/service design tool with diversified team members reaching a consensus in developing a new or an improved product/service to maximize customer satisfaction. Determining the relative importance ratings (RIRs) of customer requirements (CRs) is actually a multiple attribute decision making (MADM) problem, which is regarded as an essential step in QFD application. Although many decision making approaches have been developed to rate the CRs, few of them refer to derive the weights of evaluators, and there is paucity of literature which combines the probabilistic linguistic term sets (PLTSs) with QFD methodology. Therefore, PLTSs are introduced in our study, to simultaneously reflect the hesitancy and preference degrees of evaluators. A novel MADM technique based on probabilistic linguistic preference (PLP) is explored to calculate the RIRs among CRs under the PLP environment. Concretely, some operators, such as the normalization formulas, the probabilistic linguistic expected value (PLEV) operator as well as the standard formula, are applied to weight the decision makers; a modified grey relational analysis-technique for order preference by similarity to ideal solution (GRA-TOPSIS) method called probabilistic linguistic-based GRA-TOPSIS is also proposed to rate the relative importance over CRs. Finally, application of product improvement of a turbine engine is given to see the validity and feasibility of the proposed approach.

Optimisation of shot peening process for AISI 4140H forged connecting rod using MADM techniques

Shot peening is a process that set up compressive residual stresses on the surface of component and alters its mechanical properties, which depends on proper selection of peening parameters. Manufacturer is always in demand to improve productivity and reduce cost. The attempt of this paper is to optimise shot peening process parameters by various multiple attribute decision making (MADM) techniques such as TOPSIS, GRA, SAW and desirability approach. In the present investigation shot mix, exposure time and shot flow rate are considered as input process parameters whereas surface roughness and compressive residual stresses are considered as response variables. MADM techniques suggest that, shot flow rate of 21 amp and exposure time of 120 sec are optimum setting of peening parameters. The analysis will be useful for process planning to decide the peening parameters for best combination of performance characteristics.

A genetic algorithm approach for multi-attribute vertical handover decision making in wireless networks

Mobile terminals can typically connect to multiple wireless networks which offer varying levels of suitability for different classes of service. Due to the changing dynamics of network attributes and mobile users’ traffic needs, vertical handovers across heterogeneous networks become highly desirable. Multiple attribute decision making (MADM) techniques offer an efficient approach for ranking competing networks and selecting the best one according to specific quality of service parameters. In this paper, a genetic algorithm (GA) is applied to optimize network attributes’ weighting by emphasizing ranking differences among candidate networks, thereby aiding correct decision making by reducing unnecessary handovers and ranking abnormalities. The performance of the proposed GA-based vertical handover is investigated with typical MADM techniques including Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results show that the proposed GA-based weight determination approach reduces the abnormality observed in the conventional SAW and TOPSIS techniques substantially. The results of this paper will help ensuring the application of MADM methods to more dynamic and challenging decision making problems encountered in wireless network.

An output‐oriented classification of multiple attribute decision‐making techniques based on fuzzy c‐means clustering method

AbstractThis paper presents a new output‐oriented classification of multiple attribute decision‐making (MADM) techniques, not based on common subjective comparisons, but mostly on quantitative and computer‐aided comparisons and results. Several classifications of MADM techniques exist, all of which are either input‐oriented (based on the type of input data) or process‐oriented (depending on the process of calculating the final results using the input data). The classification provided in this paper is based on measuring the performance of 17 MADM techniques (SAW, ELECTRE I, TOPSIS, ORESTE, PROMETHEE I, EVAMIX, MAUT, REGIME, MAPPAC, TACTIC, VIKOR, ARGUS, COPRAS, SMART, PACMAN, MOORA, and ARAS) in seven performance variables (simplicity in learning and deploying, speed, complexity of calculations, the number of inputs, the quality of the underlying logic, the quality of rankings, and the rate of growth in large problems) and clustering them using fuzzy c‐means clustering method. Results indicate that the considered techniques can be best classified into two clusters.

New Interval-Valued Intuitionistic Fuzzy Behavioral MADM Method and Its Application in the Selection of Photovoltaic Cells

As one of the emerging renewable resources, the use of photovoltaic cells has become a promise for offering clean and plentiful energy. The selection of a best photovoltaic cell for a promoter plays a significant role in aspect of maximizing income, minimizing costs and conferring high maturity and reliability, which is a typical multiple attribute decision making (MADM) problem. Although many prominent MADM techniques have been developed, most of them are usually to select the optimal alternative under the hypothesis that the decision maker or expert is completely rational and the decision data are represented by crisp values. However, in the selecting processes of photovoltaic cells the decision maker is usually bounded rational and the ratings of alternatives are usually imprecise and vague. To address these kinds of complex and common issues, in this paper we develop a new interval-valued intuitionistic fuzzy behavioral MADM method. We employ interval-valued intuitionistic fuzzy numbers (IVIFNs) to express the imprecise ratings of alternatives; and we construct LINMAP-based nonlinear programming models to identify the reference points under IVIFNs contexts, which avoid the subjective randomness of selecting the reference points. Finally we develop a prospect theory-based ranking method to identify the optimal alternative, which takes fully into account the decision maker’s behavioral characteristics such as reference dependence, diminishing sensitivity and loss aversion in the decision making process.

Optimization of thermal performance of Ranque Hilsch Vortex Tube: MADM techniques

Thermal performance of vortex tube is noticeably influenced by its geometrical and operational parameters. In this study effect of various geometrical (L/D ratio: 15, 16, 17, 18; exit valve angle; 300, 450, 600, 750, 900; cold end orifice diameter: 5, 6 and 7mm, tube divergence angle: 00, 20, 30, 40) and operational parameters (inlet pressure: 2 to 6 bars) on the performance of vortex tube have been investigated experimentally. Multiple Attribute Decision Making (MADM) techniques are applied to determine the optimum combination of the vortex tube. Performance of vortex tube was analysed with optimum temperature difference on cold end, COP for cooling. The MADM (Multiple Attribute Decision Making) methods, namely WSM (Weighted Sum Method), WPM (Weighted Power Method), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and AHP (Analytical Hierarchy Process) are applied. Experimental best performing combinations are obtained for Length to Diameter ratios 15, 16, 17 with exit valve angle as 450,750 and 900 at orifice diameter 5mm for inlet pressure of 5 and 6 bar pressure. Best COP, efficiency and cold end temperature difference are 0.245, 40.6% and 38.3K respectively for the combination of 15 L/D, 450 valve angle, 5mm orifice diameter and 2 bar pressure by MADM techniques.

A Fuzzy Analytic Hierarchy Process (FAHP) Approach for Optimal Selection of Low-carbon Energy Technologies

In process systems engineering, one may encounter design problems that require choosing one among the predefined set of alternatives in a complex decision making environment. In most cases, the selection process involves multiple conflicting criteria that can be either qualitative or quantitative in nature. The use of multiple attribute decision making (MADM) technique thus has been proven effective in providing a transparent, systematic and rational approach to problem solving. In this study, a variant of the widely used MADM technique known as the Analytic Hierarchy Process (AHP) has been developed and applied for the selection of low-carbon technologies. This approach enables the selection of optimal alternative by incorporating not only the judgment of domain experts but also their degree of confidence through the use of fuzzy numbers for the pairwise comparison ratios in the AHP decision framework. The proposed fuzzy AHP then determines a set of crisp weights that maximizes the degree of consistency of all judgments taken together. The said approach can also derive crisp weights from an incomplete fuzzy pairwise comparative judgment matrix (PCJM). To illustrate the technique, a case study involving comparison of electricity storage technologies for renewable energy systems is considered. This selection problem is important due to the intermittent nature of many forms of renewable energy, which thus require cost-effective auxiliary energy storage subsystems for successful deployment.

Investigating the Quality of Electronic Services Using Multiple Attribute Decision-Making Techniques Case Study (Aria, Ghaem, and Golsar Hospitals)

Abstract. IT and its remarkable abilities created positive changes, and in addition to the possibility of increasing effectiveness and efficiency in industries and businesses, has led to an increasing interest in this technology and to presentation of more functions of it. One of these areas which is more important to people is the industry of health care and health and treatment services. Reduction of health care costs and provision of these high quality services has become a global priority. Due to the use of electronic services the need to use these services in the field of health care is felt more than ever. The purpose of this research is to answer the question of how to choose the most appropriate hospital in terms of electronic service delivery by using multiple attribute decision-making techniques.In this research, SERVQUAL method and criteria related to the electronic services is used, which has 6 major criteria of Tangible, Responsiveness, Reliability, Information quality, Assurance and Empathy factors, and 19 sub criteria. This research uses Multiple Attribute Decision Making (MADM), including the composition process of ANP to gain the criteria weight and fuzzy TOPSIS in order to rank the dimensions. According to the research results, Golsar, Arya and Ghaem hospital, respectively, earned the first to the third place. Among the criteria Information quality earned the first place, Assurance earned the second place and last place was devoted to responsiveness among the six criteria. Among the sub-criteria also Informatiom accuracy and trust, respectively ranked first and second, and customerization got the last place.

Material Selection for Dye Sensitized Solar Cells Using Multiple Attribute Decision Making Approach

Dye sensitized solar cells (DSCs) provide a potential alternative to conventional p-n junction photovoltaic devices. The semiconductor thin film plays a crucial role in the working of DSC. This paper aims at formulating a process for the selection of optimum semiconductor material for nanostructured thin film using multiple attribute decision making (MADM) approach. Various possible available semiconducting materials and their properties like band gap, cost, mobility, rate of electron injection, and static dielectric constant are considered and MADM technique is applied to select the best suited material. It was found that, out of all possible candidates, titanium dioxide (TiO2) is the best semiconductor material for application in DSC. It was observed that the proposed results are in good agreement with the experimental findings.