Technique For Order Of Preference By Similarity To Ideal Solution Technique Research Articles

Conceptual Design and Materials Selection of the FDM Composites for Passenger Vehicle’s Spoiler

One of the additive manufacturing techniques available is Fused Deposition Modeling (FDM), which offers advantages in design flexibility, cost-effectiveness, and the ability to produce intricate designs. Therefore, FDM for the 3D-printed vehicle’s car spoiler is a subject that can be explored. The FDM technology can significantly reduce time and cost before mass production, and the vehicle’s car spoiler was used as the case study in this research. The research investigates the mechanical properties of various commercial PLA composite filaments, addressing the lack of specifications provided by manufacturers. Testing four types of filaments—PLA/bamboo, PLA/coconut, PLA/wood, and PLA/metal. This research also emphasizes the conceptual design generation and selection for the passenger vehicle’s spoiler. Five design concepts were generated using the morphological chart for the passenger vehicle’s spoiler. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was used as the decision-making tool. As a result, PLA/metal, with 53.65 MPa and 70.23 MPa, showed the highest tensile and flexural strength values, respectively. Design concept 5 with the infill pattern of rib + I was the best from the finite element analysis (FEA) using SolidWorks simulation software. Finally, the TOPSIS technique revealed PLA/metal as the best PLA composite filament for car spoilers, scoring first in performance score with a value of 0.5774. This study demonstrates that by using a systematic approach, researchers may choose the best design concept and material choice by combining the conceptual design, experimental, simulation, and TOPSIS methods.

Decision-Making for Project Delivery System in Construction Projects Based on SWARA-TOPSIS Methods

The owner of construction projects often faces difficulty choosing the appropriate project delivery system. The construction project’s owner must choose a suitable project delivery system throughout the project's early decision-making stages. This choice greatly impacts the success of the project. This paper aims to construct a method for project delivery decisions. This method can provide a reference for the owner to select the proper project delivery system. This research described the factors affecting the decision about the project delivery method through a literature review. These factors were categorized into eight groups: scope, time, quality, cost, risk and relationships, owner organization, project characteristics, and external environmental factors. Then, a combined methodology of Stepwise Weight Assessment Ratio Analysis (SWARA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are proposed for project delivery system selection. The SWARA is used to determine the factors’ weights. The TOPSIS technique determines the rank of the alternatives proposed for the delivery system in the various categories. The results showed the most significant factors related to the cost with a weight of 0.335, and the design-build is the closest to the ideal solution and ranked first among the other suggested project delivery systems with a relative closeness of 0.829. This paper stipulates the basis for such a process of decision-making.

A new correlation-based measure on Fermatean fuzzy applied on multi-criteria decision making for electric vehicle selection

Purpose:Since the pollution from the transportation sector has become intolerable in urban areas, many authorities are beginning to ban vehicles powered by fossil fuels. In this scenario, electric vehicle (EV) mobility is the only viable option for the transportation sector. Despite the fact that EVs offer a number of advantages over fuel-powered vehicles, it can be challenging for a purchaser to select a model due to its technical features and their lack of knowledge about EVs. This article aims to assist purchasers of EV by constructing an MCDM problem with multiple features as criteria and EV models as alternatives. Design/methodology/approach:The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method ranks alternatives to a multi-criteria decision-making (MCDM) problem based on the distance between the positive and negative ideal solutions. Even though the TOPSIS methodology has several fuzzy enhancements, it is difficult to select an appropriate distance measure in an uncertain context. To replace the distance measure, we introduced an improved correlation coefficient measure in fermatean fuzzy contexts by considering the hesitancy function of fermatean fuzzy sets (FFS). First, we establish the proposed correlation coefficient by defining its main characteristics, including the weighted correlation coefficient, type I and type II closeness measures, and the weighted index coefficient. Then, the TOPSIS method is extended using the proposed correlation measure in a fermatean fuzzy environment. There is an algorithm that presents the proposed fermatean fuzzy TOPSIS (FF-TOPSIS) method. The proposed approach is used to determine the ranking order of the EVs. To assist the decision-maker, the sensitivity of the proposed technique is investigated by varying its parameters. Finally, the consistency of the proposed FF-TOPSIS is evaluated by comparing it to existing crisp as well as fuzzy MCDM methods. Findings:By implementing the FF-TOPSIS approach, it has been determined that the fifth EV model is the most favorable option, while the first model is deemed the least desirable choice. The primary factors to evaluate an EV are its range and its price, rather than battery life, storage, and charging time. Originality:This article presents a novel correlation coefficient for FFSs, which incorporates the hesitancy function into the calculation for the first time. The correlation-based FF-TOPSIS method is a novel extension of the TOPSIS technique in fermatean fuzzy systems.

Evaluating human–computer interaction education based on active learning methods using the CRITIC and TOPSIS approach

With the ever-growing development of information technology, the education sector has adopted advanced technologies to improve this field. Various procedures based on human–computer interaction (HCI) can be applied to enhance the efficiency of interaction between humans and computers, especially in education. Active learning is one of the major technological fields that improve the usability of active learning. The implementation of HCI approaches such as project-based learning (PBL), active learning classrooms (ALCs), and multi-user virtual environments (MUVE) can improve students' learning capabilities and teachers' teaching skills, making education more effective. After studying different applications of HCI in active learning, this study aims to extract common features and identify the most efficient ones. For the evaluation process, we presented two techniques: Criteria Importance Through Inter Correlation (CRITIC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to select the most effective HCI approach among various options. We chose nine criteria along with ten alternatives and ranked them based on their performance scores. For assigning weights to criteria, we utilized the CRITIC strategy, while the TOPSIS technique was applied to rank the alternatives. In the proposed study, we identified that the alternative with the highest performance score is placed in first place, while the alternative with the worst performance score is placed at the bottom. The study concluded that the most efficient and effective approach was selected among various options, which can be used as a suggestion for decision-related issues and future directions.

Social media analytics and their applications to evaluate an activity in online health interventions using CRITIC and TOPSIS techniques.

The usage of social media is increasing by leaps and bounds in our day-to-day lives. It affects daily routines and brings a lot of change in different departments like health and education systems during the COVID-19 pandemic. Healthcare research and practice have been significantly impacted by the astounding growth of social media. Social media are changing health information management in several ways, from offering appropriate ways to enhance healthcare professional contact and share health-related knowledge and experience to facilitating the development of innovative medical research and wisdom. Social media analytics (SMAs) are efficient and effective interaction instruments that can be useful for both patients and clinicians in health interventions. Moreover, a significant portion of those involved in clinical practices (such as clinicians, professional colleges, and departments of health) are unaware of the importance of social media, its potential applications in their daily lives, as well as the possible consequences and how these will be handled. In the presented study, we proposed MCDM-based approaches known as "Criteria Importance Through Inter Correlation" (CRITIC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in order to identify the effective alternative among several options and make a better decision. After extracting features from the literature review, we choose six significant and relevant features and assign weights to them using CRITIC techniques while utilizing the TOPSIS technique to rank the alternatives based on their performance values. After the implementation of both methods and evaluation procedure, it is determined that the alternative with the highest score is placed at the top and called the "best alternative," while the alternative with the lowest score is placed at the bottom and called the worst alternative. Finally, we suggest a variety of research initiatives and new research areas where the aforementioned procedures become extremely useful in evaluating SMAs and their uses in online health interventions.

Facilitating decision-making for the adoption of smart manufacturing technologies by SMEs via fuzzy TOPSIS

The fourth industrial revolution or Industry 4.0 has changed today's manufacturing scenario. The need to make manufacturing systems agile, adaptive, resilient, and robust, due to the pandemic, has expediated the adoption and implementation of smart manufacturing technologies. Despite the interest of manufacturers in smart manufacturing, the adoption rate has been slow. Small- and medium-sized enterprises (SMEs) can be especially hindered in adoption due to the lack of a transition strategy and identification of relevant technologies required to achieve a smart factory. Although there is literature that provides maturity and readiness models and toolkits for adoption, the decision-making models for SMEs are inadequate. This paper proposes a multi-criteria decision-making model as a tool to provide a means for evaluating a large range of smart manufacturing technologies while considering the status quo for SMEs. The aim of this project is to aid SMEs in the adoption of smart manufacturing technologies by providing a roadmap to assess performance parameters and identify an appropriate smart manufacturing technology for adoption. The recommended technology is tailored to the requirements of the SME using fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The fuzzy TOPSIS technique aggregates the opinions of decision makers and uses a fuzzy environment to account for their subjectivity. The inclusion of personnel as provided by the model from various hierarchical levels promotes favourable implementation by insertion in the transition process while also educating the personnel of the technologies. An industry case study with individuals from an SME, Levil Technology, and Florida's Manufacturing Extension Partnership (MEP) Center, FloridaMakes, is conducted to assess the preference for five smart manufacturing technologies over a range of eleven criteria pertaining to performance, sustainability, quality, cost and maintenance. The results give clarity regarding the preference for critical manufacturing criteria by assigning weightage, and identifies the most relevant technology catering to the preferred criteria. Predictive analytics for asset health monitoring was found to be most preferred followed by a digitally connected factory for visibility into production operations. The determination of rank will allow manufacturers to assess the manufacturing alternatives with respect to the key performance indicators for transition to Industry 4.0.

Solar PV cleaning techniques contribute to Sustainable Development Goals (SDGs) using Multi-criteria decision-making (MCDM): Assessment and review

• The types of cleaning techniques for Photovoltaic (PV) panels are briefly reviewed. • The Sustainable Development Goals (SDGs) and their associated targets are mapped to each cleaning technique. • SDGs 6 and 7 are the most influenced SDGs by the cleaning techniques. • The TOPSIS method is employed to compare the cleaning techniques and rank them from most favored to least favored. • Manual cleaning of the PV panels is the highest ranked cleaning technique according to the TOPSIS ranking. The efficiency and power output of photovoltaic (PV) panels are vital to the solar PV plant. Apart from overheating, and natural shading, some geographical locations are more susceptible to objectionable dust accumulation on the surfaces of PV panels. Consequently, specific cleaning techniques are required to mitigate the accumulated dust and restore the plant's efficiency. The most popular PV panel cleaning techniques include natural, manual, automatic, and electrostatic cleaning. Each cleaning technique is associated with both positive and negative impacts. Hence, this paper proposes a methodology that underlines the impact of each cleaning technique and relates it to the Sustainable Development Goals (SDGs) and their associated targets. The results exhibit the goals related to energy (SDG 7, 9,11, 12, & 13) and water (SDG 6) were the most affected SDGs by choice of cleaning technology and thus needed to be carefully considered before their deployment. This paper then employs a multi-criteria decision-making method Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to compare these cleaning techniques and find the optimal cleaning method concerning the SDGs. This method prioritizes the alternatives by considering the 18 criteria set and their relative significance to the SDGs. The TOPSIS technique showed that the manual cleaning technique outperformed the other cleaning techniques and obtained the highest ranking.

Application of TOPSIS Technique for Financial Performance Evaluation of Manufacturing Firms in US Market

For over a hundred years, manufacturing firms has been a major sector of the US economy. In today’s competitive market, being able to accurately evaluate the firm’s financial performance is becoming increasingly important. By appropriately evaluating the financial performance of the company, a firm can better protect their market position and protect their market shares while the financial statement users can better understand the firm’s financial position and have better valuation on the company. In this paper, the author utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to analyze financial ratios of twenty selected manufacturing firms in the US market. This method enables us to rank these firms according to their financial performance. Although some similar analytics has been done on multiple industries, this article focuses specifically on manufacturing companies and seeks to reach a better understanding of whether this ranking system can explain the overlap between the financial performance and the market value of manufacturing companies.

A Decision-Making Approach Based on TOPSIS Method for Ranking Smart Cities in the Context of Urban Energy

This paper presents the use of multi-criteria decision-making (MCDM) for the evaluation of smart cities. During the development of the method, the importance of the decision-making approach in the linear ordering of cities was presented. The method of using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was proposed for the preparation of ranking. The method was verified by the application in the measurement of energy performance in smart cities. The authors conducted a literature review of research papers related to urban energy and MCDM published in the period from 2010 to 2020. The paper uses data from the World Council on City Data (WCCD). The research conducted allowed for the identification of the most popular MCDM techniques in the field of urban energy such as TOPSIS, AHP and DEA. The TOPSIS technique was used to organize and group the analyzed cities. Porto took the top position, whereas Buenos Aries was the last.

Technique for order of preference by similarity to ideal solution (TOPSIS) method for the generation of external preference mapping using rapid sensometric techniques.

External preference mapping is a powerful tool to explain consumer preference or rejection. Combining the technique for order of preference by similarity to ideal solution (TOPSIS) multicriteria analysis with rapid descriptive techniques can improve preference map (PREFMAP) results. This study was conducted to compare the PREFMAPs generated with rapid descriptive flash profile (FP), check-all-that-apply (CATA), and Napping® versus PREFMAPs constructed with FP-TOPSIS, CATA-TOPSIS, and Napping-TOPSIS. Only 38.46%, 63.66%, and 42% of sensory attributes initially generated by FP, CATA, and Napping techniques respectively were considered for the determination of their weight W and allocation as positive or negative in the TOPSIS technique. The PREFMAPs constructed with FP-TOPSIS, CATA-TOPSIS, and Napping-TOPSIS presented a better explanation of the preference and rejection than the PREFMAPs directly generated with rapid sensory techniques. The results of the multiple factor analysis and coefficient Rv indicated similarities in the sensory vocabularies used after the TOPSIS technique. The combination of the TOPSIS technique with rapid sensory techniques is a reliable alternative for the construction of PREFMAPs in order to identify the sensory attributes responsible for preference and rejection of food products. © 2020 Society of Chemical Industry.

Environmental Sustainable Development and Green Economy Strategies in Khouzestan Steel Company

Background and Objectives: Limitless use of natural resources and increased environmental pollution by various industries will lead to rapid environmental destruction. The steel industry interacts with the environment in all its activities, so green economy and sustainable development is an inevitable necessity in this industry. This study aimed to identify and prioritize sustainable development and green economy strategies in Khuzestan Steel Company. Material and Methods: In this study participants are experts familiar with sustainable development and green economy strategies in Khuzestan Steel Company who were selected through purposive sampling in order to identify the strengths, weaknesses, opportunities, threats (SWOT), and developing strategies.The External and Internal Factor Evaluation Matrix as well as the SWOT matrix were formed then strategies were ranked using TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) technique. Results: 6 opportunities and 5 threats were identified as external factors and 8 strengths and 6 weaknesses were identified as internal factors and 21 strategies were developed. Among the external factors Develop and equip the company with new technologies with the final score of 0.498 and among the internal factors Waste management and pollution control with the final score of 0.31 got the highest score. Conclusion: According to the results, the sum of the final scores of External and Internal Factors Evaluation Matrixes are 2.72 and 2.57 respectively, this shows that company performance is appropriate. ST1 strategy with closeness coefficient 0/7 was also ranked first.

Analytical hierarchy process and TOPSIS for selecting best parameters of green manufacturing

Purpose Competition and globalization in the today’s scenario have posed many pressures on all manufacturing enterprises either it be small or medium scale organizations but at different levels. The purpose of this paper is to comprehend the usage of comparison for rankings between analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) approach and find out best parameters, which is a multi-decision-making method for quantifying the implementation of green manufacturing. Design/methodology/approach In this investigation, eight aspects, for instance, top management commitment and support, organizational practices, peer government pressure, peer social pressure, customer’s attributes, green supply chain management, government support and adoption of new/ supportive technologies have been identified to examine priority weights by the use of AHP. A comparative methodology is used between AHP and TOPSIS rankings and further identifying the success possibility of green manufacturing using AHP technique. Findings Green supply chain management, customer’s attributes and organizational practices have been ranked at first three places, respectively, while adoption of new/ supportive technologies, top management commitment and support have been ranked fourth and fifth, respectively, according to AHP and TOPSIS techniques, which declares that results of the study are validated by two different techniques. The important factors discussed in this research can assist the different organizations to concentrate on imperative sections and assigning obligatory resources for assuring successful green manufacturing execution in various small- and medium-scale enterprises (SMEs). Originality/value The variables identified for the research are suitable to SMEs.

Evaluation and Classification of Mobile Financial Services Sustainability Using Structural Equation Modeling and Multiple Criteria Decision-Making Methods

Despite the fast emergent of smartphones in day-to-day activity, the sustainable development of mobile financial services (MFS) remains low partially due to online consumer’s trust and perceived risk. This research broadens the trust and the perceived risk at the multi-dimensional for understanding and prioritizing alternatives of MFS decision. A combined methodology; structural equation modeling (SEM) with two multiple criteria decision-making (MCDM) methods such as a technique for order of preference by similarity to ideal solution (TOPSIS) and analytic hierarchy process (AHP) were applied for data analysis. The two steps SEM-TOPSIS techniques were adopted through a two-types survey on datasets consisting of 538 MFS users, and 74 both experienced MFS users and experts in Togo. The SEM is used for causal relationships and assigning weights for the TOPSIS input. TOPSIS was applied for providing MFS alternative classification, in which the results were compared with prior research using the SEM-AHP technique on the given population. The results via SEM revealed particularly strong support for the dispositional trust and perceived privacy risk. Trust has a negative relationship with perceived risk. Except for perceived time risk, all the antecedents of perceived risk and trust validated the proposed relationship. The findings of TOPSIS uncovered that mobile money transfer (MMT) remains the core application used, followed by mobile payment (MP) and mobile banking (MB) and, therefore, consistent with AHP. However, the TOPSIS technique is better suited to the problem of MFS selection for this study field. This research offers a novel and practical modeling and classification concept for researchers, companies’ managers, and experts in the areas of information technology. The implications, limitations, and future research are provided.

Landfill site selection via integrating multi-criteria decision techniques with geographic information systems: a case study in Naqadeh, Iran.

Sanitary landfill is still the most significant and reasonable and the least expensive waste disposal method. The process of selecting a landfill site with minimal adverse effects on the environment is a complex task, in which numerous criteria need to be taken into account. In the present research, the AHP (analytical hierarchy process) and TOPSIS (technique for order of preference by similarity to ideal solution) models were used to weigh the layers and prioritize the identified regions, respectively. In the present study, the following criteria were considered: (1) environmental criteria consisting of groundwater depth and distance from rivers, streams, and wetland/dam/lake; (2) physical criteria consisting of geology, slope percentage, distance from faults, heights, soil depth, and aspect; (3) social-economic criteria including the subcriteria land uses and distance from urban centers and villages; and finally, (4) access factors with the subcriteria distance from roads and power lines. After the preparation phase, the maps corresponding to each subscale were weighed in GIS by means of AHP. To weigh the layers and evaluate the inconsistency rate of pairwise comparisons, the Expert Choice software, in which the AHP model can be run, was used. On the output map, 11 highly suitable areas for landfill were identified via the integration of layers and the identified options and were ranked using the TOPSIS technique and five secondary criteria based on experts' views. Ultimately, area no. 12 was selected as the proposed site for the landfill in Naqadeh County. The results indicated that the combination of multicriteria decision-making models including AHP and TOPSIS can be properly utilized for the purpose of locating.

Evaluation of the Impact of Selected Weighting Methods on the Results of the TOPSIS Technique

The present research deals with selected multi-criteria methods identified according to available literature sources as a suitable instrument for the comprehensive evaluation of a set of alternatives. Further, it focuses on a group of methods including Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is often associated in practice with the use of several subjective and objective methods for the determination of the weights of selected indicators. Accordingly, five of these objective methods are used in this research. The main contribution consists in emphasizing the differences between the results achieved through various methods for the determination of the weights of input indicators. Moreover, we restate the fact that the choice of an adequate method to weigh indicators significantly affects the overall results of the TOPSIS technique. The Coefficient of Variance method clearly identifies subjects in prominent positions. Subsequently, the Mean Weight method does not consider the structure of data and their variability, while focusing on all the indicators being equal. The results obtained via the Standard Deviation and the Statistical Variance Procedure methods are comparable with the results obtained for the identical weights of individual indicators, i.e., the Mean Weight method. Therefore, based on the overall results of our research for the determination of the weights of input indicators for the purposes of the TOPSIS technique, we recommend the use of the Standard Deviation method.

An integrated approach of CCD-TOPSIS-RSM for optimizing the marine grade GFRP drilling process parameters

Abstract This article aims to optimize the drilling process parameters using an integrated approach of the central composite design (CCD), the technique for order of preference by similarity to ideal solution (TOPSIS) and response surface method (RSM) to reduce delamination in the marine grade glass fiber reinforced polyester (GFRP) drilling. Marine grade GFRP composite laminates of 6, 8 and 10 mm thickness were made using E-glass fiber and isophthalic resins. Drilling experiments were conducted using 10 mm high-speed steel (HSS) drills on the specimen as per the central composite design framework with a face center value of unity. The process parameters speed (600, 1050 and 1500 rpm) and feed (0.1, 0.3 and 0.5 mm/rev) were considered as the control variables along with the material thickness. TOPSIS technique is used to optimize the process control parameters: speed, feed and material thickness based on four output performance characteristics: thrust force, torque, entry delamination and exit delamination. Analysis of variance (ANOVA) with a 99% confidence interval is used to determine the significant main and interaction effects with the corresponding percentage contribution of the drilling parameters. The experimental results indicate feed to be the most influential factor in the drilling of marine grade glass fiber reinforced polyester composites contributing 70.38% to the variation in the overall performance index, represented by the TOPSIS grade (TG).

A Comparative Study of the AHP and TOPSIS Techniques for Dam Site Selection Using GIS: A Case Study of Sistan and Baluchestan Province, Iran

The application of multiple criteria decision-making (MCDM) techniques in real-life problems has increased in recent years. The need to build advanced decision models with higher capabilities that can support decision-making in a broad spectrum of applications, promotes the integration of MCDM techniques with applicable systems, including artificial intelligence, and Geographic Information Systems (GIS). The Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are among the most widely adopted MCDM techniques capable of resolving water resources challenges. A critical problem associated with water resource management is dam site selection. This paper presents a comparative analysis of TOPSIS and AHP in the context of decision-making using GIS for dam site selection. The comparison was made based on geographic and water quality criteria. The geographical criteria are geology, land use, sediment, erosion, slope, groundwater, and discharge. The water quality criteria include Soluble Sodium Percentage, Total Dissolved Solid, Potential of Hydrogen, and Electrical Conductivity of water. A ratio estimation procedure was used to determine the weights of these criteria. Both methods were applied for selection of optimal sites for dams in the Sistan and Baluchestan province, Iran. The results show that the TOPSIS method is better suited to the problem of dam site selection for this study area. Actual locations of dams constructed in the area were used to verify the results of both methods.

An improved memory-based collaborative filtering method based on the TOPSIS technique.

This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined preferences. Collaborative filtering (CF) is a commonly used recommendation approach that generates recommendations based on correlations among user preferences. Although several enhancements have increased the accuracy of memory-based CF through the development of improved similarity measures for finding successful neighbors, there has been less investigation into prediction score methods, in which rating/preference scores are assigned to items that have not yet been selected by a user. A TOPSIS solution for evaluating multiple alternatives based on more than one criterion is proposed as an alternative to prediction score methods for evaluating and ranking items based on the results from similar users. The recommendation accuracy of the proposed TOPSIS technique is evaluated by applying it to various common CF baseline methods, which are then used to analyze the MovieLens 100K and 1M benchmark datasets. The results show that CF based on the TOPSIS method is more accurate than baseline CF methods across a number of common evaluation metrics.

PSI and TOPSIS Based Selection of Process Parameters in WEDM

Wire electric discharge machining (WEDM) is a nontraditional machining process for machining conductive materials with complex and intricate shapes with a high surface finish and dimensional accuracy. The decision making for the selection of the best set of combinations of input process parameters is a major challenge. Therefore a proper optimization tool should be used for the optimal selection of process parameters. The resent work deals with the comparative study of Preferential Selection Index (PSI) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for the selection of process parameters during machining of EN31 tool steel. Four input parameters- Pulse on Time (Ton ), Pulse off Time (Toff ), Servo Voltage (SV) and the Wire tension (WT) are considered. Surface roughness and material removal rate are the measured output responses. Taguchi L9 orthogonal array is used for developing the experimental design. Three levels of each control factor are considered. The results show that a single parameter alone does not have a significant influence on the output responses. Thequality of the output responses depends on the combination of the various set of input parameters. The best set of combination suggested from the current input parameters for machining of EN31 Tool Steel by Wire EDM Process is found to be Pulse on Time (Ton )= 15μs, Pulse Off Time (Toff )=35μs, Servo Voltage (SV)=40V and the Wire tension (WT)=5kgf from both PSI as well as TOPSIS techniques. Confirmation experiments are performed to validate the optimal results.

Determination and priority of socioeconomic determinants of health in Iran: a mixed methods study

The socioeconomic factors have crucial effects on health inequality. The unequal distribution of socioeconomic factors may lead to the health inequality. This study aimed to determine and give priority to the socioeconomic factors affecting health in Iran in the experts’ point of view. This study was a sequential mixed methods research conducted in quantitative and qualitative phases. The study population consisted of 16 policymakers and experts in the field of economic and social determinants of health. The purposive stratified non-random sampling method was employed. The qualitative data were collected in a semistructured interview and giving priority to factors was conducted using multiple criteria decision making with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The qualitative phase showed that the main socioeconomic factors determining health were the economic factors, income, education, sex, social class, and employment. The data analysis in TOPSIS technique showed that priority parameters were income, employment, and economic factors with coefficient of 0.66, 0.60, and 0.55, respectively. Paying attention to the socioeconomic factors may result in the increased equity and eventually the promoted public health. It is essential for policy makers to take this approach into account and improve the socioeconomic factors for attaining the promoted health outcomes.