The Order Of Preference By Similarity To Ideal Solution Research Articles

Integrated Design and Assessment for Indoor Heating, Ventilation and Air-Conditioning in Hot Summer and Cold Winter Area: A Case Study in China

Integrated design of the heating, ventilation and air-conditioning (HVAC) is indispensable to green design because the increasing demand for HVAC systems has led to the diversification of indoor terminals for residential buildings, either focusing on energy efficiency or specializing in creating comfortable indoor environments, and they have different impacts on architectural and engineering design. The paper discussed the assessment-based integration design of the HVAC system, and by introducing case experiences, the whole process of the collaboration between architects and engineers was explored. Various methods were used in the research. The analytic hierarchy process (AHP) was employed to develop the assessment structure and calculate weightings; employing fuzzy comprehensive evaluation (FCE), the social performances of HVAC systems were subjectively evaluated; simulation technology was used to calculate the energy performances; the final results were ranked by the order of preference by similarity to ideal solution (TOPSIS). The research perspective of the collaboration between architects and engineers contributed to the existing literature. Besides, different indoor terminals were analyzed from the two disciplines; an assessment tool (ATI) was conducted and could be referred to; the current green building rating tools were analyzed, and suggestions were proposed to promote the integrated design.

Experimental Investigations and Optimization of Machining Parameters in CNC Turning of SS304 Using Coolant at 0 °C

During the machining process, coolant is utilized to remove chips and tiny abrasive particles created during the machining process as well as to lessen heat concentration and friction between tools and chips. The machining performances, such as tool life, surface roughness, cutting forces, retention of mechanical properties of the work material, etc., are also desired to be retained or improved at the same time. This presented research work’s main goal is to investigate and analyze the impact of coolant at 0 °C on input machining parameters when turning SS304 (an austenitic stainless steel of the 300 series with high corrosion resistance) on a CNC lathe and to optimize the input variable factors, such as feed rate, cutting speed, and depth of cut for the best machining conditions, and each input cutting parameter is given a weight using the analytic hierarchy process (AHP) technique. A novel experimental setup is created to decrease the temperature of emulsion coolant and to use it in control conditions during machining operation. To research and assess the impact on the workpiece surface roughness, forces produced during actual cutting operations, the rate of tool wear, and the rate of material removal, twenty-seven sets of experiments using the partial factorial design approach are devised and carried out. Prioritizing the many optimal solutions accessible for this work is done using the technique for the order of preference by similarity to ideal solution (TOPSIS) and grey relation grade (GRG) approaches. Further, the surface finish of the workpiece after machining, rate of tool wear, cutting force generated during machining, and material removal rate from the workpiece were compared with traditionally/conventionally used input parameters with newly obtained optimized parameters through this work. Approximately a 30% improvement is observed in output parameters compared with using traditional parameters, and was close to the 50% of the result obtained through cryogenic machining. The work piece’s chip morphology along with tool wear was observed in form of SEM images, and it supports the claim of the surface finish and tool wear. The material removal rate was physically observed during machining. SEM pictures were used to physically validate the changes in tool wear. It has also been shown that keeping the coolant temperature at 0 °C significantly improves a number of work quality and machining characteristics. This method offers a substitute for cryogenic machining, making it useful for the manufacturing sectors.

Artificial intelligence assisted technoeconomic optimization scenarios of hybrid energy systems for water management of an isolated community

Water is an essential resource demanded worldwide and it is quite debatable owing to the economic, political, and energy characteristics of any region. Off-grid water filtration plants are an alternative for communities where transportation of freshwater becomes a real challenge due to a lack of infrastructure for the water potabilization processes. For such potable water filtration plants, hybrid renewable energy systems (HRES) can be a viable solution to meet their energy demand meanwhile providing a sustainable water solution.The main contribution of this work is the unique methodology, which starts with a sizing procedure of various hybrid energy systems using a commercial software “Hybrid Optimization of Multiple Energy Resources (HOMER)” and spreadsheet algorithms, followed by a “Non-dominating Sorting Genetic Algorithm II (NSGA-II)” based multiobjective optimization. Single-objective optimization scenarios contain photovoltaic installation capacity, wind turbines, diesel generators, and battery energy storage systems including Pb-acid (Lead-acid), Li-ion (Lithium-ion), and AGM (Absorbent Glass Mat) technologies as design variables to maximize the cost of electricity or net-present-cost. Multiobjective optimization also involved environmental (CO2 emissions i.e. carbon dioxide emissions) and water cost indices as an additional packet to single-objective optimization scenarios. Afterward, a multicriteria decision-making tool using “The Order of Preference by Similarity to Ideal Solution (TOPSIS)” is applied on the Pareto front to attain the final optimization results. The analysis is further explored in depth by generating digital twins (surrogate or meta model) of HRES data using artificial intelligence techniques (artificial neural network and group-method-of-data-handling). Furthermore, calculus and statistical sensitivity analysis assist in the identification of the significant variables in the design procedure. In summary, the technical contribution of this work can be divided into two sections. The first one is the design of a hybrid energy system for the water management of an isolated community of the indigenous Mayan region of Yucatan, Mexico, which has never been considered before. Secondly, the technical contribution is related to the usage of environmental emissions as an objective function, which is not considered in the traditional design of hybrid energy systems by the software HOMER. Environmental emission as an objective function is not considered while designing a hybrid energy system in commerical softwares like HOMER, in fact, HOMER provides a list of environmental impacts but it is a secondary outcome as a result of technoeconomic optimization.Analysis of results between HOMER pro and spreadsheet has shown conformity, reporting that the optimal case consists of a photovoltaic system, diesel generator, and Li-ion technology of battery storage with capacities of ∼17 kW, ∼5kW, and 44–48 kWh, respectively, corresponding to a net present cost ranging from 70,000 United States Dollars (USD) to 79,000 USD and a cost of electricity ranging from 0.205 to 0.229 USD/kWh. The achievements obtained with multiobjective optimization indicate that the cost of electricity and net present cost can be further reduced by 0.86 % and 0.73 %, respectively, at a decrement of only 0.4% of the renewable fraction as compared to the single objective optimization scenario. It is concluded that multiobjective optimization provides an add-in feature to HOMER by using environmental emissions as an objective function.The design procedure and adapted methodology can be useful to promote sustainable development in the statewide context and can provide a scientific justification to national energy policymakers.

Multiple criteria assessment of sustainability programs in the textile industry

AbstractTo survive in the long term, business needs to profit, controlling environmental impacts with social responsibility. Sustainability programs involve the integration of social and environmental issues in business models and organizational processes. The assessment of sustainability programs is a problem of multiple criteria decision analysis (MCDA). This work presents applications of MCDA for the assessment of sustainability programs in the textile industry. Applied methods for MCDA are analytic hierarchy process (AHP) and the technique for the order of preference by similarity to ideal solution (TOPSIS). The reasons to apply AHP and TOPSIS include providing an assessment index, ranging from 0 to 1, and that the MCDA model is expected to have more criteria than alternatives. Therefore, an application of other methods, such as data envelopment analysis, could be prejudiced. Concepts from the triple bottom line, economic, social as well as environmental criteria were inserted in the proposed model. Sustainability programs of six leading companies from the Brazilian textile industry were evaluated. The main finding of the research is that AHP and TOPSIS resulted in similar evaluations for sustainability programs. Both methods resulted in the same rank of alternatives. However, with TOPSIS, companies' sustainability indices were more disperse, varying from 0.10 to 0.92 against a range from 0.23 to 0.69 with AHP.

Comparison of methods for estimating loss from water storage by evaporation and impacts on reservoir management

AbstractReservoirs are the key infrastructure of water resources management. A controlling variable of reservoir operation is evaporation, which in semi‐arid and arid regions may consume a large fraction of reservoir storage annually. This paper assesses the role of evaporation and the choice of evaporation methods on reservoir operation. The operation of the reservoir is calculated with the standard operation policy (SOP). Several efficiency criteria are employed to rank the evaporation‐calculation methods with the technique for the order of preference by similarity to ideal solution (TOPSIS). The method presented in this paper is illustrated by applying its application to Karkheh reservoir, the largest in Iran.

Combining chemometrics and the technique for the order of preference by similarity to ideal solution: A new approach to multiple-response optimization of HPLC compared to desirability function

Some devices are used in the pharmaceutical industry to produce and control the quality of a variety of pharmaceutical products; therefore, it would be helpful to identify the factors affecting the performance of the devices and optimize them in terms of time and cost. In this regard, the use of experimental design approaches to optimization is important as it can help a device achieve its highest efficiency. One of the most popular devices in the pharmaceutical industry is high-performance liquid chromatography (HPLC). In view of its wide use in the industry and the number of factors affecting it, it is necessary to design and validate a new method that can simultaneously optimize its different parameters. In this study, the method of the simultaneous optimization of the parameters was implemented using the Box-Behnken design and multiple-criteria decision-making (MCDM) by Derringer's desirability function and technique for the Order of Preference by Similarity to Ideal Solution (TOPSIS) method. In this regard, mobile phase ratio, flow rate, and pH were simultaneously changed, and capacity factor, total analysis time, and resolution were considered as responses. To validate the method, the key parameters of the method were determined and used for all the validation steps. Therefore, this approach to optimizing HPLC factors is new and very cost-effective with diverse applications in the pharmaceutical industry.

Improved Hybridization of Evolutionary Algorithms with a Sensitivity-Based Decision-Making Technique for the Optimal Planning of Shunt Capacitors in Radial Distribution Systems

In this paper, an improved hybridization of an evolutionary algorithm, named permutated oppositional differential evolution sine cosine algorithm (PODESCA) and also a sensitivity-based decision-making technique (SBDMT) are proposed to tackle the optimal planning of shunt capacitors (OPSC) problem in different-scale radial distribution systems (RDSs). The evolved PODESCA uniquely utilizes the mechanisms of differential evolution (DE) and an enhanced sine–cosine algorithm (SCA) to constitute the algorithm’s main structure. In addition, quasi-oppositional technique (QOT) is applied at the initialization stage to generate the initial population, and also inside the main loop. PODESCA is implemented to solve the OPSC problem, where the objective is to minimize the system’s total cost with the presence of capacitors subject to different operational constraints. Moreover, SBDMT is developed by using a multi-criteria decision-making (MCDM) approach; namely the technique for the order of preference by similarity to ideal solution (TOPSIS). By applying this approach, four sensitivity-based indices (SBIs) are set as inputs of TOPSIS, whereas the output is the highest potential buses for SC placement. Consequently, the OPSC problem’s search space is extensively and effectively reduced. Hence, based on the reduced search space, PODESCA is reimplemented on the OPSC problem, and the obtained results with and without reducing the search space by the proposed SBDMT are then compared. For further validation of the proposed methods, three RDSs are used, and then the results are compared with different methods from the literature. The performed comparisons demonstrate that the proposed methods overcome several previous methods and they are recommended as effective and robust techniques for solving the OPSC problem.

Bipolar fuzzy TOPSIS and bipolar fuzzy ELECTRE-I methods to diagnosis

Medical diagnosis is the process of determining which disease or condition explains a person’s symptoms or signs. The complexity and uncertainty of diagnostic information make the diagnostic process difficult to accomplish. Many theories concerned with medical diagnosis such as fuzzy set theory, rough set theory and soft set theory are presented in the past. We are concerned with bipolar fuzzy multi-criteria decision-making (MCDM) methods. The main purpose is to provide guidance for determining a patient’s health status and evaluating the factors influencing that status in bipolar fuzzy environment. Bipolar fuzzy set theory offers a useful tool for developing knowledge-based systems in MCDM methods. Since the diagnosis of an appropriate disease involves bipolar judgement, we utilize bipolar fuzzy Technique for the Order of Preference by Similarity to Ideal Solution (TOPSIS) and YinYang bipolar fuzzy Elimination and Choice Translating Reality (ELECTRE) in medical diagnosis, where the various diseases for various symptoms and weights of various symptoms are assessed using bipolar fuzzy information and fuzzy information, respectively. Comparison of BF-TOPSIS and BF-ELECTRE-I is presented. The illustrated methods demonstrate the practicability, feasibility and sustainability of diagnostic process. BF-TOPSIS gives one diseases as diagnosis, whereas BF-ELECTRE-I gives the set of diseases as diagnosis. Moreover, the comparison analysis demonstrates which diagnostic process is more credible and accurate. This article presents two powerful MCDM techniques for the diagnosis of a disease in which the pairwise comparison of diseases and symptoms is considered in bipolar behavior.

Development of Urban Road Order Index Based on Driving Behavior and Speed Variation

This study uses driving behavior and speed variation data from navigation software to propose a traffic order index (TOI) for evaluating the order of traffic on urban roads. Based on the significance analysis of driving behaviors and speed variation under different road types and congestion levels, the TOI calculation method is proposed by using the order of preference by similarity to ideal solution (TOPSIS) method. Through a case study of an urban area in Beijing, the distribution of TOI under different road types and congestion levels is described, and the hourly and daily TOI heat maps are generated to show changes in TOI for urban roads during different periods. The relationships between TOI, congestion index (CI) and crash data were explored. A nonlinear relationship of TOI and CI was discovered, and roads with more crashes or longer crash durations were associated with a lower level of traffic order. The TOI could help traffic management departments better understand the order of traffic on roads, reveal causes of the poor level of traffic order in some roads, and more reasonably dispatch traffic police to handle traffic crashes.

Application of the coupled TOPSIS–Mahalanobis distance for multi-hazard-based management of the target districts of the Golestan Province, Iran

This study is aimed at producing an improved ranking method by coupling the technique for the order of preference by similarity to ideal solution (TOPSIS) and Mahalanobis distance (MD) to prioritize the districts of Golestan Province, northeast of Iran, with respect to the prevailed natural hazards. The main idea of this work is underpinned by introducing a method that is: (1) in accordance with holistic thinking by engaging different threatening natural hazards in order to rank different threatened targets and (2) harmonized by the probabilistic context of the natural hazards. Therefore, maximum entropy (MaxEnt), a well-known data mining model, was used to model the spatial pattern of flood inundation and landslide occurrence over the study area. The area under the receiver operating characteristic (AUROC) was used to assess the goodness of fit and prediction power of the used models. As a result, the MaxEnt model showed an outstanding predictive performance with the AUROC values of 0.889 and 0.903 for landslide and flood inundation modelling, respectively. Afterwards, the revised universal soil loss equation (RUSLE) was employed to model soil erosion. This model successfully estimated the average soil loss of the study area with a value of about 33 ton/ha/yr which is in the range reported by provincial natural resources organization (10-35 ton/ha/yr). Results revealed that highly susceptible areas to the landslide, flood inundation, and water erosion potentially account for about 11, 9.6, and 6.6% of the Golestan Province surface area. Lastly, the TOPSIS–MD, TOPSIS, and Simple Additive Weight (SAW) methods were used to prioritize the districts of the Golestan Province with respect to all three susceptibility maps. As a result, TOPSIS–MD was chosen as the well-performing ranking method for further environmental managerial actions due mainly to considering the strong correlations among the criteria. According to TOPSIS–MD results, Minoodasht, Ramian, and Gorgan districts were recognized as the most threatened districts, while Gomishan, Aq Qala, and Gonbad-e Kavous districts are located at a safe zone with respect to the studied susceptibility indices. The proposed TOPSIS–MD framework merits more studies and is applicable to any multi-criteria decision-making issue in any branch of science.

Assessment and Ranking Framework for the English Skills of Pre-Service Teachers Based on Fuzzy Delphi and TOPSIS Methods

In this study, pre-service teaching refers to teaching English as a second language (TESL) to Malaysian students whose first language is not English. TESL prepares English-language learners to become future teachers of English as a second language. To date, no multi-criteria framework has been developed to evaluate and select the skills of pre-service teachers. This study presents a new framework to assess and rank the English skills of pre-service teachers on the basis of fuzzy Delphi and multi-criteria analysis. Three experiments were conducted. Firstly, criteria were identified from the literature review and the opinions of representative experts via the Delphi method. Secondly, 31 pre-service teachers were evaluated to determine the skills of pre-service teachers on the basis of Delphi criteria outcomes. English proficiency was tested through the English Language Testing Service and four language skill examinations. Each examination was evaluated by experts with vast experience in English teaching. Thirdly, pre-service teachers were ranked on the basis of a set of evaluated Delphi criteria outcomes through the technique for the order of preference by similarity to ideal solution (TOPSIS) method. Thereafter, the mean and standard deviation were utilized to ensure the identical systematic ranking of pre-service teachers. Findings are as follows. Twenty-five criteria from previous studies are representative as evaluated by the opinions of experts, which were gathered through interviews and a structured questionnaire. The validity of content was verified using a five-point Likert scale. With Delphi method outcomes, 14 criteria were selected and included in the final framework. The results of the proposed evaluation framework were tested on Malaysian pre-service teachers. TOPSIS is effective for solving the selection problems of pre-service teachers. In the final experiment, significant differences were recognized between the scores of groups, indicating identical ranking results.

A GROUP DECISION-MAKING MODEL FOR WASTEWATER TREATMENT PLANS SELECTION BASED ON INTUITIONISTIC FUZZY SETS

As the need for environmental protection and resource sustainability has increased in recent times, wastewater treatment has become increasingly important. In this paper, a group decision-making model for plans selection in wastewater treatment is proposed. In order to deal with uncertainties and multiple attributes in wastewater treatment, an intuitionistic fuzzy set is employed to evaluate wastewater treatment plans effectively. A distance measure is defined to obtain an objective weight measuring the expert’s judgment. More specifically, experts first use group decision-making on the various plans with an intuitionistic fuzzy set. Meanwhile, Due to the decision-makers psychological behavior, the prospect theory is applied. Next, the various plans are ranked by The Order of Preference by Similarity to Ideal Solution (TOPSIS) method and prospect theory. Finally, an illustrative example of wastewater treatment plans selection is used to verify the proposed model.

Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework

Structural damage detection is a well-known engineering inverse problem in which the extracting of damage information from the dynamic responses of the structure is considered a complex problem. Within that area, the damage tracking in 3D structures is evaluated as a more complex and difficult task. Swarm intelligence and evolutionary algorithms (EAs) can be well adapted for solving the problem. For this purpose, a hybrid elitist-guided search combining a multiobjective particle swarm optimization (MOPSO), Lévy flights (LFs), and the technique for the order of preference by similarity to ideal solution (TOPSIS) is evolved in this work. Modal characteristics are employed to develop the objective function by considering two subobjectives, namely, modal strain energy (MSTE) and mode shape (MS) subobjectives. The proposed framework is tested using a well-known benchmark model. The overall strong performance of the suggested method is maintained even under noisy conditions and in the case of incomplete mode shapes.

Decision-Making with Bipolar Neutrosophic TOPSIS and Bipolar Neutrosophic ELECTRE-I

Technique for the order of preference by similarity to ideal solution (TOPSIS) and elimination and choice translating reality (ELECTRE) are widely used methods to solve multi-criteria decision making problems. In this research article, we present bipolar neutrosophic TOPSIS method and bipolar neutrosophic ELECTRE-I method to solve such problems. We use the revised closeness degree to rank the alternatives in our bipolar neutrosophic TOPSIS method. We describe bipolar neutrosophic TOPSIS method and bipolar neutrosophic ELECTRE-I method by flow charts. We solve numerical examples by proposed methods. We also give a comparison of these methods.

Decision-Making with Bipolar Neutrosophic TOPSIS and Bipolar Neutrosophic ELECTRE-I

Technique for the order of preference by similarity to ideal solution (TOPSIS) and elimination and choice translating reality (ELECTRE) are widely used methods to solve multi-criteria decision making problems. In this research article, we present bipolar neutrosophic TOPSIS method and bipolar neutrosophic ELECTRE-I method to solve such problems. We use the revised closeness degree to rank the alternatives in our bipolar neutrosophic TOPSIS method. We describe bipolar neutrosophic TOPSIS method and bipolar neutrosophic ELECTRE-I method by flow charts. We solve numerical examples by proposed methods. We also give a comparison of these methods.

Failure mode and effects analysis based on D numbers and TOPSIS

AbstractFailure mode and effects analysis (FMEA) is a widely used technique for assessing the risk of potential failure modes in designs, products, processes, system, and services. One of the main problems with FMEA is the need to address a variety of assessments given by FMEA team members and the sequence of the failure modes according to the degree of risk factors. Many different methods have been proposed to improve the traditional FMEA, which is impractical when the risk assessments given by multiple experts to one failure mode are imprecise, incomplete, or inconsistent. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, a new risk priority model based on D numbers and technique for the order of preference by similarity to ideal solution (TOPSIS) is proposed to evaluate the risk in FMEA. In the proposed model, the assessments given by the FMEA team members are represented by D numbers, where a new feasible and effective method can effectively represent the uncertain information. The TOPSIS method, a multicriteria decision‐making method is presented to rank the preference of failure modes with respect to risk factors. Finally, an application of the failure modes of the rotor blades of an aircraft turbine is provided to illustrate the efficiency of the proposed method.

Toward a Customer Knowledge Management Model for Enhancing Enterprise Software Quality

Many previous research studies on software quality enhancement have only focused on the technical aspects of software quality, such as reliability, maintainability and functionality. However, the nature of enterprise software requires additional focus on transferring and integration of customer knowledge, for customization, enhancement, maintenance and training. As customers are seen as one of the most important stakeholders in software projects, it would appear that the current use of customer knowledge in software development is insufficient. This study concentrates on investigating Human, Organizational and Technological factors of customer knowledge management (CKM) in order to enhance the quality of enterprise software within software companies. After investigating CKM factors in the literature, a Technique for the Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to rank these factors regarding the importance of CKM development in enterprise software companies, in order to improve software product quality. The weight and priority of the factors were determined by 31 software development company experts. Based on the highest priority factors, a theoretical model was developed. This study proposes a fundamental model that can be used as a guideline for successful CKM applications, within enterprise software development companies, to improve software quality.

Classification of Rainfall Warnings Based on the TOPSIS Method

Extreme weather, by definition, is any unexpected, unusual, unpredictable, severe or unseasonal weather condition. A rainfall event that is considered normal in one region may be considered a torrent in a dry region and may cause flash flooding. Therefore, appropriate weather warnings need to be issued with respect to areas with different climates. Additionally, these alerts should be easy to understand—by clear classification—in order to apply reinforcements. Early warning levels not only depend on the intensity and duration of rainfall events, but also on the initial water stress conditions, land cover situations and degree of urbanization. This research has focused on defining different warning levels in northwest Iran using long-term precipitation data from 87 weather stations well distributed across the study area. Here, in order to determine alert levels, TOPSIS (The Order of Preference by Similarity to Ideal Solution), as one of the most common methods in multi-criteria decision making, has been used. Results show that five main levels of alerts can be derived, leading to the provision of spatial maps. Further, it can be deduced that these levels are highly associated to the location of a region at different times: months/seasons. It has been observed that the issuance of a warning for precipitation should correspond with the location and time. At one location during different seasons, different alert levels would be raised corresponding to the rainfall. It was also concluded that using of fixed alert levels and extending them to larger areas without considering the seasons could be grossly misleading.

A meta-analysis strategy for gene prioritization using gene expression, SNP genotype, and eQTL data.

In order to understand disease pathogenesis, improve medical diagnosis, or discover effective drug targets, it is important to identify significant genes deeply involved in human disease. For this purpose, many earlier approaches attempted to prioritize candidate genes using gene expression profiles or SNP genotype data, but they often suffer from producing many false-positive results. To address this issue, in this paper, we propose a meta-analysis strategy for gene prioritization that employs three different genetic resources—gene expression data, single nucleotide polymorphism (SNP) genotype data, and expression quantitative trait loci (eQTL) data—in an integrative manner. For integration, we utilized an improved technique for the order of preference by similarity to ideal solution (TOPSIS) to combine scores from distinct resources. This method was evaluated on two publicly available datasets regarding prostate cancer and lung cancer to identify disease-related genes. Consequently, our proposed strategy for gene prioritization showed its superiority to conventional methods in discovering significant disease-related genes with several types of genetic resources, while making good use of potential complementarities among available resources.