Technique For Order Preference By Similarity Ideal Solution Research Articles

Exergoeconomic analysis for a thermoelectric generator using mutation particle swarm optimization (M-PSO)

Efficiency and cost-effectiveness play dominant roles in the commercialization of thermoelectric generator (TEG) technology. In this paper, the exergy analysis of a TEG module with 199 TE couples was considered. Two objective functions, the exergy efficiency and levelized cost of energy (LCOE), were established for exergoeconomic analysis. The geometric structure and working conditions involving TE couple length, base area ratio, working temperature, and load resistance were varied. The particle swarm optimization (PSO) method has excellent convergence and few parameters need to be adjusted. Mutation can increase randomization for the PSO method, making it possible to improve its search direction. Therefore, the mutation-PSO (M-PSO) algorithm was used to optimize the exergy efficiency and LCOE for the TEG. Through the M-PSO algorithm, the optimum corresponds to an exergy efficiency of 29% and LCOE of 1.93 $US/kWh·m2 under a maximum temperature difference of 40 K. In order to achieve a balance between the two exergoeconomic indices, the ξ-constraint combined with the M-PSO method was used to obtain alternatives, named Pareto solutions. Then, these alternatives were ranked to acquire an ideal solution based on a technique for order preference by similarity ideal solution (TOPSIS) method. The TOPSIS ideal solution corresponds to an exergy efficiency of 22.2% and LCOE of 3.02 $US/kWh·m2.

Multi-dataset analysis to assess mineral potential of MVT-type zinc-lead deposits in Malayer-Isfahan metallogenic belt, Iran

The Malayer-Isfahan metallogenic belt (MIMB) is located in the Sanandaj-Sirjan zone. The belt hosts the important Zn-Pb deposits of Iran which are predominantly hosted by Cretaceous limestone, limestone-dolomite, limestone-shale, and carbonate sandstones. These deposits, which are associated with dolomitization, silicification, and calcitization, are believed to be mainly epigenetic stratabound Mississippi Valley-type (MVT) mineralization. This research aims at presenting an efficient geological-based exploration model through the integration of structural, lithological, alteration, and geochemical datasets from Malayer-Isfahan metallogenic belt. The main genetic factors of epigenetic stratabound Zn-Pb MVT-type mineralization and those of the well-known MVT deposits of the study area were used to extract the recognition criteria for potential mapping of these deposits. The predictive maps of geological, geochemical, structural, and alteration data were generated by fuzzy and simple weighted overlay (SWO) methods, while final favorability maps were created by SWO and technique for order preference by similarity ideal solution (TOPSIS) methods. The results were finally validated by using the known mineralized locations. The results showed that Lower Cretaceous dolomitized carbonate rocks and their contact with Jurassic shales, deep thrust faults, and joint predictive maps of Pb and Zn are the most important factors for exploration of these deposits in regional scale. The validation results showed that the moderate and high favorable areas identified by SWO and TOPSIS methods occupy 12.6% and 12.9% of the study area, which include 83.3% and 75% of the known MVT-type deposits, respectively. The well-known deposits such as Irankuh, Emarat, and Tiran occur in high potential areas of favorability maps. The results also delineated new potential targets for MVT-type mineralization in Malayer-Isfahan metallogenic belt for follow-up next exploration programs.

Multi-objective optimization of a hybrid microchannel heat sink combining manifold concept with secondary channels

A novel optimization approach, which combines optimal Latin hypercube design (Opt LHD), Pareto chart analysis, response surface methodology (RSM), the non-dominated sorting genetic algorithm II (NSGA-II) and technique for order preference by similarity ideal solution (TOPSIS), has been proposed and applied to optimize the performance of a hybrid microchannel heat sink combining manifold concept with secondary oblique channels. Four geometric parameters are selected as design variables and the optimization objective is to minimize the total thermal resistance Rt and pumping power Pp simultaneously. First, 160 sample points is generated by Opt LHD and the Pareto chart analysis is performed to identify the dominant design parameters influencing the objectives. Then, RSM is used to generate approximate models relating to the objectives and design parameters, and NSGA-II is selected to minimize Rt and Pp. 374 Pareto-optimal solutions are obtained and verified by CFD results, which indicates that the hybrid design can reduce Rt by 18.83% compared with manifold microchannel heat sink under the same Pp. Finally, the best compromise solution is obtained by TOPSIS combined with entropy weight method. The proposed optimization approach can also be applied to optimize the performance of other types of heat sinks.

NETWORK DATA ENVELOPMENT ANALYSIS WITH COMMON WEIGHTS: AN APPLICATION TO THE SUSTAINABILITY MEASUREMENT OF OECD COUNTRIES

This work considers a general common-weights network data envelopment analysis (DEA) model which is applicable to most network systems, except those with feedbacks and cycles. The principle of compromise, of the technique for order preference by similarity ideal solution (TOPSIS), is employed to find the common set of weights in the general network DEA model. The proposed method is applied to assess the environmental sustainability performance of the Organization for Economic Co-operation and Development (OECD) countries. Our results show that most countries have a large difference in the rank of efficiencies between the eco-efficiency and production efficiency stages, which reveals the source that causes the low environmental sustainability scores of the whole process.

A fast reaction-based port vulnerability assessment: Case of Tianjin Port explosion

There has been a growing international attention on port catastrophes after the 2015 Tianjin Port explosion, which calls for a realistic yet effective method to assess seaports’ vulnerability. However, quantitative vulnerability analysis of ports during the initial post-disaster stage by traditional methods reveals challenges due to the scarcity of data and the inoperability in treating diverse data formations in a utility vulnerability inference mechanism. Thus, this study proposes a fast reaction-based port vulnerability assessment framework (FRPVA) which allows the quantification of ex-post port vulnerability. The fuzzy evidential reasoning (ER) approach and the fuzzy Technique for Order Preference by Similarity Ideal Solution (TOPSIS) are embedded in the proposed framework. The FRPVA framework is applied to the case of the 2015 Tianjin Port explosion in two assessment periods – one day and four months after the explosion. Our results show that (1) the collection and distribution system as well as the storage system are bottlenecks of the Tianjin Port; (2) the storage system is the most vulnerable among the four assessment units in both assessment periods; (3) after four months’ recovery, the vulnerability condition of the loading and unloading system improves the most comparing to other three assessment units. Outputs of this framework can be useful references for various port stakeholders in prioritizing protected targets, planning routes, as well as for (re)insurance companies as a part of the catastrophe modeling.

Sustainability Performance. A Comparative Analysis in the Polish Banking Sector

This article analyses the Polish banking sector’s involvement with sustainable development through a multidimensional evaluation applying the technique for order preference by similarity ideal solution (TOPSIS) method with different weight vectors. Our results highlight numerous shortcomings in the sustainability performance of commercial banking activities. In fact, there was backsliding during the analysed period (2015–2017), which suggests that supporting sustainability performance was not one of the priorities of the Polish banking sector. However, we found a dichotomy between national and foreign banks. The government-owned banks and national banks showed greater commitment to this issue than did the banks with foreign capital. This finding suggests that banks with foreign capital were not fully interested in sponsoring activities aimed at sustainable development.

Tourist potential of Polish voivodships

The aim of the article is to compare tourist attractiveness of Polish voivodships. Data published by the Central Statistical Office for 2014 was used in the research. The Hellwig’s method of taxonomic measure of development and the TOPSIS (Technique for Order Preference by Similarity Ideal Solution) were applied to assess tourist potential. The results were compared with the corresponding values of the Schneider’s tourist traffic density and intensity rate. The analysis shows that the highest tourist potential was recorded for Małopolskie and Silesian, while the lowest for: Podlaskie and Lubelskie Voivodships. An efficient use of tourist potential was demonstrated by i.a. Małopolskie, Silesian, Lower Silesian, Pomeranian and West Pomeranian Voivodships.

COMPARING MULTI-CRITERIA DECISION-MAKING (MCDM) TOOL AND HUFF MODEL TO DETERMINE THE MOST APPROPRIATE METHOD FOR SELECTING MOUNTAIN TOURISM SITES

Population growth and urbanization in Tehran have increased the citizens demand for spending their leisure time in the nature; and the mountains near Tehran have created such opportunity. However, tourism management is facing two challenges in this regard. The first challenge is to determine sites capable of tourism development, and the second challenge is to select the optimal site among the alternatives. Therefore, this study aims to find the most appropriate method for identifying and prioritizing mountain tourism sites. To achieve this, two stages were employed. In the first stage, according to the constraint criteria, potential sites for mountain tourism with three land-uses of skiing, mountaineering, and accommodation facilities were identified using geographical information system (GIS). In the second stage, according to different approaches and by employing three methods of analytic hierarchy process (AHP), technique for order preference by similarity ideal solution (TOPSIS) and Huff, potential sites were prioritized; and then, by comparing the results, the most appropriate method among them was selected. Findings show that the prioritizing results of TOPSIS and AHP methods are close. However, AHP has prioritized the sites based on the expert opinions while TOPSIS has not privileged expert opinions as much as AHP. Even so, ranking results of the Huff model were different from the other methods. Therefore, it can be stated that each of these methods can be appropriate based on decision-makers viewpoints. However, TOPSIS method was preferred because of its greater capability in differentiating between the sites.

On the solution of Large Scale Bi-Level Linear Vector Optimization Problems through TOPSIS

In this paper, we extend TOPSIS (Technique for Order Preference by Similarity Ideal Solution) for solving Large Scale Bi-level Linear Vector Optimization Problems (LS-BL-LVOP). In order to obtain a compromise ( satisfactory) solution to the LS-BL-LVOP problems using the proposed TOPSIS approach, a modified formulas for the distance function from the positive ideal solution (PIS ) and the distance function from the negative ideal solution (NIS) are proposed and modeled to include all the objective functions of both the first and the second levels. An interactive decision making algorithm for generating a compromise ( satisfactory) solution through TOPSIS approach is provided where the first level decision maker (FLDM) is asked to specify the relative importance of the objectives. Finally, a numerical example is given to clarify the main results developed in the paper.

Multiresponse Optimization of Abrasive Water Jet Cutting Process Parameters Using TOPSIS Approach

This paper describes how optimization studies were carried out on an abrasive water jet (AWJ) cutting process with multiresponse characteristics based on Multi Criteria Decision Making Methodology (MCDM) using the Technique for Order Preference by Similarity Ideal Solution (TOPSIS) approach. The process parameters water jet pressure, traverse rate, abrasive flow rate, and standoff distance are optimized with multiresponse characteristics, including the depth of penetration (DOP), cutting rate (CR), surface roughness (Ra), taper cut ratio (TCR), and top kerf width (TKW). The optimized results obtained from this approach indicate that higher DOP and CR and lower Ra, TCR, and TKW were achieved with combinations of the AWJ cutting process parameters, such as water jet pressure of 300 MPa, traverse rate of 120 mm/min, abrasive flow rate of 360 g/min, and standoff distance of 1 mm. The experimental results indicate that the multiresponse characteristics of the AA5083-H32 unit used during the AWJ cutting process can be enhanced through the TOPSIS method. Analysis of variance was carried out to determine the significant factors for the AWJ cutting process.

Multi-Index Rain Detection: A New Approach for Regional Rain Area Detection from Remotely Sensed Data

Abstract In this article, a new approach called Multi-Index Rain Detection (MIRD) is suggested for regional rain area detection and was tested for India using Kalpana-1 satellite data. The approach was developed based on the following hypothesis: better results should be obtained for combined indices than an individual index. Different combinations (scenarios) were developed by combining six commonly used rain detection indices using AND and OR logical connectives. For the study region, an optimal rain area detection scenario and optimal threshold values of the indices were found through a statistical multi-decision-making technique called the Technique for Order Preference by Similarity Ideal Solution (TOPSIS). The TOPSIS analysis was carried out based on independent categorical statistics like probability of detection, probability of no detection, and Heidke skill score. It is noteworthy that for the first time in literature, an attempt has been made (through sensitivity analysis) to understand the influence of the proportion of rain/no-rain pixels in the calibration/validation dataset on a few commonly used statistics. Thus, the obtained results have been used to identify the above-mentioned independent categorical statistics. Based on the results obtained and the validation carried out with different independent datasets, scenario 8 (TIRt < 260 K and TIRt − WVt < 19 K, where TIRt and WVt are the brightness temperatures from thermal IR and water vapor, respectively) is found to be an optimal rain detection index. The obtained results also indicate that the texture-based indices [standard deviation and mean of 5 × 5 pixels at time t (mean5)] did not perform well, perhaps because of the coarse resolution of Kalpana-1 data. It is also to be noted that scenario 8 performs much better than the Roca method used in the Indian National Satellite (INSAT) Multispectral Rainfall Algorithm (IMSRA) developed for India.

Identification of natural hazards and classification of urban areas by TOPSIS model (case study: Bandar Abbas city, Iran)

In this paper, among multi-criteria models for complex decision-making and multiple-attribute models for assigning the most preferable choice, the technique for order preference by similarity ideal solution (TOPSIS) is implied. The main objective of this research is to identify potential natural hazards in Bandar Abbas city, Iran, using TOPSIS model, which is based on an analytical hierarchy process structure. A set of 12 relevant geomorphologic parameters, including earthquake frequency, distance from the earthquake epicentre, number of faults, flood, talus creep, landslide, land subsidence, tide, hurricane and tidal wave, dust storms with external source, wind erosion and sea level fluctuations are considered to quantify inputs of the model. The outputs of this study indicate that one region, among three assessed regions, has the maximum potential occurrence of natural hazards, while it has been urbanized at a greater rate compared to other regions. Furthermore, based on Delphi method, the earthquake frequency and the landslide are the most and the least dangerous phenomena, respectively.

An interactive algorithm for Large Scale Bi level Linear Multiple Objective programming problems through TOPSIS approach

In this paper, we extend TOPSIS (Technique for Order Preference by Similarity Ideal Solution) for solving Large Scale Bi-level Linear Multiple Objective Programming (LS-BL-LMOP) problems. In order to obtain a compromise ( satisfactory) solution to the LS-BL-LMOP problems using the proposed TOPSIS approach, a modified formulas for the distance function from the positive ideal solution (PIS ) and the distance function from the negative ideal solution (NIS) are proposed and modeled to include all the objective functions of both the first and the second levels. An interactive decision making algorithm for generating a compromise ( satisfactory) solution through TOPSIS approach is provided where the first level decision maker (FLDM) is asked to specify the relative importance of the objectives. Finally, a numerical example is given to clarify the main results developed in the paper.

A TOPSIS-BASED ENTROPIC REGULARIZATION APPROACH FOR SOLVING FUZZY MULTI-OBJECTIVE NONLINEAR PROGRAMMING PROBLEMS

In this work, a version of the technique for order preference by similarity ideal solution (TOPSIS) with entropic regularization approach is developed for solving the fuzzy multi-objective nonlinear program- ming (MONLP) problems. Applying the basic principle of compromise of TOPSIS, the fuzzy MONLP problem can be reduced into a fuzzy bi-objective nonlinear programming problem. Moreover, following the tolerance approach, the solution of the fuzzy bi-objective nonlinear programming problem can be obtained by solving a min-max problem. An entropic regularization approach is then applied for solving such a problem. Computational results are provided to illustrate the validity and efficiency of the proposed method.

A Method for the Virtual Enterprise to Select its Partners

Multiple factors are required to take into consideration when evaluating the performance of candidates. This paper formulates selecting partners for virtual enterprises as a multi-criteria analysis model, and presents an effective approach by Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity Ideal Solution (TOPSIS) for solving the problem. A relatively complete database of indicators for different types of candidate is established first. AHP and entropy are simultaneously uesd to weight these indicators. An empirical example is conducted to illustrate the application of this model. The result shows that the approach can reflect the information emitted by the ratios used.

Efficient Process Parameters Optimization of Rapid Heat Cycling Molding Technology Based on Response Surface Methodology and Improved Genetic Algorithm

An independently developed vehicle-borne high-gloss bluetooth product is taken as an example in the research of optimization of rapid heat cycle molding(RHCM) technology.Coupling with the experiment and numerical simulation technology,the center composite design(CCD) method is employed in arranging the experimental points.With an interference factor introduced artificially,the simulation results are kept in good consistency with experimental result.And the samples qualitative data is processed to more truly response the product quality by integrating entropy-based weight(EBW) method and technique for order preference by similarity ideal solution(TOPSIS) method.Then a predictive response surface model(RS) for comprehensive quality index is created by using response surface methodology(RSM) based on analysis of variance(ANOVA) method.The RS model is interfaced with an effective improved genetic algorithm(IGA) to find the optimum process parameters values.And the actual production achieves good result,which shows that the process control technology is feasible and effective in processing quality optimization of high-gloss plastic part.

An interactive algorithm for large scale multiple objective programming problems with fuzzy parameters through TOPSIS approach

In this paper, we extend TOPSIS (Technique for Order Preference by Similarity Ideal Solution) for solving Large Scale Multiple Objective Programming problems involving fuzzy parameters. These fuzzy parameters are characterized as fuzzy numbers. For such problems, the ?-Pareto optimality is introduced by extending the ordinary Pareto optimality on the basis of the ?-Level sets of fuzzy numbers. An interactive fuzzy decision making algorithm for generating ?-Pareto optimal solution through TOPSIS approach is provided, where a decision maker (DM) is asked to specify the degree ? and the relative importance of objectives. Finally, a numerical example is given to clarify the main results developed in the paper. <br><br><font color="red"><b> This article has been retracted. Link to the retraction <u><a href="http://dx.doi.org/10.2298/YJOR141008034U">10.2298/YJOR141008034U</a><u></b></font>

Application of multi-criteria analysis for parameters selection problem in rotor spinning machine

Optimum spinning machine parameter selection among feasible alternatives with different significance is a multi-criteria decision-making problem. Statistical analysis is a conventional method that is used in such a problem. However, the preferences of the producer about the yarn properties needed to obtain an end product with defined characteristics have not been taken into consideration in these approaches. Therefore, in this study, assistance in reaching acceptable solutions in order to select the appropriate doffing tube components and its adjustment for 30 Ne rotor yarn that is intended to be used for weft-knitted fabric will be provided by the technique for order preference by similarity ideal solution (TOPSIS). Yarn samples were spun considering three variables, namely the distances between the nozzle and rotor, the extractive nozzle and the draw-off tube. Possible alternatives were ranked on the basis of the yarn quality parameters by the TOPSIS method and the best alternative for increasing weft-knitting machine efficiency was presented. According to the final ranking, the spinning condition in which the sample was spun by using a spiral nozzle, a doffing tube without a torque stop, and a closer setting had the highest closeness coefficient (Rj value) to the ideal solution (0.967). Consequently, the stability of the proposed final ranking was verified after sensitivity analysis.

Application of TOPSIS approach on parameters selection problem for rotor spinning machine

Classical statistical analysis has been generally used in obtaining optimum condition such as problems for rotor spinning machine. In these methods the preferences of the producer about yarn characteristics to achieve the desired end product properties have not been taken into consideration. However, machine parameters selection from possible alternatives with different performance levels about yarn quality is difficult task and is inherently a multi-criteria decision making problem. In the present study, valuable assistance in reaching acceptable solutions in order to select the appropriate doffing tube and its adjustment for 30 Ne rotor yarn spun to raise efficiency of weft knitting machine will be provided by technique for order preference by similarity ideal solution (TOPSIS) approach. In experimental part 30 Ne rotor yarn samples were spun by considering one quantitative variable, i.e., two different distances between the nozzle and rotor, and also two qualitative variables, i.e., nozzles in 4 different shapes and a draw-off tube with and without a torque stop. Then quality parameters of the yarns were analyzed with TOPSIS.

Extensions of TOPSIS for large scale multi-objective non-linear programming problems with block angular structure

This paper focuses on multi-objective large-scale non-linear programming (MOLSNLP) problems with block angular structure. We extend the technique for order preference by similarity ideal solution (TOPSIS) to solve them. Compromise (TOPSIS) control minimizes the measure of distance, provided that the closest solution should have the shortest distance from the positive ideal solution (PIS) as well as the longest distance from the negative ideal solution (NIS). As the measure of “closeness” L P -metric is used. Thus, we reduce a q -dimensional objective space to a two-dimensional space by a first-order compromise procedure. The concept of a membership function of fuzzy set theory is used to represent the satisfaction level for both criteria. Moreover, we derive a single objective large-scale non-linear programming (LSNLP) problem using the max–min operator for the second-order compromise operation. Finally, a numerical illustrative example is given to clarify the main results developed in this paper.