Technique For Order Preference By Similarity To Ideal Solution Research Articles

Use of measurement results from a mobile laboratory in the diagnostics of anthropogenic environment - linear ordering of objects using the TOPSIS method

The measurements of the concentrations of gaseous and dust pollutants in the anthropogenic environment are an important element of environmental monitoring and for determining directions of preventive activities in the field of health protection. The article presents the results involving the concentrations of suspended dust and gaseous pollutants in the outdoor air, which were recorded at three measuring stations of air quality in the Silesian and Opole voivodeships (Wodzisław Śląski, Zabrze, Kędzierzyn-Koźle). The results were supplemented with the values recorded by the mobile laboratory located at the Center for Continuing Education - Branch of the Silesian University of Technology in Rybnik. The research results were used for a synthetic assessment of the threat level to the anthropogenic environment. In the computational layer, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed, which is included in the group of methods for solving multi-criteria decision-making problems (Multi Attribute Decision Making).

Matrix‐phase material selection for shape memory polymer composites: A comparative analysis of multi‐criteria decision‐making

AbstractIn the present study, the selection of suitable shape memory polymers (SMPs) to be used as the matrix‐phase material with various (In)organic filler materials to achieve the required optimum multi‐stimuli response in shape memory polymer composites (SMPC) systems is analyzed. The selection of these materials is based on their mechanical and physical properties as well as other underlying factors such as cost, availability, shape recovery rate, and aesthetic characteristics. In this study, the entropy method was applied to estimate the weightages of the various criteria while the gray relation analysis (GRA) and the technique for order preference by similarity to ideal solution (TOPSIS). Multi‐criteria decision‐making (MCDM) approaches have been used to rank the suitable matrix‐phase polymer materials for manufacturing shape memory polymer composites (SMPC) system. A total of eight alternative SMP matrix‐phase materials based on a set of nine criteria were analyzed and ranked. The proposed methodology and the result obtained thereof have been illustrated in detail. The results obtained from TOPSIS and GRA methods have been compared to conclude the effects of the material properties on the ranking and the selection of the SMP materials. Among all the eight alternatives considered, thermoplastic polyurethane (TPU) was found to be the best material in both the MCDM methods. The material cost, resistivity, % elongation, and hydrophobicity present the most influencing properties on the SMP material selection, whereas density presents no effect on the SMP matrix material selection. The robustness of the results for the comparative analysis was verified using TOPSIS methodology to validate its reliability. It was revealed that the TPU, polycarbonate, polypropylene, and epoxy‐resin/poly(lactic acid) are the most dominant matrix‐phase SMP material alternatives when a deviation in the entropy weights of the primary evaluation criteria is applied. The novelty of this study is the exploration and application of statistical MCDM methods of engineering material selection problems based on a set of decision criteria, which can be time‐consuming and costly while using experimental analysis methods.Highlights SMPCs are applicable engineering materials thus making their research viable. SMPC systems can undergo various shape changes under external stimuli. Selection of matrix‐phase polymer is critical in achieving desired objectives. GRA and TOPSIS MCDM approaches have been applied in the selection process. TPU was found as the best material in both methods.

Application of the TOPSIS decision-making method for selecting a manufacturing technique for children’s furniture elements with therapeutic functions

AbstractMaking furniture or furniture elements that account for the needs of children at various stages of development or with psychomotor dysfunctions is very difficult. From the point of view of exploitation and production technology, it is difficult to select a specific material and manufacturing technique. In this article, the results of using the APEKS method, which is a type of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, are presented to select the best solution for the production of children’s furniture elements with surface structures similar to those of natural materials. Wood bark was selected as a material that, due to the sensory tactile sensations of dysfunctional children, could contribute to therapy and education. Comparative analysis was performed on the basis of the subtractive and additive methods used for manufacturing furniture products. Precise multiaxis milling of ash wood and 3D printing with fused filament fabrication technology using wood PLA filaments were carried out. The method used to select the best option considered quantitative and qualitative criteria in the assessment. Various parameters characterizing the surfaces were analyzed, such as geometric dimensions, hill heights, valley depths, and 3D surface parameters. The quality and surface roughness (Sa, Sz, Ssk, Sku, Sp, and Sv) parameters obtained based on 3D microscope measurements were determined. A scale of 1 to 10 was used to assess qualitative factors (i.e., usability and aesthetics). Based on the critical values obtained from the coefficient Kcri = 79.36, it was assumed that multiaxis wood milling was the best method for producing furniture elements with the required surface characteristics for use as therapeutic and educational tools for children with dysfunctions. The applied method allowed an effective evaluation of the compared variants of the production of furniture elements for customized applications.

Genetic Algorithm Optimization with Selection Operator Decider

AbstractGenetic Algorithm (GA) is a powerful and flexible meta-heuristic tool to deal with the complexity of optimization problems, as they are directly related to real-life situations. The primary goal of an optimization problem could be to obtain a solution with less effort and near-optimal rather than slow, improbable optimal. GAs serve this purpose by broadly exploring the possible solution space and using genetic operators. The performance of GAs can vary significantly depending on the genetic operators. Although each operator type has upsides and downsides, the selection operator greatly influences the GA’s performance. Conventional GAs initialize with predetermined genetic operators and continue with the same throughout all iterations. In this paper, dynamically adjusting the selection operators to the current progress of the iteration will be shown to be a crucial strategy to improve the performance of the GA. This study aims to propose a novel GA capable of harnessing multiple selection operators by a self-deciding operator structure, which is more advantageous at the current iteration. For this, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which is known as a simple and effective multi-criteria decision-making method, will be integrated into the GA by a proposed dynamic decision matrix. The proposed Selection Operator Decider Genetic Algorithm (SODGA) has unique properties with varying selection processes and is capable of using TOPSIS as a decider of the operator inside the iterations. The effectiveness of the presented SODGA framework will be analyzed by a Capacitated Vehicle Routing Problems (CVRPs) benchmark set.

Assessment of consolidative multi-criteria decision making (C-MCDM) algorithms for optimal mapping of polymer materials in additive manufacturing: A case study of orthotic application

ObjectiveThe objectives of this research are twofold. The primary goal is to introduce, investigate, and contrast consolidative multi-criteria decision-making (C-MCDM) approaches. The second objective is the investigation of five alternative additive manufacturing materials. MethodsIt integrates the subjective and objective weights using the Bayes hypothesis in conjunction with a normal method. Chang's Extent Analysis Method under fuzzy logic is used to estimate subjective weights and the CRITIC approach is used for assessing objective weights. Ranking techniques, including the simple ranking process (SRP), multi-objective optimization based on ratio analysis (MOORA), measurement alternatives and ranking according to compromise solution (MARCOS), and technique for order preference by similarity to ideal solution (TOPSIS) are applied. It also encompasses sensitivity analysis based on Kendall's coefficient of concordance and rank reversal phenomenon analysis. Spearman's rank correlation coefficient, a weighted rank measure of correlation, and rank similarity coefficient are among the metrics used to evaluate agreement between different approaches. It entails gathering expert opinions regarding the importance of various criteria as well as conducting extensive experiments. ResultsThe findings of the study indicate that polylactic acid is the best material to use for orthoses. When compared to the other MCDM approaches being discussed, SRP is the most reliable approach. It is also demonstrated that the SRP, MARCOS, and TOPSIS methods are rank reversal-free. Furthermore, SRP exhibits a very poor association with the TOPSIS technique but a strong correlation with the MOORA and MARCOS approaches. ConclusionsTo ensure results reliability, it is necessary to consider both the subjectivity and objectivity of weights as well as apply multiple MCDM methodologies in addition to sensitivity analysis.

A hybrid MCDM optimization for utilization of novel set of biosynthesised nanofluids on thermal performance for solar thermal collectors

Recent advancements in solar technology have spurred researchers to develop a precise and cost-effective method for monitoring solar radiation across diverse environmental conditions. This study focuses on optimizing performance parameters by replacing conventional nanofluids, produced through chemical and physical processes, with biosynthesized counterparts. Biosynthesized nanoparticles, derived from biological sources like microorganisms or plants, offer a promising avenue for enhancing solar panel efficiency. Through the construction and testing of an integrated photovoltaic thermal system, varying operating parameters have been explored to maximize photovoltaic efficiency. The highest overall efficiency, reaching approximately 62%, was achieved with biosynthesized Graphene oxide nanofluid under higher Direct Normal Irradiance (DNI) levels, while the lowest was observed with Aluminium oxide nanofluid at lower DNI values. Employing Multi-Criteria Decision methods such as Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), nanofluids were ranked based on criteria including Exergy loss, Surface temperature, Overall efficiency, and Electrical efficiency, with weights of 49.58%, 28.43%, 13.45%, and 8.54% respectively. TOPSIS prioritized Graphene Oxide as the most favourable nanofluid, followed by Copper-Oxide and Cerium-Oxide, while Aluminium-Oxide received the lowest priority in optimizing PV panel performance.

Experimental investigation and optimization of heat pipe heat exchanger performance under two distinct heat transport mediums through TOPSIS approach

This paper presents a comprehensive thermal performance analysis of the heat pipe heat exchanger by varying the input process parameters and optimizing the best condition for waste heat recovery through the TOPSIS technique. In this work, the heat pipe is constructed with a copper tube and segmented into evaporator and condenser sections carrying hot and cold fluids with a half-fill ratio. The heat pipe is oriented in 0° horizontally with the wick material made from stainless steel and works based on the capillary principle. The efficiency of the HPHE is evaluated by varying the input operational parameters, namely heat input, mass flow rate of hot fluid, inlet temperature of hot fluid, and mass flow rate of cold fluid, on response variables, specifically thermal resistance, energy balance ratio, universal heat transfer coefficient, and effectiveness. The experiment was designed for four factors at three levels as per Taguchi’s L27 orthogonal array, and the best optimising condition was determined by employing the multi-objective technique for order preference by similarity to ideal solution (TOPSIS) approach. The best input condition found was A3B1C1D3, in which the heat input was at 90 W, the mass flow rate of hot fluid was at 0.2 kg min−1, the mass flow rate of cold fluid was at 0.1 kg min−1, and the inlet temperature was at 60 °C. The effect of process parameters and their significance on output responses was analysed by executing an analysis of variance (ANOVA) statistical method. The mathematical regression model was generated, and validation was made to investigate the prediction error percentage.

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

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

An integrated framework for assessing renewable-energy supply chains using multicriteria decision-making: a study on Bangladesh

Abstract The depletion in non-renewable energy sources and a fast-growing population in Bangladesh are exacerbating the already existing energy scarcity, highlighting the need for an efficient and robust renewable-energy supply chain. The primary goal of this study is to evaluate the most optimized renewable-energy supply chain based on natural resource availability and government policies of Bangladesh. In the present study, four renewable energy resources, including solar, biomass, wind and hydropower, are studied and nine subcriteria are defined under four primary criteria for each supply chain. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) are multicriteria decision-making approaches used in this study to compare and choose the best renewable-energy supply chain. The relative significance of four supply-chain criteria for primary renewable energy in this study, namely energy procurement, production, operations and maintenance costs, and social and environmental impact, is gathered via a survey. The results of this research, supported by a comprehensive sensitivity analysis, indicate that hydropower is the best renewable-energy supply chain, followed by wind as a compromise solution, biomass and solar. The study also demonstrates that no energy source can satisfy all supply-chain criteria alone; each resource is better for a specific criterion—solar is better for procurement, hydropower is significant for production and wind is remarkable for operations and social impact. Therefore, to maximize output, renewable energy sources must be integrated. From Bangladesh’s perspective, for the first time, by using TOPSIS and VIKOR together, this study offers significant insights to establish an efficient and sustainable renewable-energy supply chain for practitioners, academics and policymakers.

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

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

Recycling electric arc furnace slag through tailoring of ambient-cured alkali-activated mortar mix using hybrid AHP-GRA and TOPSIS

Utilizing Electric arc furnace slag (EAFS) as a precursor for alkali-activated mortar production reduces waste and supports alternative cement binders. Since the mix design includes multiple factors, the Taguchi DoE was employed by varying the modifier (fly ash) to precursor (EAFS) ratio (0–75% by mass), activator to precursor ratio (A/P) (0.52–0.66), sodium hydroxide (SH) concentration (8–14 M), and sodium silicate to sodium hydroxide ratio (SS/SH) (1.5–3.0). Analytic hierarchy process (AHP) weighted Grey relational analysis (GRA), and technique for order preference by similarity to ideal solution (TOPSIS), were utilized to achieve a high performing mix. Performance characteristics encompass flowability (flow%), final setting time (FST), compressive strength (f'cu), and flexural strength (f'cr). The optimized mix possessed 75% flowability, 575 min FST, 38 MPa f'cu, 16 MPa f'cr, and 3.75% water absorption. Precursor-to-modifier ratio dominates the mix design. The three mixes—optimized (M17), top-ranked (M8), and least-ranked (M1) were micro-structurally examined using powdered X-ray diffraction, X-ray fluorescence, scanning electron microscopy, energy dispersive X-ray spectroscopy, and Fourier transform infrared spectroscopy.

Safety resilience evaluation of hydrogen refueling stations based on improved TOPSIS approach

The traditional use of TOPSIS evaluation for assessing the resilience of hydrogen refueling stations results in overly one-sided outcomes and fails to identify the indicators that have a significant impact on the resilience of the stations. Therefore, this paper proposes an improved method for evaluating the safety resilience of hydrogen refueling stations based on CRITIC-DEMATEL and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The CRITIC-DEMATEL combination weighting method is employed to determine weights, thus improving the TOPSIS method. This method is then applied to assess the resilience level of hydrogen refueling stations. Then, introducing a Conditional Logistic Regression model identifies indicators with significant impact on resilience. The improved TOPSIS method enhances the reliability and accuracy of evaluation results. The Conditional Logistic Regression model provides a more accurate delineation of the impact size of indicators on the safety resilience of hydrogen refueling stations compared to traditional methods.

Evaluation of management practices in rice–wheat cropping system using multicriteria decision-making methods in conservation agriculture

In this study, we employed two multiple criteria decision-making (MCDM) methods, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and the Analytic Hierarchic Process (AHP), to determine the best management choice for the cultivation of wheat with a regime of conservation agriculture (CA) practices. By combining alternative tillage approaches, such as reduced tillage and zero tillage, with the quantity of crop residues and fertilizer application, we were able to develop the regime of CA practices. The performance of the regimes compared to the conventional ones was then evaluated using conflicting parameters relating to energy use, economics, agronomy, plant protection, and soil science. TOPSIS assigned a grade to each alternative based on how close it was to the ideal solution and how far away it was from the negative ideal solution. However, employing AHP, we determined the weights of each of the main and sub-parameters used for this study using pairwise comparison. With TOPSIS, we found ZERO1 (0% residue + 100% NPK) followed by ZERO4 (50%residue + 100% NPK), and ZERO2 (100% residue + 50% NPK) were the best performing tillage-based alternatives. To best optimize the performance of wheat crops under various CA regimes, TOPSIS assisted the decision-makers in distinguishing the effects of the parameters on the outcome and identifying the potential for maneuvering the weak links. The outcomes of this investigation could be used to improve management techniques for wheat production with CA practices for upscaling among the farmers.

Research on the Preference of Mobile Phone Photography in Leisure Life

The way of communication and thinking are reconstructed by mobile phone photography. Due to the advancement of technology and economy, mobile phone chips have become advanced and the quality of photography has improved. In addition to the communication function, the camera function of smartphones is more valued by consumers and mobile phone manufacturers. Apps with built-in cameras, photo editing and social websites are constantly being upgraded., and mobile phone photography and social media has been combined. The influence and interaction of digital images in social leisure makes mobile phone photography play an important role in people's communication on social websites. This research mainly analyzed the preferences of mobile phone photography in leisure, and the Evaluation Grid Method (EGM) of Miryoku Engineering was applied to extract the attractive attributes of mobile phone photography. William DuMouchel continuous Kano model was applied to summarize the evaluation factors of young users on the quality of mobile phone photography. SPSS.17 statistical software was applied for reliability analysis, and IPA satisfaction was applied as the weighted factor evaluation. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied to evaluate preference factors. The purpose of this research is to analyze the preferences of mobile phone photography in leisure life through these research methods, and also to provide a reference for the future promotion of mobile phone photography.

Modified jaya optimization and TOPSIS for determining the optimal frequency in LF-HVac

This study presents a novel approach of hybrid modified Jaya and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) optimization (MJTO) for efficiently determining the optimal frequency of low-frequency HVac lines. The proposed technique takes frequency constraints and static synchronous compensator (STATCOM) equations into the account. The objective functions in this study are the minimization of power losses, voltage deviation, and voltage stability index of transmission system. This study examines two types of objective functions: the traditional quadratic function and the enhanced quadratic function. These functions are used to incorporate more accurate modeling that considers low frequency. The MJTO is employed for global searching and gradient-free optimization to determine optimal control settings. The Modified Newton-Raphson method is utilized to minimize the mismatch power between power flow (PF) and STATCOM equations. The proposed method utilizes a hybrid technique to handle inequality constraints by preserving feasible solutions while keeping the original objective function intact. The suggested methodology is successfully implemented in extensive benchmark testing of IEEE systems on the 39 Bus and 14 Bus. The optimal solutions obtained are validated by comparing them to recent algorithms as mentioned in literature. The analytical and Statistical results indicates that the proposed optimization strategy is more optimistic for the LF-HVac system.

Optimization of thermal deformation process parameters of 2219 Al matrix composites

To investigate the deformation behavior and optimize the hot processing parameters for 2219 aluminum matrix composite, the constitutive equation and hot processing maps were established. Initially, hot compression experiments on 2219 aluminum alloy (2219A) were conducted using a Gleeble-3500 thermal simulation tester to obtain high-temperature rheological data. The deformation temperatures tested were 573, 623, 673, 723, and 773 K, with strain rates of 0.01, 0.1, 1, and 10 s−1, and a maximum deformation of 60%. Subsequently, material parameters such as the activation energy, Zener–Hollomon parameter, power dissipation efficiency, and instability coefficient for 2219A were calculated. Analytical expressions for these material and deformation parameters were formulated, and a hot processing map for 2219A was constructed. The hot processing map, along with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and the Entropy Weight Method (EWM), were used to optimize the thermal deformation process parameters. The stability processing area and the optimal processing area identified by both methods were largely consistent. According to the hot processing map, the stability processing areas were identified in the temperature ranges of 580-660 K and 690-773 K with strain rates of 0.01 s−1 and 0.01-0.6 s−1, respectively. Using the TOPSIS and EWM methods, the stability processing areas were defined between 573-640 K and 0.01 s−1, 640-690 K and 0.01-0.1 s−1, and 690-773 K and 0.01-1 s−1. The consistency and accuracy of these optimization results were confirmed through microstructure analysis.

Evaluating water resource carrying capacity in Pearl River-West River economic Belt based on portfolio weights and GRA-TOPSIS-CCDM

Research on the spatial differences in the water resource carrying capacity (WRCC) and their causes can effectively address the imbalance of water resources and enhance the economic, social, and ecological properties of the basin. Sixteen cities in the Pearl River-West River Economic Belt (PR-WR EB) were selected for this study from 2017 to 2021. Based on the theoretical Driving-Pressure-State-Impact-Response model and related research, an evaluation model for WRCC was constructed. The weights obtained from the entropy method and CRITIC method were combined and assigned using the geometric method, after which they were applied to a model that combines the grey relational analysis (GRA), technique for order preference by similarity to ideal solution (TOPSIS), and coupling coordination degree model (CCDM) to calculate the evaluation value of the WRCC subsystem. Meanwhile, the spatial variability and spatial correlation of WRCC were analyzed using the natural breakpoint method, Moran's index, clustering characteristics, and outlier index. The overall WRCC of the PR-WR EB showed stable characteristics from 2017 to 2021. In terms of spatial correlation, significant spatial clustering characteristics were observed. The subsystem coupling degree ranged between 0.6 and 1.0 for all subsystems, thus exhibiting good coupling efficiency; meanwhile, the coordination degree was low and ranged between 0.17 and 0.36.

A probabilistic performance-based analysis approach for a vibrator-ground interaction system

There is an increasing interest in investigating the effects of input uncertainties on dynamic systems. The probabilistic analyses for a vibrator-ground (VG) interaction system are rare and the effects of system uncertainties need to be revealed. This study aims to present an approach for the probabilistic performance-based analysis of the VG system under multi-source uncertainties. The probabilistic model of the VG system is constructed on the basis of the Monte Carlo (MC) simulation combined with the Latin Hypercube Sampling (LHS) method, while the artificial neural networks optimized by the genetic algorithms are employed to reduce the large computational expenses in the MC simulation. Then, a multi-criteria sensitivity analysis is presented by using a technique for order preference by similarity to ideal solution (TOPSIS) to evaluate the effects of input uncertainties on the dynamic performance of the vibrator. Finally, a probabilistic simulation analysis of the VG system is conducted by implementing the presented approach. The results demonstrate the effectiveness of the presented probabilistic performance-based analysis approach for the VG system and evaluate the effects of input uncertainties on the dynamic performance of the system.

A Theory of Best Choice Selection through Objective Arguments Grounded in Linear Response Theory Concepts

In this study, we propose how to use objective arguments grounded in statistical mechanics concepts in order to obtain a single number, obtained after aggregation, which would allow for the ranking of “agents”, “opinions”, etc., all defined in a very broad sense. We aim toward any process which should a priori demand or lead to some consensus in order to attain the presumably best choice among many possibilities. In order to specify the framework, we discuss previous attempts, recalling trivial means of scores—weighted or not—Condorcet paradox, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), etc. We demonstrate, through geometrical arguments on a toy example and with four criteria, that the pre-selected order of criteria in previous attempts makes a difference in the final result. However, it might be unjustified. Thus, we base our “best choice theory” on the linear response theory in statistical physics: we indicate that one should be calculating correlations functions between all possible choice evaluations, thereby avoiding an arbitrarily ordered set of criteria. We justify the point through an example with six possible criteria. Applications in many fields are suggested. Furthermore, two toy models, serving as practical examples and illustrative arguments are discussed.

Analysis comparison model of ROC-TOPSIS, ROC-WP, and ROC-Electre methods for supporting the funding decision to micro-enterprises Indonesia

Micro-business is an essential business unit that supports the economies of countries in the world, including Indonesia. Unfortunately, micro-entrepreneurs often find difficulties to obtain financial assistance from government to develop their businesses. In Indonesia, the smallest number of micro industries can be found in West Papua Province, where one of the underdeveloped areas in the province is the Sorong City. Therefore, a study was conducted to determine the application of decision support methods in selecting micro-enterprises in Sorong that are eligible for business funding. The decision support method chosen is the collaboration between the Rank-Order Centroid (ROC) method, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Weight Product (WP), and Elimination Et Choix Traduisant la Realite (Electre). With the analysis conducted on 10 samples of micro-enterprises in Sorong City, it was found that the micro-enterprises that were most eligible to receive assistance is Raya Helm based on ROC-TOPSIS, ROC-WP, and ROC-Electre. Then, sensitivity analysis results the ROC-Electre as the best method with the highest sensitivity percentage. Therefore, the implementation of these methods for supporting the funding decision to micro-enterprises in Sorong was successfully carried out with the output being the micro-enterprise rating results.