Technique Of Order Preference Similarity To The Ideal Solution Research Articles

Unveiling Afghanistan's wind and hydrogen potential: a comparative study.

Given the detrimental environmental impacts of fossil fuels, there is a gradual worldwide shift towards renewable energy sources. Wind power, renowned for its cost-effectiveness and simplicity, has been widely embraced. Despite Afghanistan facing significant challenges in its energy sector, its considerable wind energy potential offers a chance to mitigate some of these issues. This study employed a multi-criteria decision-making approach to evaluate potential wind-hydrogen project sites in Afghanistan, encompassing economic, technical, social, risk, and environmental considerations. Five criteria and sub-criteria for wind-hydrogen project implementation were analyzed using the Step-wise Weight Assessment Ratio Analysis (SWARA) method. The Weighted Aggregated Sum Product Assessment (WASPAS), Additive Ratio Assessment (ARAS), Evaluation based on Distance from Average Solution (EDAS), and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) methods were then applied to prioritize provinces for wind-hydrogen project implementation. The analysis found that the Duration of the Payback Period and Levelized Cost of Electricity (LCOE) with weight of 6.6% and 5.6% were critical sub-criteria. Farah, Herat, and Nimroz emerged as Afghanistan's most promising provinces for wind-hydrogen energy development. In Farah, utilizing 900-kW turbines, it is feasible to generate 2679.8 MWh of electricity and produce 43.4 tons of hydrogen annually, with a Levelized Cost of Electricity of 0.0690 $/kWh and Levelized Cost of Hydrogen of 1.747 $/kg.

Personnel Selection Based on the LBWA, TOPSIS and GRA Methods: A Case Study on Foreign Trade Company

Recruitment and personnel selection are affected by significant factors. Thus, personnel selection is one of the main decision-making problems for a company’s long-term survival. The objective of this study is to identify the most suitable candidate for the export department of a company operating in Mersin, using the Level Based Weight Assessment (LBWA)-based Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA) methods. The criteria were determined based on the literature review and experts’ opinions. The weight of criteria was calculated by the LBWA method, and the alternatives (candidates) were ranked using the TOPSIS and GRA methods. The LBWA results showed that fluency in a foreign language and team player were the most and least important criteria, respectively. The results from both methods (TOPSIS and GRA) suggested different candidates for the relevant positions. Furthermore, sensitivity analyses were conducted to assess the validity and robustness of the results. In conclusion, the findings of this study provide valuable insights to decision-makers involved in the personnel selection process.

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company.

Cell formation (CF) and machine cell layout are two critical issues in the design of a cellular manufacturing system (CMS). The complexity of the problem has an exponential impact on the time required to compute a solution, making it an NP-hard (complex and non-deterministic polynomial-time hard) problem. Therefore, it has been widely solved using effective meta-heuristics. The paper introduces a novel meta-heuristic strategy that utilizes the Genetic Algorithm (GA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to identify the most favorable solution for both flexible CF and machine layout within each cell. GA is employed to identify machine cells and part families based on Grouping Efficiency (GE) as a fitness function. In contrast to previous research, which considered grouping efficiency with a weight factor (q = 0.5), this study utilizes various weight factor values (0.1, 0.3, 0.7, 0.5, and 0.9). The proposed solution suggests using the TOPSIS technique to determine the most suitable value for the weighting factor. This factor is critical in enabling CMS to design the necessary flexibility to control the cell size. The proposed approach aims to arrange machines to enhance GE, System Utilization (SU), and System Flexibility (SF) while minimizing the cost of material handling between machines as well as inter- and intracellular movements (TC). The results of the proposed approach presented here show either better or comparable performance to the benchmark instances collected from existing literature.

Fuzzy TOPSIS-based Secure Neighbor Discovery Mechanism for Improving Reliable Data Dissemination in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) being an indispensable entity of the Internet of Things (IoT) are found to be more and more widely utilized for the rapid advent of IoT environment. The reliability of data dissemination in the IoT environment completely depends on the secure neighbor discovery mechanism that are utilized for effective and efficient communication among the sensor nodes. Secure neighbor discovery mechanisms that significantly determine trustworthy sensor nodes are essential for maintaining potential connectivity and sustaining reliable data delivery in the energy-constrained self organizing WSN. In this paper, Fuzzy Technique of Order Preference Similarity to the Ideal Solution (TOPSIS)-based Secure Neighbor Discovery Mechanism (FTOPSIS-SNDM) is proposed for estimating the trust of each sensor node in the established routing path for the objective of enhancing reliable data delivery in WSNs. This proposed FTOPSIS-SNDM is proposed as an attempt to integrate the merits of Fuzzy Set Theory (FST) and TOPSIS-based Multi-criteria Decision Making (MCDM) approach, since the discovery of secure neighbors involves the exchange of imprecise data and uncertain behavior of sensor nodes. This secure neighbor is also influenced by the factors of packet forwarding potential, delay, distance from the Base Station (BS) and residual energy, which in turn depends on multiple constraints that could be possibly included into the process of secure neighbor discovery. The simulation investigations of the proposed FTOPSIS-SNDM confirmed its predominance over the benchmarked approaches in terms of throughput, energy consumption, network latency, communication overhead for varying number of genuine and malicious neighboring sensor nodes in network.

Intuitionistic Fuzzy Sets with Ordered Pairs and Their Usage in Multi-Attribute Decision Making: A Novel Intuitionistic Fuzzy TOPSIS Method with Ordered Pairs

Intuitionistic Fuzzy Sets with Ordered Pairs (IFSOP) are the recent extension of intuitionistic fuzzy sets by incorporating functional and dysfunctional points of view into the definition of membership functions. This paper extends the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) method to the Intuitionistic Fuzzy TOPSIS (IF TOPSIS) with ordered pairs method and applies it to a multi-criteria risk-based supplier selection problem under fuzziness. IF TOPSIS with ordered pairs involves finding a positive ideal solution and a negative ideal solution, and measuring the distance between each alternative and these solutions. The final ranking of the alternatives is obtained based on the proportion of distances between the positive and negative ideal solutions. By asking functional and dysfunctional questions in this ranking process, the developed IF TOPSIS with ordered pairs method incorporates the accuracy and consistency of expert judgments, enhancing the decision-making process. A sensitivity analysis is also presented in order to show the robustness of the rankings obtained by IF TOPSIS with ordered pairs.

Comparative assessment between DFA, NSGA-II coupled with TOPSIS, GRA, and TOPSIS for multiobjective optimization of gray cast iron turning process using CBN insert

This work deals with the optimization of the machining process of EN-GJL-250 gray cast iron with a CBN/Tin-coated insert. The multiobjective optimization was performed by four methods, in order to find the optimal cutting conditions (cutting speed (Vc) , feed rate ( f), and depth of cut (ap)) that would satisfy the goals of improving surface quality, minimizing power consumption, and maximizing productivity simultaneously. The aim is to evaluate the performance of different techniques, namely desirability function approach, Grey relational analysis, technique of order preference similarity to the ideal solution (TOPSIS), and nondominated sorting genetic algorithm coupled with TOPSIS. The outcomes revealed that the methods propose different solutions, where significant divergences were mainly observed in power consumption and material removal rate objectives. This raises the issue of selecting the most appropriate optimization method for industrial needs and confirms the need for further efforts in this field, i.e. optimization of the machining process.

Optimal shape selection of pseudo-rigid-body model compliant centrifugal clutch manufactured by thermoplastics using TOPSIS approach

This paper discusses and analyses a compliant centrifugal clutch with different materials. Compliant centrifugal clutch specimens required for investigation are prepared by an injection moulding process. Experiments are conducted based on the experiments designed as per Taguchi’s Design of Experiment (DoE); an L27 Orthogonal Array (OA) is formulated and a multi-response optimisation approach Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) is adopted for optimising torque carrying capacity, wear rate and, contact temperature. Experiments are conducted for three different polymer materials viz., polypropylene (PP), Delrin (DE) and polyurethane (PU) with three different profiles, circular (CR), elliptical (EL) and square fillet (SF), along with varying rotating speeds. The outcome shows that by changing the clutch material and flexure profiles, a substantial variation in torque carrying capacity, wear rate and contact temperature is observed.

Optimizing the Distribution Network of a Bakery Facility: A Reduced Travelled Distance and Food-Waste Minimization Perspective

There are many logistics nuances specific to bakery factories, making the design of their distribution network especially complex. In particular, bakery products typically have a shelf life of under a week. To ensure that products are delivered to end-customers with freshness, speed, quality, health, and safety prioritized, the distribution network, facility location, and ordering system must be optimally designed. This study presents a multi-stage framework for a bakery factory comprised of a selection methodology of an optimum facility location, an effective distribution network for delivery operations, and a practical ordering system used by related supply chain actors. The operations function and distribution network are optimized using a multi-criteria decision-making method comprised of the Analytic Hierarchy Process (AHP) to establish optimization criteria and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to select the optimal facility location. The optimal distribution network strategy was found using an optimization technique. This framework was applied to a real-life problem for a bakery supply chain in the Western Region, Saudi Arabia. Using a real-life, quantitative dataset and incorporating qualitative feedback from key stakeholders in the supply chain, the developed framework enabled a reduction in overall distribution costs by 14%, decreasing the total travel distance by 16%, and decreasing estimated food waste by 22%. This result was primarily achieved by solving the facility location problem in favor of operating two factories without dedicated storage facilities and implementing the distribution network strategy of direct shipment of products from the bakery to customers.

Stochastic Triangular Fuzzy Number (S-TFN) Normalization: A New Approach for Nonmonotonic Normalization

Data normalization is essential in many fields, such as speech recognition, deep learning, machine learning, and optimization. Many researchers focus on developing various normalization techniques, such as min-max, score-based, maximum, vector, or sum, on processing data efficiently. However, classical normalization methods may need to be improved in Multi-Criteria Decision-Analysis (MCDA), where criteria are often non-monotonic. This paper proposes a new approach for identifying and normalizing nonmonotonic criteria in MCDA. The method combines a stochastic optimization technique - a genetic algorithm - with normalization based on triangular fuzzy numbers. This paper compares the proposed approach with classical normalization techniques, such as min-max, maximum, vector, or non-linear, using the classical MCDA method - the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS). The study results showed that the proposed approach to normalizing non-monotonic criteria yields a better match between decision preferences and the actual reference model than traditional normalization methods. Optimization using the genetic algorithm helps identify the means of triangular fuzzy numbers, which generate more accurate results. Analysis of the R2 coefficient confirms a very good fit of the proposed model. Therefore, the new non-linear normalization approach in MCDA presented in this work has great potential in adjusting decision preferences to non-monotonic criteria. Future research should continue on the accuracy of this approach in identifying non-linear decision models and apply it to other MCDA methods and real-world decision problems.

Multi-criteria screening of carbon dioxide utilization products combined with process optimization and evaluation

Climate change induced by the increasing concentration of carbon dioxide (CO2) in the atmosphere has exhibited widespread impacts to ecosystems. The carbon dioxide utilization (CDU) technology is forecasted to significantly contribute towards decarbonization, providing new pathways for chemical production with carbon dioxide as feedstock. The key challenge that affects the identification of a promising CDU technology is its efficient integration with chemical process design. To this end, this study proposes a multi-criteria screening framework of carbon dioxide utilization technologies combined with process optimization and evaluation. The proposed framework combines process evaluation method and multi-criteria decision analysis (MCDA) method with the aim to identify the most promising technologies, where process evaluation provides process data for multi-criteria decision analysis and multi-criteria decision analysis reaches comprehensive decision. In developing this framework, a thermodynamic-based process model is established to obtain the screening criteria, return on investment (ROI) and carbon dioxide utilization ratio (CDUR), through process simulation, optimization and evaluation. A multi-level screening scheme is presented to assess the technical, economic and environmental performance. The multi-criteria decision analysis method based on Technique of order preference similarity to the ideal solution (TOPSIS) is adopted to obtain the ranking results. The results show that ethanol, methanol and ethylene carbonate are the most promising carbon dioxide utilization products in chemical process. This study provides serviceable guidelines for future development of carbon dioxide utilization technologies combined with chemical process design.

Assessing adaptability of the water resource system to social-ecological systems in the Beijing-Tianjin-Hebei region: Based on the DPSIR-TOPSIS framework

To optimize the overall layout of water resource allocation in the Beijing-Tianjin-Hebei region, the adaptability of the water resource system to the regional social-ecological systems has to be enhanced. Based on the driver-pressure-state-impact-response (DPSIR) framework, this study constructs an evaluation index system to analyze the adaptability mechanisms of Beijing-Tianjin-Hebei’s water resource system according to the three major constituent social-ecological systems (i.e., economic, social, and ecological systems). Moreover, it adopts the technique of order preference similarity to the ideal solution (TOPSIS) to comprehensively evaluate the adaptability of Beijing-Tianjin-Hebei’s water resource system based on three constituent social-ecological systems (i.e., economic, social, and ecological systems) and identifies the spatiotemporal differentiation characteristics of the region. Our results showed that, ① from 2000 to 2020, the adaptability of Beijing-Tianjin-Hebei’s water resource system, as a whole, significantly improved. In terms of stages, from 2000 to 2007, the adaptability of the water resource social system was significantly higher than that of economic and ecological systems in the region. From 2008 to 2015, by accelerating the transformation and upgrading of industrial structures, improving the efficiency of economic water utilization, and strengthening the governance of the water ecosystem, the adaptability of water resource economic and ecological systems rapidly improved; however, that of the water resource ecological system was still the lowest. Additionally, the adaptability of the water resource economic system exceeded that of the social system. From 2016 to 2020, the gap in adaptability of the water resource system to all three major constituent systems gradually narrowed. By 2020, the three systems entered a relatively balanced development stage, with the adaptability of the entire water resource system and the three major constituent systems maintaining a high level. ② The economic system was significantly affected by per capita GDP, per capita water resources, and the efficiency of economic water utilization. Additionally, the social system was significantly affected by water consumption per unit of irrigation area. Meanwhile, the ecological system was significantly influenced by precipitation, water pollution discharge performance indicators, and the structure optimization indicators of water supply. According to the evaluation results, we propose countermeasures and provide recommendations to optimize the overall layout of water resource allocation and promote the coordinated management of water resources in the Beijing-Tianjin-Hebei region.

Cathode side transport phenomena investigation and Multi-Objective optimization of a tapered parallel flow field PEMFC

A Proton Exchange Membrane Fuel Cell (PEMFC) provides stable, emission-free, high-efficiency power. Water management and durability of PEMFCs are directly affected by transport phenomena at the cathode side. In the present study, transport phenomena are investigated and optimized in a tapered parallel flow field. Main channels in the flow field are tapered, which increases limiting current density by 41%. Two objectives, i.e. water saturation and transport resistance, are considered metrics for transport phenomena in a tapered parallel flow field PEMFC. Operating pressure, temperature, stoichiometries at both sides, and the porosity of gas diffusion layers are selected as parameters to be optimized. Two functions are generated for objectives by integrating 3D multiphase-flow computational fluid dynamics and Response Surface Methodology. Multi-Objective Optimization (MOO) is carried out with two different methods. Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are employed to produce two challenging Pareto fronts. The results demonstrate that MOPSO performs better than NSGA-II. MOPSO recognized quite the same Pareto front with lower runtime. In the last step, the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) is used to select an optimum point from the Pareto front. The results are compared against experimental data, and good correspondence is observed. The optimum features are temperature 323, pressure 1 atm, anode stoichiometry 3, cathode stoichiometry 2.62, and porosity 0.68. The porosity and pressure played the most significant roles in determining water saturation and resistance.

Evaluation of Production of Digital Twins Based on Blockchain Technology

A blockchain, as a form of distributed ledger technology, represents the unanimity of replication, synchronization, and sharing of data among various geographical sites. Blockchains have demonstrated impressive and effective applications throughout many aspects of the business. Blockchain technology can lead to the advent of the construction of Digital Twins (DTs). DTs involve the real representation of physical devices digitally as a virtual representation of both elements and dynamics prior to the building and deployment of actual devices. DT products can be built using blockchain-based technology in order to achieve sustainability. The technology of DT is one of the emerging novel technologies of Industry 4.0, along with artificial intelligence (AI) and the Internet of Things (IoT). Therefore, the present study adopts intelligent decision-making techniques to conduct a biased analysis of the drivers, barriers, and risks involved in applying blockchain technologies to the sustainable production of DTs. The proposed model illustrates the use of neutrosophic theory to handle the uncertain conditions of real-life situations and the indeterminate cases evolved in decision-makers’ judgments and perspectives. In addition, the model applies the analysis of Multi-criteria Decision Making (MCDM) methods through the use of ordered weighted averaging (OWA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to achieve optimal rankings for DT production providers based on consistent weighted decision-maker’s judgments in order to maintain and to assure sustainability. An empirical study is applied to the uncertain environment to aid decision-makers in achieving ideal decisions for DT providers with respect to various DT challenges, promoting sustainability and determining the best service providers. The Monte Carlo simulation method is used to illustrate, predict, and forecast the importance of the weights of decision-makers’ judgments as well as the direct impact on the sustainability of DT production.

Sustainable partner selection and order allocation for strategic items: an integrated multi-stage decision-making model

ABSTRACT Current environmental issues and government requirements, together with pressure from the market and other stakeholders, emphasise the importance of partner selection in constructing and operating sustainable supply chains. Strategic items, which carry both high supply risk and high importance of purchase, are particularly important in sustainable supply chains. This paper presents an integrated decision-making model, which aims to solve the partner selection and order allocation problem for strategic items in sustainable supply chains. In the proposed model, weightings of different decision-makers are first calculated using Trapezoidal Fuzzy Numbers. Then, Taguchi loss function is used to evaluate the relative importance of potential partners, with the weighting results of criteria by Best-Worst Method. Finally, considering the weights of different potential partners, Particle Swarm Optimisation (PSO) is used to solve the multi-objective programming problem, and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) is applied to identify the most appropriate Pareto solution for sustainable partner selection and order allocation of strategic items. An illustrative application of the proposed model is undertaken in a leading Chinese LED lighting manufacturer to show its effectiveness and applicability.

Graphene/Al6061 nanocomposite selection using TOPSIS and EXPROM2 multi-criteria decision-making methods

Inventions of novel nanocomposites are highly recommended due to predominant physical, mechanical characteristics and infinite design possibilities. Manufacturing of these nanocomposites is a costly and challenging task, as it can show unpredictable characteristics behavior and different order of importance considered prior to manufacturing. Therefore, the selection of the most suitable nanocomposite fulfilling characteristics requirement in specific applications is highly needed. To tackle this difficulty, a novel approach to use multicriteria decision-making (MCDM) models to the nanocomposite selection was presented in this research. The TOPSIS (technique of order preference similarity to the ideal solution) and extended PROMETHEE II (EXPROM2) (preference ranking organization method for enrichment of evaluations) multicriteria decision models were used for the best alternative selection of novel graphene/Al6061 nanocomposites. The graphene/Al6061 nanocomposites were fabricated and characterized to tackle the low strength and hardness deficiency of Al6061. The criteria weights were determined by AHP (analytical hierarchy process). All nanocomposites were ranked and results were compared. The best materials were found as 10G (10% graphene reinforcement) due to excellent ultimate tensile strength (279.69 MPa), hardness (93.73 BHN), proof stress (248.55 MPa), though material cost (506.81 Rs) is high. The results of MCDM analysis were found satisfactory and can be utilized in design and manufacturing in aerospace, automobile, transportation, defense, and other applications.

A hybrid approach based on ELECTRE III‐genetic algorithm and TOPSIS method for selection of optimal COVID‐19 vaccines

COVID‐19 pandemic poses unprecedented challenges to the world health system, prompting academics and health professionals to develop appropriate solutions. Researchers reported different COVID‐19 vaccines introduced by institutions and companies around the globe, which are at different stages of development. However, research developing an integrated framework for selecting and ranking the optimal potential vaccine against COVID‐19 is minimal. This paper aimed to fill this gap by using a hybrid methodology based on ELimination Et Choice Translating REality III (ELECTRE III)–Genetic Algorithm (GA) and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) approach to select the optimal SARS‐CoV‐2 vaccine. ELECTRE III method yields a fathomable analysis of the concordance index, while GA is known for its ability to disaggregate decision‐making preferences from holistic decisions. TOPSIS is preferred for picking an ideal and an anti‐ideal solution. Thus, combining ELECTRE III‐GA and TOPSIS is considered the best model to assess vaccines against the pandemic. The results confirm that the best vaccines rely on a high level of safety, efficacy, and availability. Our developed evaluation framework can help healthcare professionals and researchers gain research information and make critical decisions regarding potential vaccines against the disease.

A private sustainable partner selection model for green public-private partnerships and regional economic development

Many cities are struggling to keep pace with limited budgets and rapid growth. Economic development models involving public-private partnerships (P3s) can help drive economic revitalization. The choice of partners plays a vital role in the success or failure of sustainable P3 initiatives. In this study, we propose a novel integrated sustainable private partner selection framework in P3s. The proposed model is composed of the best-worst method (BWM), the weighted influence non-linear gauge system (WINGS), and the technique of order preference similarity to the ideal solution (TOPSIS). The BWM is used to identify the importance weights of the economic, environmental, social, and technological criteria. The WINGS method uses ideographic causal maps to analyze the intertwined criteria and their causal relations. TOPSIS is used to rank and select the private partners that will bring the “best value” to the partnership. We demonstrate the proposed method's applicability in a P3 initiative for sustainability, gentrification and neighborhood revitalization, and economic development in a northeastern US city. In this initiative, low density, low cost, and biodegradable agricultural waste and mushroom fibers are grown in vacant buildings to be used as a Styrofoam packaging replacement.

Multi-Criteria Analysis of Green Bonds: Hybrid Multi-Method Applications

There is an increasing pressure by the community and customers forcing companies to insert environmental concerns in their practices. To help companies initiatives, the green bonds market was created. Our research question is “How to select bonds in a growing billion-dollar market?” This paper presents a multi-criteria decision analysis (MCDA) model to enable investors identify opportunities based not only in opinions, but grounded on objective facts. Analytic hierarchy process (AHP), complex proportional assessment (COPRAS), full consistency method (FUCOM), step-wise Weights Assessment Ratio Analysis (SWARA), and technique of order preference similarity to the ideal solution (TOPSIS) are MCDA methods applied in this paper. Top-fifteen green bonds ranked by specialized media were assessed with the proposed MCDA model. Criteria included the Environmental Performance Index (EPI) proposed by Yale University, and common financial indicators as assets, risks (β), and dividends. The new ranks from MCDA are compared each other and compared with the rank published by specialized media.

Multi-criteria decision approach to select carbon dioxide and hydrogen sources as potential raw materials for the production of chemicals

The use of fossil resources has lead to great increase in concentration of carbon dioxide (CO2) in the atmosphere beyond sustainable limits, which causes environmental issues such as greenhouse gas effect, climate change and extreme weather events and threats the human life. Thus, several researches have been focused on mitigate this problem. Possible strategies involve implementing technologies of carbon capture storage and utilization. Among them, integrated processes for carbon dioxide capture and its conversion into value-added products have gained attention. Carbon dioxide hydrogenation is among the most developed technologies for its conversion, but requires an external hydrogen (H2) source. Since the conversion of carbon dioxide is highly energy-demanding, assessing its overall process sustainability requires a comprehensive study on the whole system, including its raw material sources (carbon dioxide and hydrogen). Thus, this work proposes a multi-criteria framework to select suitable sources of carbon dioxide and hydrogen to be used in the conversion of carbon dioxide. Potential sources of carbon dioxide (from power plants to ethanol fermentation) and hydrogen (from dedicated production to by-product hydrogen) were evaluated considering environmental, economic, and technical aspects associated with the usage of each source. The Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) is the multi-criteria decision analysis method used to aggregate the criteria and to rank each source individually and further in a pair-wise assessment to identify potential synergic combinations between carbon dioxide and hydrogen sources. Results suggested that using carbon dioxide from natural gas steam reforming, iron and steel industries, ethylene oxide and other high concentration point sources may be the ideal choice for sustainability. The analysis also indicated that hydrogen may be more sustainable if it is a process by-product or is produced by low-cost wind-powered electrolysis. It is important to consider that the analysis is based on several specific data inputs and assumptions, and that a lower score does not mean that the source is not worth investing in.

TOPSIS model based on entropy and similarity measure for market segment selection and evaluation

PurposeThe paper aims to propose a practical model for market segment selection and evaluation. The paper carries out a technique of order preference similarity to the ideal solution (TOPSIS) approach to make an operation systematic dealing with multi-criteria decision- making problem.Design/methodology/approachIntroducing a multi-criteria decision-making problem based on TOPSIS approach. A new entropy and new similarity measure under neutrosopic environment are proposed to evaluate the weights of criteria and the relative closeness coefficient in TOPSIS model.FindingsThe outcomes show that the TOPSIS model based on new entropy and similarity measure is effective for evaluation and selection market segment. Profitability, growth of the market, the likelihood of sustainable differential advantages are the most important insights of criteria.Originality/valueThis paper put forward an effective multi-criteria decision-making dealing with uncertain information.