Decision-making Approach Research Articles

Organizational Innovativeness: The Role of Innovation Adoption Capability

Organizations develop capabilities to manage activities and generate value for their customers. This study focuses on the adoption capability of innovation adopting organizations. Adoption capability is a complex, and multidimensional concept that includes a high level of uncertainty, making it difficult to measure. This study attempts to find a systematic methodology to measure the innovation adoption capability of firms, through which management can monitor and evaluate a firm’s strengths and weaknesses in each capability dimension compared with competitors, predict their future needs for improvements, and plan necessary activities to stay competitive in the market. The proposed model considers multiple quantitative and qualitative adoption capability dimensions simultaneously and utilizes a relatively comprehensive set of adoption capability elements by developing 35 items and classifying them into six dimensions. This study uses the fuzzy weighted aggregated sum product assessment (WASPAS) method as a multi-attribute decision-making (MADM) approach. The model is applied to three competing firms in the consumer electronics sector, and the results are discussed.

Development of a sequential sampling plan for classifying Thrips tabaci (Thysanoptera: Thripidae) populations in onion fields.

Onion thrips, Thrips tabaci Lindeman, is a global pest of onion crops, causing substantial economic damage by diminishing bulb yields and transmitting plant pathogens. Insecticides are used to manage T. tabaci infestations with control decisions traditionally based on action thresholds that require visually counting thrips on a fixed, predetermined number of onion plants per field. However, this approach for treatment decisions is inefficient when thrips populations are well above or below the action threshold. The aim of this research was to develop a sequential sampling plan that would provide a rapid and reliable classification of thrips populations in commercial onion fields above or below prespecified management thresholds. The study was conducted in a total of 24 commercial onion fields in New York in 2021 and 2022. Taylor's power law and Wald's Sequential Probability Ratio Test were used in concert to develop each sampling plan. Simulated and historical field data of thrips populations were used to further validate the efficacy of each sampling plan. Results demonstrated the sequential sampling plan required an average of 78% fewer samples to make a control decision compared with the traditional fixed-sampling approach. Treatment decisions were reached in 72% of cases after inspecting only 10 plants, while only 6% of the cases required examining more than 25 plants. Comparisons with fixed-sample sizes ranging from 23 to 68 plants revealed a 96% agreement in decision-making and a 78% reduction in sampling effort when using the sequential sampling plans.

A Multi-Criteria Decision-Making Approach for Assessing the Sustainability of an Innovative Pin-Connected Structural System

A Multi-Criteria Decision-Making Approach for Assessing the Sustainability of an Innovative Pin-Connected Structural System

Assessment and selection of a micro-hybrid renewable energy system for sustainable energy generation in rural areas of Zambia

This research focuses on the implementation of micro-hybrid renewable energy systems (MHRES) in rural Zambia, where a large part of the population lacks adequate electrical infrastructure. Using a multi-criterion decision-making approach (MCDM), specifically the Technique for Preference Order by Similarity to an Ideal Solution (TOPSIS) with an entropy-weighted method, this research aims to identify an optimal MHRES solution to replace dependence on wood and fossil fuels. Analysis is carried out in 14 scenarios, taking into account technical, economic, social, and environmental criteria. The results show that a biogas-solar photovoltaic integrated with Energy System Power Safe Storage Battery Systems 190 (SBS 190) is the most visible option, demonstrating a relative proximity of 0.98556. In contrast, a diesel generator-solar PV system is the least favorable, with a relative proximity of 0.02576. Validation of the results through a convergent distance assessment (CODAS), an evaluation based on the distance from the average solution (EDAS), and the weighted aggregate sum of the product assessment (WASPAS) confirm its robustness. The proposed MHRES solution is designed to meet the energy needs of rural communities using local biomass and solar resources, free of obstacles by high buildings and structures. This research highlights the potential of MHRES to meet energy needs and promote sustainable development in developing countries, providing valuable insights for policymakers and energy stakeholders.

A Comprehensive Approach to Biodiesel Blend Selection Using GRA-TOPSIS: A Case Study of Waste Cooking Oils in Egypt

The transition to sustainable energy sources is critical for addressing global environmental challenges. In 2017, Egypt produced about 500,000 tons of waste cooking oil from various sources including food industries, restaurants and hotels. Sadly, 90% of households choose to dispose of their used cooking oil by pouring it down the drain or into their village’s sewers instead of using proper disposal methods. The process involves converting waste cooking oil (WCO) into biodiesel.This study introduces a multi-criteria decision-making approach to identify the optimal biodiesel blend from waste cooking oils in Egypt. By leveraging the grey relational analysis (GRA) combined with the technique for order preference by similarity to the ideal solution (TOPSIS), we evaluate eight biodiesel blends (diesel, B5, B10, B20, B30, B50, B75, B100) against various performance metrics, including carbon monoxide, carbon dioxide, nitrogen oxides, hydrocarbons, particulate matter, engine power, fuel consumption, engine noise, and exhaust gas temperature. The experimental analysis used a single-cylinder, constant-speed, direct-injection eight cylinder diesel engine under varying load conditions. Our methodology involved feature engineering and model building to enhance predictive accuracy. The results demonstrated significant improvements in monitoring accuracy, with diesel, B5, and B20 emerging as the top-performing blends. Notably, the B5 blend showed the best overall performance, balancing efficiency and emissions. This study highlights the potential of integrating advanced AI-driven decision-making frameworks into biodiesel blend selection, promoting cleaner energy solutions and optimizing engine performance. Our findings underscore the substantial benefits of waste cooking oils for biodiesel production, contributing to environmental sustainability and energy efficiency.

Resource allocation strategy selection for 5G network by using multi-attribute decision-making approach based on tangent trigonometric bipolar fuzzy aggregation operators

Resource allocation strategy selection for 5G network by using multi-attribute decision-making approach based on tangent trigonometric bipolar fuzzy aggregation operators

Identifying core IoT technologies using ARM and FCM: A comprehensive data-driven method

The internet of things (IoT) technology has garnered significant attention in recent years due to its wide-ranging applications. IoT, with its high connectivity capabilities, integrates various industrial, domestic, and agricultural devices into a smart and remotely controllable software and hardware platform. The field of IoT technology is expansive and encompasses a multitude of sub-technologies. Identifying core technologies in this domain is crucial for guiding research and development efforts by companies. Given the interrelation of these core technologies and their combination with recent decision-making approaches, network-based strategies have recently received special attention. The developed methods are based on static conditions and the assumption of stability, while in emerging technologies like IoT, the pace of changes over time is high. This leads to changes in the importance of technologies under various scenarios.In this study, in order to analyze the extracted patent data, association rule mining (ARM) algorithms were applied to identify the relationships between technologies and social network analysis was used to analyze the relationships between technologies and estimate their initial weights. Finally, fuzzy cognitive map (FCM) were used to estimate the final weights of technologies and rank them. The fcm approach allows for simultaneous modeling of both static and dynamic states of the system and, on the other hand, by calculating under various scenarios, suggests a core technology that is sustainable.The research results show that digital information transmission technologies, digital or electrical data processing, and wireless communication networks are the most important sub-technologies of Internet of things.

Assessment of bio-medical waste disposal techniques using interval-valued q-rung orthopair fuzzy soft set based EDAS method

Selecting an optimum technique for disposing of biomedical waste is a frequently observed obstacle in multi-attribute group decision-making (MAGDM) problems. The MAGDM is commonly applied to tackle decision-making states originated by obscurity and vagueness. The interval-valued q-rung orthopair fuzzy soft set is a novel variant of fuzzy sets. The main objective of this study is to introduce the interval-valued q-rung orthopair fuzzy soft Einstein-ordered weighted and Einstein hybrid weighted aggregation operators. Based on developed aggregation operators, a novel decision-making approach, the Evaluation based on the Distance from the Average Solution introduced to solve the MAGDM problem. The execution of the proposed approach demonstrates the significant impact of determining the most effective strategy to handle biomedical waste. Our proposed approach's practicality is confirmed by a case study focusing on selecting the most effective technique for Biomedical Waste (BMW) treatment. This study shows that autoclaving is the most effective method for the disposal of BMW. Comparative and sensitivity analysis confirms the consistency and effectiveness of our methodology. The comparative study indicates the effects of the proposed strategy are more feasible and realistic than the prevailing techniques.

An investment decision framework for offshore CCUS project under interval-valued fermatean fuzzy environment

ABSTRACT Carbon Capture, Utilization and Storage (CCUS) is an indispensable technology for achieving a net-zero emission society. The offshore CCUS project is still in its infancy. To promote its sustainable development, developing a comprehensive framework for investment decision-making is very crucial. First, a comprehensive evaluation criteria system is established. Second, in order to characterize the ambiguity and uncertainty of information in the process of making decisions, the interval-valued fermatean fuzzy set (IVFFS) is introduced, and the extended variance method of IVFFS is proposed to systematically calculate the weights of experts. Then, the power weighted average (PWA) operator based similarity measure of IVFFSs is developed to aggregate different expert information. Meanwhile, the fuzzy-weighted zero-inconsistency (FWZIC) method and the method based on the removal effects of criteria (MEREC) are used to determine the criteria weights. In addition, considering the interactions between the criteria, we introduce the Hamacher operator into the measurement of alternatives and ranking according to the compromise solution (MARCOS) method to select the optimal alternative in the interval-valued fermatean fuzzy (IVFF) environment. The suggested framework is then used to analyse a case study. After that, sensitivity and comparative analyses are conducted to confirm its robustness and viability. This study creates a practical investment framework for offshore CCUS projects, identifies a number of investment-sensitive criteria and provides management insights. The proposed framework expands the methods and applications in the field of decision-making and provides a scientific approach for investment decision-making in offshore CCUS projects, which can be a useful reference for managers.

An algorithmic multiple attribute decision-making context to model uncertainty associated with hospital site selection problem using complex sv-neutrosophic soft information

ABSTRACT Decision-making approaches are often used in uncertain environments by people who must make difficult judgments in daily life, including elements of varied qualities and costs. These methods assist decision-makers in managing ambiguity and uncertainty, allowing for more informed and risk-reduced decisions. This research introduces an advanced framework called a complex single-valued neutrosophic soft set (csvNSS) to address uncertainties inherent in decision-making. The csvNSS framework is capable of managing information periodicity by introducing two components: amplitude and phase. The first deals with fuzzy membership, while the second manages periodicity within a complex plane. Some rudiments of csvNSS like properties, set operations and aggregations, are investigated. To make these ideas practically applicable in choosing an appropriate location for the hospital, an algorithm for handling csvNSS is proposed. An enhanced strategy is validated through the use of a specific example that takes site selection for hospital into account. The outcome demonstrates the efficacy of the suggested strategy. The method can be used in other domains where selection issues arise.

Determination of sustainable energy mix to ensure energy security in Italy amidst Russian-Ukraine crises

Energy security has become an important topic, especially after the recent Russian-Ukraine conflict. Amongst the European Union (EU) nations, Italy in particular has been the most affected by such energy crises. To address such a concern, this study aims to determine a sustainable energy mix for Italy to ensure affordable, clean, and reliable energy in the country. The existing energy mix consisting of seven energy alternatives are evaluated based on the metrics of technological, socio-political, economic, and environmental aspects. A fuzzy quantitative strategic decision-making approach known as the Fuzzy Strength, Weakness, Opportunity, and Threat (F-SWOT) tool is employed to materialize this goal. In the SWOT matrix, hydro and biopower have been placed in the first quadrant showcasing strengths and opportunities for its development in Italy. Moreover, solar and wind power have been placed in the second quadrant both showcasing considerable opportunities for its development, however face a few weaknesses as well. Moreover, geothermal and coal power have been placed in the third quadrant, both showcasing weaknesses and threats to the energy security of the country. Lastly, natural gas in the fourth quadrant showcases strengths but poses a substantial threat to the energy security of the country.

A Review of the Sustainable Siting of Offshore Wind Farms

The continued technical and economic development of offshore wind farms needs to match their sustainable siting transparently and fairly. Aiming to assess existing methodologies widely used in the field of OWFs spatial planning, as well as to identify the proposed enhancements for the improvement of such methods, this study examines 80 peer-reviewed papers over the past eight years. The analysis encompasses articles from 34 scientific journals, with a notable concentration in the journals Renewable Energy, Renewable and Sustainable Energy Reviews, and Energies, and it sheds light on geographical distribution, journal classification, funding sources, and the various methodological approaches. Most of the studies were conducted in Turkey, China, and Greece; half of the surveyed papers utilize multi-criteria decision-making approaches, predominantly addressing bottom-fixed technologies for offshore wind farms, which currently dominate the field. The 80 papers are categorized into five methodological domains: Marine Spatial Planning, Feasibility Analysis, Probabilistic Methods, Meteorological Data, and Multi-Criteria Decision Making. One hundred and seventy criteria were identified and condensed into a final set of 41 critical criteria. This article provided an overview of the site selection process and the most crucial findings and recommendations.

Critical success factors for the adoption of AVs in sustainable urban transportation

Before integrating Autonomous Vehicles (AVs) into transportation systems, it's important to evaluate various aspects to facilitate their adoption. Numerous factors are likely to be crucial in this process. This research investigates the critical success factors influencing the adoption of AVs in sustainable urban transportation through a hybrid fuzzy Multiple-Attribute Decision-Making (MADM) approach. The primary aim is to identify key criteria and sub-criteria perceived by transport experts, shedding light on the complex dynamics of AV adoption. Methodologically, a fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) is employed to unveil cause-and-effect relationships among the main criteria, determining their respective weights. Subsequently, fuzzy Analytic Network Process (ANP) is applied to calculate the weights of criteria and sub-criteria based on the fuzzy DEMATEL results. The study considers seven major factor dimensions: economic, technical, operational, environmental, safety and risk, social and regulatory, and user acceptance. Key findings underscore user acceptance, financial costs, and environmental impact as the most pivotal criteria, emphasising their significance in the eyes of transportation experts. Contrarily, technological and operational criteria hold the lowest weight in the considered criteria for AV adoption in transportation networks. This research provides valuable insights into critical barriers and opportunities in the adoption of AVs, contributing to the advancement of sustainable urban transportation.

Research on a Three-Way Decision-Making Approach, Based on Non-Additive Measurement and Prospect Theory, and Its Application in Aviation Equipment Risk Analysis.

Due to the information non-independence of attributes, combined with a complex and changeable environment, the analysis of risks faces great difficulties. In view of this problem, this paper proposes a new three-way decision-making (3WD) method, combined with prospect theory and a non-additive measure, to cope with multi-source and incomplete risk information systems. Prospect theory improves the loss function of the original 3WD model, and the combination of non-additive measurement and probability measurement provides a new perspective to understand the meaning of decision-making, which could measure the relative degree by considering expert knowledge and objective data. The theoretical basis and framework of this model are illustrated, and this model is applied to a real in-service aviation equipment structures risk evaluation problem involving multiple incomplete risk information sources. When the simulation analysis is carried out, the results show that the availability of this method is verified. This method can also evaluate and rank key risk factors in equipment structures, which provides a reliable basis for decisions in aviation safety management.

A multi-criteria compromise ranking decision-making approach for analysis and evaluation of community-integrated energy service system

With the acceleration in industrialisation and urbanisation, building a community-integrated energy service system that includes renewable energy is an effective way to resolve energy shortages and reduce environmental pollution. Energy coupling can be realised using technology, such as electricity-to-gas conversion equipment, gas turbines, and gas boilers. And a carbon-trading mechanism can be introduced to build a community-integrated energy service system. First, the composition and characteristics of the community-integrated energy service system are analysed, and an index evaluation model for the community-integrated energy system is constructed considering the four influencing factors of cost, reliability, energy consumption, and environmental protection. The weight assigned to each indicator is determined based on the binary comparison weighting method and the improved criteria importance though intercrieria correlation method, and a multi-criteria compromise ranking evaluation system is established. A community was selected as an example of sensitivity and comparative analysis to verify the correctness and rationality of evaluating a community-integrated energy service system using the multi-criteria compromise ranking evaluation system based on the binary comparison weighting method and the improved criteria importance though intercrieria correlation method.

A Green Marketing and Operations Management Decision-Making Approach Based on QFDE for Photovoltaic Systems

A Green Marketing and Operations Management Decision-Making Approach Based on QFDE for Photovoltaic Systems

Sustainable rural economy and food security: An integrated approach to the circular agricultural model

The lack of resources and increasing damage are major obstacles to sustainable economic progress. Conventional farming methods, known for their energy consumption and resulting pollution, need changes. This study focuses on agriculture, an approach inspired by natural ecological cycles that aim to promote sustainable development in rural areas through efficient resource reuse. The connection between agriculture and rural economies, along with strategies for their combined advancement, is an area that requires more exploration and thorough empirical analysis. The research involves an energy analysis methodology tailored for use in regions to assess the benefits and environmental impacts of implementing circular agriculture for sustainable rural economic growth. A multi-objective decision-making approach using TOPSIS is employed to enhance agricultural policy formulation and decision-making processes. The study concludes by developing a model that considers production, processing, recycling, and socioeconomic aspects to delve into the development of circular agriculture within rural communities and propose relevant strategies. The uniqueness of this research lies in its combination of agriculture and the coordinated advancement of rural economies, which offers a fresh viewpoint and approach to sustainable farming methods and robust rural economic development. The results contribute to the circular agriculture literature body and provide practical guidance for policymakers and farmers.

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.

Performance enhancement of reactive foam dyeing for cotton fabric through different foaming agents and stabilizers

Purpose In the textile dyeing industry, the foam dyeing has been recognized as a significantly sustainable alternative for the cotton fabrics. However, this efficient technology undergoes the many issues related to the foam generation, foam optimization and the required performance of the resultant fabrics. The purpose of this paper is to address these issues through the development and optimization of the novel reactive foam dyeing recipes for the cotton fabrics. Design/methodology/approach The foam dyeing recipes were generated and optimized using the different stabilizers, foaming agents and three primary colors of reactive dyes. The different recipes were applied onto the cotton fabric using laboratory scale foam coating machine. The performance of the foam coated and padded fabrics was evaluated using different criteria including the shade depth, rubbing fastness, air permeability, washing fastness, perspiration fastness, light fastness and tear strength. Then, a complex decision-making approach, namely, analytic hierarchy process (AHP), was applied for the ranking of the key recipes based on the main criteria. Findings The newly optimized foam dyeing recipes were found very competitive with the conventional pad dyeing process with respect to the shade-depth and the other performance properties. The optimization of foaming parameters and addition of stabilizers have advanced the foam dyeing process, which would accelerate the implementation of foam dyeing methods in the textile industry. Furthermore, significant water and energy savings would be achieved as compared to the conventional foam dyeing. AHP model offered a comprehensive and rational way to identify the most important recipes amongst the selected recipes. Originality/value In this research, novel foam dyeing recipes have been developed for the cotton fabrics through the optimization of the different stabilizers, foaming agents and the three primary colors of reactive dyes. Until now, the exiting literature has not reported the combination of these stabilizers with the different foaming agents and three primary reactive dyes for the improvement of sustainable foam cotton dyeing process.

CT-based radiomics for predicting success of shock wave lithotripsy in ureteral stones larger than 1cm.

This study aims to investigate the predictive value of CT-based radiomics in determining the success of extracorporeal shock wave lithotripsy (SWL) treatment for ureteral stones larger than 10mm in adult patients. A total of 301 eligible patients (165/136 successful/unsuccessful) who underwent SWL were retrospectively evaluated and divided into a training cohort (n = 241) and a test cohort (n = 60) following an 8:2 ratio. Univariate analysis was performed to assess clinical characteristics for constructing a nomogram. Radiomics and conventional radiological characteristics of stones were evaluated. Following feature selection, radiomics and radiological models were constructed using logistic regression (LR), support vector machine (SVM), random forest (RF), K nearest neighbor (KNN), and XGBoost. The models' performance was compared using metrics such as the area under the receiver operating characteristic curve (AUC), precision, recall, accuracy, and F1 score. Finally, a nomogram was created incorporating the best image model signature and clinical predictors. The SVM-based radiomics model showed superior predictive performance in both training and test cohorts (AUC: 0.956, 0.891, respectively). The nomogram, which combined SVM-based radiomics signature with proximal ureter diameter (PUD), demonstrated further improved predictive performance in the test cohort (AUC: 0.891 vs. 0.939, P = 0.166). Integration of CT-derived radiomics and PUD showed excellent ability to predict SWL treatment success in patients with ureteral stones larger than 10mm, providing a promising approach for clinical decision-making.