Decision-making Techniques Research Articles

Future potable water supply demand projection under climate change and socioeconomic scenarios: A case of Gshba subbasin, Northern Ethiopia

This paper aims to quantify the subbasin’s potable water supply demand forecast from 2023 to 2050 under various scenarios of climate change and socioeconomic development. The variability of the climate and the resulting problems with urbanization threaten the availability of water resources, especially in less developed countries like Ethiopia. Thus, the main objective of this study is showing the necessary to determine the amount of water needed in advance, in order to comply with the availability of water resources within a specified future period under different scenarios. Our indicator-based approach used a multicriteria decision-making technique. Accordingly, several important variables were considered, including climatological, anthropological, demographic, socioeconomic, and economic variables, in addition to water engineering-related factors (e.g. Water losses). The method also considered a number of factors, such as unexpected and extreme temperature changes, and forecasting factors studied by the Ethiopian Ministry of Water and Energy. The projected population in the subbasin is estimated at 2.52 million, so the total projected water supply demand i.e., for domestic, non-domestic, industrial, commercial, public, and institutional is approximately 126.53 MCM/yr by 2050. Our results revealed how changes in both climatic and socioeconomic factors strongly influence future water resource system performance, and this will help the water services provider better prioritize the refurbishment of existing infrastructure and investment in new infrastructure, and more importantly, manage the subbasin effectively by introducing resilient adaptation options.

Optimizing electric vehicles charging for enhancing environmental sustainability and reducing carbon emissions of freight transport: case of Czech Republic

The limited infrastructure of charging stations, which is crucial in route planning and total journey time and creates uncertainty in efficiency and operating costs, calls for new economic and statistical methods in sustainability development and environmental economics. This paper aims to examine the challenges of integrating electric vehicles into freight transport to improve distribution logistics’ environmental sustainability, which represents one of the pathways for reducing environmental risk. The analysis results underscore the inadequacy of the truck charging station network in the Czech Republic. This insufficiency presents an opportunity to enhance environmental sustainability and reduce carbon emissions through strategic analysis and optimizing charging station locations. The difficulty of identifying optimal locations for these stations, given truck availability, requires using multi-criteria decision-making techniques such as the Analytical Network Process (ANP). Municipalities with limited access to existing logistics facilities were considered during the simulation. This way, 15 new locations were identified for municipalities with insufficient distance to a charging station.By implementing the ANP method, the study contributes to a more environmentally sustainable transportation infrastructure, highlighting the potential for significant reductions in carbon emissions through improved charging station networks. These results apply to other countries and can provide novel insights on optimizing charging station locations for sustainable economic development and reducing freight transport’s carbon emissions and environmental risks.

Archimedean Heronian mean operators based on complex intuitionistic fuzzy sets and their applications in decision-making problems

In this article, we derive the Archimedean aggregation operators for complex intuitionistic fuzzy sets, for this, first, we evaluate some Archimedean operational laws based on complex intuitionistic fuzzy values and then we discuss their special cases because the Archimedean norms are the general form of all existing norms, for instance, algebraic, Einstein, Hamacher, and Frank operational laws. Furthermore, we present the complex intuitionistic fuzzy Archimedean Heronian aggregation operator and complex intuitionistic fuzzy weighted Archimedean Heronian aggregation operator. Several special cases and the basic properties of the above-proposed operators are also diagnosed, because proposing the Heronian mean operators based on Archimedean norms are very challenging and complicated tasks, because of their features and structure. Additionally, a decision-making process is developed under the identified operators by using complex intuitionistic fuzzy information. Finally, we illustrate several examples to show the multi-attribute decision-making technique is more flexible than the prevailing works with the help of sensitive analysis between explored and certain prevailing works.

Multicriteria Decision-Making Technique for Choosing the Optimal Ammonia Energy Share in an Ammonia-Biodiesel-Fueled Reactivity-Controlled Compression Ignition Engine.

Low-temperature combustion paired with the use of carbon-free ammonia and carbon-neutral biofuels is a novel approach for improving performance, reducing greenhouse gases, and reducing regulated emissions. Reactivity-controlled compression ignition (RCCI), a low-temperature combustion technology, dramatically reduces NOx and smoke emissions compared to traditional engines. Ammonia can be projected as a good transit fuel in the journey toward achieving net zero emissions and cleaner energy. This study examines the impact of ammonia energy premixing fraction (AEPF) (20, 30, 40, and 50%) as a low-reactive fuel (LRF) and algal biodiesel as a high-reactive fuel on the performance and emission characteristics of a single-cylinder, water-cooled 3.5 kW CI engine at a constant speed of 1500 rpm under various loading conditions. The research results indicate that the 40% ammonia share RCCI mode exhibited a reduction in carbon dioxide (CO2) by 14.16%, nitrogen oxide (NOx) by 22.6%, and smoke by 42.1%, with an 11.5% improvement in thermal efficiency compared to the neat biodiesel-fueled conventional engine. Furthermore, the analytical hierarchy process (AHP) will be used in conjunction with the technique for order of preference by similarity to ideal solution (TOPSIS) of multiple criteria decision-making techniques to determine the optimal energy share in the RCCI combustion with the goal of achieving superior thermal efficiency and lower emissions. According to the AHP-TOPSIS study findings, AEPF40 is the best choice for all engine loads.

Fuzzy Cash Flow Analysis of Fluctuations in Interest Rates at the State Bank of Pakistan using Present Worth Criterion

A systematic approach is required to analyze present worth by incorporating annual interest rate fluctuations. This is a key factor in attracting local and foreign investors and maintaining a level of economic advancement. This study primarily focuses on the domain of the State Bank of Pakistan (SBP) and contributes towards achieving three major objectives. The first objective includes modeling interest rate values as triangular fuzzy numbers, and using the Analytic Hierarchy Process (AHP), a Multi-criteria Decision-Making technique (MCDM). Second, the triangular fuzzy interest rates are forecasted over a future timespan. Subsequently, the last objective includes the use of the Present Worth Analysis (PWA) to calculate the final net present value. The results demonstrate a trend in interest rates with net present values of specific future years. This research is a good initiative for the SBP and helpful for both foreign and local investors. The study is novel in that it involves the merging of qualitative and quantitative data. Furthermore, the combination of the forecasting technique, the AHP, fuzzy set theory, and present worth criteria renders a unique approach to formulating present worth. There is no previous research of this magnitude that has been conducted on this topic in a developing country such as Pakistan. This study delves into the annual interest rate fluctuations, providing valuable insights for present worth calculations.

Frequent Errors in Modeling by Machine Learning: A Prototype Case of Predicting the Timely Evolution of COVID-19 Pandemic

Background: The development and application of machine learning (ML) methods have become so fast that almost nobody can follow their developments in every detail. It is no wonder that numerous errors and inconsistencies in their usage have also spread with a similar speed independently from the tasks: regression and classification. This work summarizes frequent errors committed by certain authors with the aim of helping scientists to avoid them. Methods: The principle of parsimony governs the train of thought. Fair method comparison can be completed with multicriteria decision-making techniques, preferably by the sum of ranking differences (SRD). Its coupling with analysis of variance (ANOVA) decomposes the effects of several factors. Earlier findings are summarized in a review-like manner: the abuse of the correlation coefficient and proper practices for model discrimination are also outlined. Results: Using an illustrative example, the correct practice and the methodology are summarized as guidelines for model discrimination, and for minimizing the prediction errors. The following factors are all prerequisites for successful modeling: proper data preprocessing, statistical tests, suitable performance parameters, appropriate degrees of freedom, fair comparison of models, and outlier detection, just to name a few. A checklist is provided in a tutorial manner on how to present ML modeling properly. The advocated practices are reviewed shortly in the discussion. Conclusions: Many of the errors can easily be filtered out with careful reviewing. Every authors’ responsibility is to adhere to the rules of modeling and validation. A representative sampling of recent literature outlines correct practices and emphasizes that no error-free publication exists.

A novel decision-making technique based on T-rough bipolar fuzzy sets

A novel decision-making technique based on T-rough bipolar fuzzy sets

Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach

The purpose of this paper is to propose a novel hybrid framework for evaluating and benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi-criteria decision-making (MCDM) techniques under a new fuzzy environment. To develop such a framework, a new decision matrix has been built, and then integrated with q-ROF2TL-FWZIC (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Fuzzy-Weighted Zero-Inconsistency) and q-ROF2TL-CODAS (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Combinative Distance-Based Assessment). In this integration, q-ROF2TL-FWZIC is utilized for assigning the weights of evaluation attributes of trustworthy AI, while q-ROF2TL-CODAS is employed for benchmarking trustworthy AI applications. Findings show that the q-ROF2TL-FWZIC method effectively weights the evaluation attributes. The transparency attribute receives the highest importance weight (0.173566825), whereas the human agency and oversight criterion has the lowest weight (0.105741901). The remaining attributes are distributed in between. Moreover, alternative_4 receives the highest rank order (score of 7.370410417), while alternative_13 receives the lowest rank order (score of −4.759794397). To evaluate the validity of the proposed framework, systematic ranking and sensitivity analysis assessments were employed.

Prioritization of types of wireless sensor networks by applying decision-making technique based on bipolar complex fuzzy linguistic heronian mean operators

Wireless sensor networks are flexible monitoring systems that save track of, data, and communicate multipoint digital information interpretations to other devices. Wireless sensor networks meaningly enhance the accuracy, breadth, and extent of local data collection, commonly doing away with the requirement for expensive data wiring and recurring manual checks at risky, remote, or inaccessible locations. As a result, it is utilized to keep an eye on systems and environmental or physical parameters. In this manuscript, we expand the Heronian mean operators in the model of bipolar complex fuzzy linguistic set to concoct bipolar complex fuzzy linguistic arithmetic Heronian mean, bipolar complex fuzzy linguistic weighted arithmetic Heronian mean, bipolar complex fuzzy linguistic geometric Heronian mean and bipolar complex fuzzy linguistic weighted geometric Heronian mean operators. We also inspect the special cases of the invented bipolar complex fuzzy linguistic arithmetic Heronian mean and bipolar complex fuzzy linguistic geometric Heronian mean operators. Moreover, in this manuscript, we concoct a technique of decision-making in the model of a bipolar complex fuzzy linguistic set with the assistance of the invented operators. As the selection and prioritization of the various types of Wireless sensor networks is the decision-making dilemma, we prioritize various types of Wireless sensor networks by employing the concocted technique of decision-making and by taking artificial data in the model of the bipolar complex fuzzy linguistic set. To reveal the influence and excellence of the concocted work, a comparative study is given in this manuscript.

Development of a project complexity measurement model for metro rail projects

PurposeThe purpose of this research is to develop a project complexity index (PCI) model using the best and worst method (BWM) to quantitatively analyze the impact of project complexities on the performance of metro rail projects.Design/methodology/approachThis study employed a two-phase research methodology. The first phase identifies complexities through a literature review and expert discussions and categorizes different types of complexities in metro rail projects. In the second phase, BWM, a robust multi-criteria decision-making (MCDM) technique, was used to prioritize key complexities, and a PCI model was developed. Further, the developed PCI was validated through case studies, and sensitivity analysis was performed to check the accuracy and applicability of the developed PCI model.FindingsThe analysis revealed that location complexity exerted the most substantial influence on project performance, followed by environmental, organizational, technological and contractual complexities. Sensitivity analysis revealed the varying impacts of complexity indices on the overall project complexity.Practical implicationsThe study's findings offer a novel approach for measuring project complexity's impact on metro rail projects. This allows stakeholders to make informed decisions, allocate resources efficiently and plan strategically.Originality/valueThe existing studies on project complexity identification and quantification were limited to megaprojects other than metro rail projects. Efforts to quantitatively study and analyze the impact of project complexity on metro rail projects are left unattended. The developed PCI model and its validation contribute to the field by providing a definite method to measure and manage complexity in metro rail projects.

Mapping cultivation potential index of rainfed wheat from climatic perspective using Shannon entropy catastrophe scheme

Mapping the Cultivation Potential Index (CPI) for a specific crop is a topical research activity in agro-climate studies, which can lead to environmental and water resources protection and agriculture efficiency increase. This study formulates a framework to delineate the CPI of rainfed wheat from the climatic perspective of precipitation and temperature. Four precipitation layers were prepared using the frequency analysis of annual, autumn, spring, and June precipitation time series; also, three temperature layers comprise autumn, spring, and June temperatures obtained by remote sensing techniques. The Shannon-Entropy Catastrophe Scheme (ECS) was also formulated to combine seven layers of precipitation and temperature besides the land use layer while decreasing the inherent subjectivity. The formulated methodology was implemented in the Maragheh city, northwest of Iran. Some contrasts exist between the spatial distributions of precipitation in different time scales, which necessitates the formulation of ECS as a multi-criteria decision-making technique. ECS identifies the eastern half of the study area as the medium to relatively high CPI, which can be employed as a decision support system for planning and managing agriculture.

Modifying Hellwig’s Method for Multi-Criteria Decision-Making with Mahalanobis Distance for Addressing Asymmetrical Relationships

Hellwig’s method is a multi-criteria decision-making technique designed to facilitate the ranking of alternatives based on their proximity to the ideal solution. Typically, this approach calculates distances using the Euclidean norm, assuming implicitly that the considered criteria are independent. However, in real-world situations, the assumption of criteria independence is rarely met. The paper aims to propose an extension of Hellwig’s method by incorporating the Mahalanobis distance. Substituting the Euclidean distance with the Mahalanobis distance has proven to be effective in handling correlations among criteria, especially in the context of asymmetrical relationships between criteria. Subsequently, we investigate the impact of the Euclidean and Mahalanobis distance measures on the several variants of Hellwig procedures, analyzing examples based on various illustrative data with 10 alternatives and 4 criteria. Additionally, we examine the influence of three normalization formulas in Hellwig’s aggregation procedures. The investigation results indicate that both the distance measure and normalization formulas have some impact on the final rankings. The evaluation and ranking of alternatives using the Euclidean distance measure are influenced by the normalization formula, albeit to a limited extent. In contrast, the Mahalanobis distance-based Hellwig’s method remains unaffected by the choice of normalization formulas. The study concludes that the ranking of alternatives is strongly dependent on the distance measure employed, whether it is Euclidean or Mahalanobis. The Mahalanobis distance-based Hellwig method is deemed a valuable tool for decision-makers in real-life situations. It enables the evaluation of alternatives by considering interactions between criteria, providing a more comprehensive perspective for decision-making.

Solution of Z-number-based multi-objective linear programming models with different membership functions

In a universe full of fuzziness and uncertainty, it is an absolute blessing if some information is reliable to some degree. Considering the amount of uncertainty, Zadeh proposed the idea of Z-number, which carries both uncertainty and the reliability of the information. Uncertain information can be reliably conveyed using Z-numbers. On the other hand, one of the important and widely used decision-making techniques is linear programming. Currently, linear programming has been extensively explored using fuzzy information. Decisions made using linear programming with uncertain data are highly uncertain due to the lack of reliability of the information. An effective decision-making method is multi-objective linear programming (MOLP), in which the decision-maker attempts to draw conclusions from information containing conflicting attributes. In the MOLP problem, there may be different solution vectors corresponding to each objective function. One needs to find a compromise solution vector that satisfies each objective function to a certain extent based on the decision maker's suggested preferences. In this study, we use Z-number and MOLP to model the Z-number MOLP problem (ZMOLPP). Furthermore, we propose a strategy for solving ZMOLPP by transforming it into a crisp MOLP problem, and finally converting this crisp MOLP problem into a single-objective linear programming problem (LPP) using different shape functions. Additionally, we compare the results with existing MOLP problems in fuzzy settings to validate the proposed strategy.

A software trustworthiness evaluation methodology for cloud services with picture fuzzy information

VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is one of the most commonly used decision-making technique. This work provides a software trustworthiness evaluation method based on VIKOR technique in a group decision-making (GDM) environment. Entropy is an important measure that refers to the amount of disorder in a decision support system. To quantify the decision quality of decision maker (DM), a new method for determining DM weights is proposed according to the entropy value of decision matrix, which is provided by that DM. A new normalization-projection measure is developed in order to test the closeness between two evaluation matrices in software trustworthiness evaluation process. The group regret measure in VIKOR-based GDM method is based on two negative reference matrices in this model. A new ranking method with classification is also developed in this work. Aiming at the complex and changeable environment, these new measures and methods are expected to enrich VIKOR techniques and to play a constructive role in group decision support systems. The feasibility and practicability of developed method are verified by some experiments. This study finds that the different methods (models) may have different results in situations where the same decision data are provided by DMs; and this study also finds that the different parameters may have different results in situation where the decision model is same. The practical implications of developed GDM method can apply not only to software trustworthiness evaluation, but also to many other product quality evaluations.

Comparative Analysis of Cement Production Methods Using a Life Cycle Assessment and a Multicriteria Decision-Making Approach

Manufacturing cement has a major impact on climate change, resource depletion, and pollution. Selecting sustainable cement alternatives is vital but entails difficult trade-offs between numerous variables. The objective of this study is to determine the most environmentally beneficial method of cement manufacturing by employing an integrated life cycle assessment multi-criteria decision-making technique. The LCA is employed to quantitatively evaluate the environmental effects of ten different methods of cement production across eighteen distinct categories. Meanwhile, the CRITIC weighted TOPSIS and EDAS MCDM approaches are utilized to rank the various alternatives by determining their proximity to the optimal solution. The LCA results showed that CEM III/A slag cement had lower environmental impacts than Portland cement. With a ranking score of 0.9094 and 1.7228 for EDAS and TOPSIS techniques, respectively, both MCDM identified CM10: ground granulated blast furnace slag (GGBFS) as the most recommended. In addition, midpoint characterization revealed that clinker production was responsible for 55% of the global warming impact. Based on these findings, slag cements are more environmentally friendly than Portland cement. Furthermore, an integrated LCA-MCDM approach offers a thorough sustainability evaluation that incorporates many aspects. Overall, this research shows that blast furnace slag cements, notably CM10, are ideal alternatives for reducing the environmental consequences of cement production in a variety of areas. This integrated methodology provides a systematic framework for making informed decisions in the production of sustainable cement.

Optimizing Priority Sequencing Rules in Parallel Machine Scheduling: An Evaluation and Selection Approach using Hybrid MCDM Techniques

Priority sequencing criteria are of utmost importance in the determination of the sequence in which jobs are processed at workstations in parallel machine scheduling. The utilization of diverse priority rules can result in varied sequencing arrangements, hence requiring more experimentation to ascertain the optimal rule. Hence, it is imperative to formulate a thorough approach for the selection of the most suitable priority sequencing rule from the standpoint of management decision-making. The objective of this research is to analyze and compare six different priority sequencing rules in the context of parallel machine scheduling. Additionally, a methodology is proposed for the assessment and selection of the most suitable rule. This methodology combines the full consistency method (FUCOM) with the measurement of alternatives and ranking according to compromise solution (MARCOS) method, which are both multi-criteria decision-making techniques. When reviewing and selecting the optimal priority sequencing rule, seven parameters are taken into consideration. The weights of these criteria are computed using the FUCOM method, while the relative proximity values of all priority sequencing rules are derived by the MARCOS method. The data indicate that the priority sequencing rules are prioritized according to their level of importance. The approach outlined in this study is essential for workstation management to make well-informed decisions regarding the choice of the most advantageous priority sequencing rule for parallel machine scheduling.

Novel drying cum floral water extraction technique for rose petals

This study focussed on experimental investigation of a novel drying cum floral water extraction technique for rose petals (Rosa rubiginosa). The proposed novel technique extracted floral water from rose petals while it was drying only thus giving both dried petals and floral waters which has not been done before. Air flow rate and temperature were found to be the most influencing parameters in the drying cum floral water extraction process. Using a multi-criteria decision-making technique, the best alternative for maximum drying and condenser efficiency with lower energy consumption was found to be 2.5 m/s air flow rate and target temperature of 55 °C. The quality of rose water extracted was found to be chemically similar to the commercial one. The dried petals obtained had a vibrant color and retained the characteristic aroma of fresh rose petals. The texture obtained was dry and crisp. The moisture content of the dry rose petals was typically lower than 20%w.b. All these properties indicated that the obtained dried petals and floral waters were suitable to be used in culinary, cosmetic, and herbal preparations and solved the problem faced by current methods of petals being useless after floral water extraction. Further enhancement in the setup for large-scale implementation will require numerical simulation to obtain optimized parameters for the higher capacity setup that is to be developed.

Evaluating Blockchain Cybersecurity Based on Tree Soft and Opinion Weight Criteria Method under Uncertainty Climate

In the era of digital transformation (DT), many digital technologies have emerged and have had a positive impact on society. Nevertheless, because of certain issues with existing technologies, innovative technology has developed to eradicate them. Fog computing (FC) plays a vital role as an intermediate between edge layer and cloud computing (CC) to resolve limited resources and capabilities. In the same vein, blockchain technology (BCT) is responsible for resolving privacy and security issues that IoT suffers from. Due to using cryptography rules and hashing which is utilized in BCT to prevent any trickery. Hence, BC shows promise as a possible remedy for the cybersecurity issue, especially in financial services and transactions. In the context of cybersecurity, it's critical to comprehend how financial sector businesses might handle these issues by investigating the use of BCT for financial transactions. Thereby, we constructed Tree Soft Evaluator (TrSEV) framework to evaluate security for enterprises (ENs) that embrace BCT as a cyber security method. The initial and important step in the evaluation process is determining the influenced criteria. In our problem, we modeled the determined criteria of BCT in Tree soft (TrSo) model. After that these criteria are analyzed and obtaining weights for it. Hence, we harnessed Opinion Weight Criteria Method (OWCM) as a novel multi-criteria decision-making (MCDM) technique to obtain criteria weights, and weighted sum model (WSM) is utilized for ranking enterprises and recommending optimal and worst enterprise. Finally, the techniques harnessed in our constructed framework are employed under the authority of Neutrosophic theory, precisely Single value Neutrosophic sets (SVNSs).

Sustainable utilisation of rice husk for cleaner energy: A circular economy between agricultural, energy and construction sectors

Global research focuses on cleaner and sustainable energy alternatives like biomass due to the limited availability of coal and high CO2 emissions associated with coal combustion. Rice husk-based electricity generation is hindered by the absence of cogeneration facilities in rice mills. Rice (Oryza sativa) cultivation is wide-spread and constructing several biomass plants at various locations is uneconomical. Hence, instead of using as biomass, rice husk is field burnt as a method of disposal. Construction of only few rice cogenerations facilitates and using already existing sugar mills and coal based thermal plants cogeneration facilities in a combined way is cost-effective and sustainable solution. Therefore, the present study examines the synergistic utilisation of sugar mills, coal-based power plants and only few newly proposed biomass power plants for effective combustion of rice husk. Maharashtra, one of the largest states in India is considered as the study area. Already existing 181 sugar mills, 18 thermal power plants are considered in 36 districts of Maharashtra state. Site suitability analysis was performed using a multi-criteria decision-making technique and ArcGIS. Furthermore, sensitivity analysis was conducted to assess the robustness of the suitability index values. From the analysis, it is found that nine districts require new biomass power plants. In remaining 27 districts use of already existing cogeneration facilities at sugar mills and coal-based power plants are found to be economical than new plants. In addition, 98.7 % of carbon emission was reduced when rice husk is used instead of coal for generating the equivalent quantity of power.

Implementation of multi-criteria decision making for the ranking of drugs used to treat bone-cancer

<abstract> <p>The concept of "topological index" refers to a numerical value determined by the structure of a chemical network. It serves to determine the physicochemical and biological properties of diverse medications, offering a more precise depiction of the theoretical properties of organic materials, this is achieved through the utilization of degree-based topological indices. Because of the development of resistance to existing treatments and the unpleasant side effects associated with some current drugs, the hunt for new drugs remains a priority. In drug discovery, QSPR approaches are widely used to predict, from a chemical structure, the biological activity of potential novel drugs. Researchers can prioritize compounds for synthesis and optimize them to improve potency, preference, and other desired attributes by establishing a correlation between chemical features and biological activity. Rational drug design approaches incorporate research methodologies such as quantitative structure-activity relationships (QSAR) and quantitative structure-property relationships (QSPR), along with decision-making strategies. The goal of these strategies is to improve the biological activity and physicochemical qualities of existing leads. This research includes mathematical modeling of drug mechanisms utilizing multiple-criteria decision analysis and QSPR analysis. Furthermore, using decision-making techniques, I can determine the order of production for various drugs used to treat bone cancer based on their examination using QSPR analysis and topological indices.</p> </abstract>