Order Preference Research Articles

Effect of fly ash chemical components on epoxy mortar composite material performance

This study focuses on the application of fly ash (FA)-filled epoxy mortar composite material (EMCM) as a bed material for precision machine tools, emphasizing the impact of fly ash’s primary chemical components (SiO2, Al2O3, Fe2O3, and CaO) as mono-component or two-component fillers on the mechanical properties of EMCM composite material. The research thoroughly analyzed the porosity, macroscopic mechanical properties, and microstructure of the EMCM. The results revealed that increasing the filler content up to 40% significantly enhances the elastic modulus and compressive strength of the EMCM specimens, despite an increase in porosity. Specific filler combinations, such as SiO2/CaO and CaO/Fe2O3, exhibit superior performance. Additionally, Fe2O3 helps prevent sedimentation, enhancing the material’s uniformity. A comprehensive performance evaluation using the Entropy Weighted Technique for Order Preference by Similarity to Ideal Solution (EW-TOPSIS) method showed that specimens containing CaO/Fe2O3 exhibited optimal performance, even when considering cost factors.

Analysis of barriers to Maintenance 4.0 in manufacturing industries

PurposeAsset maintenance in manufacturing industries is a critical issue as organizations are highly sensitive towards maximizing asset uptime. In the advent of Industry 4.0, maintenance is increasingly becoming technology driven and is being termed as Maintenance 4.0. Several barriers impede the implementation of Maintenance 4.0. This article aims at - exploring the barriers to implementation of Maintenance 4.0 in manufacturing industries, categorizing them, analysing them to prioritize and suggesting the digital technologies to overcome them.Design/methodology/approachTwenty barriers to the implementation of Maintenance 4.0 were identified through literature survey and discussion with the industry experts. The identified barriers were divided into five categories based on their source of occurrence and prioritized using fuzzy-technique for order preference by similarity to ideal solution (TOPSIS), sensitivity analysis was carried out to check the robustness of the solution.Findings“Data security issues” has been ranked as the most influencing barrier towards the implementation of Maintenance 4.0, whereas “lack of skilled engineers and data scientists” is the least influencing barrier that impacts the implementation of Maintenance 4.0 in the manufacwturing industries.Practical implicationsThe outcomes of this research will help manufacturing industries, maintenance engineers/managers, policymakers, and industry professionals for detailed understanding of barriers and identify easy pickings while implementing Maintenance 4.0.Originality/valueManufacturing industries are witnessing a paradigm shift due to digitization and maintenance 4.0 forms the cornerstone. Little research has been carried in Maintenance 4.0 and its implementation; this article will help in bridging the gap. The prioritization of the barriers and digital course of actions to overcome those is a unique contribution of this article.

Multi-Objective Optimization of the Pre-Swirl System in a Twin-Web Turbine Disc Cavity

Enhancing thermal efficiency and minimizing weight are prevailing issues in aero engines. Owing to its hollow structure, the twin-web turbine disc exhibits remarkable weight reduction properties, while its enhanced cooling constitutes a novel challenge. In this study, a twin-web turbine disc cavity system is numerically investigated. To enhance the cooling effect and minimize pressure loss, a multi-objective genetic algorithm and Kriging surrogate model are employed to optimize the radial height of the pre-swirl nozzle and receiver hole in the disc cavity system. The results indicate that the overall performance of Opt-3, derived from the Technique for Order Preference by Similarity to the Ideal Solution method within the Pareto frontier, is superior. This configuration achieves a uniform low distribution of rotor temperatures while maintaining moderate pressure losses. Notably, the maximum temperature is reduced by 21.1 K compared to the basic model, with pressure losses remaining largely unchanged. Additionally, an increase in the flow ratio leads to a reduction in both the maximum temperature and average temperature of the back web while simultaneously increasing the temperature of the front web and augmenting pressure losses. However, it is important to note that the degree of variation in these parameters diminishes with increasing flow ratios.

A TOPSIS analysis of regional competitiveness at European level

PurposeThe measurement of regional competitiveness is becoming essential for policymakers to address territorial disparities, while considering the issue of correlations among indicators. Therefore, the purpose of this paper is to measure regional competitiveness using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) by considering different distance measures and two levels of analysis to provide a comparative and comprehensive measurement of regional competitiveness in Europe.Design/methodology/approachThe authors apply TOPSIS based on three different distance measures (the Manhattan, the Euclidean and the Mahalanobis distance measures) to the regions of the EU Regional Competitiveness Index (RCI) 2019, which is taken as the frame of reference.FindingsThe authors replicate the RCI by using TOPSIS with a less preferred choice of distance measure, indicating TOPSIS as a valuable method for policymakers in the analysis of regional competitiveness. The authors argue in favour of the Mahalanobis distance measure as the best of the three, as it considers correlations among macro-economic indicators.Originality/valueThis study aims to make three contributions. Firstly, by replicating the RCI by means of TOPSIS with a less preferred choice of distance measure, the paper provides a benchmark for future research on regional competitiveness. Secondly, by suggesting the use of TOPSIS with the use of the Mahalanobis distance measure, the authors show how to measure regional competitiveness by taking into account correlations among pillars. Thirdly, the authors argue in favour of considering clusters of regions when measuring regional competitiveness.

INVESTIGATIONS ON THE MACHINABILITY CHARACTERISTICS OF Al–Zn(5.4)–Mg(2.4)–Cu(1.4) ALLOY COMPOSITE USING TOPSIS APPROACH

In this research, stir casting was used to create the aluminium alloy (AA7075) composite filled with 10[Formula: see text]wt.% zirconia (ZrO2) particles as reinforcement. Die sinking electric discharge machining (EDM) was used to examine the machining characteristics of the proposed composite. The machining experiments were executed in accordance with the [Formula: see text] orthogonal layout. Here, the material removal rate (MRR) and surface roughness (SR) were taken into account as the output responses, and the discharge current ([Formula: see text]), pulse on-time ([Formula: see text]), and pulse off-time ([Formula: see text]) were taken as the machining parameters. To determine the optimal parameter conditions for the output responses, the technique for order preference by similarity to ideal solution (TOPSIS) approach was applied. According to the experimental findings, the optimum settings of the parameters are determined at 15[Formula: see text]amps of ‘[Formula: see text]’, 750[Formula: see text][Formula: see text]s of ‘[Formula: see text]’ and 50[Formula: see text][Formula: see text]s of ‘[Formula: see text]’. ANOVA results stated that ‘[Formula: see text]’ has the most notable factor with a contribution of 90.85%. The interaction effect of parameters on the responses was shown by the contour plots. The confirmatory experiment was finally conducted at the optimum machining parameter settings, and it was found that the error only occurred in 8.4%.

Holistic approach for selecting chatbot development tools: combining AHP and TOPSIS methodologies

ABSTRACT The proliferation of chatbot development tools presents a significant challenge in identifying the most suitable ones for specific needs. This study addresses this issue by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to create a comprehensive methodology for selecting chatbot development tools. The study aims to provide a transparent, objective decision-making framework by evaluating tools across multiple dimensions including deployment characteristics, input processing capabilities, dialogue management, response generation, and economic considerations. Utilizing AHP for criteria weighting and TOPSIS for ranking alternatives, the methodology systematically evaluates various tools to identify the optimal choice. Key findings highlight the strengths and weaknesses of different tools, offering insights that guide developers and organizations in choosing the most suitable chatbot development tool aligned with their specific needs and strategic goals. The implications of this methodology are significant, enhancing decision-making processes within technological tool selection and contributing to the fields of artificial intelligence and automated systems development.

The evolution of two‐stage production and order equilibrium in a random yield supply chain with demand information updating

AbstractThe mismatch between production, order, and demand seriously affects supply chain performance. However, most research focus on the mismatch between the retailer's order and customer's demand, which ignores the influence of the supplier's random yield on supply chain members' decision‐making. This paper investigates a two‐stage optimization problem within a two‐echelon supply chain, featuring a supplier with random yield and a retailer updating demand information in real‐time. Faced with a long production lead time, the retailer can either place advance orders at the production season's onset (first‐stage advance order) or opt for instant orders at the beginning of the sales season (second‐stage instant order). To ensure timely order fulfillment, the supplier initially employs a cost‐effective regular production mode with random yield during the production season. If yields are insufficient during sales, a pricier emergency production mode with guaranteed output becomes available. Utilizing a dynamic programming approach, we formulate the two‐stage optimization problem to derive optimal production and order decisions. Our analysis uncovers how realized random yield and stochastic market signals influence emergency production and instant order quantities in the second stage. We compare expected profits in scenarios with perfect and imperfect market signals, probing the members' preferences regarding order strategies. An intriguing finding emerges: as instant wholesale prices rising, the supplier's preferred order strategy diverges from the retailer. By strategic adjustments to the instant wholesale price, we demonstrate the potential for unanimous agreement on preferred order strategies among supply chain members — a quality enhancing the chain's flexibility and performance. Moreover, we extend the model to hybrid order strategies and identify conditions for unanimous preference among the three strategies. To bolster our theoretical findings, we provide numerical examples, lending practical support to our study.

Delphi/AHP-Based Method for Biomass Sustainable Assessment in the Sugar Industry

Multi-criteria methods are highly attractive tools to address the inherent complexity of evaluating problems in various scientific areas. The combination of methods such as Delphi/AHP is emerging as a robust alternative to evaluate the sustainability of renewable energy sources. In this theoretical-descriptive research, the use of the Delphi method is proposed to select criteria and sub-criteria to obtain a high level of reliability, while the AHP method is used to establish an order of preference among the alternatives analyzed. This process requires the support of a committee of experts, whose role is to identify the various biomass alternatives that can be used in the sugar industry, considering aspects related to sustainability. The selected experts have identified energy, exergy, and emergetic indicators, in which economic, environmental, and social aspects are integrated. The multi-criteria analysis shows that the V1 variant was the most satisfactory in terms of biomass sustainability, representing 45% and 53% of the overall priorities in the evaluated case studies. In addition, the sensitivity analysis under an equal-weighted scenario for both study cases evidenced that variant V1 acquired the highest score (38.17%) among all alternatives. Variant V4 achieved the second highest score (31.79%), while alternative V2 achieved only 29.04%, respectively. The integration of Delphi/AHP methods emerges as a novel tool to assess sustainability in different industries of the energy sector.

A Qualitative Study of the Knowledge of Metabolic Syndrome, Attitudes about Lifestyle Modifications, and Preferences for Lifestyle Interventions among Patients with Cancer and Metabolic Syndrome.

Metabolic syndrome is a group of disorders-hypertension, dyslipidemia, obesity, and insulin resistance-that together increase the risk of coronary heart disease, stroke, and diabetes. Although ~60% of cancer patients have metabolic syndrome, which increases their risk of mortality, oncology providers lack clear guidance about its management. The development of metabolic syndrome lifestyle interventions requires a better understanding of these patients' knowledge, attitudes, and intervention preferences in order to inform management. During 2022-2023, 19 adult cancer patients with metabolic syndrome engaged in semistructured interviews focused on metabolic syndrome and lifestyle interventions. Interviews were analyzed using hybrid thematic analysis involving deductive and inductive coding. The findings indicated that patients lack knowledge about metabolic syndrome, were motivated to prioritize lifestyle modifications, and expressed strong interest in personalized care plans focused on healthy lifestyle rather than simply on weight loss. As part of their tailored intervention plans, participants desired clear communication with, and coordination of care among, their medical team and shared decision-making with providers about treatment decisions. The findings indicate that patients with metabolic syndrome want collaborative, patient-centered care, tailored interventions, and practical implementation strategies. This research will be used to inform the development of future lifestyle interventions for patients diagnosed with metabolic syndrome based on their identified needs and preferences.

Enhancing biometric system selection: A hybrid AHP-neutrosophic fuzzy TOPSIS approach

A biometric system is essential in improving security and authentication processes across a variety of fields. Due to multiple criteria and alternatives, selecting the most suitable biometric system is a complex decision. We employ a hybrid approach in this study, combining the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the Analytic Hierarchical Process (AHP). Biometric technologies are ranked using the TOPSIS method according to the relative weights that AHP determines. By applying the neutrosophic set theory, this approach effectively handles the ambiguity and vagueness inherent in decision-making. Fingerprint, face, Iris, Voice, Hand Veins, Hand geometry and signature are the seven biometric technologies that are incorporated in the framework. Seven essential characteristics are accuracy, security, acceptability, speed and efficiency, ease of collection, universality, distinctiveness used to evaluate these technologies. The model seeks to determine which biometric technology is best suited for a particular application or situation by taking these factors into account. This technique may be applied in other domains in the future.

A novel energy-efficient and cost-effective task offloading approach for UAV-enabled MEC with LEO enhancement in Internet of Remote Things networks

The Internet of Remote Things (IoRT) involves networks of devices deployed in extensive and often remote areas, collecting data for transmission and processing. In such networks, Unmanned Aerial Vehicles (UAVs) gather data, which is then sent to Low Earth Orbit (LEO) satellites for processing. These systems often face significant challenges, particularly in task offloading. Conventional methods typically rely on static routing and scheduling algorithms that do not adapt to changing conditions and usually overlook the complexity of dynamic decision-making in harsh or isolated environments, thus failing to address the critical challenges of energy efficiency and latency. In this paper, we introduce a method comprised of a three-layer architecture. The first layer, the IoRT computing layer, uses Deep Q-Network (DQN) to optimize local decisions based on device constraints and task urgency. The second layer features UAVs serve as Mobile Edge Computing (MEC), which not only processes data but also decides whether to process tasks locally or offload them to LEO satellites, utilizing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for this decision-making process. The third LEO Satellite Layer has a high computational capacity to handle offloaded tasks. Simulation results demonstrate notable improvements: compared to another method, the proposed model shows a 14.73 % reduction in energy consumption and a 23.13 % decrease in latency while reducing execution costs by an average of 28.7 %.

Performance Assessment of Public Hospitals with the Entropy-Weighted TOPSIS Method: The Case of Turkey

Background: Hospitals make up the main cost factor of health systems and also face increasing pressure to improve efficiency. Improving the performance of hospitals, which are vital for public health, is very important for developing countries such as Turkey. Aims: We conducted this study to assess the performance of 544 secondary-level public hospitals in Turkey by geographical region. Methods: In the study, we used the entropy weighting method to obtain the objective weights of indicators with the evaluation criteria, and then we utilized the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to rank the performance of hospitals by geographical region. Results: The comparison of hospital performances by geographical region indicated that the highest and the lowest mean scores belonged to the Mediterranean Region and the Eastern Anatolia Region, respectively. Conclusions: The socio-economic differences between geographical regions in Turkey were in parallel with the findings of the study. Although there is a need for professional management in resource allocation and use to improve the performance of public hospitals, it is necessary to focus on the origin of the problems first.

Identification of important nodes in multi-layer hypergraphs based on fuzzy gravity model and node centrality distribution characteristics

Hyperedge is a common structure that represents high-order interactions between nodes in complex networks, and multi-layer networks provide more diverse node interactions than single-layer networks. Therefore, multi-layer hypergraphs can more clearly represent the relationships between nodes. However, there are few studies on identifying important nodes in this framework. This paper proposes a method called HCT to fill this gap. It consists of three parts, namely: Hypergraph Fuzzy Gravity Model (HFGM), Layer Weight Calculation Method based on Node Centrality Distribution Characteristics (CDLW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). HCT progressively analyzes the importance of a node from three levels: local (the node itself), semi-local (the hyperedges and community to which the node belongs), and global (the layer to which the node belongs). By combining these three results, the global centrality of a node in the entire network can be calculated. Simulation experiments demonstrate that the important nodes identified by HCT exhibit stronger contagion capabilities in nine networks compared to nine combinatorial methods and removing these nodes will seriously damage the connectivity and robustness of the network. The centrality of each node calculated by HCT is also consistent with its actual importance.

Characterization of indigenous lactobacilli from dairy fermented foods of Haryana as potential probiotics utilizing multiple attribute decision-making approach

The interest in region-specific ethnic fermented foods and their functional microbiota is rising. The demands for functional foods are continuously rising, so research is going on to develop nutritious food with many beneficial attributes and low safety concerns. The present study was designed to isolate and characterize lactobacilli probiotic candidates from locally resourced fermented foods (dahi, lassi, and raabadi) to make ready-to-eat fermented functional products later. Cultures were isolated from 82 fermented food samples collected from different villages. The initial experiments of gram staining, catalase test, and carbohydrate fermentation were assessed for the morphology, purity, and primary characterization on the genus level, which was verified through molecular characterization using PCR. Seven lactobacilli strains (no. MS001-MS007) were then assessed for safety, probiotic candidacy, phytase degradation, and biofilm forming abilities. All seven bacterial cultures showed no hemolytic activity and antibiotic sensitivity against more than 14 antibiotics out of 20. All seven lactobacilli isolates were able to tolerate pH 3.0, 0.3% bile 0.5% pancreatin, lysozyme (100 mg/L to 300 mg/L) and also shown possessed phytase degradation ability. All the cultures showed antioxidative potential and biofilm formation ability. Culture MS007 showed considerably higher bile salt hydrolase activity among all the isolates, whereas MS005 possessed excellent phytate degradation ability among others. Bacterial strains were identified using 16S rRNA gene sequencing. Moreover, the order of preference of isolates was calculated using the multidimensional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based on probiotic and other functional properties. The most promising attributes showing cultures were recognised as Limosilactobacillus fermentum MS005 and Lactiplantibacillus plantarum MS007, which could be further used for functional food product development.Graphical

Half‐Metallic Ferromagnetism in 2D Janus Monolayers: Mn2GeX (X = As, Sb)

Two‐dimensional (2D) Janus materials are a fascinating class of materials resulting from their unique electronic and magnetic properties induced by mirror symmetry breaking. However, 2D Janus materials with intrinsic magnetism remain rather rare, casting a mysterious veil over magnetism. In this work, the electronic and magnetic properties of Janus Mn2GeX (X = As, Sb) monolayers using the first‐principles calculations are investigated. The results demonstrate that these Janus materials exhibit excellent mechanical and dynamic stability, indicating their potential for future applications in nanoscale spintronic devices. Interestingly, the Janus and monolayers possess exciting half‐metallic character with wide half‐metallic gaps of 0.29 and 0.18 eV, and spin gaps of 1.68 and 1.62 eV, respectively. Their calculated ground state exhibits a strong preference for ferromagnetic ordering, with a Curie temperature () of 630 and 590 K, respectively. Additionally, the ferromagnetism of Janus Mn2GeX (X = As, Sb) monolayers is robust against biaxial strain ranging from −6% to 6%. Under 6% tensile strain, the calculated of the monolayer is 639 K, which represents a 9% increase compared to the observed in the unstrained condition. All these intriguing electronic and magnetic properties make the Janus Mn2GeX (X = As, Sb) monolayers an appealing candidate for applications in nanoscale spintronic devices.

Analyzing the Digital Infrastructure Enabling Project Management Success: A Hybrid FAHP-FTOPSIS Approach

This research paper examines the digital infrastructure required to achieve project management success by analyzing the enabling elements of this digital infrastructure in terms of three pillars: digital readiness, digital fitness, and digital tools. A comprehensive literature review was conducted to identify these enabling elements and to develop a list of project management success indicators through which the success of project management can be measured. To evaluate and rank the digital infrastructure enabling elements, a Multi-Criteria Analysis (MCA) was implemented using a hybrid approach combining Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). The study used the digital infrastructure enabling elements as MCA alternatives and the project management success indicators identified in the literature review as MCA criteria. The results indicated that the enabling elements associated with digital tools are the most significant for project management success, with a FTOPSIS closeness coefficient (CCi) of 0.8525, followed by those related to digital fitness (CCi = 0.6481) and digital readiness (CCi = 0.1602). These findings have proven to be robust, as they remained consistent even when weights of the MCA criteria were adjusted in three new scenarios proposed in a scenario analysis. This research highlights the critical role of digital enabling elements in enhancing project management practice and achieving project management success. It also offers a strategic framework for organizations to develop and strengthen their digital infrastructure.

Assessing Heterogeneity Among Cyclists Towards Importance of Bicycle Infrastructural Elements in Urban Areas

Promoting bicycling and making it attractive requires appropriate infrastructure. Sociodemographic characteristics, frequency and experiences of bike use, and purpose of bicycle trips can affect preferences towards bicycle infrastructure facilities in urban areas. Hence, this study aims to explore the heterogeneity in the perceived importance of bicycle infrastructure facility attributes in various cyclist groups based on gender, age, weekly biking frequency, daily cycling distance, cycling experience, and bicycle trip purpose. Data were collected from bicycle users through a questionnaire disseminated via social media platforms and QR code brochures distributed in Hasselt, Belgium. A 5-point Likert-type ordinal scale was used to collect data on the perceived importance of bicycle infrastructure facility indicators. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank the indicators. At the same time, Mann–Whitney U and Kruskal–Wallis tests were utilized to verify the heterogeneity among the groups. The findings reveal that bicycle infrastructure, i.e., bicycle lanes or paths, is the most critical variable, while the slope was considered the least important. No heterogeneity was found regarding the importance of bicycle infrastructure indicators based on gender. However, heterogeneity was observed based on age, daily bicycle use, cycling experience, weekly bicycle use, and bicycle trip purpose. The findings of this research help urban and transport planners develop improvement strategies for the city’s existing bicycling facilities and prioritize future developments by considering various cyclist groups’ preferences.

Should the pricing or advertising decision come first in a supply chain with a network externality?

PurposeThis paper aims to study how the timing of these decisions affects the total profit and the individual profits of the two agents.Design/methodology/approachThis paper study a supply chain for a network good where there is a manufacturer and a retailer. The manufacturer determines its wholesale price and its share in the retailer’s advertising cost while the retailer decides the retail price and the advertising cost.FindingsThis paper finds that a stronger network externality leads to higher prices and higher advertising efforts. This increases the profits of both manufacturer and retailer, but the manufacturer’s share of advertising costs depends on the order in which the supply chain enterprise make their decisions, the strength of network externality and the effect of advertising determines which decision timeline results in a higher price and greater advertising effort. The manufacturer prefers the price decision to be made before the advertising decision, while the retailer prefers these decisions to be made simultaneously.Research limitations/implicationsAlthough this paper studies the price and advertising decision-making order preferences of channel members based on network externalities, this research can also be expanded from the following aspects based on network effects. First, network externality affects advertising cooperation between both parties in the situation such that the pricing power of retail prices is transferred from the retailer to the manufacturer and the retailer relies on revenue sharing (revenue sharing contract, nonwholesale price contract. Second, the manufacturer dominates the issues in the supply chain, but in reality, a retailer can also be the dominator or there are no dominators (Nash equilibrium). Finally, it is possible to consider pricing and advertising decisions in situations where two manufacturers or retailers compete.Practical implicationsWhen the price is reasonable, advertising investment is the main determinant of product sales. The greater the intensity of network externalities the more retailers will be willing to invest in advertising. An increase in the intensity of network externalities may not necessarily enhance manufacturers’ motivation or cooperative advertising, but it depends on the decision-making sequence. The strength of network externalities determines the decision-making sequence preferences of supply chain channel members whose preferences vary leading to conflicts of interest.Originality/valueThe impact of cooperative advertising or decision sequence on corporate decision-making has not been considered. To fill this gap, the paper integrates network externality and supply chain cooperative advertising models, focusing on the impact of network externality on pricing and advertising decisions, as well as on the sequence of decisions.

Multi-attribute approach for selection of polymeric materials for manufacturing gears: A case study in the automotive industry

Automotive industry is characterized by mass production, a large share in the gross national income and high employment of workers with different knowledge and skills. Improving the production process, as well as product quality, is one of the most important tasks of automotive enterprise as well as government management. This research promotes a new fuzzy hybrid model for determining the priority of polymeric materials for manufacturing gears in an exact manner, which leads to an incremental improvement in the quality of the considered product. The analysis of polymeric materials and their characteristic is based on data from the relevant literature and experiences of the best practice. The relative importance of material characteristics is stated as fuzzy group decision making problem. The weights vector is determined by using the fuzzy Analytic Hierarchical Process and fuzzy geometric mean. The rank of polymeric materials is obtained by employing the proposed Technique for Order Preference by Similarity to Ideal Solution with triangular fuzzy numbers. The fuzzy algebra rules have been used for determining: (i) distances from Fuzzy Positive Ideal Solutions and Fuzzy Negative Ideal Solutions and (ii) closeness coefficient values. The proposed fuzzy hybrid model testing and verification are performed on real data in an automotive enterprise.

Integrated assessment of groundwater quality for sustainable irrigation in drought-prone central districts of Odisha, India

Crop productivity relies on the quantity and quality of irrigation water, with groundwater quality playing a major role. Exploitation of groundwater for irrigation is slowly increasing in central districts of Odisha. A study was carried out in these areas to assess the groundwater quality for irrigation for sustainable use of the groundwater resources. This was done by developing a Water Quality Index (WQI) for eight parameters. The groundwater quality was assessed using a framework that utilised the entropy weight approach and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This framework was employed to determine the relative proximity of each solution to the ideal solution. The TOPSIS range is divided into three categories: low quality (less than 0.1), medium quality (0.1–0.5), and good quality (0.5–0.9). The low-quality category has a percentage of 6.45%, categorized as poor quality and unfit for drinking and irrigation uses; the medium-quality category has 52.07%, and the good quality category has 41.47% of the area falls under the very good category. These parameters are essential when considering water usage for agriculture, especially in regions where farmers use fertilizers and pesticides extensively to enhance crop production.