Multi-criteria Decision-making Process Research Articles

A multi-criteria decision-making method based on discrete Z-numbers and Aczel-Alsina aggregation operators and its application on early diagnosis of depression

In mental health diagnostics, the questionnaire is an effective and cost-effective method. However, the traditional questionnaire test methods for depression and anxiety have great ambiguity. The discrete Z-numbers (DZs) provide solutions for describing and resolving complex fuzzy issues in the intelligent multi-criteria decision-making (MCDM) process. However, large-scale datasets are not suited for the present MCDM techniques due to their extremely high computational cost. Additionally, these techniques are less stable and flexible. To address the above issues, a novel MCDM method is introduced, which is based on the DZs theory and the Aczel-Alsina (AA) aggregation operator (AO) for large-scale datasets. To begin with, centroid points are calculated for DZs, and a series of novel AOs are introduced. And then a score function with a parameter is introduced to balance the influence between the possibility restriction and the fuzzy restriction of DZs. Thirdly, a new MCDM method under DZs is presented based on the proposed AA AOs and score function. Finally, to support the early diagnosis of depression and anxiety, we apply our method to the real-life online Depression, Anxiety, and Stress Scale (DASS) which can be transformed into DZs by our proposed preprocessing method. According to experimental results, our method is applicable to large-scale datasets and has much lower complexity as well as higher flexibility and stability.

Fault tolerant & priority basis task offloading and scheduling model for IoT logistics

The Internet of Things (IoT) has connected millions of devices to the internet for communication and computation purposes. Due to constraints such as limited battery power, processing capabilities, and storage capacity, IoT devices often rely on remote fog nodes for task execution, a practice known as task offloading. The NP-hard nature of offloading and scheduling IoT tasks onto fog nodes poses significant challenges. Current IoT task offloading often relies on single parameters like device MIPS, RAM, or battery power. Similarly, task allocation on fog nodes frequently prioritizes VM MIPS. To address energy consumption and task failures in IoT systems, robust fault tolerance, efficient task offloading, and effective scheduling are crucial. This paper introduces a novel Fault-Tolerant, Priority-Based Task Offloading and Scheduling Model (FP-TOSM) for IoT logistics. Utilizing the Analytic Hierarchy Process (AHP) for multi-criteria decision-making, IoT tasks are prioritized. Tasks below a specified threshold are executed locally, while those within a defined range are offloaded to a fog node. Tasks exceeding this range are delegated to the cloud. The Euclidean formula is used to determine the proximity between the IoT logistics vehicle and fog nodes for offloading decisions. Dynamic re-clustering fault tolerance mechanisms are utilized to manage task failures after offloading. Successful offloaded tasks are allocated to suitable VMs in the cloud and fog nodes. The proposed strategy selects the most suitable VM for offloaded tasks using a multi-criteria decision-making process to reduce energy consumption, SLA violations, and execution costs. The model's performance is evaluated through simulations in iFogSim2. Results demonstrate reductions in energy consumption by up to 5.8 % and 10.4 %, decreases in SLA violations by up to 25.2 %, and a 16.28 % improvement in response time compared to an ACO-based model with a response time of 17.8 %. Additionally, the task failure ratio shows a significant reduction of up to 22.1 %. These findings highlight the effectiveness of FP-TOSM in enhancing efficiency and reliability within fog computing environments.

Organizational culture and leadership style in Spanish Hospitals: Effects on knowledge management and efficiency

Hospital culture and leadership style have attracted considerable attention in research, with compelling evidence indicating their potential competitive advantages, including their crucial role in ensuring the successful implementation of knowledge management and its impact on hospital efficiency. The aim of this paper is to identify the effects of organizational culture and leadership style on knowledge management and hospital efficiency. Fuzzy cognitive maps (FCMs) are relational models that can be used to represent the opinions and knowledge of expert to infer cause-effect relationships among different concepts. The use of FCMs as a simulation tool enables the evaluation of potential scenarios based on different organizational cultures and leadership styles in hospitals. Developing an FCM for this study involved several steps. Firstly, data were collected through interviews with 21 experts in hospital management. The interviews were conducted between May and September 2023 either face-to-face or via videoconference. Once individual cognitive maps had been created, consensus among them was achieved through a multicriteria decision-making process, wherein the expert opinions were averaged. The separate cognitive maps of each expert were then integrated to produce a single FCM using the augmented FCM approach. Reflecting expert insights from the FCM, hospitals with a hierarchy culture exhibit diminished levels of knowledge creation, management, and overall hospital efficiency, whereas those with an adhocracy culture show improvements in knowledge creation, knowledge exploitation, and overall hospital efficiency in comparison to alternative ones. From the experts' FCM perspective regarding leadership style, transformational leadership achieves the highest level of knowledge management and hospital efficiency in hospitals with an adhocracy culture. Finally, this paper offers a reference for practising knowledge management and improving hospital efficiency through adhocracy culture and transformational leadership.

Multi-Criteria Decision Making for Selecting the Location of a Solar Photovoltaic Park: A Case Study in UAE

The high availability of solar energy in the Gulf Cooperation Council (GCC) makes it the most attractive source of energy in this region, especially due to the global shift toward eco-friendly systems. A significant increase in the implementation of solar PV projects has been noticed in the United Arab Emirates (UAE). For this reason, this study conducted a multi-criteria decision-making process to compare four locations for building a solar PV park in the UAE, namely, Abu Dhabi, Dubai, Sharjah, and Umm Al Quwain. Various criteria were taken into account, including the solar radiation, wind speed, distance from the electricity grid, distance from seaports, and land cost. A hybrid AHP-TOPSIS model was employed to evaluate the criteria weights and alternatives’ scores, which were also compared with the no priority-TOPSIS to check the effects of the criteria weights on the overall ranking. According to the findings, Dubai and Abu Dhabi were ranked first and second, with final scores of 0.7108 and 0.4427, respectively, when using the AHP-TOPSIS model. Furthermore, Umm Al Quwain scored slightly less than that of second place, with a value of 0.4252. The top two ranked alternatives were consistent between the two compared models, as Dubai also performed the best among all locations, which confirmed the reliability of the proposed approach and verified the obtained results and rankings.

Correlation coefficients between normal wiggly hesitant fuzzy sets and their applications

The multi-criteria decision-making (MCDM) field has long sought tools capable of adeptly capturing the intricacies of human decision-making amidst uncertainty. Hesitant fuzzy sets (HFS) have become a cornerstone in the MCDM field due to their ability to capture the intricacies of human decision-making under uncertainty. Nonetheless, we identified a significant gap in traditional HFS formulations, which often fail to fully harness the nuanced and implicit preferences of decision-makers (DMs). This shortcoming can lead to suboptimal decision outcomes in complex and uncertain environments. We introduce the normal wiggly hesitant fuzzy set (NWHFS), a novel construct that encapsulates both explicit and implicit preferences within a more representative framework. This study pioneers the development of new correlation coefficients for NWHFSs, offering a robust quantitative measure to elucidate the intricate relationships between variables. Our findings demonstrate that NWHFSs significantly enhance the MCDM process, providing a nuanced perspective that traditional HFS models cannot match. The proposed correlation coefficients not only reveal the concealed preferences of DMs but also broaden the decision-making spectrum, offering a more profound understanding of the relationships between alternatives and criteria. We illustrate the superiority of our approach through comparative analysis with existing methods, highlighting its ability to discern subtleties that other models overlook. Moreover, we integrate NWHFSs into clustering analysis, showcasing their potential to classify data sources with shared attributes effectively. This integration is particularly noteworthy for its ability to navigate complex datasets, offering a new dimension in data mining and resource retrieval. In essence, our study redefines the MCDM paradigm by introducing NWHFSs and their correlation coefficients, setting a new standard for decision-making accuracy and insight. The implications of our work extend beyond theory, offering practical solutions to real-world decision-making challenges.

Proactive framework for obsolescence management of electrical equipment in oil and gas industry

PurposeEnergy consumption has increased significantly since the 1970s, which has increased demand for sufficient infrastructure in the oil and gas industry. Many top-notch oil and gas companies invested in and equipped their facilities with high-capacity electrical equipment to meet high demand and benefit from high revenues. This is becoming a challenge nowadays for old facilities in the oil and gas industry, as most of the electrical equipment installed has reached or even exceeded its lifetime. Moreover, many of the original equipment manufacturers (OEMs) for electrical equipment from the 1980s are no longer in market today. Therefore, the aim of this study is to develop a proactive, cost-effective obsolescence management framework for electrical equipment in the oil and gas industry, considering the aging factor of the equipment.Design/methodology/approachFirstly, the study begins with gathering available information and identifying criteria. Secondly, the data collection is evaluated by subject-matter-experts (SMEs) in asset management field to ensure compliance with updated international standards and relevant regulatory requirements. Thirdly, a multi-criteria decision-making process is used to rank criteria. Finally, a scoring system is developed to measure the electrical equipment obsoleteness.FindingsThe developed framework will assist decision-makers in making informed decisions about maintenance, replacement or upgrades, using knowledge from previous studies and experts’ input. The result finding indicates that considering aging correction factors when measuring equipment obsoleteness leads to accurately and correctly predicting the electrical equipment obsoleteness score.Originality/valuePrevious studies have addressed obsolescence management without taking equipment age into account, regardless of how the equipment is performing. Thus, the lack of a comprehensive obsolescence management framework that accounts for both cost-effectiveness and the aging factor in the oil and gas industry poses a critical challenge.

Research on expanding the scope of application of the MARA method

Data normalization is a crucial step in multi-criteria decision-making (MCDM) processes. The choice of data normalization method significantly influences the ranking of alternatives. The available data normalization methods in the MARA (Magnitude of the Area for the Ranking of Alternatives) method may not be applicable in certain cases. This study aims to broaden the application scope of the MARA method. Therefore, an investigation into the compatibility of data normalization methods when combined with the MARA method has been conducted. Ten data normalization methods were utilized, including Linear normalization, Weitendorf normalization, Sum linear normalization, Vector normalization, Logarithmic normalization, Max linear normalization, Min linear normalization, Jüttler-Körth normalization, Peldschus normalization, and Stop normalization. The compatibility between the MARA method and these ten data normalization methods was tested in five different scenarios. In the initial four scenarios, variations in the number of alternatives, criteria, and criterion types were introduced. Eight out of the ten data normalization methods were proven to be suitable for integration with the MARA method. In the fifth scenario, a hypothetical situation was presented where the data normalization methods available in the MARA method could not be used. Alternative data normalization methods were employed, and their combination with the MARA method was compared to using other multi-criteria decision-making methods. The results affirmed the accuracy of these combinations. This exploration has expanded the application scope of the MARA method compared to its original version

Development of a heavy vehicle torque rod applying continuous fiber reinforced thermoplastic composite materials instead of forged steel material: Design, analysis and optimization

The torque rod is an important component of the suspension system that connects the axle to the chassis in heavy commercial vehicles. The main motivation of this study is the development of a torque rod made of (1) plus cross-section, (2) continuous carbon and glass fiber reinforced hybrid thermoplastic composites, which can replace a forged steel torque rod used in heavy vehicles, has superior mechanical properties, provides minimum cost and weight. This study aims to develop a torque rod before its production within the framework of integration with Computer Aided Design (CAD), Finite Element Analysis (FEA), and Multi-Criteria Decision Making (MCDM). In this study, the design data and the properties of the composite materials were chosen as control factors. The minimum displacement, the best mechanical properties such as tensile stress, compressive stress, torsional shear stress, maximum critical buckling load, and the minimum part weight minimum production cost per piece were selected as quality characteristics. As a result of the FEA study, considering the experimental set of the Taguchi Method L25 orthogonal array, the data on mechanical properties, weight, and production cost per piece were subjected to the MCDM process using the Entropy Weighted TOPSIS method. As a result of the MCDM study, a torque rod made of continuous fiber-reinforced thermoplastic composite material, instead of a torque rod produced by forged steel, had the highest mechanical properties produced, the weight of the torque rod can be reduced by 64.76%, and the production costs per piece can be reduced by 37.5%. This study’s findings have shown that the torque rod produced from continuous fiber-reinforced thermoplastic composite materials in a new geometry with a plus cross-section can be substituted for the torque rod produced by steel forging. Thus, it will contribute to reducing the fixed vehicle weight, especially in heavy commercial vehicles, reducing CO2 emissions, and increasing the range of the vehicles.

Development of RAMDOE: a new method for rapidly ranking alternatives with supplementary options and considering changes in criteria values

This paper delves into the development and validation of the RAMDOE method, a pioneering approach in multi-criteria decision making (MCDM) that seamlessly integrates the root assessment method (RAM) and design of experiments (DOE) techniques, addressing the inflexibility of traditional MCDM methods in accommodating adjustments in criteria ranges and the addition of new alternatives without necessitating a complete overhaul of the decision framework. Through empirical analysis, the study demonstrates the RAMDOE method's remarkable efficacy in precisely ranking alternatives, as illustrated through a practical case study focused on the selection of a supplier from a pool of seven candidates. One of the most notable aspects of the RAMDOE method lies in its capacity to formulate a regression equation that accurately captures the intricate relationship between alternative scores and criteria values, enabling decision-makers to seamlessly integrate new alternatives into the decision-making process without the cumbersome task of recalibration, thereby distinguishing it from conventional MCDM techniques such as TOPSIS (technique for order of preference by similarity to ideal solution), COPRAS (complex proportional assessment), MOORA (multiobjective optimization on the basis of ratio analysis), EDAS (evaluation based on distance from average solution) and CODAS (combinative distance-based assessment). The practical implications of these findings are profound, offering decision-makers across various domains a more efficient and adaptable framework to navigate complex decision scenarios. Particularly in contexts like supplier selection, where criteria ranges may vary significantly, the RAMDOE method provides decision-makers with a robust toolset to make informed decisions, presenting a promising avenue for addressing the dynamic nature of decision-making environments and enhancing the overall robustness and flexibility of MCDM processes in real-world applications

Spherical Fuzzy Multicriteria Decision Making for Evaluating Healthcare Service Quality of Hospitals During the Global Pandemic

AbstractThis study identifies hospitals in Iran that have demonstrated exceptional performance in service quality during the COVID-19 pandemic based on the proposed integrated multicriteria decision-making (MCDM) process. Although the coronavirus has been eradicated in most countries, occasional outbreaks of COVID-19 variants have occurred, affecting many individuals, particularly in Iran. The pandemic caused an influx of hospital visits, with people seeking treatment for various illnesses. However, the abrupt onset of the pandemic and its global impact challenged hospitals’ ability to provide timely care, leading to a noticeable decline in service quality. Identifying the top-performing hospitals is crucial for benchmarking and enhancing healthcare quality. To assess hospital service quality, the study employed a customized SERVQUAL model, which helped identify key factors that served as criteria and subcriteria for the evaluation process. The priority weights of these factors were then obtained using the spherical fuzzy analytic hierarchy process. For each SERVQUAL criterion, the hospitals were evaluated using the spherical fuzzy weighted aggregated sum product assessment method, resulting in respective rankings of the hospitals. Finally, an integrated Borda−Copeland method was utilized to generate the aggregate evaluation ranking, a feature that serves as an important departure from the literature. The contribution of this work lies in developing an integrated approach that intends to serve as a benchmark not only for hospitals in different countries but also for those confronting similar challenges and offers guidance for seeking insights from top-performing hospitals in comparable situations.

Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics.

IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodes' priority for task allocation and scheduling. The AHP decides the fog nodes' priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7% reduction in response time, 16% reduction in energy consumption, and 22% reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin.

Fermatean fuzzy Z-analytic hierarchy process: An application to third party logistics providers

To decrease the impact of ambiguity, fuzzy sets are frequently used to decide if an element belongs to a set. Various fuzzy set extensions differ from others in how uncertainty is expressed in the definition of membership functions. This paper presents a novel extension named Fermatean fuzzy Z-numbers, introducing fuzzy restriction and fuzzy reliability functions with Fermatean fuzzy membership functions. By utilizing fuzzy Z-numbers to delineate membership degrees, considering both restriction and its reliability, in tandem with Fermatean membership functions offering an expanded value spectrum, the assessment of linguistic evaluations using Fermatean fuzzy Z-numbers can yield more pragmatic outcomes in decision evaluations amidst high uncertainty. Moreover, the interval-valued Fermatean fuzzy-Z-analytic hierarchy process is developed to explore the impact of proposed interval-valued Fermatean fuzzy-Z numbers on the multi-criteria decision-making process, demonstrated in the context of third-party logistics provider selection. Adding fuzzy reliability functions to the decision-making process improves how uncertainty is represented by considering variations in confidence levels associated with each decision, thus aiding in the clearer expression of decisions. Additionally, the proposed method's outcomes are compared with the outcomes of the current approaches.

Analysis of the Post-Cyclonic Physical Flood Susceptibility and Changes of Mangrove Forest Area Using Multi-Criteria Decision-Making Process and Geospatial Analysis in Indian Sundarbans

Tropical cyclones, one of the most extreme and destructive meteorological incidents, cause extensive damage to lives and livelihoods worldwide. This study utilized remotely sensed data along with multi-criteria decision-making, geospatial techniques, and major cyclonic events Aila, Amphan, and Yaas to identify the changes in the vulnerability of cyclone-induced floods in the 19 community development blocks of Indian Sundarbans in the years 2009–2010, 2020–2021, and 2021–2022 (the post-cyclonic timespan). The Sundarbans are a distinctive bioclimatic region located in a characteristic geographical setting along the West Bengal and Bangladesh coasts. In this area, several cyclonic storms had an impact between 2009 and 2022. Using the variables NDVI, MNDWI, NDMI, NDBI, BSI, and NDTI, Landsat 8 Operational Land Imager, Thermal Infrared Sensor, Resourcesat LISS-III, and AWiFS data were primarily utilized to map the cyclonic flood-effective zones in the research area. The findings indicated that the coastline, which was most impacted by tropical storms, has significant physical susceptibility to floods, as determined by the AHP-weighted overlay analysis. Significant positive relationships (p < 0.05, n = 19 administrative units) were observed between mangrove damage, NDFI, and physical flood susceptibility indicators. Mangrove damage increased with an increase in the flood index, and vice versa. To mitigate the consequences and impacts of the vulnerability of cyclonic events, subsequent flood occurrences, and mangrove damage in the Sundarbans, a ground-level implementation of disaster management plans proposed by the associated state government, integrated measures of cyclone forecasting, mangrove plantation, coastal conservation, flood preparedness, mitigation, and management by the Sundarban Development Board are appreciably recommended.

Assessing and enhancing the regional sustainability of electricity generation technologies in an energy-importing megaregion in China

Decision-makers are increasingly concerned about the sustainability of power generation technologies to achieve a secure and sustainable electricity supply in the future. This study aims to assess the sustainability of the eight key electricity generation technologies in the energy-importing Yangtze River Delta region of China and further enhance the regional sustainability of the electricity generation mix. We employed the multi-criteria decision-making process to rank the sustainability of the eight electricity generation technologiesfrom the perspective of various decision-makers. First, the results revealed that no technology is absolutely most sustainable. Second, the subjective findings show that hydropower is the most sustainable technology among the assessed ones, followed by nuclear and onshore wind power. Third, policymakers regard fossil fuel energy as more sustainable than investors and experts. Furthermore, local renewable diffusion and substantial electricity imports from renewable-rich areas can enhance the sustainability of the localelectricity system. In addition,an interregional electricity transmission grid with the neighbor Anhui province as the primary backup power supplier can sufficiently enhance the sustainability of the electricity system in the Yangtze River Delta region.

Material Selection for Artificial Femur Bone Using PROMETHEE-GAIA Method

Abstract When replacing bones and implants, choosing the right materials for the artificial bone and orthopedic implants is crucial to the procedure’s success. In this work, a thorough assessment of the literature was followed by a thorough and rigorous evaluation of prospective materials for prosthetic femurs using a multicriteria decision-making process known as PROMETHEE-GAIA (Preference Ranking Organization METHod for Enrichment Evaluation and Geometric Analysis for Interactive Assistance). The proposed approach was validated using a total of 12 assessment parameters, including density, tensile strength, and ultimate tensile strength, and 17 candidate materials. The significance of the chosen criteria is well described. These 17 candidate implant materials and the 12 assessment criteria were used to develop a choice matrix. Rankings over the prepared matrix were produced using the PROMETHEE-GAIA program. Ti-6Al-7Nb, Ti-6Al-4V, and ASTM F1537, Standard Specification for Wrought Cobalt-28Chromium-6Molybdenum Alloys for Surgical Implants (UNS R31537, UNS R31538, and UNS R31539), Co-Cr-W emerged as the top contenders and were demonstrated as possible materials for effective artificial femur materials because of the assessment. With a large number of pertinent criteria and a wide range of materials, this study offers a framework for the selection of implant materials. It also emphasizes how choosing materials carefully may increase the durability and efficiency of orthopedic implants.

Optimization of thermophysical properties of nanofluids using a hybrid procedure based on machine learning, multi-objective optimization, and multi-criteria decision-making

The rheological and thermal behavior of nanofluids in real-world scenarios is significantly affected by their thermophysical properties (TPPs). Therefore, optimizing TPPs can remarkably improve the performance of nanofluids. In this regard, in the present study, a hybrid strategy is proposed that combines machine learning (ML), multi-objective optimization (MOO), and multi-criteria decision-making (MCDM) to select optimal parameters for water-based multi-walled carbon nanotubes (MWCNTs)-oxide hybrid nanofluids. In the first step, four critical TPPs, including density ratio (DR), viscosity ratio (VR), specific heat capacity ratio (SHCR), and thermal conductivity ratio (TCR), are modeled using two efficient ML techniques, the group method of data handling neural network (GMDH-NN) and combinatorial (COMBI) algorithm. In the next step, the superior models are subjected to a four-objective optimization by the well-known non-dominated sorting genetic algorithm II (NSGA-II), which aims to minimize DR/VR and maximize SHCR/TCR. This study considers volume fraction (VF), oxide nanoparticle (NP) type, and system temperature as optimization variables. In the final step, two prominent MCDM techniques, TOPSIS and VIKOR, were used to identify the desirable optimal points from the Pareto fronts generated by the MOO algorithm. ML results reveal the COMBI algorithm's superior reliability in accurately modeling various TPPs. The pattern of Pareto fronts for all oxide-NPs indicated that over one-third of the optimal points have a VF > 1.5 %. On the other hand, the distribution of optimal points across different temperature ranges varied significantly depending on the type of oxide-NPs. For Al2O3-based nanofluid, around 90 % of the optimal points were within 40–50 °C. Conversely, for nanofluids containing CeO2 NPs, only approximately 24 % of the optimal points were found within the same temperature range. Considering diverse scenarios for weighting TPPs in the MCDM process implied that combining CeO2/ZnO oxide-NPs with MWCNTs in water-based nanofluids is highly effective across various real-world applications.

Agricultural Implement Industry Using WPM Method

Agricultural implements are agricultural Human labor in activities that Reduce field crop yield Tools that can be used to improve is reapers, Traction, disc harrows, Cultivators, seed drills, Harrows, Spades, Baggage, Plows, and other agricultural Tools are very common. In Modern Agricultural Practices Agricultural implements play an important role play These are commercial and Widespread in organic farming are used. This Tools are for sowing, field preparation, Planting, threshing, and irrigation and are used for harvesting. Agricultural machinery industry or Agricultural engineering is a profession as part of the industry, it is in agriculture or other agriculture used tractors, Agricultural machinery and Manufacturing agricultural implements maintain. This branch is mechanically Considered part of the profession.Agricultural implements are agricultural to carry out procedures The necessary tools are: In today's farming operations Many agricultural implements are used. Agriculture means crops and livestock Production, Aquaculture, Aquaculture and food and food Forests for non-food products Includes. Seated man Agriculture at the Rise of Civilization A major development was Raised by this Cultivation of species is food Generated surpluses, which helped people live in cities. Humans are at least 105,000 grains years ago Although started to collect, New farmers are about 11,500 years ago They started planting. Sheep Goats, pigs, and cattle About 10,000 years ago were raised. World's lowest Plants are native to 11 regions and Cultivated as fodder. twentieth in century, large-scale Based on monocultures Industrial agriculture with dominated agricultural production.The weighted product method is a multi-criteria decision-making process is there are many alternatives, and based on several criteria we must determine the best alternative.DuPont India, Rallis India Limited, Nuziveedu Seeds Limited, Lemken India Agro Equipments Private Limited, Advanta Limited.Technical capability, Product quality capability, Delivery capability, Financial/cost capability from the result, it is seen that Lemken India Agro Equipments Private Limited is got the first rank where as Nuziveedu Seeds Limited is having the lowest rank

Addressing a decision problem through a bipolar Pythagorean fuzzy approach: A novel methodology applied to digital marketing

Decision-making in real-world scenarios faces uncertainty. Fuzzy theory has been a means to represent such uncertainty. In this study, we propose an approach that incorporates bipolarity into multi-criteria decision-making processes applied to digital marketing. The proposal considers both the positive and negative dimensions of data, leading to better-informed decisions. Our contribution integrates bipolarity into Pythagorean fuzzy matrices, a framework that broadens the utility of bipolar fuzzy theory. Through computational experimentation, we identify the most effective strategy for digital marketing platforms. When compared to existing techniques, our approach shows advantages, underlining its potential to improve decision-making in uncertain scenarios and offering insights for businesses that refine their digital marketing strategies.

Fuzzy Predictive Modeling for the Hierarchical Structure of Strategic Innovation Management

Total interpretive structural modeling (TISM) constructs a hierarchical structure with interpretations of nodes and interrelationship links. While TISM performs qualitative research when conceptual modeling is preferred, it lacks quantitative capabilities at present. Conducting a quantitative study on TISM is difficult, especially when the model elements are qualitative. TISM enables the development of a predictive model based on fuzzy set theory. The proposed methodology benefits from using surrogate variables to quantify qualitative variables and fuzzy set theory to simulate the hierarchical model. The proposed method is exercised to conduct a simulation-based study on the existing hierarchical model of strategic innovation to generate multiple scenarios. Additionally, through simulation and scenario building, relevant decisions can be made to enhance productivity and performance in particular functional areas. In this article, multiple scenarios generated by the simulation provide a comprehensive view of the system's behavior in new ventures, which aids in the multicriteria decision-making process.

The selection of washing machine programs with fuzzy dematel and moora-ratio multi-criteria decision-making methods considering environmental and cost criteria

The washing machine is the prevalent white household equipment in contemporary society. These machines provide consumers with a range of program options that encompass several variables, including temperature and detergent type. Nevertheless, the selection made by individual customers about the washing machine program they opt for carries substantial environmental consequences during the use stage of textile products. According to studies on the life cycle of clothes, it has been established that the use stage, following the extraction of raw materials, exerts the most substantial influence on environmental impacts. The objective of this research is to assess the washing machine programs provided by the manufacturer through the application of a comprehensive systematic approach for analysis. The evaluation of scenarios for washing machine programs was conducted using the MOORA-Ratio multi-criteria decision-making process. This evaluation considered various parameters, including environmental impact and cost. The life cycle assessment methodology was employed to quantify the environmental impact of the specified criteria. Based on the comprehensive study conducted by integrating criteria across numerous dimensions, it has been determined that the most favorable scenario wass scenario 1, which was developed for the Cotton 20 C program. The primary objective of this research endeavor is to fill a significant need in the current body of literature by undertaking a comprehensive review of washing machine programs that have not been previously recorded. This study employs a comprehensive methodology to investigate the environmental and economic implications linked to these activities, with the objective of delivering significant insights to producers and users.