Multi-criteria Decision-making Approach Research Articles

Evaluating the factors influencing the sustainable refrigerant selection by fuzzy decision making approach

Considering that cooling in cooling systems is more costly than heating, the importance of refrigerant selection in cooling systems is even more obvious. Due to the complexity of the refrigerant selection problem, a multi-criteria decision approach must be used to implement a thorough and organized evaluation of the factors. The purpose of this study is to evaluate the criteria to be considered when choosing refrigerants using the interval type-2 trapezoidal fuzzy Analytic Hierarchy Process (AHP). As a result, the most important and least crucial refrigerant selection criteria are determined by calculating the weights and obtaining the ranking of the requirements. In this way, the refrigerant selection criteria are prioritized, and the most crucial factor in refrigerant selection has emerged as energy efficiency. In light of the results, it has become clear that it is now essential for everyone in the world to use environ- mentally friendly, highly effective refrigerants.

Flood risk assessment using Neutrosophic Analytical Hierarchy Process (N-AHP) and GIS techniques in the Melet Basin (Türkiye)

Today, knowing the spatial distribution of flood risk using GIS (Geographic Information Systems)-based MCDM (Multi-Criteria Decision Making) approaches has been a topic addressed by many researchers. In this context, the current study focuses on the spatial distribution of flood risk using the N-AHP (Neutrosophic Analytical Hierarchy Process)-based GIS approach. The Melet Basin (Türkiye) is a case study for the suggested methodology. Four decision-makers used linguistic phrases to compare and assess the flood criteria during the method's application phase. The opinions of the decision makers were combined with the N-AHP, and the criteria were weighted. The results determined that precipitation, distance from the river, drainage density, land use, and slope were the most important factors affecting the flood and contributed approximately 74%. Apart from this, it has been observed that 10% of the basin is in the high and very high flood risk classes, and these risky areas overlap with the flood points of past periods. The proposed approach and findings are anticipated to have theoretical and practical implications.

A Taguchi-based hybrid multi-criteria decision-making approach for optimization of performance characteristics of diesel engine fuelled with blends of biodiesel-diesel and cerium oxide nano-additive

Given the pressing demand and ongoing necessity for fossil fuels, there is an imperative to actively seek alternative resources to replace petroleum-based fuels. The presents study considers a problem of experimentally investigating the effect of varying levels of important input parameters of a diesel engine fuelled with a novel blend of biodiesel-diesel and cerium oxide nano-additive on the sustainable performance characteristics of a diesel engine. Four input parameters, i.e., blend percentage (B in %), nanoparticle concentration (NPC in ppm), engine load (LD in %) and ignition pressure (IP in bar) each at four levels are considered. Experiments are conducted as per the Taguchi’s L16 standard orthogonal array and for each experiment, performance parameters (such as Brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC)), emission measures (Carbon monoxide (CO), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC) and Vibration level (VL)) of the diesel engine are collected. A hybrid multi-criteria decision-making (MCDM) approach, i.e., integrated MEREC-MARCOS method along with signal-to-noise (S/N) ratio and analysis of mean (ANOM) is employed to determine optimal setting of the input parameters that yield optimal multiple performance characteristics. The results reveal that B at 40%, NPC at 80 ppm, LD at 50% and IP at 200 bar is the optimal setting of the input parameters that produce optimum values of the output responses considered simultaneously. Further, results of the analysis of variance (ANOVA) show that Nanoparticle concentration percentage contribution is the maximum (79.63%) followed by engine load (8.40%), ignition pressure (6.28%), and blend percentage (2.11%). The optimization results are: BTE is 32.87%, BSEC is 0.285, CO is 0.018%, NOx is 559.6 ppm, UBHC is 28.1 ppm and VL= 19.57m2/sec which were validated with a confirmation test. Henceforth, such hybrid fuels provide sustainable energy solutions and environmental conservation simultaneously addressing the current and future demands.

Mapping coastal groundwater potential zones using remote sensing based AHP model in Al Qunfudhah region along Red Sea, Saudi Arabia

Due to the increases in agriculture and industry sector as well as high population, lack of water is becoming a major problem in the Middle East especially in arid regions. Saudi Arabia needs more groundwater research and explorations because of its higher water use and no source of freshwater. Assessing groundwater zonation in semi-arid locations is essential due to the significant degree of variation in groundwater depth, aquifer features, topographical characteristics, and insufficient precipitation. Mapping prospective groundwater zones in Al Qunfudhah region of southwestern Saudi Arabia has utilized the capability of the multi-criteria decision approaches (MCDA), and the Geographic information system (GIS). We have used the analytical hierarchy process (AHP) as one of the MCDA that is applied to achieve the objective of the current study by integrating twelve controlling factors. These factors are represented by the thematic layers; slope, precipitation, soil type, land use/cover (LULC), drainage density (DD), normalized difference vegetation index (NDVI), curvature, topographic position index (TPI), Terrain Ruggedness Index (TRI), drainage density (DD), and Lineament Density (LD). These thematic layers are combined with GIS to delineate the zones of groundwater potentialities. All factors were classified and weighted according to their importance and its effect on groundwater zones. Their normalized weights were evaluated using a pairwise comparison matrix. The present study shows that the groundwater potential zones (GWPZs) map is represented by five groups ranging between a very high zone with an area of 23781.06 Km2 that represents 4.04 % of the studied area, and a very poor GWPZ with an area of 182944.4 Km2 that represents 31.09 % of the studied area. The AHP model suggests that lineament density, slope, and drainage density are more important for determining the groundwater potentiality than other physiographic factors. The study's findings will be helpful in developing practical strategies for the region's groundwater supply. This analysis shows how the methodology may be used to study a broad coastal groundwater basin. The current study will give the decision makers to select suitable sites with a high groundwater potential.

A risk-based multi-criteria decision-making framework for offshore green hydrogen system developments: Pathways for utilizing existing and new infrastructure

Unlocking the potential of offshore renewables for green hydrogen (GH2) production can be a game-changer, empowering economies with their visionary clean energy policies, amplifying energy security, and promoting economic growth. However, their novelty entails uncertainty and risk, necessitating a robust framework for facility deployment and infrastructure planning. To optimize offshore GH2 infrastructure placement, this work proposes a novel and robust GIS-based multi-criteria decision-making (MCDM) framework. Encompassing thirty-two techno-socio-economic-safety factors and ocean environmental impact analysis this methodology facilitates informed decision-making for sustainable and safe GH2 development. Utilizing the synergies between offshore wind and solar resources, this study investigates the potential of hybrid ocean technologies to enhance space utilization and optimize efficiency. To illustrate the practical application of the proposed framework, a case study examining a GH2 system in Australia's marine region and its potential nexus with nearby offshore industries has been conducted. The performed life cycle assessment (LCA) explored various configurations of GH2 production, storage, and transportation technologies. A Bayesian objective weight integrating technique has been introduced and contrasted statistically with the hybrid CRITIC, Entropy, MEREC and MARCOS-based MCDM approaches. Various locations are ranked based on the net present value of life cycle cost, GH2 production capacity, risk, availability, and environment sustainability factors, illustrating their compatibility. A sensitivity analysis is conducted to confirm that a Bayesian approach improves the decision-making outcomes through identifying optimal criteria weights and alternative ranks more effectively. Empowering strategic GH2 decisions globally, the proposed approach optimizes system performances, cost, sustainability, and safety, excelling in harsh environments.

Integrating Multi-Criteria Decision Models in Smart Urban Planning: A Case Study of Architectural and Urban Design Competitions

The focus of this study is to integrate the DEX (Decision EXpert) decision-modeling method in architectural and urban design (A & UD) competitions. This study aims to assess the effectiveness of integrating the DEX (Decision EXpert) decision-modeling method into the evaluation process of A & UD competitions to enhance decision-making transparency, objectivity, and efficiency. By using symbolic values in decision models, the approach offers a more user-friendly alternative to the conventional jury decision-making process. The practical application of the DEX method is demonstrated in the Rhinoceros 3D environment to show its effectiveness in evaluating A & UD competition project solutions related to the development of the smart city. The results indicate that the DEX method, with its hierarchical and symbolic values, significantly improves the simplicity of the evaluation process in A & UD competitions, aligning it with the objectives of the smart cities. This method provides an efficient, accessible, and viable alternative to other multi-criteria decision-making approaches. This study importantly contributes to the field of architectural decision making by merging qualitative multi-criteria decision models into the CAD environment, thus supporting more informed, objective, and transparent decision-making processes in the planning and development of smart cities.

Double-layer multi-criteria group decision-making approach using neutralized possibility degree-based decision matrix with fuzzy information

Multi-criteria group decision-making (MCGDM) problems are fundamentally characterized by uncertainty and ambiguity. The quantification of such data is vital for determining the most optimal course of action. In this paper, a double-layer possibility degree-based interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs) Vlsekriterijumska Optimizacija i Kompromisno Resenje (VIKOR) approach is presented. First, the possibility degrees of IVq-ROFSs are calculated and processed to establish a new neutralized decision matrix. Second, the components of the new decision matrix are taken into account for obtaining a new pair of positive and negative ideal solutions. The utility and regret measures in the VIKOR approach are ultimately determined by combining criteria weights and cumulative possibility degrees to rank the alternatives. The results of the study show that the proposed approach contributes to acquiring accurate alternative rankings in situations where traditional approaches have had difficulty generating ranks. Also, by eliminating the early normalization stage, which has a direct impact on the final ranking, the proposed approach concludes a unique ranking for each decision-making problem. Several comparative and sensitivity analysis are also conducted to exhibit robustness and applicability of the proposed approach in real world.

Selection of automotive brake friction composites reinforced by agro-waste and natural fiber: An integrated multi-criteria decision-making approach

The aim of this study is to develop a method for selecting and ranking automotive brake friction composite materials made from agro-waste and natural fibers. This is achieved by using an integrated multi-criteria decision-making methodology. Consequently, six composite samples are produced by reinforcing rice husk, rice husk ash, and Grewia optiva fiber. These samples are then assessed for their frictional properties using a laboratory-scale brake tribometer, in accordance with the requirements outlined in SAE J2522 standard. The selection criteria are based on the analyzed tribological parameters, which include friction coefficients, wear, and friction fluctuations fade-recovery performances. The CRITIC (criteria importance through intercriteria correlation) method was used to determine the importance of criteria for performance evaluation. Based on CRITIC analysis, the average friction coefficient (0.1592), recovery-3 (0.1363), friction fluctuations (0.1347), and performance friction coefficient (0.1240) were identified as the key criteria. The use of MARCOS (measurement of alternatives and ranking according to compromise solution) for ranking assessment reveals that composite materials reinforced with rice husk ash exhibit the most favorable tribological characteristics. The validation of the ranking using different decision-making tools demonstrates the reliability and effectiveness of the proposed approach.

Extracting of prospective groundwater potential zones using remote sensing data, GIS, and multi-criteria decision-making approach in the Sub-Himalayan Dooars region of West Bengal, India

Groundwater is one of the fundamental sources of fresh water. Currently, the unequal distribution of groundwater poses a significant environmental crisis, leading to scarcity in certain regions and affecting the livelihood of living organisms worldwide. The present study introduces the Sub-Himalayan foothills Dooars, a highly populated and agriculturally based area. To delineate the groundwater potentiality eight different effective parameters have been employed such as slope, geomorphology, soil, LULC, lineament, rainfall, hydrogeology, and drainage density. For the process and development of these eight thematic layers, geographic information systems and satellite images (Landsat 8) have been used to give the normalized weight and rank value in each thematic layer and their sub-criteria. MCDA-AHP-based model is used for overlay analysis to delineate groundwater potentiality (GWPZ). After overlay analysis in ArcGIS 10.2.2 final groundwater potentiality map is further grouped into four potentiality zones, i.e., very high, high, moderate, and low potential zones. A very high potential zone encompasses 22.45 sq. km. in the southern portion of the selected study region. The high potential zone encompasses 1613.32 sq. km in the southeastern part, the moderate zone is covered by 1063.84 sq.km, and the 71.03 sq.km (2.56% area of the region) area is covered by the low potential zone. The available well data from CGWB have been utilized to assess the potential of groundwater in the district, ensuring originality and authentic validation. It is found that there is a correlation between the observed potentiality zone and existing well data. With the growing population in the Sub-Himalayan Dooars region, there has been a corresponding rise in the demand for water. Hence, this research would assist the authorities and policymakers in effectively managing the groundwater resources in the district.

A Multi-Criteria Decision-Making Framework for Zero Emission Vehicle Fleet Renewal Considering Lifecycle and Scenario Uncertainty

In the last decade, with the increased concerns about the global environment, attempts have been made to promote the replacement of fossil fuels with sustainable sources. For transport, which accounts for around a quarter of total greenhouse gas emissions, meeting climate neutrality goals will require replacing existing fleets with electric or hydrogen-propelled vehicles. However, the lack of adequate decision support approach makes the introduction of new propulsion technologies in the transportation sector a complex strategic decision problem where distorted non-optimal decisions may easily result in long-term negative effects on the performance of logistic operators. This research addresses the problem of transport fleet renewal by proposing a multi-criteria decision-making approach and takes into account the multiple propulsion technologies currently available and the objectives of the EU Green Deal, as well as the inherent scenario uncertainty. The proposed approach, based on the TOPSIS model, involves a novel decision framework referred to as a generalized life cycle evaluation of the environmental and cost objectives, which is necessary when comparing green and traditional propulsion systems in a long-term perspective to avoid distorted decisions. Since the objective of the study is to provide a practical methodology to support strategic decisions, the framework proposed has been validated against a practical case referred to the strategic fleet renewal decision process. The results obtained demonstrate how the decision maker’s perception of the technological evolution of the propulsion technologies influences the decision process, thus leading to different optimal choices.

Spherical fuzzy sets based integrated DEMATEL, ANP, VIKOR approach and its application for renewable energy selection in Turkey

Renewable energy continues occupying the global agenda for the low-carbon transition. Identifying suitable sources for renewable energy involves different criteria and alternatives, which can be defined as a Multi-Criteria Decision-Making (MCDM) problem. MCDM methods can be enriched with Group Decision Making (GDM) to merge individual judgments into group opinions, as well as with fuzzy sets to better simulate human opinions. To handle uncertainties of DMs’ preferences, a recently developed fuzzy set extension, the Spherical Fuzzy Sets (SFSs), is combined in this study with different MCDM tools to establish a decision support tool for the energy source selection problem. This article proposes a novel MCDM approach that integrates SF-DEMATEL (Spherical Fuzzy Decision-Making Trial and Evaluation Laboratory), SF-ANP (Spherical Fuzzy Analytic Network Process), and SF-VIKOR (Spherical Fuzzy Vlse Kriterijumska Optimizacija Kompromisno Resenje) algorithms in a GDM environment and presents its application in a case study for selecting renewable energy sources in Turkey. Comparing wind, geothermal, solar, hydropower, and biogas, the proposed method highlighted both wind and solar energy as a compromise solution as the most suitable options for Turkey. It has been found that compared to similar fuzzy sets, SFS can better reflect the hesitation degree to address the lack of knowledge and errors in the membership function definitions. The robustness and plausibility of these findings are demonstrated with sensitivity and comparative analyses, suggesting this MCDM method can also be applied to similar problems in other settings.

Multi-criteria assessment of climate change due to green house effect based on Sugeno Weber model under spherical fuzzy Z-numbers

Using the multi-criteria decision-making (MCDM) approach, this research piece addresses the urgent problems of environmental degradation and climate change. The method provides a structured way to examine and compare different criteria and options, which improves the precision of decision-making. In order to make this method even better, we combine Zadeh's Z´-numbers with limitations and reliability factors in an effort to fill the current knowledge vacuum about their maximum potential. We investigate Z´-numbers, which are unexpected and fuzzy, in an effort to use them to our advantage by combining them with spherical fuzzy sets (SFSs). We present the idea of spherical fuzzy Z´-numbers (SFZ´Ns) that allow for flexible and adaptive handling of uncertain data by facilitating fast pairwise comparisons of decision-making options. We develop a full set of operational rules and create aggregation operators (AOs) based on the Sugeno Weber Γ¯-norm and Γ¯-conorm to formalize this new method. We use the average and geometric AOs, two of these operators, to show how our suggested technique works and how practical it is. In decision-making situations including climate change assessment, our comparison study highlights the importance of the suggested operators and approaches, especially in relation to the greenhouse effect. Our study helps us understand Z´-numbers better and how they might be used to handle uncertain data by tackling the interaction between fuzziness and randomness. By emphasizing clarity and specificity, our research provides a solid basis for better decision-making and handling of ambiguity in several fields.

An innovative distance measure for quantifying the dissimilarity between Q-Rung orthopair fuzzy sets

Q-Rung orthopair fuzzy set (Q-ROFSs) is a generation of quartic fuzzy sets (QFSs), Fermatean fuzzy sets (FFSs), Pythagorean fuzzy sets (PFSs), Intuitionistic fuzzy sets (IFSs), and fuzzy sets (FSs). Fuzzy similarity measures find wide-ranging applications in several fields, such as pattern recognition (PR), classification, and multi-criteria decision-making (MCDM). The computation of criteria weights in MCDM heavily relies on the fuzzy environment. Therefore, this study focuses on developing a similarity measure for Q-ROFS based on Hausdorff distance, a valuable tool for establishing object similarities. We introduce an innovative distance measure designed to quantify the dissimilarity between Q-ROFSs. Our approach leverages this distance measure to establish a similarity measure between Q-ROFSs. Furthermore, we present a set of properties associated with the suggested similarity measures. To demonstrate the usefulness of these measures, we provide numerous numerical examples in the fields of PR and linguistic variable characterization. By leveraging our innovative techniques, we also develop a novel algorithm for orthopair fuzzy Tomada de Decisao Iterativa Multicriterio (TODIM), a highly interactive and effective MCDM approach. Moreover, we have successfully applied the newly devised orthopair fuzzy TODIM method to address MCDM challenges encountered in diverse real-life problems. The obtained algebraic outcomes affirm that the suggested similarity measures are appropriate, practical, and applicable for queries involving fuzzy linguistic variables in MCDM contexts.

Optimizing photovoltaic thermal (PVT) collector selection: A multi-criteria decision-making (MCDM) approach for renewable energy systems

In the realm of renewable energy systems, the effective selection of Photovoltaic Thermal (PVT) collectors is important. This study delves into the intricacies of choosing optimal PVT collectors available in the market, emphasizing the utility of Multiple Criteria Decision Making (MCDM) methodologies. PVT collectors are differentiated based on various aspects such as their transparent front cover presence and the medium—liquid or air—used for heat transfer. The study methodologically identifies commercially available PVT collectors and establishes key performance indicators for comparison. Using an 11-point fuzzy scale, a weight matrix is determined. Subsequently, advanced MCDM techniques, including PROMETHEE II, TOPSIS, EDAS, and VIKOR, are employed to rank the alternatives. This research not only provides a systematic approach to PVT collector selection but also extends to a MATLAB-based tool for facilitating the process. The results underscore the value of the proposed methodologies in refining the selection of PVT collectors and their potential adaptability to other applications, such as PVT collector design.

A single-valued neutrosophic CIMAS-CRITIC-RBNAR decision support model for the financial performance analysis: A study of technology companies

Financial performance stands as a fundamental performance indicator that reflects companies' current financial conditions, providing information to investors and stakeholders about companies' financial well-being. In this research, a decision support system is developed for identifying the financial performance of companies traded on stock exchanges. This decision support system is based on a multicriteria decision-making (MCDM) approach and neutrosophic logic. The research introduces a novel method for calculating and ranking financial performance, using financial ratio indicators as selection criteria. The importance levels of financial ratio indicators are weighted through two different approaches. In the first approach, expert opinions and criteria importance assessment (CIMAS)-based on single-valued neutrosophic (SVN) sets are utilized for calculation. In the second approach, criteria importance through intercriteria correlation (CRITIC)-based on reference-based normalization processes is employed for criteria weighting. Subsequently, the results of the two criterion weightings are combined to calculate the final criterion weights. The SVN reference-based normalization alternative ranking (RBNAR) method is presented for ranking companies based on their financial performance. Thus, SVN-CIMAS-CRITIC-RBNAR is developed and its algorithm is presented. The novel hybrid decision support model is applied to a case study of technology companies traded on the Borsa Istanbul. The research results support the applicability of the SVN-CIMAS-CRITIC-RBNAR hybrid method. The results of the case study and sensitivity analyses affirm the applicability and robustness of the SVN-CIMAS-CRITIC-RBNAR hybrid model. The research provides detailed implications and insights for financial managers.

An evidential linguistic ELECTRE method for selection of emergency shelter sites

In many circumstances, decisions are based on subjective experience. However, some views can be vague, meaning that policymakers do not know exactly how they should express their opinions. Therefore, it is necessary for researchers to provide scientific decision frameworks, among which the multi-criteria decision making (MCDM) method in the linguistic environment is gradually favored by scholars. A large body of literature reports relevant approaches with regard to linguistic term sets, but existing approaches are insufficient to express the subjective thoughts of policymakers in a complex and uncertain environment. In this paper, we address this problem by introducing the concept of evidential linguistic term set (ELTS). ELTS generalizes many other uncertainty representations under linguistic context, such as fuzzy sets, probabilities, or possibility distributions. Measures on ELTS, such as uncertainty measure, dissimilarity measure and expectation function, provide general frameworks to handle uncertain information. Modeling and reasoning of information expressed by ELTSs are realized by the proposed aggregation operators. Subsequently, this paper presents a novel MCDM approach called evidential linguistic ELECTRE method, and applies it to the case of selection of emergency shelter sites. The findings demonstrate the effectiveness of the proposed method for MCDM problems under linguistic context and highlight the significance of the developed ELTS.

Integrated analytical hierarchy process-grey relational analysis approach for mechanical recycling scenarios of plastics waste in India.

Mechanical recycling is an indispensable tool for plastic waste (PW) recycling and has the highest share in the PW recycling sector in India. The transition to the circular economy of plastics (CEoP) needs a systemic perspective on the mechanical recycling processes. Nevertheless, the assessment of multiple parameters influencing the mechanical recycling of PW is a complex decision-making problem for the development of triple-bottom-line mechanical recycling. A systemic perspective of various mechanical recycling scenarios was performed by employing a multi-criteria decision-making approach to examine the complexity of interlinked factors in the present investigation. Analytical hierarchy process (AHP) integrated with grey relational analysis (GRA) was used to evaluate the criteria that directly influence quality-oriented mechanical recycling. Data were collected by conducting semi-structured interviews using a framed questionnaire in stakeholder engagement with mechanical recyclers of PW. The first level hierarchy included economy, technical, resource consumption and environmental criteria. These criteria were further categorized into various significant indices such as quality of recyclate, recyclability, water and energy consumption during recycling. The results of the integrated grey relational analysis indicated that the technical parameters including quality of recyclate, resource efficiency, PW processing rate and recyclability have a significant influence on mechanical recycling. Based on AHP-GRA, scenario MR6, i.e. manufacturing of PET strap from recycled PET flakes, was ranked the optimal mechanical process amongst the various scenarios. MR6 was followed by Straps and Films at the second and third rank. The lowest ranking was observed for polymer blend recycling. These processes with higher ranks produced good quality recyclate with better efficiency and recyclability. Moreover, these processes consumed optimal resources during manufacturing. These processes also exhibited less maintenance cost, high production rate, low chemical consumption and waste generation as well as implemented pollution control practices.

A Decision Support Model for Mitigating Supply Chain Risks Based on a Modified FMEA, Multi-Objective Optimization and Multi-Criteria Decision-Making Approach

Abstract Recent years have seen a huge development in the subject of supply chain risk management. In this increasingly uncertain world, the use of practical and effective tools for decision making and risk mitigation has become more necessary than ever. In this research, mitigation strategies for a tier one multinational company operating in the automotive industry and providing an assembly operation to final customer Renault Tanger and Renault SOMACA were prioritized according to their effectiveness, as well as their implementation costs. Based on research in the literature and the opinions of experts in the field. 44 risks and 55 mitigation strategies were identified. FMEA (Failure Modes and Effects Analysis) method was used based on the latest AIAG 2019 edition to filter and identify the risks to be prioritized, we used then a multi-objective optimization approach to identify the mitigation strategies that constitute the Pareto front for each of the risks and finally used the EDAS method for the final ranking of the strategies. Our case revealed that strategies like ensuring elaborating a contingency planning and defining the responsibilities, imposing contractual obligations on subcontractors, applying a flexible supply contract were found to be relevant risk mitigation strategies for the company. Managers interested in mitigating risk can deploy this model to prioritize risk mitigation strategies.

Simplified Novel Approach for Accurate Employee Churn Categorization using MCDM, De-Pareto Principle Approach, and Machine Learning

يعد طرد الموظفين من المنظمات مشكلة خطيرة. يجب حل مشكلة تدوير الموظفين أو اطردهم من داخل المنظمة نظرًا لأن لها تأثيرًا سلبيًا على المنظمة. يعد الاكتشاف اليدوي لتسرب الموظفين أمرًا صعبًا للغاية، لذلك تم استخدام خوارزميات التعلم الآلي ML بشكل متكرر لاكتشاف تسرب الموظفين بالإضافة إلى تصنيف الموظفين وفقًا للاستبدالهم. باستخدام التعلم الآلي، بحثت دراسة واحدة فقط في تصنيف الموظفين حتى الآن. تم اقتراح نهج جديد لاتخاذ القرار متعدد المعايير MCDM إلى جانب مبدأ DE-PARETO لتصنيف الموظفين. يشار إلى هذا باسم مخطط SNEC . تم تصميم نموذج AHP-TOPSIS DE-PARETO PRINCIPLE (AHPTOPDE) الذي يستخدم نظام MCDM على مرحلتين لتصنيف الموظفين. في المرحلة الأولى، تم استخدام عملية التسلسل الهرمي التحليلي AHP لتعيين الأوزان النسبية لعوامل إنجاز الموظف. في المرحلة الثانية، تم استخدام TOPSIS للتعبير عن أهمية الموظفين في إجراء تصنيف الموظفين. تم تطبيق قاعدة 20-30-50 البسيطة في مبدأ DE PARETO لتصنيف الموظفين إلى ثلاث مجموعات رئيسية وهي الموظفون المتحمسون والسلوكيون والمضطربون. يتم بعد ذلك تطبيق خوارزمية الغابة العشوائية كخوارزمية أساسية لإطار عمل الموظفين المقترح للتنبؤ بخسارة الموظفين على أساس الفصل والذي يتم اختباره على مجموعة بيانات قياسية لنظام معلومات الموارد البشرية HRIS، ويتم تقييم النتائج التي تم الحصول عليها باستخدام طرق تعلم الآلة الأخرى. تتمتع خوارزمية Random Forest ML في مخطط SNEC بدقة إجمالية مماثلة أو أفضل قليلاً وMCC مع تعقيد زمني أقل مقارنةً بمخطط ECPR باستخدام خوارزمية CATBOOST

A Fuzzy-AHP Multi-Criteria Decision-Making Approach for a Sustainable Supply Chain of Rice Farming Stakeholders in Edu-Patigi LGA, Kwara State, Nigeria

The adoption of sustainable practices in decision making in supply chain operations among rice farmers and other stakeholders in Nigeria could be an effective tool to drive food security. The triple bottom line identifies the environmental, economic, and social dimensions of sustainability. These could be harnessed to improve the supply chain of locally produced rice, ultimately improving food security in the region. This paper adopts a quantitative method that uses 21 criteria from the three sustainability dimensions to measure rice stakeholders’ decision-making activities against sustainable rice practices (SRPs) using a Fuzzy Analytical Hierarchy Process multi-criteria decision-making approach. The findings reveal that decisions with regard to water usage, supplier diversity, and a safe working environment were ranked high, while government intervention, quality assurance, transparency, and power usage ranked low. This implies that improved performance of other stakeholders across the rice value chain such as the government and extension officers would enhance productivity and drive food security in the region.