Rock Mass Quality Rating Based on the Multi-Criteria Grey Metric Space

Assessment of rock mass quality significantly impacts the design and construction of underground and open-pit mines from the point of stability and economy.This study develops the novel Gromov-Hausdorff distance for rock quality(GHDQR)methodology for rock mass quality rating based on multi-criteria grey metric space.It usually presents the quality of surrounding rock by classes(metric spaces)with specified properties and adequate interval-grey numbers.Measuring the distance between surrounding rock sample characteristics and existing classes represents the core of this study.The Gromov-Hausdorff distance is an especially useful discriminant function,i.e.,a classifier to calculate these distances,and assess the quality of the surrounding rock.The efficiency of the developed methodology is analyzed using the Mean Absolute Percentage Error(MAPE)technique.Seven existing methods,such as the Gaussian cloud method,Discriminant method,Mutation series method,Artificial neural network(ANN),Support vector machine(SVM),Grey wolf optimizer and Support vector classification method(GWO-SVC)and Rock mass rating method(RMR)are used for comparison with the proposed GHDQR method.The share of the highly accurate category of 85.71%clearly indicates compliance with actual values obtained by the compared methods.The results of comparisons showed that the model enables objective,efficient,and reliable assessment of rock mass quality.

Determination of the best materials for development and designing product using a multi-criteria decision-making

Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are effective.An approach of MCDM is needed to cater to criteria of material assortment simultaneously.More firms are now concerned about increasing their productivity using mathematical tools.To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material.In addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this research.It goes on to construct a mathematical bi-objective model for determining the best item to purchase.Design/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system.The intermediate pipes are used to join the components of the exhaust systems.The materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS 439L.These seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this paper.This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes.After that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)C.The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes,on their performance and on the cost.In this structure,the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment.This essentially measures the quantity of information each criterion contribution,indicating the relative importance of these criteria better.Subsequently,the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative.The results show that SUS 309,SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.Findings–The material matrix of the decision presented in Table 3 was normalized through Equation 5,as shown in Table 5,and the matrix was multiplied with weighting criteriaß_j.The obtained weighted normalized matrix V_ij is presented in Table 6.However,the ideal,worst and best value was ascertained by employing Equation 7.This study is based on the selection of material for the development of intermediate pipe using MCDM,and it involves four basic stages,i.e.method of translation criteria,screening process,method of ranking and search for methods.The selection was done through the TOPSIS method,and the criteria weight was obtained by the entropy method.The result showed that the top three materials are SUS 309,SUS 432L and SUS 436 LM,respectively.For the future work,it is suggested to select more alternatives and criteria.The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality(ELECTRE),Decision-Making Trial and Evaluation Laboratory(DEMATEL)and Preference Ranking Organization Method for Enrichment Evaluation(PROMETHEE).Originality/value–The results provide important conclusions for material selection in this targeted application,verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

Contribution of GIS to Soil Landscape Mapping by Multi-Criteria Analysis Using Weighting: The Case of the Square Degrees of M’Bahiakro (Centre) and Daloa (Centre-West) in Ivory Coast

As part of the drive to improve coffee and cocoa production in Ivory Coast, studies are carried out to identify soils that are favourable for these crops. It is therefore necessary to orientate soil investigations based on reliable criteria that best discriminate soil cover. With this in mind, this study is being carried out to help improve survey methods by mapping soil landscapes. It uses GIS and weighted multicriteria analysis. To do this, satellite images were processed and the geological map of the square degrees of M’Bahiakro and Daloa was reclassified. The results show that relief is the main factor in soil landscape differentiation, with respective weights of 0.58 and 0.67 for the forest and pre-forest zones. In contrast, the weight of geological formation in soil landscape differentiation remains low (0.05 for the forest zone and 0.07 for the pre-forest zone). The criteria used on the base of aggregation sum methods have made it possible to formulate soil landscape mapping prediction functions according to agro-ecological environments in the humid intertropical zone. This is essential for the orientation of soil survey work. Nevertheless, other comparative methods, such as the coding mapping method, could provide elements for discussion to validate the models.

Multi-Criteria Wildfire Risk Hazard Assessment in GIS Environment: Projection for the Future and Impact on RES Projects Installation Planning

It is alarming for the fact that Wildfires number, severity and consequently impact have significantly increased during the last years, an aftermath of the Climate Change. One of the most affected areas worldwide is Mediterranean, due to the unique combination of its type of vegetation and demanding climatic conditions. This research is focused on the Region of Epirus in Greece, an area with significant natural vegetation and a range of geomorphological aspects. In order to estimate the Wildfire Risk Hazard, several factors have been used: geomorphological (slope, aspect, elevation, TWI, Hydrographic network), social (Settlements and landfils, roads, overhead lines and substations), environmental (land cover) and climatic (Fire Weather Index). Through a multi-criteria decision analysis (MCDA) and an analytic hierarchy process (AHP) in a GIS environment, the Wildfire Risk Hazard has been estimated not only for current conditions but also for future projections for the near future (2031-2060) and the far future (2071-2100). The selected case study includes the potential impact of the Wildfires to the installed (or targeted to be installed) RES projects in the studied region.

Using multi-criteria decision-making and machine learning for football player selection and performance prediction:a systematic review

Evaluating and selecting players to suit football clubs and decision-makers (coaches, managers, technical, and medical staff) is a difficult process from a managerial-financial and sporting perspective. Football is a highly competitive sport where sponsors and fans are attracted by success. The most successful players, based on their characteristics (criteria and sub-criteria), can influence the outcome of a football game at any given time. Consequently, the D-day of selection should employ a more appropriate approach to human resource management. To effectively address this issue, a detailed study and analysis of the available literature are needed to assist practitioners and professionals in making decisions about football player selection and hiring. Peer-reviewed journals were selected for collecting published papers between 2018 and 2023. A total of 66 relevant articles (journal articles, conference articles, book sections, and review articles) were selected for evaluation and analysis. The purpose of the study is to present a systematic literature review (SLR) on how to solve this problem and organize the published research papers that answer our four research questions.

Aspect based sentiment analysis using multi-criteria decision-making and deep learning under COVID-19 pandemic in India

The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.

A Deep Learning Based Approach for Context-Aware Multi-Criteria Recommender Systems

Recommender systems are similar to an informationfiltering system that helps identify items that best satisfy the users’demands based on their pre-ference profiles.Context-aware recommender systems(CARSs)and multi-criteria recommender systems(MCRSs)are extensions of traditional recommender sys-tems.CARSs have integrated additional contextual information such as time,place,and so on for providing better recommendations.However,the majority of CARSs use ratings as a unique criterion for building communities.Meanwhile,MCRSs utilize user preferences in multiple criteria to better generate recommen-dations.Up to now,how to exploit context in MCRSs is still an open issue.This paper proposes a novel approach,which relies on deep learning for context-aware multi-criteria recommender systems.We apply deep neural network(DNN)mod-els to predict the context-aware multi-criteria ratings and learn the aggregation function.We conduct experiments to evaluate the effect of this approach on the real-world dataset.A significant result is that our method outperforms other state-of-the-art methods for recommendation effectiveness.

Assessing Criteria Weights by the Symmetry Point of Criterion (Novel SPC Method)–Application in the Efficiency Evaluation of the Mineral Deposit Multi-Criteria Partitioning Algorithm

Information about the relative importance of each criterion or theweights of criteria can have a significant influence on the ultimate rank of alternatives.Accordingly,assessing the weights of criteria is a very important task in solving multi-criteria decision-making problems.Three methods are commonly used for assessing the weights of criteria:objective,subjective,and integrated methods.In this study,an objective approach is proposed to assess the weights of criteria,called SPCmethod(Symmetry Point of Criterion).This point enriches the criterion so that it is balanced and easy to implement in the process of the evaluation of its influence on decision-making.The SPC methodology is systematically presented and supported by detailed calculations related to an artificial example.To validate the developed method,we used our numerical example and calculated the weights of criteria by CRITIC,Entropy,Standard Deviation and MEREC methods.Comparative analysis between these methods and the SPC method reveals that the developedmethod is a very reliable objective way to determine the weights of criteria.Additionally,in this study,we proposed the application of SPCmethod to evaluate the efficiency of themulti-criteria partitioning algorithm.The main idea of the evaluation is based on the following fact:the greater the uniformity of the weights of criteria,the higher the efficiency of the partitioning algorithm.The research demonstrates that the SPC method can be applied to solving different multi-criteria problems.

Multi-Criteria Group Decision-Making Method Based on an Improved Single-Valued Neutrosophic Hamacher Weighted Average Operator and Grey Relational Analysis

This paper proposes a multi-criteria decision-making (MCGDM) method based on the improved single-valued neutrosophic Hamacher weighted averaging (ISNHWA) operator and grey relational analysis (GRA) to overcome the limitations of present methods based on aggregation operators. First, the limitations of several existing single-valued neutrosophic weighted averaging aggregation operators (i.e. , the single-valued neutrosophic weighted averaging, single-valued neutrosophic weighted algebraic averaging, single-valued neutrosophic weighted Einstein averaging, single-valued neutrosophic Frank weighted averaging, and single-valued neutrosophic Hamacher weighted averaging operators), which can produce some indeterminate terms in the aggregation process, are discussed. Second, an ISNHWA operator was developed to overcome the limitations of existing operators. Third, the properties of the proposed operator, including idempotency, boundedness, monotonicity, and commutativity, were analyzed. Application examples confirmed that the ISNHWA operator and the proposed MCGDM method are rational and effective. The proposed improved ISNHWA operator and MCGDM method can overcome the indeterminate results in some special cases in existing single-valued neutrosophic weighted averaging aggregation operators and MCGDM methods.

Application of a Multi-Criteria Approach for the Assessment of Groundwater Potential in the Basement Zone: A Case of Tamou (South West Niger)

The commune of Tamou, located in the Department of Say in Niger, occupies the southwestern part of the Liptako crystallophyllian basement domain. In this area, the problem of the drinking water supply of the populations is acute, because of the low flow rates of the drillings capturing the crystallophyllian formations and the Voltaian sandstones, the failure rates of the drillings are very high there. Therefore, the main objective of this study is to improve the knowledge of the areas potentially favorable for the implantation of drillings likely to give more satisfactory flow rates. The methodological approach, based on the collection of data (Landsat 7 ETM+ satellite imagery, borehole data, geological and topographical maps) and their processing by a combination of remote sensing and GIS tools and a field check, allowed the elaboration of maps of availability, accessibility and exploitability of the groundwater resources in the study area. The maps developed were analyzed with a Spatial Reference Hydrogeological Information System following the technique of aggregation by weighting to generate the map of productive drilling sites. The results show that the area is moderately rich in groundwater (58%) and that only 31% of the potential is exploitable. The groundwater potential map shows that 46% of the study area is suitable for drilling.

Studies on Multi-Criteria Decision-Making-Based Healthcare Systems Using the Machine Learning

There is a lot of information in healthcare and medical records.However,it is challenging for humans to turn data into information and spot hidden patterns in today’s digitally based culture.Effective decision support technologies can help medical professionals find critical information concealed in voluminous data and support their clinical judgments and in different healthcare management activities.This paper presented an extensive literature survey for healthcare systems using machine learning based on multi-criteria decision-making.Various existing studies are considered for review,and a critical analysis is being done through the reviews study,which can help the researchers to explore other research areas to cater for the need of the field.

Synergizing Wind,Solar,and Biomass Power:Ranking Analysis of Off-Grid System for Different Weather Conditions of Iran

Nowadays,the use of renewable energies,especially wind,solar,and biomass,is essential as an effective solution to address global environmental and economic challenges.Therefore,the current study examines the energy-economic-environmental analysis of off-grid electricity generation systems using solar panels,wind turbines,and biomass generators in various weather conditions in Iran.Simulations over 25 years were conducted using HOMER v2.81 software,aiming to determine the potential of each region and find the lowest cost of electricity production per kWh.In the end,to identify the most suitable location,the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)method was employed to rank different stations based on simulation output parameters and some other influential factors.Considering the evaluation of various parameters,the stations in Yazd,Marand,and Dezful achieved the best results,while the stations in Ramsar,Shahrekord,and Gonbad presented the least favorable outcomes.In Yazd,the wind turbine is an economic priority,and a 100 kW wind turbine is utilized in the optimal system.In Yazd,where the simultaneous use of renewable energies is most prominent,the lowest pollutant production occurred with a quantity of 1174 kg/year.Annual energy losses are highest in Jask station and lowest in Yazd.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

Enabling weakened hedges in linguistic multi-criteria decision making

A semantics-based model is proposed to enable weakened hedges, such as ＂more or less＂ and ＂roughly＂ in the context of linguistic multi-criteria decision making. First, the resemblance relations are defined based on the semantics of terms on the domain. Then, the hedges can be represented after the upper and loose upper approximations of a linguistic term are derived. Accordingly, some compact formulae can be derived for the semantics of linguistic expressions with hedges. Parameters in these formulae are objectively determined according to the semantics of original terms. The proposed model presents a more natural way to express the decision information under uncertainties and its semantics is clear. The proposed model is clarified by solving the problem of evaluation and selection of sustainable innovative energy technologies. Computational results demonstrate that the model can deal with various uncertainties of the problem. Finally, the model is compared with existing techniques and extended to the case when the semantics of terms are represented by trapezoidal fuzzy numbers.

An Evaluation of the Critical Success Factors in Sustainable Food Supply Chains in Developing Countries

Food is one of the biggest industries in developed and underdeveloped countries. Supply chain sustainability is essential in established and emerging economies because of the rising acceptance of cost-based outsourcing and the growing technological, social, and environmental concerns. The food business faces serious sustainability and growth challenges in developing countries. A comprehensive analysis of the critical success factors (CSFs) influencing the performance outcome and the sustainable supply chain management (SSCM) process. A theoretical framework is established to explain how they are used to examine the organizational aspect of the food supply chain life cycle analysis. This study examined the CSFs and revealed the relationships between them using a methodology that included a review of literature, interpretative structural modeling (ISM), and cross-impact matrix multiplication applied in classification (MICMAC) tool analysis of soil liquefaction factors. The findings of this research demonstrate that the quality and safety of food are important factors and have a direct effect on other factors. To make sustainable food supply chain management more adequate, legislators, managers, and experts need to pay attention to this factor. In this work. It also shows that companies aiming to create a sustainable business model must make sustainability a fundamental tenet of their organization. Practitioners and managers may devise effective long-term plans for establishing a sustainable food supply chain utilizing the recommended methodology.

Integrated use of GIS, remote sensing and multi-criteria decision analysis to assess ecological land suitability in multi-functional forestry

The Zagros forests are a treasure of valuable oak forests, but they have been severely degraded from long-term misuse. Geographic information systems （GIS） and multi-criteria decision analysis （MCDA） have been increasingly used to improve the management of vulnerable ecosystems to prevent further degradation and increase the sustainability of land use. This study presents a methodology to assess land suitability using remote sensing （RS） to obtain wall-to-wall data for the calculations, GIS to analyze the data, and MCDA to rank alternative land uses. The criteria and subcriteria affecting the suitability of land for different uses were identified and weighted using an analytic hierarchy process. Variables used as subcriteria were assessed using satellite data and other sources of information such as existing maps and field surveys. Numerical values for the subcriteria were classified, and each class was given a priority rating according to expert judgments. Based on the ratings and weights of the subcriteria, a priority map was created for each land use using the weighted linear combination method. The priority maps for different land uses were overlaid to obtain a preliminary land use map, which often indicated several simultaneous land uses for the same location. The preliminary map was further edited by removing unrealistic, mutually exclusive land-use combinations. The study tested and demonstrated the potential of integrating RS, G1S and MCDA techniques for solving complicated land allocation problems in forested regions using a scientifically sound and practical approach for efficient and sustainable allocation of forestland for different uses.

Aggregation operators on intuitionistic trapezoidal fuzzy number and its application to multi-criteria decision making problems

Intuitionistic trapezoidal fuzzy numbers and their operational laws are defined. Based on these operational laws, some aggregation operators, including intuitionistic trapezoidal fuzzy weighted arithmetic averaging operator and weighted geometric averaging operator are proposed. Expected values, score function, and accuracy function of intuitionitsic trapezoidal fuzzy numbers are defined. Based on these, a kind of intuitionistic trapezoidal fuzzy multi-criteria decision making method is proposed. By using these aggregation operators, criteria values are aggregated and integrated intuitionistic trapezoidal fuzzy numbers of alternatives are attained. By comparing score function and accuracy function values of integrated fuzzy numbers, a ranking of the whole alternative set can be attained. An example is given to show the feasibility and availability of the method.

Potential landfill site selection for solid waste disposal using GIS and multi-criteria decision analysis(MCDA)

Proper solid waste disposal is an important socioeconomic concern for all developing countries.Municipalities have their own policies,individual approaches and methods to manage the solid wastes.They consider wastelands outside the urban area as the best suitable for the solid waste disposal.Such improper site selection will create morphological changes that lead to environmental hazards in the urban and its surrounding areas.In this research,the site selection for urban solid waste disposal in the Coimbatore district used geographical information system(GIS)and multi-criteria decision analysis(MCDA).Thematic layers of lineament density,landuse/landcover,population density,groundwater depth,drainage density,slope,soil texture,geology and geomorphology were considered as primary criteria and weights for criteria,and sub-criteria were assigned by MCDA analysis.The resultant weight score was validated by consistency ratio so that the efficiency of the selected criteria was justified.The overlay analysis in GIS environment provides 17 potential zones in Coimbatore district,among which,four suitable sites were screened and refined with the help of field investigation and visual interpretation of satellite image.The result of landfill suitability map shows the effectiveness of the proposed method.

Multi-criteria decision-making approach with incomplete certain information based on ternary AHP

It is not uncommon in multiple criteria decision-making that the numerical values of alternatives of some criteria are subject to imprecision, uncertainty and indetermination and the information on weights of criteria is incomplete certain. A new multiple criteria decision- making method with incomplete certain information based on ternary AHP is proposed. This improves on Takeda＇s method. In this method, the ternary comparison matrix of the alternatives under each pseudo-criteria is constructed, the eigenvector associated with the maximum eigenvalue of the ternary comparison matrix is attained as to normalize priority vector of the alternatives, then the order of alternatives is obtained by solving two kinds of linear programming problems. Finally, an example is given to show the feasibility and effectiveness of the method.

Urban Construction Land Suitability Evaluation Based on Improved Multi-criteria Evaluation Based on GIS(MCE-GIS): Case of New Hefei City,China

Urban construction land suitability evaluation （UCLSE） is a complex system engineering and the basis for rational use of the limited urban land resources in China. It has an important practical value on urban construction land use planning and management from the angle of methodology. As a widely used technique, traditional multi-criteria evaluation based on GIS （MCE-GIS）, is not suitable for UCLSE. This study develops an improved MCE-GIS method which could be more suitable for UCLSE based on urban complex ecological system theory and the summary of the shortcomings of traditional MCE-GIS. The improvements include three aspects： a composite evaluation index system rather than natural indexes alone, an index weight calculated by using fuzzy Analytic Hierarchy Process （AHP） method rather than the common AHP method, and the integrated overlay rule, which includes selecting the minimum value, weighted linear combination （WLC） and simple summation. The main advantage of this improved technique is that it can make UCLSE more comprehensive, more operational and more reasonable. It can provide a scientific basis for decision making in the planning and management of urban construction land use. The improved MCE-GIS system has been adopted in the New Hefei City, Anhui Province, China. Based on the results of UCLSE in New Hefei, three functional areas including construction-appropriate areas, construction-restricted areas and construction-forbidden areas could be worked out, in which 36.90% of the total study area could be developed as urban construction land and the remaining 63.10% should be protected as reserves land or as ecological land. Furthermore, the results can orovide scientific decision suooort for spatial planning and eco-environment nrotection in New Hefei.