Copeland Method Research Articles

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.

Determining criteria weights with genetic algorithms for multi-criteria decision making methods: The case of logistics performance index rankings of European Union countries

One of the most critical metrics for evaluating countries’ logistics performance is the Logistics Performance Index (LPI). Although the LPI is an effective tool, it is also an index with some concerns regarding performance evaluation. The two main concerns with LPI are assuming equal criterion weights and ignoring operational logistics performance and macroeconomic indicators. To overcome these problems, this study presents a logistics performance evaluation model in which the criteria weights are determined by GA. This study used 11 techniques to determine the logistics performances of the EU countries across 33 indicators. Employing more than one MCDM techniques enabled robustness and consistency. Median, Mean, Linear and K nearest neighbor techniques are implemented to impute the missing values in the dataset. The Linear Regression method is found as the best-performing imputation technique among other techniques for the data of this research. The performance of the GA is compared with other well-known criteria weight determination procedures namely CRITIC, Entropy and equal weight. The ranking results of the MCDM tools were integrated with the Copeland method. The genetic algorithm assigned the highest three weights to the Goods, Value of Exports, Quality of roads, and GDP per capita, indicating that these criteria play an essential role in the countries' logistics performance. The importance of these criteria is not captured by other weight sets. The correlation with the actual LPI scores is higher with Genetic algorithm compared with other weight determination techniques (CRITIC, Entropy, equal weight). Moreover, random weights are created and the results are examined. Weight simulation indicates that, some of the countries ranking are higher than the other countries, regardless of the weights of the criteria. The paper's novelty is to use the Genetic Algorithm (GA) as a criteria weight determination tool for MCDM methods. The proposed system can be used as a decision support system for evaluating the logistics performances of countries. The LPI index is published every two years, but if an automatic or semi-automatic system can be developed using the proposed approach, the logistics performance ranking of countries can be examined more frequently. This can help countries that want to rank high on the LPI ranking determine which criteria to focus on to improve their logistics performance.

Design of sustainable campus modal in Istanbul Gelisim University using fuzzy multicriteria decision making methods1

This study aims to investigate the sustainable campus criteria, the variations made or require to be done to become an ecologically sustainable campus. In this context, the major goal of the research is assessing the sustainable campus design principles and indicators, setting the targets and deciding the precedencies with the Fuzzy Multi-Criteria Decision-Making methods (MCDM) for the sustainable campus design at Istanbul Gelisim University. In this study, model-based methods have been used to evaluate the sustainable campus performance of universities. In this respect, the study differs from other studies in the literature. Another difference of this study is that three different Fuzzy Multi-Criteria Decision-Making methods has been used, these methods are Fuzzy-AHP, Fuzzy-TOPSIS and Fuzzy-ELECTRE. All three have different inference mechanisms. A common solution has been obtained by using the results of these three different Fuzzy-MCDM methods as hybrid dominance and superiority criteria. Here, the Copeland method, which takes the superiority criterion as a reference, has been used in the options where we could not provide the dominance criterion. At the end of this study, a recommendation report has been prepared according to these results.

Forklift Seçim Probleminin Kapsamlı Çözümü için MEREC Yöntemine Dayalı Çok Kriterli Bir Karar Verme Çerçevesi

Forklift vehicles are used for lifting, lowering, loading, and transporting loads. Aimed of this study is the selection of a forklift vehicle with a combination of multi-criteria decision-making (MCDM) methods. It was used six criteria, 13 alternatives, and the MEREC method. This study also presents a double normalization (DNMEREC) to contribute to a more convenient, and stable ranking of alternatives. 21 different methods were used to evaluate the alternatives: ARAS, CODAS, COPRAS, CoCoSo, ELECTRE, MABAC, EDAS, VIKOR, TOPSIS, SAW, WASPAS, PROMETHEE, MOORA, MOOSRA, MAIRCA, OCRA, PIV, GRA, ROV, MARCOS, PSI. The different methods rankings are integrated using the Borda, and Copeland methods.

An integrated multi-criteria decision-making approach for overcoming barriers to green supply chain management and prioritizing alternative solutions

Green practices are no longer a wish but a must in business. Despite that, businesses interested in implementing green supply-chain practices encounter several barriers. This study aims to analyze the barriers and solution proposals related to green supply chain practices in the manufacturing sector. Face-to-face interviews with eleven decision-makers in the manufacturing industry who know about green supply chain practices provided the data for the study. In the analysis phase, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was used to analyze the barriers that are effective in implementing green supply chain practices. Combining it with fuzzy logic and the Evaluation based on Distance from Average Solution (EDAS) and Complex Proportional Assessment (COPRAS) methods were used to evaluate solution proposals. The Copeland method was used to combine the results and find the final rankings. The three most important barriers are identified as the lack of technological hardware and software infrastructure, fear of failure, and non-adoption of technological improvements. The solutions that can be used in the implementation phase are training employees, collaborating with other businesses, and improving government support and incentives.

Evaluation of Green Marketing Strategies by Considering Sustainability Criteria

The purpose of this study is to identify and prioritize green marketing strategies in the food industry by taking sustainability goals into consideration since it is very difficult to prioritize and select green marketing strategies when considering sustainability criteria in their implementation. Therefore, it is necessary to use an appropriate solution to prioritize these strategies. For this purpose, sustainability criteria and green marketing strategies are recognized. Then, sustainability criteria are screened using the fuzzy Delphi method. Subsequently, using the fuzzy stepwise weight assessment ratio analysis method, the criteria are weighted according to expert recommendations. Next, green marketing strategies in the Iranian food industry are ranked using several fuzzy multiple criteria decision-making methods: fuzzy additive ratio assessment, fuzzy complex proportional assessment, fuzzy technique for order preference by similarity to the ideal solution, and fuzzy Viekriterijumsko Kompromisno Rangiranje methods. Finally, the results are integrated with the help of the Copeland method to choose the best strategies. The results indicated that the criteria of ‘minimizing waste’, ‘minimizing environmental impacts’, and ‘consumer participation’ had the highest weight. In addition, the strategies for the ‘development of waste reduction technology, ‘development of market research to detect green needs’, and ‘use of recycled or reusable materials in products’ received a higher rank.

A Multi-Criteria Based Stock Selection Framework in Emerging Market

The present study aims to compare the stock performances of the Fast Moving Consumer Goods (FMCG) and Consumer Durables (CD) firms at the Bombay Stock Exchange (BSE), India. It is evident from the extant literature that investment in the stock market depends on two broad objectives such as maximization of return while minimization of risk. Besides, investment decisions are also influenced by the behavioral nature of the investors. To this end, the current work considers the earning prospect (average market return, return on net worth, earning per share, and yield), market-centric risk (beta), market perception (price to book value, shares traded), momentum (turnover) and benchmarked performance (alpha) to set the criteria for comparison. The study period considers seven consecutive financial years to discern the performance. For the comparative analysis, a combined multi-criteria decision-making (MCDM) framework of Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) (used to determine criteria weights) and Evaluation Based on Distance from Average Solution (EDAS) (for ranking) methods has been utilized. Borda Count Method (BC), Copeland Method, and Simple Additive Weighting (SAW) have been used to aggregate the year-wise rankings. The calculated weights show consistency to the modern portfolio theory as average return, beta, and return on net worth obtain higher weightage than others. It is observed that there are variations in the year-wise comparative ranking, while on aggregation, FMCG firms dominate the top positions. The analysis reveals that Avanti Feeds Ltd., Hindustan Unilever Ltd., Procter & Gamble Hygiene & Health Care Ltd., Britannia Industries Ltd., and Nestle India Ltd. are the top five performers, while Godfrey Phillips India Ltd., E I D-Parry (India) Ltd., United Breweries Ltd., Rajesh Exports Ltd., and Radico Khaitan Ltd. hold the bottom five positions during the same period. The results also indicate that, more or less, the firms having higher market capitalizations have performed well. The results obtained using the EDAS method and other popular MCDM models, such as multi-attributive border approximation area comparison (MABAC) and the Complex Proportional Assessment (COPRAS), show a significant correlation. Further, the outcome of the sensitivity analysis confirms the stability of the performance-based ranking results.

A multi-criteria based analytic framework for exploring the impact of Covid-19 on firm performance in emerging market

The recent pandemic has been the greatest catastrophic event in the last century that has left its irrevocable effect on the socio-economic and cultural environment. The current work presents a combined multi-criteria decision making (MCDM) framework of logarithmic percentage-change driven objective weighting (LOPCOW) and evaluation is based on distance from average solution (EDAS) method to enfold the early impact of COVID-19 on firm performance from fast moving consumer goods and consumer durables sectors in emerging market. Five aspects such as stock performance, dividend payout capability, sales and operational performance, financial stability and economic sustainability are considered for comparing 30 firms over seven consecutive financial years (FY 2013–14 to FY 2020–21). For aggregation of year wise rankings popular methods like Borda count and Copeland methods have been applied. A comparison of results of the LOPCOW-EDAS model with other MCDM methods has been made. It is noticed that the firms which held the overall top positions prior to COVID-19 suffer more afterward than the bottom performers. However, there has not been any major effect of COVID-19 on firms’ financial health and long-term growth prospect. The result shows significant reliability and stability as revealed by the comparative ranking and sensitivity analysis. The proposed framework shall enable the organizations for detailed performance analysis. Some of the policy and managerial implications are also discussed.

A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian FMCG and consumer durable sector

In this paper, we aim to carry out a comparative analysis of the Dividend Pay Capabilities (DPC) of the selected organizations belonging to the Fast Moving Consumer Goods (FMCG) and Consumer Durables (CD) sectors listed in BSE, India during the period FY 2013-14 to FY 2019-20. We select top 25 companies from FMCG group and top 5 firms from the CD sector on the basis of average market capitalization. For comparison purpose, we have considered six aspects (grounded on the extant theories on dividend policy) such as ownership, size, profitability, growth, liquidity and risk. We have used a new integrated Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) and Evaluation based on Distance from Average Solutions (EDAS) framework for our analysis. The result shows that companies do not show consistent performance over the years. We further have noticed that FMCG organizations show comparatively better capabilities that CD firms vis-à-vis dividend payment. Since, there are considerable variations in the ranking, we apply aggregation methods like Borda Count (BC), Copeland method (CM) and Simple additive weighting (SAW). We use two other popular Multi-Criteria Decision Making (MCDM) methods like MABAC and COPRAS for comparison with our framework to ascertain the reliability of our result.

Comparative Analysis of MCDM Methods for the Assessment of Corporate Sustainability Performance in Energy Sector

Energy is an important input for production, growth, and development. A sustainable energy sector, where energy production and consumption balance is ensured, constitutes a key point for nature and humanity. In this study, a multidimensional framework is presented to measure corporate sustainability in the energy sector. Based on this framework, the sustainability performance of energy companies operating in the Asia and Europe regions is measured by hybrid multiple-criteria decision-making (MCDM) methods, taking into account the economic, environmental, and social dimensions of sustainability. The Entropy method is used to determine the weights of the criteria, the Proximity Indexed Value (PIV) - Range of Value (ROV) - Grey relational analysis (GRA) - Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) methods are used to rank the alternatives. The Copeland method is used to obtain a single rational ranking from different rankings. Based on the obtained results, it is concluded that energy companies in the Asian region are more sustainable than in the European region. Moreover, Thailand is the most sustainable country in the Asian region. The proposed framework can be contribute to the development of the energy sector.

An approach for measuring spatial similarity among COVID-19 epicenters

ABSTRACT Most countries and territories worldwide are affected by coronavirus disease 2019 (COVID-19), and some cities have become known as epicenters owing to high outbreaks. Because of the changeable and unknown nature of the virus, managers of different cities could learn from the experiences of cities that have been successful in controlling COVID-19 instead of wasting time exploring different methods. It would be even more beneficial if they analyzed the experiences of similar cities. The similarity of such cities could be examined within a geographic information system based on various criteria. This study investigated the similarities among eight cities – Wuhan, Tehran, Bergamo, Madrid, Paris, Daegu, New York, and Berlin – in terms of the COVID-19 situation (target) in these locations based on proximity factors, weather, and demographic criteria. First, the factor and target layers were prepared, and then similar cities were identified using a similarity model and different distance metrics. The results were aggregated using the Copeland method because of the different outcomes for each metric. The most similar city was identified for each selected city, and its similarity level was determined based on these criteria. The results suggested the following pairs of similar cities: Wuhan–Berlin, Tehran–Berlin, Daegu–Wuhan, Bergamo–Madrid, Paris–Madrid, and New York–Paris based on COVID-19 related data up to 15 April 2020 (target T1), and Daegu–Wuhan, Tehran–Madrid, Bergamo–Paris, Berlin–Paris, and New York–Madrid up to 8 December 2021 (target T2) with a minimum and maximum similarity rate of 82.85% and 92.36%, respectively. For similar cities, the most similar factors among the proximity criteria are the distance from bus and metro stations; among weather, the criteria are humidity and pressure; and among demographics, the criteria are male and female population ratios, literacy ratio, and death ratio from asthma and cancer, with a minimum and maximum difference of 0% and 64.94%, respectively. In addition, according to the random forests ranking results (with root mean squared error = 0.23), temperature, distance from the bank, and gender were the most important criteria for the eight studied cities. Identifying these important factors helps to determine hotspots or places of future outbreaks to choose control strategies according to the cultural and ecological conditions of each city.

Green supplier selection with integrated multi-criteria decision making techniques

The concept of Green Supply Chain Management (GSCM), whose importance has increased in recent years, is considered as a supply chain that prioritizes environmental impacts in all its activities. Nowadays, businesses are considered not only in terms of their economic performance, but also in terms of their environmental performance which makes GSCM important. However, GSCM cannot be conducted without the participation of the suppliers. Due to this fact, correct determination of their suppliers has vital importance in developing a better GSCM performance. While there is a plenty of research in many sectors in GSCM literature, the research in the paint subfield of the chemical sector, which has a great environmental impact, is limited. It is seen that Multi-Criteria Decision Making (MCDM) techniques are frequently used in supplier selection studies, however, there are few studies where the results of different approaches are compared and integrated. In this research, green supplier selection is conducted using different MCDM technics, and the results are compared and combined. In the study, green supplier selection was carried out in the paint sub-branch of the chemical industry with the MOORA-Ratio Method, MOORA-Reference Point Approach, GRA and COPRAS methods, each of which is integrated with the ANP Then, the COPELAND method was used to obtain a general ranking. This research contributes to the literature by proposing a unique model in terms of the applied sector and integrating MCDM method results. The model proposed in this study can help the decision makers in order to build a green supplier selection model.

An integrated multicriteria group decision making methodology for property concealment risk assessment under Z-number environment

The complexity and opacity of individual property concealment seriously impede the court's judicial execution. Property concealment risk assessment can assist law enforcement officers in predicting the probability of judgment debtor’s concealment behavior in advance and improve enforcement efficiency. However, few studies have explored the property concealment risk assessment from the perspective of qualitative multicriteria decision making, considering the uncertainty of expert’s evaluation and the non-complementarity between assessing criteria. To fill this gap, we propose a novel integrated multicriteria group decision-making methodology (MCGDM) under the Z-numbers environment to rank the concealing risk of judgment debtors. First, Z-numbers are employed to characterize the uncertainty and reliability of cognitive information simultaneously. Next, we present an expert weight determining method based on the degree of group consistency and reliability under the Z-numbers environment (WCRZ). Then, an innovative score function is presented, extending the ELECTRE- III method to Z-number environment to obtain individual outranking relations. Moreover, we develop the weighted Copeland method to aggregate individual rankings and obtain comprehensive risk prioritizations. Finally, an application case in concealing property risk ranking illustrates the proposed method. Results show that the proposed method can be successfully applied to practical complex decision-making problems, and it obtains more reasonable outcomes than conventional methods, with lower computational complexity and higher group consensus. It is the first study to model the concealing risk of judgment debtors, and the proposed method can be employed to address complex, uncertain decision problems in the real world.

Evaluation MCDM Multi-disjoint Paths Selection Algorithms Using Fuzzy-Copeland Ranking Method

To increase the Internet's reliability and to have greater control over traffic transmission, reliable path selection is important and Multipath routing is promising technique that are used in the communication networks. Finding reliable end-end paths and backup can increase network performance. So, using proper decision metrics and algorithm should be used to paths and backup selection phase in these networks. For this goal, in this paper selecting a more reliable multi disjoint paths is addressed as a multi-criteria decision making (MCDM) problem and availability factor is defined and calculated based on network histories. For decision algorithm, a new fuzzy evaluation method is proposed to rank these multi disjoint paths selection algorithms and it is compared with bandwidth based, TOPSIS, FuzzyTOPSIS and AHP methods as candidate techniques to select more appropriate global disjoint paths in the IP/MPLS networks with packet loss, delay and availability parameters as decision making metrics. The proposed method combines fuzzy theory and Copeland method to evaluate the rank of each proposed method base on bandwidth, delay and new defined availability metric of selected end to end paths. Simulation results show that this method selects more reliable backup paths with better bandwidth in compared with others and can be used to path selection in IP/MPLS networks.

Evaluating Barriers and Drivers to Sustainable Food Supply Chains

Today, with the daily increase in population, the demand for agricultural and/or, in general, food products continuously raises and so does the natural resources consumption which induces challenges to the supply chain management of important food products. In the present study, drivers as well as barriers of the agriculture sustainable supply chain are identified and ranked with the help of multicriteria decision-making (MCDM) techniques. We identify six drivers and seven barriers with the help of experts’ opinions in the field and apply ranking methods including TOPSIS-AHP, AHP, and COPRAS-AHP as well as Borda rule and Copeland method to merge the ratings. The results highlight that the economic dimension of sustainability is more important than the environmental and social dimension. Distrust of consumers, lack of understanding and awareness of managers, and performance appraisal problems were found to be the most important barriers. Furthermore, informing the community by the media, entering the global market, exporting products, and producing sustainable products as a competitive advantage were identified to be the most important drivers. The study also shows that public awareness and demands can push the food supply chains toward sustainability goals with the cooperation of governments and suppliers.

COMBINING DIFFERENT MCDM METHODS WITH THE COPELAND METHOD: AN INVESTIGATION ON MOTORCYCLE SELECTION

There are many different multi-criteria decision making methods in the literature. These methods, which enable criteria with different measurement units to be examined together, allow choosing between alternatives. However, different methods can produce different results depending on the data set. The aim of this study is to combine the results obtained by applying different methods to the data set with the Copeland method. To this end, a problem with real data was first addressed. Technical data of motorcycle alternatives that can be preferred for individual needs were collected in terms of different criteria. The weights of these criteria were found by the PIPRECIA method. Six different multi-criteria decision making methods were used to evaluate motorcycle alternatives. These methods are MOPA, MOOSRA, COPRAS, SAW, WPM and ROV. The sequencing results obtained from these methods were combined with the Copeland method and the results were discussed.

Prioritizing suitable locations for underground dam construction in south-east of Bushehr Province

The solutions to deal with the water crisis are summarized into two strategies for managing water resources and extraction of new sources of water. In Iran, due to geographical and climatic conditions, water resources management has a high priority. One of the methods to control and store water in wet periods for using in dry periods is construction of underground dams. The most important problem in the development and creation of underground dams is the complexity of determining suitable areas for the dams. The study area has been confronted with critical water condition due to improper temporal and spatial distribution of rainfall, uncontrolled abstraction of groundwater, drop in groundwater level and interference of saline and fresh water. Therefore, the construction of underground dams can be a practical solution to prevent groundwater levels from falling, land use change, and gradual groundwater salinity. The present study was conducted to find the underground dams using Boolean logic and for priority of underground dam sites using MCDM methods including AHP, ANP, VIKOR, TOPSIS and ELECTRE III in four counties of Bushehr, Tangestan, Dashti, and Jam in southern Iran. The results obtained by Boolean logic showed that the 305 km2 of the studied site has potential for underground dam construction. In the next step, according to Google Earth images and based on different indices including axial length, reservoir, lithology and distance from village and roads, 23 potential axes were identified afterward, by extensive field surveys. Among these 23 potential axes, 6 locations were identified as suitable. To prioritize these six areas, the MCDM models were used. Finally, the results of the MCDM models showed that the sites have different ranks of 1–6 for constructing underground dams. Based on the rank from the Copeland method, Zayer Abbasi and Faghih Hasenan sites achieved the first and the last (sixth) priority, respectively.

Design of a Microbial Remediation Inoculation Program for Petroleum Hydrocarbon Contaminated Sites Based on Degradation Pathways.

This paper analyzed the degradation pathways of petroleum hydrocarbon degradation bacteria, screened the main degradation pathways, and found the petroleum hydrocarbon degradation enzymes corresponding to each step of the degradation pathway. Through the Copeland method, the best inoculation program of petroleum hydrocarbon degradation bacteria in a polluted site was selected as follows: single oxygenation path was dominated by Streptomyces avermitilis, hydroxylation path was dominated by Methylosinus trichosporium OB3b, secondary oxygenation path was dominated by Pseudomonas aeruginosa, secondary hydroxylation path was dominated by Methylococcus capsulatus, double oxygenation path was dominated by Acinetobacter baylyi ADP1, hydrolysis path was dominated by Rhodococcus erythropolis, and CoA path was dominated by Geobacter metallireducens GS-15 to repair petroleum hydrocarbon contaminated sites. The Copeland method score for this solution is 22, which is the highest among the 375 solutions designed in this paper, indicating that it has the best degradation effect. Meanwhile, we verified its effect by the Cdocker method, and the Cdocker energy of this solution is −285.811 kcal/mol, which has the highest absolute value. Among the inoculation programs of the top 13 petroleum hydrocarbon degradation bacteria, the effect of the best inoculation program of petroleum hydrocarbon degradation bacteria was 18% higher than that of the 13th group, verifying that this solution has the best overall degradation effect. The inoculation program of petroleum hydrocarbon degradation bacteria designed in this paper considered the main pathways of petroleum hydrocarbon pollutant degradation, especially highlighting the degradability of petroleum hydrocarbon intermediate degradation products, and enriching the theoretical program of microbial remediation of petroleum hydrocarbon contaminated sites.

Evaluating innovation capability of Chinese listed companies based on comprehensive methods

PurposeThis paper aims to evaluate innovation capability of companies based on comprehensive methods.Design/methodology/approachThis paper used principal component analysis (PCA), kernel principal component analysis (KPCA) and principal component cluster (PCC) analysis to analyze the listed companies’ innovation capability. On these bases, mean method, Borda method, Copeland method, alienation coefficient method and fuzzy Borda method were used separately for the comprehensive evaluation.FindingsThe results show that the comprehensive evaluation can overcome the shortage of the single method and improve the reliability of the innovation ability evaluation. In addition, the method also reveals that the innovation ability of the listed companies is closely related to the innovation investment and their industry and further regional economic development level of each province (city and area).Originality/valueThis paper uses PCA, KPCA and PCC to evaluate and study their innovation ability. On the basis of these, five methods (mean method, Copeland method, Borda method, divorced coefficient method and fuzzy Borda method) are applied respectively to combine the sort results of PCA, KPCA and PCC. The results show that combination methods have better theoretical and practical significance for innovation ability.

Prediction of the tricone rotary bits wear by combining principal component analysis and regression methods in blast holes of Sarcheshmeh copper mine

The first production cycle in open-pit mines is the drilling of blast holes, which is a major part of the exploitation costs. Bits are considered as one of the most important parts of drilling in operational processes due to the type of application and high costs. Examining the parameters affecting the bits wear can reduce the effect of wear, prevent wasting time and additional costs of the drilling process. Therefore, in this study, first, by determining the effective parameters based on the results of univariate regression and Principal Component Analysis (PCA), the relationship between these factors and the tricone rotary bits wear has been determined through statistical methods. Then, 100 linear and nonlinear models were examined to predict the rate of the tricone rotary bits wears during drilling, of which only 21 models, including 6 linear models and 15 nonlinear models, were approved. The performance of these models is evaluated based on the root mean square error (RMSE), coefficient of determination (R2), Variance accounted for (VAF), and mean absolute percentage error (MAPE). then the priority of each method is determined based on performance evaluation and prioritization strategies. In this research, three strategies of prioritization, the Rank average method, the Borda method, the Copeland method, and finally the Aggregate of these methods have been used. The ranking results show that the best model for predicting the tricone rotary bits wear is the regression model with independent variables of Weight on Bits (WOB), Uniaxial Compressive Strength (UCS), and Geological Strength Index (GSI).