# A New Hybrid MCDM Model for Personnel Selection in the Mining Industry Based on the PIPRECIA-S, CRITIC, and WISP Methods

Maja Stanujkić, Vuk Mirčetić, Dragiša Stanujkić, Darjan Karabašević, Gabrijela Popović, Muzafer Saračević, Pavle Brzaković

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Maja Stanujkić, Vuk Mirčetić, Dragiša Stanujkić, Darjan Karabašević, Gabrijela Popović, et al. A New Hybrid MCDM Model for Personnel Selection in the Mining Industry Based on the PIPRECIA-S, CRITIC, and WISP Methods. Acta Montanistica Slovaca, 2025, 10.46544/ams.v30i3.18. hal-05475452

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# A New Hybrid MCDM Model for Personnel Selection in the Mining Industry Based on the PIPRECIA-S, CRITIC, and WISP Methods

Maja STANUJKIĆ¹, Vuk MIRČETIĆ², Dragiša STANUJKIĆ³, Darjan KARABAŠEVIĆ²,⁴*, Gabrijela POPOVIĆ², Muzafer SARAČEVIĆ⁵ and Pavle BRZAKOVIĆ²

**Authors' affiliations and addresses:**

¹ReMade DOO, Industrijska 3b, 21000 Novi Sad, Serbia
e-mail: maja.stanujkic@gmail.com

²Faculty of Applied Management, Economics and Finance, University Business Academy in Novi Sad, Belgrade, Serbia, Jevrejska 24, 11000 Belgrade, Serbia
e-mail: vuk.mircetic@mef.edu.rs
e-mail: gabrijela.popovic@mef.edu.rs
e-mail: pavle.brzakovic@mef.edu.rs

³Technical Faculty in Bor, University of Belgrade, Vojske Jugoslavije 12, 19210 Bor, Serbia
e-mail: dstanujkic@tfbor.bg.ac.rs

⁴Division of Convergence Business, College of Global Business, Korea University, Sejong 30019, Republic of Korea
e-mail: darjankarabasevic@korea.ac.kr

⁵Department of Computer Sciences, University of Novi Pazar, Dimitrija Tucovica bb, 36300 Novi Pazar, Serbia
e-mail: muzafers@uninp.edu.rs

**\*Correspondence:**

Darjan Karabašević, Division of Convergence Business, College of Global Business, Korea University, Sejong 30019, Republic of Korea
tel.: +381 60 0006282
e-mail: darjankarabasevic@korea.ac.kr

**How to cite this article:**

Stanujkić, M., Mirčetić, V., Stanujkić, D., Karabašević, D., Popović, G., Saračević, M. and Brzaković, P. (2025). A New Hybrid MCDM Model for Personnel Selection in the Mining Industry Based on the PIPRECIA-S, CRITIC, and WISP Methods. *Acta Montanistica Slovaca*, Volume 30 (3), 787-801

**DOI:**

https://doi.org/10.46544/AMS.v30i3.18

**Abstract**

A myriad of factors impact various areas of the contemporary mining environment, making it more challenging. As in other industries, the ultimate goal in mining is optimising organisational operations and achieving the best results. A prerequisite to effectively achieving organisational goals is hiring the best personnel. Different innovative methods are implemented for optimisations in various areas of the mining industry. Numerous researches have been conducted so far on the use of various Multi-criteria decision-making (MCDM) methods in different areas of the mining industry. However, despite the plenitude of significant research in this context, a particular gap exists regarding personnel selection in the mining industry. This paper endeavours to narrow a current gap by providing a systematic literature review regarding MCDM methods in the mining industry. Therefore, the aim of the paper is to provide a novel hybrid integrated MCDM approach that is based on the PIPRECIA-S, CRITIC, and WISP methods for personnel selection in the mining industry. The weights of the criteria are determined by one subjective method (PIPRECIA-S) and by one objective method (CRITIC). Whereas, the final ranking is determined by the WISP method. The proposed integrated MCDM model shows itself as a very suitable technique for use in the process of evaluation of personnel in the mining industry.

**Keywords**

MCDM, PIPRECIA-S, CRITIC, WISP, Mining Industry, Human Resources Management, Personnel Selection
Introduction

The contemporary business environment's disruptiveness is further enhanced by globalisation and its challenges, hyper-competition, periodic crises, both financial and health, migrations, wars, internal organisational changes, and changes in people, both employees and clients, as well as the development of information and communication technologies (ICT). Situational factors imply that uncertainty is the only certainty in the contemporary business environment (Mirčetić et al., 2022). Contemporary challenges and persisting changes impact all modern and traditional industry spheres. As a traditional industry, mining represents one of the earliest activities in human history, it is fundamental to the development of civilisation (Bell & Donnelly, 2006), and contemporary trends also influence it. Because the ultimate goal in every industry is to optimise business and achieve the best results, the mining industry is also particularly interesting for different analyses, innovational approaches, and developments.

As in other industries, the contemporary mining environment, with all the challenges, drives organisations to innovate if they want to maintain or achieve a comparative and competitive advantage. Innovations improve the performance of organisations by enhancing productivity, decreasing costs and opening new markets (Crespi & Pianta, 2008). Jurgelevicius and Tvaronaviciene (2021) underlined that technological innovations are crucial for traditional industries because they can add value and improve productivity. Innovations have become an integral part of the mining industry for all organisations that seriously approach today's challenges. Numerous scholars have conducted various research studies in this context. For example, Polishchuk et al. (2021) proposed an improved fuzzy mathematical model for assessing the creditworthiness of enterprises, Gupta et al. (2021) constructed an optimisation model for sustainable transportation in the mining industry, and Baloyi & Meyer (2020) conducted research for the best mining method selection.

One of the popular innovative approaches in the mining industry, used in academic frameworks and practice, is the multi-criteria decision-making (MCDM) method. Multi-criteria decision-making aims to enable decision-makers to analyse possible decisions and determine the most adequate one from the set of available alternatives (Özdağoğlu et al., 2021; Karamaşa, 2021). Roy (1981) underlines that MCDM is developed to tackle different problematics: choice (selecting the best option from a set of alternatives), sorting (assigning a set of alternatives to the categories created before), ranking (partially or completely ranking alternatives), and description (elaborating alternatives, building a set of criteria, and determining the performance of alternatives). MCDM is now a well-established, determined and thoroughly explored area of operational research used in many different areas (Urošević et al., 2021).

Many MCDM methods are widely used in different areas of the mining industry. The Analytical Hierarchy Process (AHP) method, developed by Saaty (1980), has been the most used MCDM method in the mining industry (Sitorus, Cilliers & Brito-Parada, 2019). The AHP method is used for selection of the proper storage location for mines waste (Straka, Bindzár & Kaduková, 2014), selecting mining equipment or technology such as a loading-hauling system (Basçetin, 2004), appropriate roadheaders (Acaroglu, Ergin & Eskikaya, 2006), an alternative energy-delivery system for stopping in narrow-reef hard rock mines (Petit & Fraser, 2013), or the most suitable fan for an underground coal mine (Kursunoglu & Onder, 2015). The AHP method has an application also in different suitable mining method selection (Ataei et al., 2008; Mohsen et al., 2009), or even more precise, selecting an appropriate stopping method in underground mining (Gupta & Kumar, 2012), and also techno-economic optimisation of the level and raise spacing in a platinum mine (Musingwini, 2010).

Apart from the AHP method, other MCDM methods have also been implemented in the mining industry. For example, ELECTRE III (Roy, 1978), from Elimination et choix traduisant la realit (ELECTRE) family method, is used for the selection of the off-highway dump truck in opencast mining (Bodziony, Kasztelewicz & Sawicki, 2016) and selection of sustainability criteria of equipment (Patyk, Bodziony & Krysa, 2021). The Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) method, initially proposed by Brans (1982), is used for the selection of the most appropriate underground ore transport system (Elevli & Demirci, 2004) and the technological system in an open pit mine (Vujić, Hudej & Miljanović, 2013). The technique for order of preference by similarity to ideal solution (TOPSIS) method, presented by Chen and Hwang (1992), with reference to Hwang and Yoon (1981), is used for maintenance strategy (Pourjavad, Shirouyehzad & Shahin, 2013) and green supply chain practices evaluation (Kusi-Sarpong et al., 2015). Multi-criteria optimisation and compromise solution (VIKOR) method, developed by Opricović (1998), for selection of the optimal mining block size (Hayati, Rajabzadeh & Darabi, 2015). The Analytic Network Process (ANP) method, proposed by Saaty and Vargas (2001), is used to examine alternative planning scenarios for mining activities in a particular region (Assumma et al., 2020). Step-wise Weight Assessment Ratio Analysis (SWARA) method, developed by Keršuliene et al. (2010), analyse the risks in coal supply chain management (Sivageerthi et al., 2022) and evaluation of risks impeding sustainable mining (Deveci et al., 2023). Scholars periodically introduce new MCDM methods in various areas of mining, such as multiple criteria ranking by alternative trace (MCRAT) method and ranking alternatives by perimeter similarity (RAPS) method (Urošević et al., 2021).
One of the most important strategic decisions is selecting the appropriate personnel. Consequently, hiring the best individuals is crucial in the mining industry to obtain the best results and optimise work. The selection process is a critical factor in hiring and involves the systematic identification, assessment and hiring of qualified individuals for available positions within the organisation. Personnel selection represents the selection of the most adequate individuals for certain positions (Hirst et al., 2021). It is a complex process that entails planning (Armstrong & Taylor, 2023), and it begins with working position analysis and specifying the characteristics of a particular job (Karabašević et al., 2015). During selection, decision-makers use diverse methods, tests, and techniques to predict and hire the appropriate candidate.

Many researchers have confirmed that MCDM methods are innovative methods that can help select the best solution for many challenges. MCDM methods have also been used in the selection process, for example, for leader selection (Vujić et al., 2016), personnel selection (Korkmaz, 2019; Ulutaş et al., 2020; López et al., 2022; Tuğrul, 2022), more precise the selection of information technology personnel (Gelen & Demir, 2019) or selection, training and maintaining skills for the safe work of personnel (Gendler, Tumanov & Levin, 2021).

As can be observed, an abundance of research has been conducted on the use of various MCDM methods in different areas of the mining industry so far. However, despite the plenitude of considerable studies in this regard in the past decades, a particular theoretical gap exists regarding personnel selection in the mining industry. Building on the paper by Karabašević et al. (2015), this paper endeavours to narrow a current gap, provide a systematic literature review regarding MCDM methods in the mining industry and particularly elaborate on PIPRECIA-S (Simple PIVot Pairwise RElative Criteria Importance Assessment), CRITIC (CRiteria Importance Through Intercriteria Correlation), and WISP (Simple Weighted Sum Product) methods as an appropriate solution for methods regarding personnel selection in the mining industry, and discuss obtained results. Therefore, aim of the paper is to provide a hybrid integrated MCDM model based on PIPRECIA-S, CRITIC, and WISP methods for personnel selection in the mining industry. Weights of the criteria are determined by one subjective method (PIPRECIA-S) and by one objective method (CRITIC). Whereas, final ranking is determined by WISP method. The proposed integrated MCDM model shows itself as a very suitable technique for using in the process of evaluation of personnel in the mining industry.

The remainder of the article is organised as follows. The material and methods used in this paper are presented and explained in the following section. This paper analyses groundbreaking, respectable, and highly cited scientific papers from the last five years to provide a fine-grained perspective on the effectiveness of MCDM methods in different areas of the mining industry and on three MCDM methods that were used in this paper. This chapter is segmented into three subchapters, each introducing the observed MCDM method, presenting the computational procedure of the method and presenting different fields of application in which that method is used, reasoning the selection of that MCDM method for this paper. The first subchapter of this chapter examines the PIPRECIA-S method, the following segment of this chapter analyses the CRITIC method, and the third subchapter discusses the WISP method. The next chapter elaborates on the numerical illustration and results, and discusses combining subjective and objective MCDM methods and critically evaluates the results and implications, providing a holistic perspective of the study's contributions to the field of personnel selection in the mining industry. The last chapter consists of conclusions, limitations and suggestions for future analysis.

## Material and Methods

This chapter presents the methodological frameworks of this article and is thematically differentiated into three subchapters that analyse the multi-criteria methods used in this paper. The subchapters explore the MCDM methods used in the research in the following order: PIPRECIA-S, CRITIC, and the WISP method.

### The PIPRECIA-S method

PIPRECIA-S (Simple PIVot Pairwise RElative Criteria Importance Assessment) was developed by Stanujkić et al. (2021a) as a modification of the original PIPRECIA method (Stanujkić et al., 2017), making it more easy to use by the respondents. PIPRECIA method was based on SWARA method (Keršuliene, Zavadskas & Turskis, 2010).

The computational procedure of PIPRECIA-S method can be shown through the following steps (Stanujkić et al., 2021a):

**Step 1.** Determine the set of evaluation criteria.

**Step 2.** Set the relative significance $s_j$ of each criterion, except the first, as follows:

$$s_j = \begin{cases} > 1 & \text{if } C_j > C_1 \\ 1 & \text{if } C_j = C_1. \\ < 1 & \text{if } C_j < C_1 \end{cases} \qquad (1)$$

where $j \neq 1$.
Similar to the PIPRECIA method, the value of $s_1$ is set to 1, while values of $s_j$ belong to the interval (1, 1.9] when $C_j > C_1$, that is to the interval [0.1, 1) when $C_j < C_1$.

**Step 3.** Calculate the value of coefficient $k_j$ as follows:

$$k_j = \begin{cases} 1 & \text{if } j = 1 \\ 2 - s_j & \text{if } j > 1 \end{cases}. \qquad (2)$$

**Step 4.** Calculate the recalculated weight $q_j$ as follows:

$$q_j = \begin{cases} 1 & \text{if } j = 1 \\ \frac{1}{k_j} & \text{if } j > 1 \end{cases}. \qquad (3)$$

**Step 5.** Determine the relative weights of the evaluation criteria as follows:

$$w_j = \frac{q_j}{\sum_{k=1}^{n} q_k}. \qquad (4)$$

The PIPRECIA-S method was applied in different fields. Because of the similarity between the PIPRECIA-S and PIPRECIA methods, Table 1 presents an overview of fifteen respectable and novel studies that used the abovementioned methods in different fields.

<table><caption>Tab. 1. Overview of the application of the PIPRECIA-S method in different fields</caption><thead><tr><th>Study</th><th>Field of Application</th></tr></thead><tbody><tr><td>Mirčetić, Popović & Vukotić (2024)</td><td>Determining characteristics of the charismatic leaders in EU</td></tr><tr><td>Sarbat (2024)</td><td>Analysis of job satisfaction</td></tr><tr><td>Setiawansyah et al. (2024)</td><td>Personnel selection</td></tr><tr><td>Stanujskić et al. (2024)</td><td>Approach to the personnel selection in a group decision-making environment</td></tr><tr><td>Hadad et al. (2023)</td><td>Student ranking based on learning assessment</td></tr><tr><td>Mladenović, Đukić & Popović (2023)</td><td>Analysis of financial platforms reporting</td></tr><tr><td>Setiawansyah & Saputra (2023)</td><td>Selecting the head of the school organization</td></tr><tr><td>Stanujskić et al. (2023a)</td><td>Improvement of the decision-making process in the IT industry</td></tr><tr><td>Sulistiani et al. (2023)</td><td>Evaluation of employees in an educational institution</td></tr><tr><td>Aytekin (2022)</td><td>Vehicle tracking system</td></tr><tr><td>Đukić, Karabašević & Popović (2022)</td><td>Evaluation of different aspects of cognitive skills</td></tr><tr><td>Ivanov & Stanujskić (2022)</td><td>Evaluation of electric vehicles</td></tr><tr><td>Popović, Milovanović & Pucar (2022)</td><td>Choice of RFID solution provider</td></tr><tr><td>Ulutaş & Topal (2022)</td><td>Evaluation of the criteria used in the selection of renewable energy sources</td></tr><tr><td>Jauković Jocic, Karabašević & Jocić (2020)</td><td>Assessing the quality of e-learning materials</td></tr></tbody></table>

## The CRITIC Method

Proposed by Diakoulaki, Mavrotas & Papayannakis (1995), CRITIC method aims to determine objective weights of relative importance in MCDM problems. The weights derived include contrast intensity and conflict contained in the structure of the decision problem. CRITIC can be applied in numerous multicriteria problems to define objective weights when a decision maker is absent, to facilitate the decision maker's voicing of his argument or belief on the relative importance of the criteria, to decrease the subjective character of the decision-making process, or to discard the non-salient attributes in a primary weighting of the evaluation criteria. (Diakoulaki et al. 1995).

The computational procedure of CRITIC method can be shown through the following steps (Diakoulaki et al. 1995):

**Step 1.** Form decision-making matrix $D$ as follows:

$$D = [x_{ij}]_{m \times n}. \qquad (5)$$

where $x_{ij}$ denotes ratings of alternative $i$ according to criterion $j$, $m$ denotes number of alternatives and $j$ denotes number of criteria.

**Step 2.** Construct normalized decision-making matrix $R$ as follows:

$$R = [r_{ij}]. \qquad (6)$$

where $r_{ij}$ denotes normalized ratings of alternative $i$ according to criterion $j$, and it is calculated as follows:

$$r_{ij} = \frac{x_{ij} - \min_i x_{ij}}{\max_i x_{ij} - \min_i x_{ij}}. \qquad (7)$$

**Step 3.** Determine the weights of criteria $w_j$ as follows:

$$R = \frac{c_j}{\sum_{j=1}^{n} c_j}. \qquad (8)$$
where $C_{ij}$ denotes quantity of information contained in criterion j, and it is calculated as follows:

$$C_j = \sigma_j \sum_{j=1}^{n} (1 - c r_{jj}). \qquad (9)$$

and $\sigma_j$ denotes standard deviation of criterion j, $c r_{jj}$ denotes correlation coefficient between two criteria.

The CRITIC method was used in different fields. Table 2 presents an overview of eighteen respectable and novel studies that used the CRITIC method in different fields.

<table><caption>Tab. 2. Overview of the application of the CRITIC method in different fields</caption><thead><tr><td>Study</td><td>Field of Application</td></tr></thead><tbody><tr><td>Chang (2024)</td><td>Design and Application of Evaluation Method for Civics Classroom</td></tr><tr><td>Krishnan (2024)</td><td>Research trends in CRITIC method</td></tr><tr><td>Saensuk, Witchakool & Choompol (2024)</td><td>Effective fake news detection</td></tr><tr><td>Shrinivas Balraj et al. (2024)</td><td>Optimization of machining parameters</td></tr><tr><td>Hassan, Alhamrouni & Azhan (2023)</td><td>Selection of a location for a solar power plant</td></tr><tr><td>Hosseinzadeh Lotfi et al. (2023)</td><td>Evaluation and prioritization of projects based on seven specific criteria</td></tr><tr><td>Mishra, Chen & Rani (2023)</td><td>Proposing a model based on Fermatian fuzzy numbers</td></tr><tr><td>Silva et al. (2023)</td><td>Investment portfolio selection</td></tr><tr><td>Zhang et al. (2023)</td><td>Evaluation of the Rock Burst Intensity</td></tr><tr><td>Bhadra, Dhar & Salam (2022)</td><td>Selection of natural fibers</td></tr><tr><td>Haktanır & Kahraman (2022)</td><td>Selection of wearable health applications</td></tr><tr><td>Kumari & Acherjee (2022)</td><td>Selection of an unconventional method of processing</td></tr><tr><td>Pamučar, Žižović & Đuričić (2022)</td><td>Modification of the CRITIC method using fuzzy rough numbers</td></tr><tr><td>Mukhametzyanov (2021)</td><td>Comparison of methods</td></tr><tr><td>Zafar, Alamgir & Rehman (2021)</td><td>Evaluation of the blockchain system</td></tr><tr><td>Peng & Huang (2020)</td><td>Analysis of financial risks</td></tr><tr><td>Peng, Zhang & Luo (2020)</td><td>5G Industry Analysis</td></tr><tr><td>Tuş & Aytaç Adalı (2019)</td><td>Software selection</td></tr></tbody></table>

## The WISP Method

The WISP (Integrated Simple Weighted Sum Product) method, created by Stanujkić et al. (2023b), is partially based on the multiobjective optimisation by ratio analysis plus the whole multiplicative form method. It also incorporates weighted aggregated sum product assessment elements and combined compromise solution methods.

According to Stanujkić (2023), the procedure of the WISP method for evaluating alternatives in the case when all criteria are income can be presented in the following way:

**Step 1.** Construct a normalized decision-making matrix as follows:

$$r_{ij} = \frac{x_{ij}}{\max_{i} x_{ij}}. \qquad (10)$$

**Step 2.** Calculate the values of two utility measures, as follows:

$$u_i^s = \sum_{j=1}^{n} r_{ij} w_j, \text{ and} \qquad (11)$$

$$u_i^p = \prod_{j=1}^{n} r_{ij} w_j. \qquad (12)$$

where: $u_i^s$ and $u_i^p$ denote the weighted sum and the weighted product of normalized ratings of alternative i, respectively.

**Step 3.** Recalculate values of two utility measures, as follows:

$$\bar{u}_i^s = \frac{1+u_i^s}{1+\max_i u_i^s}, \text{ and} \qquad (13)$$

$$\bar{u}_i^p = \frac{1+u_i^p}{1+\max_i u_i^p}. \qquad (14)$$

where: $\bar{u}_i^s$ and $\bar{u}_i^p$ denote recalculated values of $u_i^s$ of $u_i^p$.

**Step 4.** Determine the overall utility $u_i$ of each alternative as follows:

$$u_i = \frac{\bar{u}_i^s + \bar{u}_i^p}{2}. \qquad (15)$$

**Step 5.** Rank the alternatives and select the most suitable one, in the same way as in the ordinary WISP method.

The WISP method, its extensions, and methods integrating this method have different fields of application. Table 3 presents an overview of nineteen respectable and novel studies that used the abovementioned methods in different fields.
<table><caption>Tab. 3. Overview of the application of the WISP method in different fields</caption><thead><tr><th>Study</th><th>Field of Application</th></tr></thead><tbody><tr><td>Kara et al. (2024)</td><td>Blockchain platform selection in digital projects</td></tr><tr><td>Rani et al. (2024)</td><td>Location selection for offshore wind power station</td></tr><tr><td>Altıntaş (2023)</td><td>Measuring peace performances of G7 group</td></tr><tr><td>Hezam et. al. (2023)</td><td>Gerontechnology selection problem for aging persons and people with disability</td></tr><tr><td>Kirmizi, Karakas & Uçar (2023)</td><td>Selecting the optimal naval ship drainage system design alternative</td></tr><tr><td>Rahman (2023)</td><td>Efficient requirement prioritization for software projects</td></tr><tr><td>Stanujkić (2023)</td><td>Comparative analysis of WISP method and other methods regarding candidate selection</td></tr><tr><td>Stanujkić, Fedajev & Santos (2023)</td><td>Investment projects evaluation</td></tr><tr><td>Yılmaz (2023)</td><td>Examination of the banking sector performance in a particular country</td></tr><tr><td>Demir & Arslan (2022)</td><td>Sensitivity analysis in multi-criterion decision-making problems</td></tr><tr><td>Deveci et al. (2022)</td><td>Assessing and prioritising sustainable urban transportation in metaverse</td></tr><tr><td>Karabašević et al. (2022)</td><td>A New Fuzzy Extension of the Simple WISP Method</td></tr><tr><td>Pala (2022)</td><td>Financial performance analysis in the food industry</td></tr><tr><td>Stanujkić (2022)</td><td>Development of the Simple WISP method and its extensions</td></tr><tr><td>Ulutaş et al. (2022a)</td><td>Pallet truck selection</td></tr><tr><td>Ulutaş et. al. (2022b)</td><td>Sustainable supplier selection</td></tr><tr><td>Zavadskas et al. (2022)</td><td>An Intuitionistic Extension of the Simple WISP Method</td></tr><tr><td>Gokmen (2021)</td><td>Analyse of the future educational leaders' metaphoric perceptions regarding sustainability</td></tr><tr><td>Stanujkić et al. (2021b)</td><td>Comparative analysis of the Simple WISP and other MCDM methods</td></tr></tbody></table>

## A Numerical Illustration and Results

This chapter presents the combined use of PIPRECIA-S, CRITIC and WISP methods for evaluating personnel i.e. candidates in the mining industry, based on an example adapted from Karabašević et al. (2015).

In the given example, three candidates were evaluated based on seven criteria by one decision-maker (DM). However, in this case the example is extended so that four candidates are evaluated by three decision makers (DMs), also based on the seven criteria listed below:

- C₁ Education and knowledge EK

- C₂ Personal characteristics PC

- C₃ Organisational capability

- C₄ Physical ability PA

- C₅ Computer skills CS

- C₆ Foreign languages FL

- C₇ The research spirit RS

In the first step, criteria weights were determined by using the PIPRECIA-S method. The procedure for determining criteria weights using the PIPRECIA-S method is shown in Table 4.

<table><caption>Tab. 4. Criteria weights determined using the PIPRECIA-S method based on the attitudes of the first DM</caption><thead><tr><th>Criteria</th><th>s<sub>j</sub></th><th>k<sub>j</sub></th><th>q<sub>j</sub></th><th>w<sub>j</sub></th></tr></thead><tbody><tr><td>C<sub>1</sub></td><td></td><td>1.00</td><td>1.00</td><td>0.15</td></tr><tr><td>C<sub>2</sub></td><td>0.90</td><td>1.10</td><td>0.91</td><td>0.13</td></tr><tr><td>C<sub>3</sub></td><td>1.05</td><td>0.95</td><td>1.05</td><td>0.15</td></tr><tr><td>C<sub>4</sub></td><td>1.10</td><td>0.90</td><td>1.11</td><td>0.16</td></tr><tr><td>C<sub>5</sub></td><td>1.00</td><td>1.00</td><td>1.00</td><td>0.15</td></tr><tr><td>C<sub>6</sub></td><td>0.90</td><td>1.10</td><td>0.91</td><td>0.13</td></tr><tr><td>C<sub>7</sub></td><td>0.80</td><td>1.20</td><td>0.83</td><td>0.12</td></tr></tbody><tfoot><tr><td></td><td></td><td></td><td>6.82</td><td>1.00</td></tr></tfoot></table>
The weights obtained from three DMs are shown in Table 5, as well as in Figure 1.

<table><caption>Tab. 5. Criteria weights determined using the PIPRECIA-S method based on the attitudes of three DMs</caption><thead><tr><th>DM<sub>s</sub></th><th>DM<sub>1</sub></th><th>DM<sub>2</sub></th><th>DM<sub>3</sub></th></tr><tr><th>Criteria</th><th>w<sub>1</sub></th><th>w<sub>2</sub></th><th>w<sub>3</sub></th></tr></thead><tbody><tr><td>C<sub>1</sub></td><td>0.147</td><td>0.144</td><td>0.129</td></tr><tr><td>C<sub>2</sub></td><td>0.133</td><td>0.144</td><td>0.117</td></tr><tr><td>C<sub>3</sub></td><td>0.154</td><td>0.160</td><td>0.184</td></tr><tr><td>C<sub>4</sub></td><td>0.163</td><td>0.131</td><td>0.161</td></tr><tr><td>C<sub>5</sub></td><td>0.147</td><td>0.144</td><td>0.143</td></tr><tr><td>C<sub>6</sub></td><td>0.133</td><td>0.160</td><td>0.143</td></tr><tr><td>C<sub>7</sub></td><td>0.122</td><td>0.120</td><td>0.123</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>Fig. 1. Criteria weights determined using the PIPRECIA-S method based on the attitudes of three DMs</figcaption></figure>

The ratings of the evaluated candidates concerning the selected evaluation criteria are shown in Tables 6 to 8.

<table><caption>Tab. 6. Candidate ratings obtained from the first of three DMs</caption><thead><tr><th></th><th>C<sub>1</sub></th><th>C<sub>2</sub></th><th>C<sub>3</sub></th><th>C<sub>4</sub></th><th>C<sub>5</sub></th><th>C<sub>6</sub></th><th>C<sub>7</sub></th></tr></thead><tbody><tr><th>A<sub>1</sub></th><td>4</td><td>3</td><td>3</td><td>4</td><td>3</td><td>2</td><td>4</td></tr><tr><th>A<sub>2</sub></th><td>3</td><td>3</td><td>4</td><td>3</td><td>5</td><td>4</td><td>4</td></tr><tr><th>A<sub>3</sub></th><td>5</td><td>4</td><td>4</td><td>5</td><td>5</td><td>3</td><td>4</td></tr><tr><th>A<sub>4</sub></th><td>3</td><td>3</td><td>4</td><td>5</td><td>4</td><td>3</td><td>5</td></tr></tbody></table>

<table><caption>Tab. 7. Candidate ratings obtained from the second of three DMs</caption><thead><tr><th></th><th>C<sub>1</sub></th><th>C<sub>2</sub></th><th>C<sub>3</sub></th><th>C<sub>4</sub></th><th>C<sub>5</sub></th><th>C<sub>6</sub></th><th>C<sub>7</sub></th></tr></thead><tbody><tr><th>A<sub>1</sub></th><td>4</td><td>4</td><td>3</td><td>4</td><td>4</td><td>3</td><td>3</td></tr><tr><th>A<sub>2</sub></th><td>4</td><td>3</td><td>3</td><td>3</td><td>4</td><td>4</td><td>4</td></tr><tr><th>A<sub>3</sub></th><td>3</td><td>4</td><td>5</td><td>4</td><td>3</td><td>5</td><td>3</td></tr><tr><th>A<sub>4</sub></th><td>3</td><td>3</td><td>4</td><td>5</td><td>4</td><td>3</td><td>5</td></tr></tbody></table>
<table><caption>Tab. 8. Candidate ratings obtained from the third of three DMs</caption><thead><tr><th></th><th>C<sub>1</sub></th><th>C<sub>2</sub></th><th>C<sub>3</sub></th><th>C<sub>4</sub></th><th>C<sub>5</sub></th><th>C<sub>6</sub></th><th>C<sub>7</sub></th></tr></thead><tbody><tr><th>A<sub>1</sub></th><td>2</td><td>3</td><td>3</td><td>4</td><td>5</td><td>4</td><td>4</td></tr><tr><th>A<sub>2</sub></th><td>4</td><td>4</td><td>5</td><td>4</td><td>3</td><td>4</td><td>3</td></tr><tr><th>A<sub>3</sub></th><td>3</td><td>4</td><td>5</td><td>4</td><td>4</td><td>3</td><td>4</td></tr><tr><th>A<sub>4</sub></th><td>4</td><td>3</td><td>4</td><td>5</td><td>4</td><td>4</td><td>4</td></tr></tbody></table>

The weights of the criteria obtained by the CRITIC method are summarised in Table 9, as well as presented in Figure 2.

<table><caption>Tab. 9. Criteria weights determined using the CRITIC method based on the attitudes of three DMs</caption><thead><tr><th>DMs</th><th>DM1</th><th>DM2</th><th>DM3</th></tr><tr><th>Criteria</th><th>w<sub>j</sub></th><th>w<sub>j</sub></th><th>w<sub>j</sub></th></tr></thead><tbody><tr><th>C<sub>1</sub></th><td>0.171</td><td>0.181</td><td>0.121</td></tr><tr><th>C<sub>2</sub></th><td>0.128</td><td>0.163</td><td>0.159</td></tr><tr><th>C<sub>3</sub></th><td>0.114</td><td>0.128</td><td>0.125</td></tr><tr><th>C<sub>4</sub></th><td>0.145</td><td>0.105</td><td>0.134</td></tr><tr><th>C<sub>5</sub></th><td>0.118</td><td>0.148</td><td>0.130</td></tr><tr><th>C<sub>6</sub></th><td>0.132</td><td>0.140</td><td>0.148</td></tr><tr><th>C<sub>7</sub></th><td>0.193</td><td>0.134</td><td>0.183</td></tr></tbody></table>

<caption>Fig. 2. Criteria weights determined using the CRITIC method based on the attitudes of three DMs</caption>

Table 10 presents the procedure for evaluating candidates using the WISP method and the subjective weights obtained from the first DMs, while Table 11 summarises the ranking order of evaluated candidates achieved using the WISP method and subjective weights based on the attitudes of the three DMs.

<table><caption>Tab. 10. Details of calculations using the WISP method and subjective weights based on the attitudes of the first of three DMs</caption><thead><tr><th></th><th>u<sub>i</sub><sup>s</sup></th><th>u<sub>i</sub><sup>p</sup></th><th>u&#773;<sub>i</sub><sup>s</sup></th><th>u&#773;<sub>i</sub><sup>p</sup></th><th>u<sub>i</sub></th><th>Ranks</th></tr></thead><tbody><tr><th>A<sub>1</sub></th><td>0.73</td><td>0.00000013</td><td>0.884</td><td>0.999999</td><td>0.942</td><td>4</td></tr><tr><th>A<sub>2</sub></th><td>0.84</td><td>0.00000032</td><td>0.940</td><td>0.999999</td><td>0.970</td><td>3</td></tr><tr><th>A<sub>3</sub></th><td>0.96</td><td>0.00000088</td><td>1.000</td><td>1.000000</td><td>1.000</td><td>1</td></tr><tr><th>A<sub>4</sub></th><td>0.87</td><td>0.00000040</td><td>0.951</td><td>1.000000</td><td>0.976</td><td>2</td></tr></tbody></table>

<table><caption>Tab. 11. Ranking orders of candidates obtained using the WISP method and subjective weights</caption><thead><tr><th></th><th>DM1</th><th>DM2</th><th>DM3</th></tr></thead><tbody><tr><th>A<sub>1</sub></th><td>3</td><td>4</td><td>2</td></tr><tr><th>A<sub>2</sub></th><td>1</td><td>1</td><td>3</td></tr><tr><th>A<sub>3</sub></th><td>2</td><td>1</td><td>1</td></tr><tr><th>A<sub>4</sub></th><td>3</td><td>4</td><td>2</td></tr></tbody></table>

Similarly, the procedure for evaluating candidates using the WISP method and the objective weights obtained from the first DMs is presented in Table 12.

<table><caption>Tab. 12. Details of calculations using the WISP method and objective weights based on the attitudes of the first of three DMs</caption><thead><tr><th></th><th>u<sub>i</sub><sup>s</sup></th><th>u<sub>i</sub><sup>p</sup></th><th>u&#773;<sub>i</sub><sup>s</sup></th><th>u&#773;<sub>i</sub><sup>p</sup></th><th>u<sub>i</sub></th><th>Ranks</th></tr></thead><tbody><tr><th>A<sub>1</sub></th><td>0.73</td><td>0.00000013</td><td>0.884</td><td>0.999999</td><td>0.942</td><td>4</td></tr><tr><th>A<sub>2</sub></th><td>0.84</td><td>0.00000032</td><td>0.940</td><td>0.999999</td><td>0.970</td><td>3</td></tr><tr><th>A<sub>3</sub></th><td>0.96</td><td>0.00000088</td><td>1.000</td><td>1.000000</td><td>1.000</td><td>1</td></tr><tr><th>A<sub>4</sub></th><td>0.87</td><td>0.00000040</td><td>0.951</td><td>1.000000</td><td>0.976</td><td>2</td></tr></tbody></table>
The ranking order of evaluated candidates achieved using the WISP method and objective weights based on the attitudes of the three DMs are summarised in Table 13.

<table><caption>Tab. 13. Ranking orders of candidates obtained using the WISP method and objective weights</caption><thead><tr><th></th><th>DM1</th><th>DM2</th><th>DM3</th></tr></thead><tbody><tr><th>A1</th><td>4</td><td>3</td><td>4</td></tr><tr><th>A2</th><td>3</td><td>4</td><td>1</td></tr><tr><th>A3</th><td>1</td><td>1</td><td>2</td></tr><tr><th>A4</th><td>2</td><td>1</td><td>2</td></tr></tbody></table>

Finally, the rankings orders of evaluated candidates using the WISP method and the subjective and objective methods are summarised in Table 14.

<table><caption>Tab. 14. Ranking orders of candidates obtained using the WISP method</caption><thead><tr><th rowspan="2"></th><th colspan="3">Subjective weights</th><th colspan="3">Objective weights</th></tr><tr><th>DM1</th><th>DM2</th><th>DM3</th><th>DM1</th><th>DM2</th><th>DM3</th></tr></thead><tbody><tr><th>A1</th><td>3</td><td>4</td><td>2</td><td>4</td><td>3</td><td>4</td></tr><tr><th>A2</th><td>1</td><td>1</td><td>3</td><td>3</td><td>4</td><td>1</td></tr><tr><th>A3</th><td>2</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td></tr><tr><th>A4</th><td>3</td><td>4</td><td>2</td><td>2</td><td>1</td><td>2</td></tr></tbody></table>

In Table 14, it can be seen that the candidates denoted as $A_2$ and $A_3$ are the most suitable based on the evaluation performed using the WISP method and subjective weights, i.e., using the PIPRECIA-S method, while the alternative denoted as $A_3$ is the most suitable based on the evaluation performed using the WISP method and objective weights, i.e., by applying the CRITIC method.

Based on the evaluation performed using subjective and objective weights, the candidate denoted as $A_3$ is the most suitable because, based on the theory of dominance, he has the highest number of appearances in the first position.

## Conclusions

Mining is one of the earliest activities in human history, and it is fundamental to the development of civilisation. Therefore, it is crucial to optimise organisational operations to achieve best results. One of the ways to achieve that is by using different innovations because they can add value and improve productivity. One of the popular innovative approaches in the mining industry, used in academic frameworks and practice, are the MCDM methods, which can also be used for solving problem of personnel selection, as important segment in the mining industry.

Firstly, this manuscript provides a systematic literature review regarding MCDM methods in the mining industry and particularly elaborate on PIPRECIA-S (Simple PIvot Pairwise Relative Criteria Importance Assessment), CRITIC (CRiteria Importance Through Intercriteria Correlation), and WISP (Simple Weighted Sum Product) methods as an appropriate solution for methods regarding personnel selection in the mining industry, and, based on previous research, proposed different and innovative approach for personnel selection in the mining industry.

The study discusses the combined use of one subjective and one objective method for determining the importance (weights) of the criteria, namely the PIPRECIA-S and CRITIC methods, intending to evaluate candidates using the WISP method. It is known that with subjective techniques, decision-makers can directly express their preferences in terms of the importance (weight) of the criteria, while with objective techniques, the weights of the criteria are determined based on the evaluation of the alternatives concerning the criteria. In the first case, decision-makers can express their preferences more easily and precisely, while in the second case, modifying the criteria weights requires modifying the ratings of the alternatives. By combining subjective and objective techniques, decision-makers can check whether there is a significant difference in the weights of the criteria obtained by applying subjective and objective techniques and, if necessary, make proper corrections.

When it comes to the ranking results, after the applied MCDM model, it can be noted that the candidates denoted as $A_2$ and $A_3$ are the best ranked based on the conducted evaluation by using the WISP method and subjective weights, i.e., using the PIPRECIA-S method, whereas the alternative denoted as $A_3$ is the best ranked based on the evaluation performed by using the WISP method and objective weights, i.e., by applying the CRITIC method. Also, it is important to state that theory of dominance is applied for final ranking (highest number of
apperances in the first position). By applying proposed MCDM model, it can be seen that the proposed approach is easy to use, effective and easily it can be adapted if necessary for additional criteria or sub-criteria. Also, proposed approach can be used for solving problems in other areas as well.

The proposed MCDM based PIPRECIA-S, CRITIC, and WISP model can successfully be used to resolve personnel selection issues in the mining industry. Combining subjective and objective criteria weights can achieve a more adequate selection of candidates. Therefore, the paper's particular contribution is the modification and improvement of the existing MCDM model by combining new subjective and objective MCDM methods. The proposed MCDM-based model (PIPRECIA-S, CRITIC and WISP) proved to be adequate when it comes to solving the problem of personnel selection in the mining industry. The applicability of the proposed model is reflected through the use of MCDM methods that are based on crisp numbers. Therefore, the advantage of the proposed model is its simplicity and ease of use, especially from the standpoint of the DMs.

This article is not exempt from certain limitations that may be addressed by future research. First, the proposed hybrid MCDM model is based on crisp numbers. Also, it is known that sometimes decision-making environment is characterized by vagueness, and in those cases, evaluation cannot be done via crisp numbers.

Second, the paper focuses on the microenvironment, providing insufficient information regarding possible differences in results in different industries. Future research can be conducted in another industry. Finally, because of the constant changes and challenges and the importance of new technologies, it would be beneficial for future analysis to incorporate new criteria. Adding new or adopting current criteria will help get insight in a more granular manner, and observing the industry's development can assist in achieving a more effective strategic approach to personnel selection in mining or other industries. Besides, in order to better cope with uncertainty in the decision-making process MCDM methods based on fuzzy, neutrosophic, rough numbers can be used in future.

## Acknowledgement

The research presented in this article was partly financially supported by of the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, within the funding of the scientific research work at the University of Belgrade, Technical Faculty in Bor, according to the contract with registration number 451-03-47/2023-01/200131.

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