Multiple Criteria Decision Making Methods Research Articles

Software component selection methods and techniques: a systematic review

<p>Software component selection is critical in software engineering due to its vital role in reducing software development cost and time. This study analyzes software component selection research studies on methodologies, criteria, and multi-criteria decision-making (MCDM) techniques. The key study findings are: first, comprehensive standardized criteria for software component selection are lacking, with ambiguous terminology used in research. Second, current ad hoc selection processes need streamlining to reduce time, cost, and effort. Thus, an integrated approach is required to aid decision-makers. The review suggests developing automated tools or decision support systems combining multiple criteria decision methods to improve selection accuracy and efficiency. Standardized criteria catalogs can also assist software developers in the evaluation. The findings highlight that despite extensive academic research, component selection in practice remains sub-optimal. By informing future research and tool development, this review can benefit practitioners to systematically select the most appropriate software components meeting software requirements.</p>

Multi-criteria model for selection of optical linear terminals based on FUZZY TOPSIS method

Optical networks are an integral part of modern telecommunication systems. Huge traffic and ever-increasing requirements for data transmission capacity and quality encourage the wider use of modern optical technologies in telecommunications. The rational choice of equipment for the design of optical networks is an urgent task at present. The subject of this study is the process of selecting optical line terminals, which is associated with the evaluation of possible options by a set of indicators. The OLT (optical line terminal) in PON technology implements the function of organizing subscriber lines and is also a node equipment - an L4 switch that combines the functions of routing (IP), traffic fragmentation (VLAN), switching (MAC), quality of service (QoS), and some necessary network service functions. The optimal structuring of PON access networks demands a judicious selection of software and hardware, guided by their tactical and technical characteristics, in view of the substantial information load they entail. One of the effective approaches to solving such problems is the use of MCDM (Multiple Criteria Decision Making) methods. Purpose: to construct a TOPSIS model of the optimal choice of optical linear terminals in the conditions of unclear information for different cases of aggregation of evaluations of decision makers. Task: to formalize the process of selecting optical linear terminals; develop a multi-criteria mathematical model for the effective selection of optical line terminals. Methods used in the study: The fuzzy TOPSIS method, four methods of aggregating the opinions of decision makers. The following results were obtained. Alternatives and criteria for their evaluation are defined. On the basis of interviews with decision makers, evaluations of the degree of importance of the criteria and alternatives to the criteria were determined. Linguistic changes were used to describe decision-makers’ evaluations, which were interpreted as triangular fuzzy numbers. During this study, four methods of aggregating the evaluations of decision makers were used. A fuzzy TOPSIS model for selecting an optical line terminal is constructed. The ranking of the selected optical line terminals is obtained and the best alternative is determined. The results of modeling with different methods of aggregating assessments of decision makers are compared. Conclusions. The use of the Fuzzy TOPSIS method for optimal selection of optical linear terminals is proposed. The influence of the methods of aggregation of evaluations of decision-makers is analyzed.

A total distance ranking approach to fuzzy AHP-based MCDM method for selecting sustainable manufacturing facility location

Selecting a sustainable facility location is a crucial strategy for manufacturing companies to achieve long-term success in today’s competitive environment. Various quantitative and qualitative criteria with different importance in a multiple level structure must be considered and aggregated to assist the company in decision-making. How to determine these criteria weights and select the sustainable manufacturing facility locations have become research questions. To resolve this problem, this paper proposes a total distance ranking approach to fuzzy analytic hierarchical process (AHP)-based multiple criteria decision-making (MCDM) method. Currently, the membership functions of fuzzy weighted ratings in the MCDM with a multilevel structure cannot be developed. A ranking method is needed to defuzzify those fuzzy numbers used for fuzzy AHP and qualitative criteria in the MCDM for better executing decision-making procedure. The total distance ranking method related to the centroid on x-axis, centroid on y-axis and the distances of centroids point to the two tangent lines of a fuzzy number are considered in the proposed ranking approach. Formulas of the proposed approach will be presented, and some properties will be investigated to derive formulas for trapezoidal and triangular fuzzy numbers. A comparison with relevant ranking methods will be made to show the advantages of the proposed ranking approach. The proposed ranking approach is then applied to defuzzify the fuzzy numbers used in fuzzy AHP and linguistic values under qualitative criteria to obtain the criteria weights under multi-level structure and crisp values under qualitative criteria, respectively. The final scores of alternatives can be obtained by aggregating crisp criteria values and their corresponding weights by simple additive weighting method to obtain the ranking result. A numerical example will be conducted to show the effectiveness of the proposed model. Finally, a comparison with Best-Worst method (BWM) will be presented to show the persuasiveness of the proposed method.

Determining a framework for institutional support of instructors engaged in distance education programmes using the multiple-criteria decision-making method

Determining a framework for institutional support of instructors engaged in distance education programmes using the multiple-criteria decision-making method

Interpretable decision-making model with uncertain weights for sustainable digital economy

In recent years, sustainable digital economy development goals have received increasing attention because of the serious influence environment with the development of the rapid economy. Meanwhile, Multiple Criteria Decision-Making (MCDM) methods are efficient ways to measure sustainable developments, which can controls environmental pollution and protects sustainability improvement. However, assessment results are unconvincing by using the common MCDM methods because various areas that caused pollution conditions are inconsistent. Therefore, the purpose of this paper is to design an interpretable MCDM method with uncertain weights for various areas evaluation. In particular, MCDM can be used to evaluate environmental sustainability development efficiently, and deep neural networks can supply credible weights to MCDM. Interpretability is used to understand key factors in the decision-making process. Referring to the national policy and urgent areas from central areas in China, six areas are conducted to assess the sustainable digital economy problem, which could be interpreted as demonstrating the effectiveness and feasibility of the proposed approach. Besides, the experimental results demonstrate that the sustainable developmental level of Hubei is the best in the latest year.

The Development of a Hybrid Model for Dam Site Selection Using a Fuzzy Hypersoft Set and a Plithogenic Multipolar Fuzzy Hypersoft Set

Inrecent years, there has been a notable increase in utilising multiple criteria decision-making (MCDM) methods in practical problem solving. The advancement of enhanced decision models with greater capabilities, coupled with technologies like geographic information systems (GIS) and artificial intelligence (AI), has fueled the application of MCDM techniques across various domains. To address the scarcity of irrigation water resources in Bortala, Northwest China, the selection of a dam site has been approached using a hybrid model integrating a multipolar Fuzzy set and a plithogenic Fuzzy hypersoft set along with a GIS. This study considered criteria such as a geological layer, slope, soil type, and land cover. Four potential and reasonably suitable dam locations were identified using a dam construction suitability map developed for Bortala. Ultimately, we showcased the benefits of the innovative method, emphasizing an open, transparent, and science-based approach to selecting optimal dam sites through local studies and group discussions. The results highlight the effectiveness of the hybrid approach involving a fuzzy hypersoft set and plithogenic multipolar fuzzy hypersoft set in addressing the challenges of dam site selection.

A simulation study on the probabilities of rank reversal, tie making, and tie breaking for multiple criteria decision making methods

Multiple criteria decision making (MCDM) methods can be affected by preference reversal, meaning that the order of two alternatives is reversed after adding or deleting another alternative. Here, we focus on methods that produce rankings with ties (i.e., weak orders). In this context, one usually talks about rank reversal. Existing rank reversal probability simulation experiments are subject to improvement on the following points: (1) the small number of MCDM methods included, (2) the unclear relation between the rank reversal probability and the rank of the deleted alternative, and (3) the lack of consideration of ties. In this paper, considering both strict preferences and ties, we distinguish two new phenomena: tie breaking, i.e., the shift from a tie to a strict preference, and tie making, i.e., the shift from a strict preference to a tie. To investigate the probabilities of rank reversal, tie making, and tie breaking, a simulation study involving thirty versions of twelve MCDM methods and six simulation factors is set up. Results demonstrate that for MCDM methods using pairwise comparison data, deleting an alternative ranked first or last leads to smaller probabilities than deleting an alternative in the middle, while the opposite holds for the methods using evaluation data under criteria. Four findings and three suggestions are given to help decision makers select MCDM methods to use.

Determinantes de desempenho para projetos agroindustriais em ações coletivas de pequenos agricultores

Abstract The important economic and social role of agroindustrial collective actions does not eliminate the challenges related to the sustained feasibility of these enterprises. This article presents and discusses the main determinants that affect the performance and feasibility of collective agroindustrial projects, assessing the importance of each determinant for establishing the future performance of enterprises. A systematic literature review supported the structuring of the proposed analytical framework, which suggested the use of 24 determinants grouped into five factors. Afterwards, a panel of 20 experts used a multiple-criteria decision-making method, the Simple Multi-Attribute Rating Technique (SMART), to evaluate the relative importance of the determinants proposed by the model. The framework indicates the importance of each determinant on the performance of the collective agroindustrial projects, allowing the user to apply it with a decision-making tool. The proposed model combines and incorporates a wide number of sparse determinants in other works and highlights the factor “management, operation and finance” as a factor of success in making projects feasible.

A Comprehensive Review of Multiple Criteria Decision-Making (MCDM) Methods: Advancements, Applications, and Future Directions

This research paper presents a comprehensive review of Multiple Criteria Decision-Making (MCDM) methods, encompassing their advancements, applications, and future directions. The study begins with an introduction emphasizing the significance of MCDM in complex decision-making scenarios. Through a systematic literature review, recent developments in MCDM techniques are examined, including multi-objective methods, fuzzy-based approaches, data-driven models, and hybrid methodologies. The strengths and limitations of each method are critically analyzed. Furthermore, the paper investigates the diverse applications of MCDM in domains such as business, engineering, environment, healthcare, and public policy, highlighting the practical implications through real-world case studies. The study then identifies emerging trends and challenges in MCDM research, discussing the integration of MCDM with emerging technologies, enhancing robustness and adaptability, addressing uncertainty, and identifying unexplored domains for potential application. This comprehensive review serves as a valuable resource for decision-makers and researchers, providing insights into the advancements, applications, and future directions of MCDM methods.

Renewable Energy Transition Task Solution for the Oil Countries Using Scenario-Driven Fuzzy Multiple-Criteria Decision-Making Models: The Case of Azerbaijan

The renewable energy transition of oil- and gas-producing countries has specific peculiarities due to the ambivalent position of these countries in the global energy market, both as producers and consumers of energy resources. This task becomes even more challenging when the share of oil and gas in the country’s GDP is very high. These circumstances pose serious challenges for long-term energy policy development and require compromising decisions to better align the existing and newly created energy policies of the country. The scale, scope, and pace of changes in the transition process must be well balanced, considering the increasing pressure of economic and environmental factors. The objective of this paper is to develop models that allow the selection of the most appropriate scenario for renewable energy transition in an oil- and gas-producing country. The distinguishing feature of the proposed model is that alternatives in the decision matrix are presented as scenarios, composed of a set of energy resources and the level of their use. Linguistic descriptions of the alternative scenarios are formalized in the form of fuzzy statements. For the problem solution, four different Multiple-Criteria Decision-Making (MCDM) methods were used: the fuzzy simple additive weighting (F-SAW) method, the distance-based fuzzy TOPSIS method (Technique of Order Preference Similarity to the Ideal Solution), the ratio-analysis-based fuzzy MOORA method (Multi-Objective Optimization Model Based on the Ratio Analysis), and the fuzzy multi-criteria optimization and compromise solution method VIKOR (Serbian: VIekriterijumsko Kompromisno Rangiranje). This approach is illustrated using the example of the energy sector of Azerbaijan. The recommended solution for the country involves increasing natural gas (NG) moderately, maintaining hydro, and increasing solar notably and wind moderately.

A decision support system for supplier quality evaluation based on MCDM-aggregation and machine learning

Evaluating suppliers’ quality performance is one of critical tasks of quality management since it is directly related to quality assurance, improvement and development, especially for manufacturing companies. However, the supplier quality evaluation remains challenging due to the involvement of a large amount of qualitative and quantitative data, multi-dimensional attributes, as well as a large number of items and suppliers, etc. In addition, while various multiple criteria decision making methods (MCDMs) exist and can be used for supplier quality evaluation, individual choice and application of the methods often result in inconsistent or conflicting evaluation results. To overcome the challenges, in this study, we develop a decision support system for supplier quality evaluation to deal with the practical complexity and to take advantage of the availability of the big industrial data. The system is presented as a conceptual framework including three modules: the decision matrix and criteria initialization module, the MCDM method selection and implementation module, and the aggregation module. The sequential conduction of each module formulates and solves the multiple supplier and item quality evaluation problem. The implementation of the system can be applied to dynamically monitor and evaluate supplier and item quality performance. Moreover, as the core intelligent part of the decision support system, in the aggregation module, we propose an aggregated MCDM model based on the machine learning concepts. It aims at providing more reliable evaluations across multiple suppliers and items. The aggregation model also contributes to the MCDM literature as an independent method which is not restricted to the purpose of supplier quality evaluation. The methods shed lights on how to construct and solve MCDM problems in the big-data era. Finally, the application of the decision support system is illustrated by a case of a large automotive company. The case shows that our decision support system provide more robust and reliable evaluation results than the traditional individual multiple criteria decision making method.

The Optimal Sustainable Insulation Material by Using MCDM Analysis

Increasing fuel costs is the new norm, and an increase in energy consumption leads to many troubles, such as a growing of harmful environmental emissions and rise in energy consumption bills. Hence, it’s desirable and recommended to estimate the value of energy consumed for buildings in the first design state in order to reduce the impact of these issues, and obtain a sustainable building using thermal insulation for the buildings (walls, roofs, floors...).
 The purpose of this experimental analysis is to determine the best insulating material among 12 selected materials, as the properties of these materials were analysed based on several criteria.
 The study focuses on using multiple analysis methods in order to achieve the best results and to eliminate potential inconsistencies of data with available scientific methods. The evaluation is conducted via implementing three multiple criteria decision-making methods (MCDM)the full Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) multiplication, rating the choices and selection the solution nearest to ideal, VIekriterijumsko KOmpromisno Rangiranje (VIKOR) and the method of ordering options by analogy with the optimal method (TOPSIS). In addition to the significance of the criteria, find the weights of the criterion in the analysis by using the correlation between criteria (CRITIC). Results based on pre-established methods showed consistent results, with the highest and worst performing materials identified in buildings. Extruded polystyrene foam (XPS) is ranked the best among other materials. The number of articles related to MCDM isn’t that high. Therefore, this study will contribute to filling the gap.

Utilizing multiple criteria decision-making optimization methods to machine LM25Al/VC material under electro-discharge machining processes

In this work, sparking electro-discharge machining (EDM) process parameters are optimized by using multiple criteria decision-making (MCDM) methods. The work piece utilized for the investigation was LM25 aluminum alloy reinforced with Vanadium carbide (VC), processed through a stir casting technique. The EDM process parameters that have been considered in this work are by peak current, pulse-on-time, and pulse-off-time, with the effects of each variable and their combinations on the performance metrics of EDM like material removal rate, electrode wearing rate, and surface roughness. In this study, three MCDM methodologies were applied to evaluate EDM performance. Then, the obtained MCDM scores were compared using two different objective verification mechanisms. In this case, the VIKOR technique delivered the best-desired results relative to TOPSIS method, and factorial analyzing method. Also, the factorial method is simpler than the other methods, though it produced nearly identical results as the sophisticated MDCM method.

IMPROVING PERFORMANCE OF THE BANANA AGROINDUSTRY SUPPLY CHAIN USING MCDM (MULTI CRITERIA DECISION MAKING)

The problems faced by the banana agro-industry include the procurement process of raw materials from banana suppliers, which has not been able to meet the industry's needs. The selection process of banana suppliers has not been a factor considered by the industry in its strategic planning. The technology used should be able to produce quality processed banana products, but in reality, production is still below the available normal capacity. The distribution of processed banana products to consumers has not been well identified, and market access is still limited, resulting in stagnant industry development. The aim of this research is to identify the problems related to the performance of the banana agro-industry supply chain, measure the performance of the banana agro-industry supply chain, and provide and evaluate solutions that can be used to improve the performance of the banana agro-industry supply chain. The method used in this research is to measure the performance of the supply chain using weighting with the multiple criteria decision-making (MCDM) method, which consists of the Analytical Hierarchy Process (AHP), determining alternative solutions using the VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) method. The results show that the identification of problems in the banana agro-industry supply chain consists of 5 main criteria and 20 sub-criteria, as well as 7 proposed solutions. The measurement of the supply chain performance resulted in a score of 32.032 (poor), indicating that the performance of the banana agro-industry supply chain is still low. The alternative solution provided to improve the performance of the banana agro-industry supply chain is to establish collaboration and partnerships among supply chain stakeholders.

Multiple-criteria decision-making for hydrogen production approaches based on economic, social, and environmental impacts

Hydrogen H2 has emerged as the most viable energy source in recent years for decarbonizing energy sector. Therefore, all stakeholders must understand the various H2 production approaches, along with their economic viability and environmental impacts. Additionally, it is crucial for policymakers and decision-makers to comprehend the role that H2 plays in accomplishing the various Sustainable Development Goals (SDGs) set by the United Nations (UN). In this work, the most common H2 production technologies have been investigated and assessed. Three different multiple-criteria decision-making (MCDM) methods, namely weighted aggregated sum product assessment (WASPAS), weighted sum model and weighted product model (WSMWPM), and technique for order of preference by similarity to ideal solution (TOPSIS) were used to choose the most feasible routes for H2 production considering 25 criteria. Three weighting methods, namely no priority, consistency-based ranking index for decision making (CRITIC), and Entropy were employed for weighing different criteria, to further ensure the reliability of the results obtained. Additionally, the potential of fulfilling the SDGs has been examined, and the most relevant terms have been highlighted. Among the different investigated H2 production technologies, the MCDM results showed that biomass gasification followed by steam methane reforming (SMR) outperformed other technologies. The work reveals that hydrogen's primary influence on the SDGs stemmed from its potential to provide a clean source of energy (SDG-7: Affordable and Clean Energy) and lower greenhouse gas emissions (GHGs) (SDG-13: Climate Action).

The Significance of the Factors Increasing the Asphalt Pavement Recycling Rate in the Country, Determined Using Multiple-Criteria Decision-Making Methods

Asphalt pavement, a component of transport infrastructure, deteriorates under the influence of destructive factors. The damages which have occurred during its period of use are repaired and the asphalt pavement is recycled when its further rehabilitation is economically and technically irrational. The material from the asphalt pavement layer that has reached its limit state is milled out or broken and crushed and then is repeatedly used in the production of hot-mix asphalt (HMA) or warm-mix asphalt (WMA) mixtures. In this paper, the dynamics of the percentage recycling ratio (RR) of old asphalt pavement material was investigated. RR represents the quantity of reclaimed asphalt pavement (RAP) used in the production of HMA and WMA mixtures in Europe and the USA, divided by the total amount of RAP prepared in the country. Factors and goals affecting it are analyzed. An original system of 10 criteria that increase the RR country has been created. Decision makers do not know which factors have the maximum influence in increasing the use of RAP and this should go in connection with environmental pollution reduction and achieving economic utility. By applying different multiple-criteria decision-making (MCDM) methods and using the importance given to these criteria by 14 experts, the normalized subjective weights of the criteria were determined. Analytic hierarchy process (AHP), rank correlation, average rank transformation into weight—linear (ARTIW-L) and non-linear (ARTIW-N), and direct percentage weight (DPW) methods were used in the study. The results show that the RAP recycle rate is close to 100% in countries with a sustainable economic background. In the Baltic countries, it is mostly increased by the adequacy of regulatory documents, the strategy promoting asphalt recycling in the country, and the homogeneity and classifying of RAP. The number and capacity of RAP stocks, the number and productivity of asphalt milling equipment and the wear and tear of the asphalt pavement have the least influence on the increase in RR. The opinions of experts in assessing the significance of all criteria are consistent. The averages of the weights of criteria determined by four MCDM methods (AHP, ARTIW-L, ARTIW-N, DPW) made it possible to obtain more reliable results. These results can be used to make strategic decisions and to create a plan of practical actions to increase the RAP recycling rate in the developing countries.

Development and Practical Application of Hybrid Decision-Making Model for Selection of Third-Party Logistics Service Providers

Abstract In this study a problematic of logistics service provider selection is discussed. Traditional decision-making techniques are functionally limited and must be adjusted. Modern approach must be based on contemporary tools and information. The aim of this study is to develop a modern, industry-oriented decision-making model for selection of strategic logistics service providers. Main tasks of this study include: to perform critical analysis and comparison of multiple-criteria decision-making methods; to adapt decision-making framework for specific needs of selection of third-party logistics service providers; to collect and analyze industry data related to procurement projects; to develop modern decision-making model for selection of logistics service providers; to perform practical application of developed decision-making model based on collected industry data. The key novelties of this study are developed methodology for collection of industry data for decision-making process, developed hybrid decision-making model for selection of third-party logistics service provider, as well as application of the model. As the result, there is developed decision-making model for selection of 3PL service providers that includes 6 steps.

Measuring the readiness level for Kaizen projects: a multi-layer Bayesian decision-making framework

PurposeAcquainting organisations regarding the concepts of Total Quality Management (TQM) and its implementation is one measure that effectively improves their global position and performance. Kaizen is one of the concepts of TQM, which focuses on low-cost organisational transformational methods and often saves consuming significant resources (time, capital, etc.). Using Kaizen in organisational transformation sets efficient guidelines to improve processes agility and leanness and increase manufacturing productivity. Hence, this study aims to identify the key success factors in Kaizen projects and presents a score function that measures the readiness level of organisations to implement Kaizen projects.Design/methodology/approachA literature review first extracts the key success factors in Kaizen projects. Afterwards, the selected factors are screened via the fuzzy Delphi method using expert opinions from the manufacturing sector of an emerging economy. Subsequently, their importance is cross-examined by the Bayesian best–worst Method (BBWM). The BBWM is one of the most recent multiple criteria decision-making (MCDM) methods that lead to stable, dynamic and robust pairwise comparisons. After analysing the weights of the key factors, a score function is designed so that organisations can understand how much they are ready to launch Kaizen projects.FindingsAccording to the findings, “Training and education” and “Employee attitude” played an important role in the success of Kaizen projects. The literature extracted 22 success factors of Kaizen projects, and 10 factors were eliminated through the fuzzy Delphi method. Twelve success factors in Kaizen projects were evaluated and investigated through the BBWM. Matching to this method, “Training and education” and “Employee attitude” weighed 0.119 and 0.112, relatively. Furthermore, “Support from senior management” was the least important factor.Originality/valueTo the best knowledge of the authors, this is the first research in which the success factors of Kaizen projects have been identified and analysed through an integrated multi-layer decision-making framework. Although some studies have investigated the key success factors of Kaizen projects and analysed them through statistical approaches, research that examines the success factors of Kaizen projects through MCDM methods is yet to be reported. Moreover, the score function that measures the level of readiness of each organisation for the successful implementation of Kaizen projects is a unique contribution to this research.

Implementation of Vikor Algorithm in Web-Based Recommendation System for Obstetrician Selection

The process of childbirth is a natural event for women; with the increase in birth rates, selecting a suitable obstetrician becomes essential. However, many couples find it challenging to choose the right obstetrician, from consultations to hospital births. This study aims to develop an obstetrician recommendation system to overcome decision-making challenges that involve many criteria. The method used is VIseKriterijumska Optimizacija Kompromisno Resenje (VIKOR), which is one of the best methods in Multiple Criteria Decision Making (MCDM) for ranking and selecting alternatives. This method involves several steps, including determining the weight of the criteria, normalizing the matrix, calculating the Utility Measure and Regret Measure, and calculating the VIKOR index. Testing was carried out through scenario tests and user survey tests using the USE questionnaire. The scenario test results show the successful implementation of VIKOR in this system. The results of the user survey test indicated a high level of satisfaction with a score of 83.34%, which fulfilled the four variables in the USE questionnaire: usability (79.76%), ease of use (84.81%), ease of learning (84.81), and satisfaction (83.31%). The results of this study have important implications for increasing the effectiveness of decision-making in selecting the right obstetrician. By incorporating the VIKOR method into an application, this research provides a valuable contribution to developing a VIKOR-based recommendation system. It emphasizes the importance of applying this method in complex decision-making environments.

MCDM Methods for Selection of Handling Equipment in Logistics: A Brief Review

This review considers the application of Multiple Criteria Decision Making (MCDM) methods in the evaluation process of handling equipment in logistics. In order to present a current review of research papers, 21 articles, published in the period from 2010 to date, were analyzed. All research papers were searched using the Google scholar web search engine. This paper presents various MCDM methods used to select equipment such as forklifts, cranes, conveyors, AGVs (automatically guided vehicles), AMRs (autonomous mobile robots), reach stackers both in warehouses, ports, city logistics and in intermodal transport. The results of research papers published in various journals related to the application of MCDM methods including AHP, MABAC, SWARA, ARAS, FUCOM, CRITIC, MARCOS and various fuzzy methods were also analyzed. As the studies analyzed in the paper has been published from 2010 to the present, this indicates the importance and relevance of this area of research. The purpose of this paper is to encourage additional research in this area and to provide a brief summary of the most recent studies in this field. The results of the paper showed that the most used handling equipment was the conveyor.