Multi-criteria Decision-making Method Research Articles

Multi-Criteria Assessment of Timber-Based Structural Systems for a Grocery Store

To reduce the negative impact on the environment, architects, designers, and construction companies need to find and apply eco-friendly and sustainable building solutions. Due to its renewable nature and numerous advantages, timber has become an attractive substitute for steel and concrete in both residential and non-residential construction projects. However, timber application in the construction of grocery stores is a relatively new concept. The purpose of this research is to propose three alternative timber-based structural systems for a grocery store in Lithuania and to select the most efficient option based on multi-criteria decision-making methods. Three alternative glued laminated timber (glulam) structural systems—the glulam column and truss system, the glulam three-hinge frame system, and the glulam column and double-tapered beam system—were designed. The systems were evaluated against ten criteria, reflecting structural properties, cost efficiency, assembling complexity, and aesthetics. Multiple-criteria assessments by the COmplex PRoportional ASsessment (COPRAS) method and simple additive weighting (SAW) method revealed that the best-performing alternative is the glulam column and double-tapered beam system due to the lower cost of load-bearing structures, the smaller quantity of required steel details and fittings, and the highest maximum utility ratio according to serviceability limit states compared to other alternatives.

A Combined Ranking and Sensitivity Analysis of Power Generation Using Multi-Criteria Decision-Making and Monte-Carlo Simulation

This research examined energy sources that can be employed in a region to assist policymakers in determining energy priorities. Three key components were analyzed in this research to rank these energy sources: Levelized Cost of Energy (LCOE), CO2 emissions, and power density. A combination of multi-criteria decision-making (MCDM) methods, namely the Analytical Hierarchy Process (AHP)-Entropy-the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), was used to assess these criteria, which had not been previously applied to rank energy sources. Additionally, the Monte-Carlo method was utilized to detect changes in sensitivity throughout the rankings. Results of the study indicated that gas energy topped the list, followed by Solar Photovoltaic (PV)-crystalline, geothermal, wind, nuclear, Solar PV Commercial & Industrial (C&I), Solar Thermal Tower with Storage, and residential PV rooftop solar. Moreover, nuclear energy ranked the highest when looking at the sensitivity of parameters, while utility-scale Solar PV and wind energy ranked the next highest. Thus, this research can be used to increase objectivity in the assessment and selection of power generation technology to be implemented.

Multi-objective capacity configuration optimization of a nuclear-renewable hybrid system

Integrating the energy storage and the base-load energy can be an efficient solution to cover the fluctuation of renewable energy. A nuclear-renewable hybrid energy system consisting of a small modular thorium molten salt reactor, solar photovoltaics, wind turbines, thermal energy storage and battery storage with two operation modes is proposed to meet the community load demand. The methodology combining multi-objective evolutionary algorithms with multi-criteria decision-making method is used to identify the optimal operation mode and gain the optimal configuration of the proposed nuclear-renewable hybrid energy system. In the first stage, the multi-objective evolutionary algorithm with better performance is utilized to gain Pareto solutions to minimize the levelized cost of energy while ensuring an acceptable level of deficiency of power supply probability. In the second stage, the multi-criteria decision-making method is adopted to select the best solution from the obtained Pareto frontiers. The optimization results demonstrate that the nondominated sorting genetic algorithm has the best performance compared with other algorithms and the operation mode with battery discharged first is more cost competitive. It is concluded from the results analysis that the selection of optimal mode is influenced by seasonal variation that contributes to large energy storage capacity requirement.

Groundwater potential zone mapping of middle Andaman using multi-criteria decision-making and support vector machine

Groundwater is a vital resource crucial to human survival worldwide and plays a significant role in addressing climate change. The multi-criteria decision method and support vector machine technique were used to demarcate the groundwater potential zones (GWPZ) of Middle Andaman. A total of 11 factors including geology, geomorphology, LULC map, lineament density, distance from Active Fault, groundwater level, distance from the sea, slope, drainage density, distance from major earthquakes, and distance from epicenter hotspots were used to calculate GWPZ. Spatially distributed maps/thematic layers of all of the above factors were generated using remotely sensed data as well as ground data. Each factor was assigned weights based on preceding global and regional research and the influence of each layer on groundwater. Finally, all thematic layers were used for multi-criteria decision analysis to create a map depicting the GWPZ of Middle Andaman. A support vector machine (SVM) tool was used to demarcate the potential zone based on the signature obtained from overlay analysis. The resulting map was divided into four categories: very high, high, low, and very low groundwater potential zones. SVM classification result showed that most of Middle Andaman's areas come under the low to very low GWPZ (63.4%), followed by high GWPZ (27%), and 10.6% of very high GWPZ. SVM classification resulted in overall accuracy, kappa coefficient, and the area in the ROC curve of 91.66%, 0.87, and 0.64 respectively. The analysis revealed a correlation between each factor and the Multi-Criteria Decision Method (MCDM) and Support Vector Machine (SVM), indicating that the majority of geological and geomorphological features are linked to lower groundwater potential. Further, the result was compared with groundwater fluctuation data of the middle Andaman. Finally, this study provides an expedient approach to delineating the GWPZ for practical management and use of the area's groundwater resources to maintain the island's sustainability.

Multi-Criteria Optimization of Al2O3-ZrO2(Y2O3)-based Self-lubricating Composite Cutting Tools for Increasing Shear Angle, and Wear Resistance in Machining Processes

Abstract In the present investigation, self-lubricating cutting tools were fabricated by adding various elements such as NiCr, Ag, Mo, SrSO4, and CaF2 to Zirconia Toughened Alumina in order to overcome the difficulties that occurred in dry machining. Three significant process parameters; cutting speed (50, 125, 200, and 275 m/min), feed rate (0.1 and 0.2 mm/rev), and depth of cut (0.1 and 0.2 mm) were selected for the machining process. The performance measures such as chip thickness ratio, shear angle, and tool wear, were investigated. The experimental runs were conducted with Taguchi L8 mixed-level orthogonal array. Technique for Order of Preference by Similarity to Ideal Solution is a multi-criteria decision-making method used to determine the best alternative from a set of options. Turning experiments have been performed on the AISI 4340 steel workpiece with various combinations of machining parameters. The findings revealed the incorporation of SrSO4 and Mo at 10Wt.% and CaF2 at 5Wt.% (i.e., self-lubricating cutting tool-4) achieved better mechanical properties and wear resistance due to the formation of a self-lubrication layer on the rake face of the cutting tool. A confirmation experiment is executed in order to verify the outcomes in the end. Finally, there is a noticeable improvement in the outcomes compared to the base cutting tool, i.e., chip thickness ratio is 35.77%, shear angle is 27.31%, and tool wear is 58%. The mechanism for improving performance measures is discussed in detail.

Matrix‐phase material selection for shape memory polymer composites: A comparative analysis of multi‐criteria decision‐making

AbstractIn the present study, the selection of suitable shape memory polymers (SMPs) to be used as the matrix‐phase material with various (In)organic filler materials to achieve the required optimum multi‐stimuli response in shape memory polymer composites (SMPC) systems is analyzed. The selection of these materials is based on their mechanical and physical properties as well as other underlying factors such as cost, availability, shape recovery rate, and aesthetic characteristics. In this study, the entropy method was applied to estimate the weightages of the various criteria while the gray relation analysis (GRA) and the technique for order preference by similarity to ideal solution (TOPSIS). Multi‐criteria decision‐making (MCDM) approaches have been used to rank the suitable matrix‐phase polymer materials for manufacturing shape memory polymer composites (SMPC) system. A total of eight alternative SMP matrix‐phase materials based on a set of nine criteria were analyzed and ranked. The proposed methodology and the result obtained thereof have been illustrated in detail. The results obtained from TOPSIS and GRA methods have been compared to conclude the effects of the material properties on the ranking and the selection of the SMP materials. Among all the eight alternatives considered, thermoplastic polyurethane (TPU) was found to be the best material in both the MCDM methods. The material cost, resistivity, % elongation, and hydrophobicity present the most influencing properties on the SMP material selection, whereas density presents no effect on the SMP matrix material selection. The robustness of the results for the comparative analysis was verified using TOPSIS methodology to validate its reliability. It was revealed that the TPU, polycarbonate, polypropylene, and epoxy‐resin/poly(lactic acid) are the most dominant matrix‐phase SMP material alternatives when a deviation in the entropy weights of the primary evaluation criteria is applied. The novelty of this study is the exploration and application of statistical MCDM methods of engineering material selection problems based on a set of decision criteria, which can be time‐consuming and costly while using experimental analysis methods.Highlights SMPCs are applicable engineering materials thus making their research viable. SMPC systems can undergo various shape changes under external stimuli. Selection of matrix‐phase polymer is critical in achieving desired objectives. GRA and TOPSIS MCDM approaches have been applied in the selection process. TPU was found as the best material in both methods.

Genetic Algorithm Optimization with Selection Operator Decider

AbstractGenetic Algorithm (GA) is a powerful and flexible meta-heuristic tool to deal with the complexity of optimization problems, as they are directly related to real-life situations. The primary goal of an optimization problem could be to obtain a solution with less effort and near-optimal rather than slow, improbable optimal. GAs serve this purpose by broadly exploring the possible solution space and using genetic operators. The performance of GAs can vary significantly depending on the genetic operators. Although each operator type has upsides and downsides, the selection operator greatly influences the GA’s performance. Conventional GAs initialize with predetermined genetic operators and continue with the same throughout all iterations. In this paper, dynamically adjusting the selection operators to the current progress of the iteration will be shown to be a crucial strategy to improve the performance of the GA. This study aims to propose a novel GA capable of harnessing multiple selection operators by a self-deciding operator structure, which is more advantageous at the current iteration. For this, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which is known as a simple and effective multi-criteria decision-making method, will be integrated into the GA by a proposed dynamic decision matrix. The proposed Selection Operator Decider Genetic Algorithm (SODGA) has unique properties with varying selection processes and is capable of using TOPSIS as a decider of the operator inside the iterations. The effectiveness of the presented SODGA framework will be analyzed by a Capacitated Vehicle Routing Problems (CVRPs) benchmark set.

Dynamic decision making for situational awareness using drones: Requirements, identification and comparison of decision support methods

Decision makers increasingly operate in real-time information-rich environments, where limited time is available for interpreting data to inform decisions. These environments are driven by static or mobile sensing devices that can provide numerous dynamic data points. A prominent approach in this space is to utilise drones, which can be deployed to gather targeted information. However, deciding how best to deploy available drones is nontrivial, and stands to benefit from decision support aids that plan routes. Such a system must operate under time constraints created by the changing attributes of routes as the situation unfolds. This study describes a dynamic decision support system (DSS) for situational awareness with drones. The system applies Multi-Criteria Decision Making (MCDM) methods within a dynamic genetic algorithm to provide a continuously revised ranking of routes. Five desiderata for dynamic decision support are presented. It is shown how a dynamic DSS can be equipped with declarative specification of preferences (Desiderata 1), dynamic revision of recommendations (Desiderata 2), and high diversity of options (Desiderata 3). The study then compares four MCDM methods, namely the Weighted Product Model (WPM), the Analytic Hierarchy Process (AHP), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), with regards to how consistently they trade-off between criteria (Desiderata 4) and the stability of results under small changes to criteria values (Desiderata 5). To evaluate the trade-offs between criteria we analyse the smoothness of change in criteria outcomes as criteria weightings increase for each algorithm. The outcomes are calculated by automating the selection of routes in a case study that applies drones to the task of harbour management. The stability of results for the different MCDM methods are compared. Perturbations were applied to sets of routes ranked by each algorithm then each algorithm was reapplied and the magnitude of the changes in ranking was assessed. Overall, TOPSIS was found to be the algorithm which made the most consistent trade-offs between criteria, only under-performing another algorithm with respect to a single criterion. AHP and WPM were the next most consistent algorithms and PROMETHEE was the least consistent algorithm. TOPSIS was also found to be the most stable method under small changes to criteria values. AHP was the second most stable, followed by PROMETHEE and WPM respectively. The results show that TOPSIS achieves the best result for both Desiderata 4 and 5 and consequently the study finds TOPSIS to be an appropriate MCDM method for dynamic decision support.

Data-guided for discovering high-strength, cost-effective, and low-carbon rice husk ash concrete

This study aims to present a data-guided intelligent design framework for discovering high-strength, cost-effective, and low-carbon rice husk ash concrete (RHC). First, gradient boosting (GB) models were developed to predict compressive strength of RHC based on a dataset with 1150 samples. The CatBoost model outperforms XGBoost and LightGBM models on the test set, and its determination of coefficient (R2), root mean squared error (RMSE), and mean absolute error (MAE) are 0.984, 3.270 MPa, and 2.290 MPa respectively. With the best prediction model, SHapley Additive exPlanations (SHAP) was utilized to shed light on the model outputs. The curing age is the most dominant features effecting the compressive strength of RHC, and the next two are the contents of cement and water. Then, a computational design framework was proposed by integrating the machine learning-based model and the evolutionary algorithm. Considering compressive strength, materials cost, embodied carbon, and embodied energy, the multi-objective optimization was conducted leveraging the improved adaptive geometry estimation based many-objective evolutionary algorithm (AGE-MOEA II). The optimal solutions of the different design scenarios were achieved using the multi-criteria decision-making method. The research can provide a theoretical reference for the capability of developing RHC mixture using data-guided approachesto promote the sustainable development of concrete industry.

Assessment of Wave Energy Location, Technology, and Converter Toward Sustainability Using Integrated Spherical Fuzzy MCDM Approach

Due to climate change, fossil fuel reduction, growing energy usage, and ecological impacts, the need for renewable energy sources is rising quickly. A large amount of energy comes from the ocean through the tide; renewable energy must be exploited. One important energy source is wave energy converters (WECs), which enhance efficiency and grow energy. This study presented a case study from three layers of the Red Sea in Egypt for selecting optimal wave energy location (WEL), wave energy technology (WET), and WEC. The spherical fuzzy set (SFS) framework deals with uncertainty and vague information in this selection process. The SFS is integrated with multi-criteria decision-making (MCDM) to deal with various criteria in each layer. This study uses two MCDM methods under the SFS environment: The Stepwise Weighted Assessment Ratio Analysis (SWARA) method and the Weighted Euclidean Distance Based Approach (WEDBA) method. The SF-SWARA method is used to compute the criteria weights in each layer. Then, the SF-WEDBA method ranks the alternatives in each layer. The main results in each layer show that the accessibility criterion, Economic factors, and energy production criterion have the highest weight in the three layers. Sensitivity analysis and comparative analysis are conducted in each layer.

A linguistic Z-number-based sorting method for three-way multi-criterion decision-making in ecological function zoning

Traditional multi-criterion decision-making methods can help decision-makers decide whether to accept or reject an alternative, but they cannot ensure these results fully meet decision-makers’ requirements. Three-way decision methods overcome this limitation by dividing alternatives into three regions. However, traditional three-way decision methods rarely considered the reliability of evaluation information and individual regrets under some criteria. Moreover, most weighting methods in three-way decisions ignored the influence of abnormal values. To fill the above gaps, this paper develops a linguistic Z-number-based sorting method combining the three-way decision with the gain and lost dominance score (GLDS) method that reflects individual regrets under some criteria. Firstly, to avoid individual regrets, we introduce the linguistic Z-number-based GLDS sorting (LZ-GLDS-Sort) method, which obtains two states of alternatives that will be used in the three-way decision. Linguistic Z-numbers can express linguistic evaluation and credibility. Two parameters are introduced in the LZ-GLDS-Sorting method to reflect the relative importance of evaluation information and credibility. Then, a three-way decision method is introduced to divide alternatives into three regions, in which the variation-coefficient weighting method is introduced to avoid the effect of abnormal values. Finally, the ecological function zoning as an example is provided to validate the applicability of this model.

A hybrid MCDM optimization for utilization of novel set of biosynthesised nanofluids on thermal performance for solar thermal collectors

Recent advancements in solar technology have spurred researchers to develop a precise and cost-effective method for monitoring solar radiation across diverse environmental conditions. This study focuses on optimizing performance parameters by replacing conventional nanofluids, produced through chemical and physical processes, with biosynthesized counterparts. Biosynthesized nanoparticles, derived from biological sources like microorganisms or plants, offer a promising avenue for enhancing solar panel efficiency. Through the construction and testing of an integrated photovoltaic thermal system, varying operating parameters have been explored to maximize photovoltaic efficiency. The highest overall efficiency, reaching approximately 62%, was achieved with biosynthesized Graphene oxide nanofluid under higher Direct Normal Irradiance (DNI) levels, while the lowest was observed with Aluminium oxide nanofluid at lower DNI values. Employing Multi-Criteria Decision methods such as Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), nanofluids were ranked based on criteria including Exergy loss, Surface temperature, Overall efficiency, and Electrical efficiency, with weights of 49.58%, 28.43%, 13.45%, and 8.54% respectively. TOPSIS prioritized Graphene Oxide as the most favourable nanofluid, followed by Copper-Oxide and Cerium-Oxide, while Aluminium-Oxide received the lowest priority in optimizing PV panel performance.

Literature Review: Application of MCDM Methodology to Logistics Location Problems

This article provides a comprehensive overview of the application of Multi-Criteria Decision Making (MCDM) methods in the field of logistics location selection. It traces the historical development of MCDM methods from their inception in the 1970s to the sophisticated models and algorithms used today, highlighting the evolution from simple models to complex, intelligent algorithms that incorporate environmental and social considerations. The article emphasizes the importance of combining various MCDM methods to leverage their strengths and address their limitations. It showcases the successful application of these hybrid models in diverse scenarios, such as urban transit hub optimization, inland dry port selection, and the positioning of emergency logistics centers. The article concludes by identifying current challenges, such as handling uncertainties and the complexity of models, and suggests future directions for research, including the development of adaptive models and the integration of artificial intelligence and big data analytics to enhance decision-making in logistics location selection.

Forecasting the probability of successful deployment of internet of things and cloud computing in the building sector through consistent fuzzy preference relations method

Purpose This study aims to identify significant factors, analyse them using the consistent fuzzy preference relations (CFPR) method and forecast the probability of successful deployment of the internet of things (IoT) and cloud computing (CC) in Gujarat, India’s building sector. Design/methodology/approach From the previous studies, 25 significant factors were identified, and a questionnaire survey with personal interviews obtained 120 responses from building experts in Gujarat, India. The questionnaire survey data’s validity, reliability and descriptive statistics were also assessed. Building experts’ opinions are inputted into the CFPR method, and priority weights and ratings for probable outcomes are obtained to forecast success and failure. Findings The findings demonstrate that the most important factors are affordable system and ease of use and battery life and size of sensors, whereas less important ones include poor collaboration between IoT and cloud developer community and building sector and suitable location. The forecasting values demonstrate that the factor suitable location has a high probability of success; however, factors such as loss of jobs and data governance have a high probability of failure. Based on the forecasted values, the probability of success (0.6420) is almost twice that of failure (0.3580). It shows that deploying IoT and CC in the building sector of Gujarat, India, is very much feasible. Originality/value Previous studies analysed IoT and CC factors using different multi-criteria decision-making (MCDM) methods to merely prioritise ranking in the building sector, but forecasting success/failure makes this study unique. This research is generally applicable, and its findings may be utilised for decision-making and deployment of IoT and CC in the building sector anywhere globally.

Development of RAMDOE: a new method for rapidly ranking alternatives with supplementary options and considering changes in criteria values

This paper delves into the development and validation of the RAMDOE method, a pioneering approach in multi-criteria decision making (MCDM) that seamlessly integrates the root assessment method (RAM) and design of experiments (DOE) techniques, addressing the inflexibility of traditional MCDM methods in accommodating adjustments in criteria ranges and the addition of new alternatives without necessitating a complete overhaul of the decision framework. Through empirical analysis, the study demonstrates the RAMDOE method's remarkable efficacy in precisely ranking alternatives, as illustrated through a practical case study focused on the selection of a supplier from a pool of seven candidates. One of the most notable aspects of the RAMDOE method lies in its capacity to formulate a regression equation that accurately captures the intricate relationship between alternative scores and criteria values, enabling decision-makers to seamlessly integrate new alternatives into the decision-making process without the cumbersome task of recalibration, thereby distinguishing it from conventional MCDM techniques such as TOPSIS (technique for order of preference by similarity to ideal solution), COPRAS (complex proportional assessment), MOORA (multiobjective optimization on the basis of ratio analysis), EDAS (evaluation based on distance from average solution) and CODAS (combinative distance-based assessment). The practical implications of these findings are profound, offering decision-makers across various domains a more efficient and adaptable framework to navigate complex decision scenarios. Particularly in contexts like supplier selection, where criteria ranges may vary significantly, the RAMDOE method provides decision-makers with a robust toolset to make informed decisions, presenting a promising avenue for addressing the dynamic nature of decision-making environments and enhancing the overall robustness and flexibility of MCDM processes in real-world applications

Renewable energy supplying strategy for a greenhouse based on the water-energy-economy nexus

Nowadays, global population growth has raised issues regarding food production worldwide and greenhouses can play a critical role in addressing this issue. They increase agricultural productivity by creating controlled environmental conditions. In this research, a novel approach regarding optimizing greenhouse energy use in Iran, focusing on employing renewable energy sources is presented. It explores five cities of Tehran, Tabriz, Rasht, Bandar Abbas, and Yazd - to identify the most suitable location for a renewable-energy-driven greenhouse. The study integrates thermal modeling for environmental control within the greenhouse and calculates the required heating and cooling load which are supplied using renewable-energy driven components. The research assesses each location based on energy and water consumption, environmental impact, and cost by employing multi-criteria decision-making methods. The findings reveal Yazd, a city with hot and dry weather, as the most energy-efficient location, having 190.9 MW h of electricity consumption as well as the lowest amount of fuel consumption (18.32 Tons), while Bandar Abbas, having hot and humid weather, stands out for minimal water usage which is 2.76 m3. Therefore, it can be concluded that, this type of renewable-energy driven greenhouse would be much suitable for cities with hot and dry weather condition.

RESEARCH ON THE DYNAMICS OF THE PERFORMANCE POSITIONING OF THE TRADE IN SERBIA USING THE LOPCOW AND EDAS METHODS

Researching the dynamics of the performance positioning of trade is a very challenging, continuously current, significant, and complex issue, especially in the conditions of the application of multi-criteria decision-making methods. Based on that, this paper investigates the dynamics of the performance positioning of trade in Serbia for the period 2017 - 2022 using the LAPCOW and EDAS methods. The results of this research show the following: Serbian trade, according to the obtained results of the research on the dynamics of performance positioning using the LOPCOW-EDAS method, was the best in 2022. The following are 2021, 2020, 2019, 2018 and 2017. Overall, the performance of trade in Serbia continuously improved. The factors that influenced the improvement of the dynamics of the performance positioning of trade in Serbia are geopolitical situation, economy, inflation, interest rate, employment, standard of living of the population, trade policy and strategy, foreign direct investments, new business models (multichannel sales - store and electronic, private label, sale of organic products, etc.), concept of sustainable development, energy crisis, management of human resources, asset, capital, sales and profit, digitisation of the entire business, and others. The target dynamics of the performance positioning of trade in Serbia can be achieved by effective control of these and other influential factors.

Stakeholders’ view on selecting a prospective location for marine alternative fuels bunkering station

In times of energy transition and global efforts being made toward net zero, maritime transportation with its global fleet operating worldwide plays a key role. However, despite sufficient technology readiness in both ship- and shore-side solutions, bunkering facilities dedicated to green fuels are not widely available in seaports. With massive investments at stake, it is crucial to identify the perspectives of all potential stakeholders and the factors they find relevant for deciding on the location of bunkering infrastructure. Therefore, to select the most suitable location for such a bunkering station, combined Multi-Criteria Decision-Making methods were used along with expert elicitation. The identified criteria were subsequently validated through their application using a case study of Polish seaports. The research was conducted for marine alternative fuels in general as well as for bio-methanol, which was selected by stakeholders as having the highest potential for early implementation. In order to increase the practical application of the results, a model vessel was defined and used to verify the criteria obtained. The results of the case study indicate that the most essential decision-making criterion in both research scenarios is the reliability of the fuel supply chains. Concerning a potential location for launching bunkering stations among considered major Polish seaports is the port of Świnoujście followed by the port of Gdańsk or Szczecin depending on the case study scenario. The results of this study may be considered useful for strategic decision-makers engaged in maritime transportation, fuel supply chains, and environmental protection.

Evaluating the supply chain resilience strategies using discrete event simulation and hybrid multi-criteria decision-making (case study: natural stone industry)

ABSTRACT One of the challenges that organisations face is the risk reduction through creating resilient supply chains. There are different strategies to reach the resiliency. In this paper, we analysed five important strategies: redundancy, backup supplier, reserved capacity, reserved inventory, and increasing work-shifts. These supply chain resilience (SCR) strategies are evaluated using discrete event simulation and hybrid multi-criteria decision-making methods. For this end, Bayesian best-worst method (BWM) and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were used for weighting the SCR evaluation criteria and ranking the SCR strategies, respectively. Six discrete event simulation models were generated and run for evaluating each strategy in order to achieve quantitative criteria. On the other hand, the ideas of experts were used for weighting qualitative criteria and also ranking the alternatives. Results showed that the increasing work-shift strategy has the first rank and the reserved capacity strategy has the lowest rank.

Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) Method: A Comprehensive Bibliometric Analysis

This paper explores the evolution, applications, and prospective developments of a very popular multi-criteria decision-making (MCDM) method called Measurement of Alternatives and Ranking according to COmpromise Solution Method (MARCOS). Employing an extensive bibliometric analysis, the study examines 115 pertinent articles sourced from the Scopus database spanning over the years from 2020 to 2024. This study also provides an evaluation of the methodological significance and outlines potential future directions of MARCOS method. The outcomes indicate "Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS)" by Stević et al. (2020) as the most cited paper. Journals such as "Sustainability (Switzerland)", "Mathematics" and "Expert Systems with Applications" stand out among the most cited journals. "University of East Sarajevo" is an institution distinguished for its prolific research in this field. "Stević Ž." Has been identified as the most cited and published author. The most frequently used keywords are "MARCOS", "MARCOS method", and "MCDM". CRiteria Importance Through Intercriteria. Correlation (CRITIC) method is a weighting model often integrated with MARCOS method. The results of the study provide researchers and practitioners in the field of MCDM with an important insight into the current state of the MARCOS methodology, highlighted studies and potential future developments. It also provides a comprehensive overview of the importance of this method in the multi-criteria decision-making literature, shedding light on future research directions.