Multi-criteria Decision-making Techniques Research Articles

PEMODELAN SPASIAL DALAM MENGIDENTIFIKASI TINGKAT KERENTANAN BANJIR DI KECAMATAN MEJOBO, KABUPATEN KUDUS, JAWA TENGAH, INDONESIA

The occurrence of flood disasters is an annual unavoidable event in the Mejobo District, Kudus Regency. This study aims to map the vulnerable areas to floods in the Mejobo District using spatial modelling. The parameters used to determine the vulnerability areas include soil type, elevation, population density, river buffer, rainfall, and land use. The research location is situated in the Mejobo District. The research method employed is the models and multicriteria decision-making (MCDM) techniques, with the assistance of ArcGIS software for data processing and analysis. The research findings yield a final map depicting flood vulnerability, which indicates the distribution of flood-prone areas. Areas with a high level of flood vulnerability are dispersed across agricultural fields and areas adjacent to rivers, covering an area of 1,770 hectares. Meanwhile, areas with a moderate level of vulnerability are predominantly distributed in residential areas, covering an area of 1,907 hectares.

Selection of an alternate cementitious mortar using ceramic tile dust waste: a hybrid MCDM approach.

Selection of a suitable alternative material from a pool of alternatives with many conflicting criteria becomes a Multi-Criteria Decision Making (MCDM) problem. In the present study, ternary blended mortars were prepared using ceramic tile dust waste (CTD), fly ash (FA), and ground granulated blast furnace slag (GGBFS) as binder components. Crusher dust (CD) was used as a fine aggregate component. Binder to aggregate ratios of 1:3 and 1:1 were prepared considering suitable flow. A total of 16 mortar mixes were cast. These mortars were tested for various conflicting criteria compressive strength, flexural strength, porosity, water absorption, bulk density, thermal conductivity, specific heat, thermal diffusivity, and thermal effusivity whose weightages obtained were 29.09%, 20.08%, 12.77%, 10.60%, 8.74%, 6.74%, 5.54%, 4.47%, and 1.97%, respectively, as per AHP analysis. Later, considering these different criteria and alternate mortars, it was observed that a 1:1 mortar with 20% CTD, 30% FA, and 50% GGBFS (RC20F30G50) is found to be the suitable mortar with the highest relative closeness coefficient of 0.861 and the highest net outranking flow of 0.316 with respect to MCDM techniques: technique for order of preference by similarity to ideal solution (TOPSIS) and preference ranking organization method for enrichment of evaluations (PROMETHEE-II), respectively. The ranking of the mortar in both methods complies with the relative weightages of the criteria and the performance of the mortars with respect to the above criteria.

Ranking the Performances of Goalkeepers By WASPAS Method: AFC Asian Cup Qatar 2023

This study aims to evaluate and rank goalkeepers’ performances who competed in the Asian Cup Qatar 2023 according to some parameters. The sample size of the study was 22 goalkeepers. Data were obtained from the official website of the Asian Football Confederation (AFC) and Transfermarkt. Criteria used in the analysis to rank the performances of the goalkeepers were save percentage per match, number of catches and punches, and successful passes percentage per match. The criteria were weighted via the ranking method. Five steps of the WASPAS method among multi-criteria decision-making (MCDM) techniques were used to rank goalkeepers. According to the results of the analysis, GKs were ranked from 1 to 22 in terms of their performances, and the mean composite optimal value (Qi) was 0.696. The highest Qi value was 0.866, while the lowest was calculated as 0.310. According to the performance ranking, the top 3 GKs were determined as Rustam Yatimov (Tajikistan), Meshaal Barsham (Qatar), and Mathew Ryan (Australia). On the other hand, the lowest-ranked GKs were determined as Enando Ari (Indonesia), Zion Suzuki (Japan), and Rami Hamada (Palestine). Key Words: Goalkeeper, goalkeeper performance, ranking goalkeeper, WASPAS method, MCDM

Framework for mapping large-scale nature-based solutions for drought mitigation: Regional application in Flanders

Droughts are classified as the most expensive climate disasters as they leave long-term and chronic impacts on the ecosystem, agriculture, and human society. The intensity, frequency, and duration of drought events have increased in the past and are expected to continue rising at global, continental, and regional scales. Nature-based solutions (NBS) are highlighted as effective solutions to cope with the future impacts of these events. Despite this, there has been limited comprehensive research on the effectiveness of NBS for drought mitigation, and existing suitability mapping frameworks often overlook drought-specific criteria. To address this gap, a new framework is proposed to identify areas suitable for two drought-coping NBS types at a regional scale: detention basins and managed aquifer recharge. Two multi-criteria decision-making techniques (MCDM), i.e. Boolean logic and Analytic- Hierarchy Process (AHP), were used to map suitable large-scale NBS. The new framework accounts for unique criteria to specifically address drought conditions. By incorporating climate change scenarios for both surface and groundwater, recharge, and different groundwater characteristics, it identifies suitable and sustainable locations capable of managing extreme drought events. Executed through Boolean logic at a regional scale in Flanders (Belgium), the framework's strict approach yields significant potential areas for detention basins (298.7 km²) and managed aquifer recharge (867.5 km²). Incorporating AHP with the same criteria introduces a higher degree of flexibility for decision-makers. This approach shows a notable expansion across Flanders, varying with the level of suitability. The results underscore the highly suitable potential for detention basins (2552.2 km²) and managed aquifer recharge (2538.7 km²), emphasizing the adaptability and scalability of the framework for addressing drought in the region. The comparison between potential recharge volume due to detention basin and groundwater use in the region indicated that the detention basins could partially compensate for the high water demand. Therefore, creating a framework targeting drought is vital for the sustainable management of water scarcity scenarios.

A Hybrid Approach for the Multi-Criteria-Based Optimization of Sequence-Dependent Setup-Based Flow Shop Scheduling

A key challenge in production management and operational research is the flow shop scheduling problem, characterized by its complexity in manufacturing processes. Traditional models often assume deterministic conditions, overlooking real-world uncertainties like fluctuating demand, variable processing times, and equipment failures, significantly impacting productivity and efficiency. The increasing demand for more adaptive and robust scheduling frameworks that can handle these uncertainties effectively drives the need for research in this area. Existing methods do not adequately capture modern manufacturing environments’ dynamic and unpredictable nature, resulting in inefficiencies and higher operational costs; they do not employ a fuzzy approach to benefit from human intuition. This study successfully demonstrates the application of Hexagonal Type-2 Fuzzy Sets (HT2FS) for the accurate modeling of the importance of jobs, thereby advancing fuzzy logic applications in scheduling problems. Additionally, it employs a novel Multi-Criteria Decision-Making (MCDM) approach employing Proportional Picture Fuzzy AHP (PPF-AHP) for group decision-making in a flow shop scheduling context. The research outlines the methodology involving three stages: group weight assessment through a PPF-AHP for the objectives, weight determination using HT2FS for the jobs, and optimization via Genetic Algorithm (GA), a method that gave us the optimal solution. This study contributes significantly to operational research and production scheduling by proposing a sophisticated, hybrid model that adeptly navigates the complexities of flow shop scheduling. The integration of HT2FS and MCDM techniques, particularly PPF-AHP, offers a novel approach that enhances decision-making accuracy and paves the way for future advancements in manufacturing optimization.

The emergence of digitalization to the manufacturing sector in the sustainability context: A multi-stakeholder perspective analysis

Sustainable digitalization in industry requires extensive preplanning to integrate business strategies with the concept of digitalized sustainability. However, stakeholders often have different perspectives, making alignment more challenging. Stakeholders in the digitalized sustainable manufacturing industry must be identified and prioritized for effective implementation. Therefore, the present study combines a Systematic Literature Review and Hybrid Multi-Criteria Decision Techniques, including the Full Consistency Method and Fuzzy-Evaluation based on the Distance from Average Solution method, to prioritize stakeholders' roles in digitalized sustainable manufacturing and associated strategies. This study explores various implementation methodologies and their relations to different stakeholders. It categorizes stakeholders, such as manufacturers, research and development teams, managers, mergers and acquisitions, education programs, the 6R strategy, and eco-efficiency, to illustrate corresponding strategies. Overall, the study highlights the urgent need for meticulous planning to synchronize business strategies with digitalized sustainability while recognizing the difficulty in harmonizing different stakeholders' assessments. The research findings offer valuable insights for industry professionals aiming to navigate the complexities of sustainable digitalization successfully.

The Use of Multi-Criteria Decision-Making Techniques in The Domain of Ergonomics: A Literature Review

The Use of Multi-Criteria Decision-Making Techniques in The Domain of Ergonomics: A Literature Review

Multicriteria-based optimization of roller compacted concrete pavement containing crumb rubber and nano-silica

Abstract Roller-compacted concrete pavement (RCCP) is a brittle material with low tensile strength that does not contain steel or dowel bars. This, in addition to the rigidity of the RCCP, causes degradation or cracking before the RCCP reaches its service life. To improve the performance of the RCCP, crumb rubber (CR) can be used as an aggregate. Hence, in this study, CR was used to replace 0, 10, 20, and 30% of the fine aggregate in the RCCP. To mitigate the adverse effect of the CR on the properties of the RCCP, nano-silica (NS) was added by weight of cement in proportions of 0, 1, 2, and 3%. To select an optimal mix based on various performance criteria, multicriteria-based optimization was carried out using techniques such as order of preference by similarity to ideal solution, evaluation based on distance from average solution, weighted sum model, and weighted product model techniques. During experimentation, CR improved the consistency and reduced the mechanical and durability properties of the RCCP, while NS reduced the consistency and improved the mechanical and durability performance of the RCCP. The M2 mix (mix containing 0% CR and 1% NS) is consistently ranked as the best choice for multi-criteria decision-making techniques and sensitivity analyses due to its exceptional physical, mechanical, and durability attributes, ensuring reliability across various decision-making scenarios. This study provides insights into the decision-making process for the choice of appropriate RCCP mix produced with CR and NS for improved performance in pavement applications and the importance of utilizing waste tire rubber in concrete pavements to promote sustainability.

Optimization of Highway Performance and Safety by Integrated Multi-Criteria Decision-Making Techniques

Optimization of Highway Performance and Safety by Integrated Multi-Criteria Decision-Making Techniques

Prioritizing evaluation criteria of IoT-driven warehousing startups: a silver lining to the unorganized sector in food supply chain

PurposeThis research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain.Design/methodology/approachA blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups.FindingsThe critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups.Practical implicationsWarehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience.Originality/valueDespite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work.

Decision Support System for Ranking Active Waste Bank in Makassar City Using TOPSIS and VIKOR Methods

In the city of Makassar, there were initially around 1000 waste banks, but this number has decreased significantly, and by 2023 only 381 waste banks remain active. The decline in the number of waste banks is primarily due to the society's lack of knowledge regarding the utilization of waste banks. This research aims to rank active waste banks in Makassar using the MCDM (Multi-Criteria Decision Making) technique. Two MCDM methods will be utilized in this study: the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method and the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method. Both methods share a common goal of finding the closest value to the ideal solution, but they differ in their normalization and aggregation functions. TOPSIS calculates the criteria weight values first, followed by the criteria values, whereas VIKOR starts with the highest criteria values and then calculates the criteria weights. The results of this research indicate that some alternatives received the same ranking using TOPSIS and VIKOR methods. The criteria used to calculate data for Waste Banks are Operational Hours, Operational Schedule, Total Customers, Total Employees, and Amount of Collected Waste. These criteria are determined based on Regulation Minister of Environment and Forestry Republic of Indonesia Number 14 of 2021 concerning Waste Management at Waste Banks.

Evaluation of carbon capture technologies in the oil and gas industry using a socio-technical systems perspective-based decision support system under interval type-2 trapezoidal fuzzy set

Concerns in relation to consequences of global warming and climate change have activated worldwide attempts for mitigating the concentration of carbon dioxide (CO2) produced by the industrial sector. Decarbonizing the oil and gas refining (OGR) industries is a challenging problem for policy-makers owing to its potential to prevent economic, environmental, and health risks. In this regard, CO2 capture, utilization, and storage (CCUS) technologies are the most encouraging options to decarbonize. The technologies related to the part of CO2 capture can play a vital role in solving the mentioned problem. Various technologies have been employed for CO2 capture, and choosing the appropriate technology is a complex multi-criteria decision-making (MCDM) issue. This work develops a novel and robust decision support system (DSS). The DSS integrates MCDM techniques of the Delphi and Entropy integration method (DAEIM) and complex proportional assessment of alternatives (COPRAS) method with the interval type-2 trapezoidal fuzzy (IT2TF) environment. The proposed DSS is used to evaluate, prioritize, and choose technologies for CO2 capture. A hybrid criteria system, which involves elements of socio-technical systems perspective has been used for evaluating the candidate technologies. For implementing the DSS of this work, five capture technologies of post-combustion (A_cc1), pre-combustion (A_cc2), oxy-fuel combustion (A_cc3), direct air capture (A_cc4), and indirect air capture (A_cc5) have been chosen for evaluation. The final value of each technology is A_cc1 (0. 2907), A_cc2 (0.2602), A_cc3 (0.1005), A_cc4 (0.2304), and A_cc5 (0.1181) and the preferences of the technologies are A_cc1> A_cc2> A_cc4> A_cc5> A_cc3. The evaluation findings reveal that post-combustion technology with the value of 0.2907 is the most suitable scenario for the capture of CO2 emissions from Iran's OGR systems. The computation results demonstrate that the suggested DSS is feasible and applicable and give reliable and robust findings for acquiring the optimal CO2 capture technology.

Multi-criteria decision-making techniques for groundwater potentiality mapping in arid regions: A case study of Wadi Yiba, Kingdom of Saudi Arabia

Freshwater demand is further stressed due to the impacts of climate change and the rapid expansion of the population, particularly in dry and extremely dry regions where groundwater serves as a crucial water source. This study employed various multi-criteria decision making (MCDM) techniques to identify areas with potential for groundwater in Wadi Yiba, Kingdom of Saudi Arabia (KSA). A total of 14 parameters, including Geology, land use and land cover (LULC), Drainage Density (DD), Lineament density (LD), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), rainfall, and others, were selected to create thematic layers for the delineation of groundwater potential zones (GWPZ). Using three MCDM techniques, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacijaik Ompromisno Resenje (VIKOR), and Evaluation Based on Distance from Average Solution (EDAS) methods, maps were generated to indicate the GWPZ in the study area. These maps were categorized into five categories: very low, low, medium, high, and very high. Results indicate that the GWPZ assessed by TOPSIS in Wadi Yiba categorizes 3% of the area as having very low groundwater potential, 9% as low, 29% as medium, 36% as high, and 23% as very high. In contrast, VIKOR classified the study region as 7%, 20%, 18%, 28%, and 26% for the five classes; while the EDAS study reveals a more balanced distribution, with 5.81%, 24.80%, 33.74%, 22.32%, and 13.33% for the five classes. We evaluated the accuracy of the final maps using the receiver operating characteristic-area under the curve (ROC-AUC) evaluation method. TOPSIS exhibited the highest accuracy (0.703), closely followed by VIKOR (0.701) and EDAS (0.557). TOPSIS and VIKOR were rated "excellent," while EDAS as "good". The findings are crucial for sustainable groundwater management in Wadi Yiba and underscore the importance of selecting appropriate methods in environmental modeling.

Multi-Criteria Decision-Making Methods: A Case of Software Vendor Selection

Various multi-criteria decision-making methods have been utilized in literature to address vendor selection challenges in various contexts. This study reflects on these decision-making techniques for vendor selection in the software ecosystem during software outsourcing. For the same, firstly, a requirement framework for the decision-making process for software vendor selection is proposed. Afterwards, five selected different multi-criteria decision-making techniques are compared and critically analysed against this requirement framework. This study supports software practitioners and decision-makers by providing information about which decision-making method to choose, considering the trade-off between the benefits and drawbacks of adopting each method or using hybrid approaches by combining two or more techniques with fuzzy logic. It is found that decision-making techniques generally lack in modelling the problem itself and handling the conflicts and uncertainties that may prevail during the decision-making process. Furthermore, it has been identified that artificial neural networks (ANN) and analytic network process (ANP) can support the dependencies between alternatives and criteria while handling complex interactions in the software ecosystem. On the other hand, due to its comparative ease of use, analytic hierarchy process (AHP) can serve better in scenarios where decision-makers are not experienced, or past data to train the ANN or ANP models is unavailable.

Blending Uncertainty Theory Innovative into Decision Support Framework for Selecting Agricultural Machinery Suppliers

The rivalry among suppliers and stakeholders created pressure, making the selection of the best provider a crucial subject based on a set of criteria for the responsiveness of clients. Consequently, several criteria need to be considered to select the most suitable supplier. As a result, certain criteria may overlap and contradict one another. Multi-criteria decision-making (MCDM) techniques are widely used in many fields to address selection problems when there are numerous competing criteria and multiple alternatives. Hence, we are leveraging MCDM techniques in constructing a decision support framework (DSF) as MEthod based on the Removal Effects of Criteria (MEREC) and Multi-Attributive Border Approximation Area Comparison (MABAC). In our DSF, we harnessed the uncertainty theory of triangular neutrosophic sets (TrNSs) which is considered one of the advantages that DSF provides.TrNSs support experts in their judgments when facing problems such as imprecise judgments and insufficient data. As well, we applied the constructed DSF in a real case study for five suppliers for machinery agriculture and evaluated the suppliers based on ten criteria. The DSF’s findings indicated that supplier 5 is the optimal one for machinery agriculture otherwise supplier 2 is the worst one. Also, we applied another ranker technique of MCDM technique entailed in the weighted sum model (WSM) and compared WSM results with MABAC under the authority of TrNSs.

An Analysis of the Círcular Economy in Europe through Comparative Research Employing the CRITIC-Based MAUT and COPRAS Methods

Purpose: The circular economy is an approach to sustainability that takes into account both the economic and environmental benefits of repurposing waste or underutilized inert assets into new, useable products or services. The research aims to present a comparative analysis of EU countries with multi-criteria decision-making methods using various indicators covering many topics, including emphasis on recycling, use of circular materials, material efficiency and collective management of waste. Methodology: The research aims to determine the weights through the application of the CRITIC method. It is gathered in 22 sub-indicators under three main categories, including sustainable resource management, social behaviour, and business operations, so as to assess effectiveness with regard to this notion while comparing nations. Additionally, every nation in the EU that has indicator data is assessed employing cluster analysis, a method of data mining, in addition to multi-criteria decision-making techniques like MAUT and COPRAS. Findings: Based on the assessments of nations, Denmark, Luxembourg, Finland, Austria, Sweden, the United Kingdom, and Germany exhibit a favourable and noteworthy distinction from other nations. Originality: This study offers the opportunity to make comparisons with a multi-criteria decision-making approach when it comes to environmental and circular economy goals.

Investigation of rank order centroid method for optimal generation control.

Multi-criteria decision-making (MCDM) presents a significant challenge in decision-making processes, aiming to ascertain optimal choice by considering multiple criteria. This paper proposes rank order centroid (ROC) method, MCDM technique, to determine weights for sub-objective functions, specifically, addressing issue of automatic generation control (AGC) within two area interconnected power system (TAIPS). The sub-objective functions include integral time absolute errors (ITAE) for frequency deviations and control errors in both areas, along with ITAE of fluctuation in tie-line power. These are integrated into an overall objective function, with ROC method systematically assigning weights to each sub-objective. Subsequently, a PID controller is designed based on this objective function. To further optimize objective function, Jaya optimization algorithm (JOA) is implemented, alongside other optimization algorithms such as teacher-learner based optimization algorithm (TLBOA), Luus-Jaakola algorithm (LJA), Nelder-Mead simplex algorithm (NMSA), elephant herding optimization algorithm (EHOA), and differential evolution algorithm (DEA). Six distinct case analyses are conducted to evaluate controller's performance under various load conditions, plotting data to illustrate responses to frequency and tie-line exchange fluctuations. Additionally, statistical analysis is performed to provide further insights into efficacy of JOA-based PID controller. Furthermore, to prove the efficacy of JOA-based proposed controller through non-parametric test, Friedman rank test is utilized.

Natural refrigerant mixtures in low-charge heat pumps: An analysis of the potential for performance enhancements

The study investigated the characteristics of different natural refrigerants and their mixtures in a residential heat pump with low refrigerant charge. Three main evaluation criteria were utilized for comparing different mixtures: Coefficient of Performance (COP), Volumetric Heating Capacity (VHC), and a newly proposed indicator, the heating capacity at charge limit. Propane was used as a reference refrigerant. It was found that some mixtures significantly improved both COP and the heating capacity at charge limit while maintaining similar volumetric heating capacity and operating conditions. Alternative multi-criteria decision-making techniques were adopted to rank the best refrigerant mixtures. Mixtures rich in Dimethyl Ether (DME), such as DME-CO2 [0.96-0.04] and DME-Propylene [0.75-0.25] were found consistently among the best options. Those were followed by Propylene-rich mixtures such as Propylene-CO2, Propylene-Isobutane and Propylene-Butane. The levelized cost of heat (LCOH) could be improved by up to 12 %. To accelerate the transition to natural refrigerants, without compromises on efficiency and costs, further research and certification activities on non-fluorinated refrigerants based on Dimethyl Ether (R-E170), CO2 (R-744) and Propylene (R-1270) are recommended.

The use of ASTER and landsat remote sensing data for exploring hydrothermal mineral in Arabian-Nubian Shield

Multispectral remote sensing data provided valuable information for hydrothermal minerals exploration. In this article, Landsat-8 Operational Land Imager (OLI) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were processed and analyzed using band ratio and principal component techniques to demarcate areas of hydrothermal mineral resources by highlighting the most influenced zone of hydrothermal alteration (high-grade) in Egypt. The area is a part of the Arabian-Nubian Shield, covering 1068 km2, and comprises wide varieties of Pre- and Pan-African rock assemblages. Preparing and integrating thematic layers consisting of the grade of hydrothermal intensity in a GIS technique supported generating a hydrothermal mineral predictive map (HMPM) using a multi-criteria decision-making technique. The plausible zone of very high mineral resources covers about 6.57 %. Analysis of ASTER data revealed that this zone is reliable with phyllic and argillic minerals and iron-oxides. The results of OLI data helped in demarcating potential areas of hydrothermal alteration areas (Al–OH) mixed with iron oxides. Field observations and investigations enabled the validation of the HMPM. The spatial distribution of hydrothermal zones throughout the study was reliable to that observed in the mine areas. Field and ore microscopic examination verified the existence of Au-sulfide minerals connected with hydrothermal alteration zones (HAZs) in different areas e.g., Abu-Marawat, Gidami, Semna, Abu-Gaharish, and Hamama mines. Overall, integrating multi-spectral data through overlay analysis techniques can provide valuable information about hydrothermal mineral resources in an accurate and cost-effective approach.

Decision-Making Support in Vehicle Routing Problems: A Review of Recent Literature

The Vehicle Routing Problem (VRP) involves a set of customers with known locations, each having a certain demand for goods or services. There is also a fleet of vehicles available, each with limited capacity and often a fixed starting point. With the aid of mathematical programming tools, this paper offers an overview of the most recent VRP research. This study also examines the algorithms for solving VRP models and categorizes them in terms of their application areas. For these reasons, related publications that appeared in the international journal have been compiled and studied. According to the literature review, multi-criteria decision-making (MCDM) techniques have yet to constitute the most mathematical programming methods used to solve the VRP problem.