Multi-criteria Decision Research Articles

Performance Evaluation of Turkish Ports: Integrated Fuzzy Entropy- Fuzzy MARCOS Analysis

Purpose: The aim of this study is to propose the Fuzzy Entropy based Fuzzy MARCOS method to solve the Multi Criteria Decision Making (MCDM) problem, which involves analyzing the performance of Turkish ports according to quantitative evaluation criteria. Methodology: The uncertainty of quantitative criteria is based on the different values they take at different time periods. To overcome this problem, in this study, the importance levels of the criteria were determined by the Fuzzy Entropy method. Then, 11 port alternatives with a share of over 1% in transportation in Turkish ports were ranked according to their performance using the Fuzzy Measurement of Alternatives and Ranking to Compromise Solution (MARCOS) method. Findings: According to the analysis results, the most important evaluation criterion used in the performance evaluation of container ports, that is, the criterion with the highest weight, is the “port area” criterion. The port with the highest performance value among the ports is Kocaeli port. This method can provide a more accurate evaluation of the performance level of ports and its use in the planning and effective use of port investments. Originality: This research fills the gap in the literature in three ways: (1) Evaluatee the application of triangular fuzzy numbers to the panel data, which will provide effective inferences about the performanse level of the selected ports, (2) Evaluated a weighting approach using Entropy method that takes into account the distances of triangular fuzzy numbers consisting of real numbers instead of linguistic expressions, (3) An Entropy-based MARCOS method is proposed for solving the Multi-Criteria Decision Making (MCDM) problem involving the performanse analysis of Turkish ports.

How Criteria Weights Influence Performance in Evaluating Logistic Productivity: An Application in the Emerging Markets Logistics Index

Purpose: The differences between the criteria affecting the logistics performance of countries and their importance levels are meaningful in terms of policy development processes. It has been determined that the criteria are weighted equally in the emerging markets logistics index. For this reason, the study reweighted the criteria of the Emerging Markets Logistics Index and investigated the effects of weighting on the ranking. In this respect, the study aims to make the index more objective. Methodology: In the study, Multi-Criteria Decision Making methods were utilized. Within this context, MEREC (Method Based on the Removal Effects of Criteria) was used to determine the criteria weights, while MABAC (Multi Attributive Border Approximation Area Comparison) and MAIRCA (Multi Attributive Ideal Real Comparative Analysis) methods were preferred to rank the alternatives. Findings: In the study, it was concluded that the weighted values of the criteria are more consistent with the literature. Additionally, the new weights obtained have an effect on the ranking values of the countries. Orginality: It is important that emerging markets provide an opportunity to develop infrastructure to increase logistics productivity and provide a platform for the implementation of new technologies in logistics operations. Furthermore, these markets enable the diversification and development of logistics services through the expanding consumer demand. This study differs from other studies in the literature because it preferred the Agility Emerging Markets Logistics Index (AEMLI) instead of the Logistic Performance Index (LPI) and used MEREC-based MABAC-MAIRCA methods.

Determination of Best Input Parameters for Internal Grinding SKD11 Tool Steel using MCDM

This article presents the results of an optimization study on the determination of the best input parameters for the internal grinding process when processing cylindrical-shaped parts of SKD11 tool steel. For this purpose, three Multi-Criteria Decision Making (MCDM) methods including the Evaluation based on Distance from Average Solution (EDAS), Multi-Attributive Border Approximation Area Comparison (MABAC), and Multi Attribute Utility Theory (MAUT) were used to solve the MCDM problem, while the entropy method was applied to find the criteria weights. Three objectives, namely the Surface Roughness (SR), Material Removal Rate (MRR), and wheel life (Tw), were also investigated. Six input factors, involving the coarse dressing depth (ar), coarse dressing times (nr), fine dressing depth (af), fine dressing times (nf), non-feeding dressing (n0), and dressing feed rate (Sd), were examined. Additionally, the Taguchi method with the L16 (44+22) design and the Minitab R19 program were deployed to design this study’s experiment and investigate its outcomes. The MCDM work was successfully solved and the best process factors are proposed.

Application of Multi Criteria Decision Making Methods for the Determination of the Best Dressing Factors for Surface Grinding Hardox 500

This study applies Multi-Criteria Decision-Making (MCDM) methods to identify the optimal dressing parameters for the surface grinding of Hardox 500 steel. The investigation focuses on three key objectives: Surface Roughness (SR), Material Removal Rate (MRR), and Wheel lifespan (Lw). Five dressing variables were considered: non-feeding dressing (nn), fine dressing depth (df), fine dressing times (nf), rough dressing depth (dr), and rough dressing times (nr). Three MCDM methods—Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Simple Additive Weighting (SAW), and Evaluation based on Distance from Average Solution (EDAS)—were employed to solve the MCDM problem. Additionally, the Entropy technique was used to determine the criterion weights. A total of 16 experimental runs were conducted based on the L16 (44 x 21) design configuration. The analysis identified Option 7 as the optimal dressing mode, characterized by the input parameters: dr = 0.02 mm, nr = 3 times, df = 0.05 mm, nf = 3 times, and nn = 0. To validate the consistency of rankings obtained from the three MCDM methods, the Spearman’s rank correlation coefficient (R) was employed. The results demonstrated a strong correlation among the rankings, confirming the reliability of the proposed approach. These findings provide a robust framework for optimizing surface grinding parameters to enhance performance and productivity.

Hybrid Multi-Criteria Decision Making Methods: Combination of Preference Selection Index Method with Faire Un Choix Adèquat, Root Assessment Method, and Proximity Indexed Value

This study presents an investigation into the hybridization of Multiple Criteria Decision Making (MCDM) methods. The Preference Selection Index (PSI) method is used in two distinct ways: first, for its traditional purpose of ranking alternatives, and second, to calculate criteria weights. These criteria weights are utilized to rank the alternatives provided by other MCDM methods, including the Faire Un Choix Adéquat (FUCA), Root Assessment Method (RAM), and Proximity Indexed Value (PIV), resulting in the creation of three hybrid models: FUCA-PSI, RAM-PSI, and PIV-PSI. The effectiveness of these hybrid approaches is tested by ranking 20 Vietnamese cities based on their digital transformation efforts. The results demonstrate that the hybrid approaches produce a highly correlated ranking, as evidenced by the Spearman rank correlation coefficient found among these methods, with the lowest being 0.8571. Both the PSI method and the three hybrid models identified the same top alternative, confirming the reliability and accuracy of the rankings.

Evaluating the Impact of Weighting Methods on the Stability of Scores for Alternatives in Multi-Criteria Decision-Making Problems

Criteria weights play a crucial role in Multi-Criteria Decision Making (MCDM) problems when selecting the best alternative from a set of options. This study aims to compare three objective weighting methods: MEthod based on the Removal Effects of Criteria (MEREC), Entropy, and Symmetry Point of Criterion (SPC). These methods were applied to a case study involving the ranking of eight sustainable energy development alternatives, each characterized by seventeen criteria. Four representative MCDM methods, the Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Proximity Indexed Value (PIV), and Root Assessment Method (RAM), were also deployed. The results revealed that the Entropy method provided the most stable and consistent performance, followed by the MEREC method, with the SPC method showing the least stability.

Determination of Best Input Factors in Powder-Mixed Electrical Discharge Machining 90CrSi Steel using Multi-Criteria Decision Making Methods

This article outlines the results of a Multi-Criteria Decision Making (MCDM) analysis conducted on the Powder-Mixed Electrical Discharge Machining (PMEDM) process for cylindrical parts fabricated from 90CrSi tool steel, using graphite electrodes. The study aims to identify the optimal input factors to simultaneously minimize Surface Roughness (SR) and Electrode Wear Rate (EWR), while maximizing Material Removal Speed (MRS). Five input factors were selected: powder concentration (CP), pulse-on time (Ton), pulse-off time (Toff), pulse current (IP), and servo voltage (SV). Experimental data were generated using the Taguchi method with an L18 design. The optimization process was performed using the Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Evaluation by an Area-based Method of Ranking (EAMR) methods. Criteria weights were calculated utilizing the Entropy and the Multi-Expert Ranking Evaluation with Compensation (MEREC) techniques. The analysis identified the best PMEDM input factor, providing an optimal solution for enhancing the efficiency of machining cylindrically shaped components.

Multi-Objective Optimization of a Two-Stage Helical Gearbox with Two Gear Sets in First Stage to Reduce Volume and Enhance Efficiency using the EAMR Technique

This study presents the results of a work employing the Evaluation by an Area-based Method of Ranking (EAMR) methodology to address the Multi-Objective Optimization Problem (MOOP) of a two-stage helical gearbox comprising two gear sets in the initial stage. The objective of this study is to identify the most critical design parameters for minimizing the volume of the gearbox while optimizing its efficiency. In this study, three key design parameters were selected for analysis: the wheel face width coefficients Xba for the first and second stages, as well as the gear ratio of the first stage u1. Furthermore, the EAMR technique was employed to address the Multi-Criteria Decision-Making (MCDM) challenge, with the entropy method used to ascertain the weight criterion for resolving the MOOP. The study's findings offer valuable insights into the optimal values for three primary design parameters, which are essential for the development of a two-stage helical gearbox with two gear sets in the initial stage.

Barriers to adoption of reusable packaging from supply chain perspective: multi criteria decision making analysis and industry 4.0 strategies

Barriers to adoption of reusable packaging from supply chain perspective: multi criteria decision making analysis and industry 4.0 strategies

A Standardized Measurement and Valuation Scale of Genomic Utility for Policy Decisions: The GUV Scale.

The multifaceted ways in which genomics can be valuable to clinicians, patients, families, and society are important for informing prioritization decisions by policy makers. This study aims to develop a standardized, cumulative, and preference-weighted genomic utility valuation (GUV) on a scale of 0% to 100%. A multicriteria decision analysis was conducted with experts involved in policy, clinical, research, and consumer advocacy leadership in Australia for the valuation of policy priority indicators of genomic utility. The use of the GUV scale to support policy decisions is illustrated through a stylized example, and benchmark scoring thresholds of genomic utility were identified by mapping evidence from real-world health technology assessments leading to the public reimbursement of genomic testing in Australia onto the GUV scale. In total, 33 (73%) invited experts participated in the study. Clinical utility had the highest priority, followed by societal, diagnostic, economic, and family utilities. Improving health outcomes had the highest preference value (29.5%), followed by improving equity (22.6%), Having high diagnostic yield (22.2%), improving symptom management (15.5%), being cost saving (14.3%), having average diagnostic yield (13.1%), enabling access to clinical trials (12.3%), and enabling reproductive family planning (11.5%). Genomic testing scores from real-world health technology assessments ranged from 46% for syndromic and nonsyndromic intellectual disability to about 60% for mitochondrial conditions and genetic kidney diseases. Comparisons of genomic utility across different clinical contexts may seem difficult because of the multiple criteria required to be weighted to support policy decisions. This comparison is now facilitated in a standardized manner with the GUV scale.

Analytic hierarchy process geographic information system based model for sustainable construction and demolition waste landfill site selection

Properly managing waste generated by buildings and public works is a significant challenge in Tunisia, particularly in the city of Bizerte. The inadequate disposal of such waste can cause substantial harm to human life, property, and the environment. This paper proposes an multi-criteria decision making (MCDM) that utilizes the analytic hierarchy process (AHP) decision support tool to identify suitable landfill sites for construction and demolition waste (CDW) in Bizerte. The AHP method is widely used in MCDM applications. The approach involves classifying different scenarios based on various exclusion and appreciation criteria to determine the optimal locations for future landfills. Furthermore, the paper develops a conceptual approach for identifying better sites for the disposal of CDW, resulting in a comprehensive database capable of storing, accessing, and extracting information at both conceptual and operational levels. The proposed model considers spatial, technical, and environmental criteria in the selection of a suitable landfill site. The proposed methodology offers an effective and practical solution for properly managing CDW waste in Bizerte, Tunisia, and can be applied to other regions facing similar challenges.

Strategic evaluation of European Inland transport companies for European green deal compliance: A multi-criteria decision making-analysis.

The purpose of this study is to evaluate the readiness of European inland transport companies for the European Green Deal by analyzing the causal relationships between key factors essential for EGD implementation. The research identifies six critical factors: Investment in Green Technologies, Policy Alignment and Compliance, Stakeholder Collaboration, Resource Management, Employee Training and Engagement, and Emission Reduction Initiatives. This research offers the first integrated evaluation of EGD readiness utilizing a combination of fuzzy-DEMATEL, multiple regression analysis, and Structural Equation Modeling aimed at uncovering systemic interactions among the factors in the system. The study revealed Resource Management and Employee Training as significant drivers in enhancing the readiness, highlighting the role of human capital and resource optimization in environmental sustainability. Expert panel, consisting of 19 industry representatives selected through purposive sampling, provided insights on the effective relationships which offer practical strategies for managers to prioritize resource management and employee engagement to achieve Green Deal compliance and guiding more strategic and efficient allocation of resources to enhance alignment with European Green Deal stipulations.

DecideXpert: Collaborative system using AHP-TOPSIS and fuzzy techniques for multicriteria group decision-making

decideXpert: Collaborative system using AHP-TOPSIS and fuzzy techniques for multicriteria group decision-making

Optimizing building energy solutions: A guideline for choosing proper multi criteria decision making methods in energy problems

Optimizing building energy solutions: A guideline for choosing proper multi criteria decision making methods in energy problems

Mapping the social-ecological suitability of agroforestry in the US Midwest

Abstract Agroforestry practices offer the potential to increase agricultural sustainability, but their adoption remains limited, especially in some of the world’s most highly productive regions like the United States (US) Midwest. Integrated assessment of the environmental, social, and economic factors that determine agroforestry’s potential benefits and social acceptability is needed to effectively target agroforestry expansion. To meet this need, we used geospatial multi-criteria decision analysis to map agroforestry’s suitability across the US Midwest. We mapped priority areas where agroforestry is expected to reduce the risk of environmental degradation, provide productive tree growth, and be socially and economically viable. We show that integrating social and economic factors dramatically shifted priorities compared to an environmental-only suitability assessment. Using COMET-Planner, we estimated that expanding agroforestry to the top 5% most suitable land from our analysis (totaling 18.3 million acres) has the potential to store 43 [29–58] Mt of CO2e per year over the business-as-usual scenario. Our integrated approach can help stakeholders identify target areas for agroforestry and provides a theoretical foundation for interdisciplinary suitability mapping that can be adapted for use in other global regions.

Multicriteria Decision Analysis Coupled with Type-2 Fuzzy Bi-Level Programming for Water Resource Optimization: A Case Study of the Jiulong River Basin, China

Multicriteria Decision Analysis Coupled with Type-2 Fuzzy Bi-Level Programming for Water Resource Optimization: A Case Study of the Jiulong River Basin, China

Prioritising the location of gold refining facility in Nigeria: An application of advanced multi-criteria decision making methods based on Fermatean fuzzy sets

Prioritising the location of gold refining facility in Nigeria: An application of advanced multi-criteria decision making methods based on Fermatean fuzzy sets

Evaluation of simultaneous energy production and biotic and abiotic nitrate removal in microbial fuel cells (MFCs): Selection of the most suitable MFC by a multi-criteria decision methodology

Evaluation of simultaneous energy production and biotic and abiotic nitrate removal in microbial fuel cells (MFCs): Selection of the most suitable MFC by a multi-criteria decision methodology

Multicriteria decision making-based approach to classify loose-leaf teas

Multicriteria decision making-based approach to classify loose-leaf teas

Multi-objective optimization and posteriori multi-criteria decision making on an integrative solid oxide fuel cell cooling, heating and power system with semi-empirical model-driven co-simulation

An integrative solid oxide fuel cell combined cooling, heating and power system in green buildings with hydrogen energy of byproduct water enables carbon neutrality transformation. However, underlying mechanisms on capacity sizing of combined cooling, heating and power system devices and its impacts on system techno-economy have not been figured out especially considering dynamic degradation and efficiency of associated devices. In this study, a multi-software optimization platform is established by MATLAB-TRNSYS co-simulation for sizing parametrical analysis, with well balance of modelling complexity and computational efficiency. A self-sufficient combined cooling, heating and power system is modelled integrating with a semi-empirical surrogate model of solid oxide fuel cell to interact with other balance of plant types efficiently. Total energy efficiency and annual total cost are optimized through parametrical analysis on device size of each component (battery, electrolyzer and solid oxide fuel cell) and analysis of variance for contribution ratio quantification. Results indicate that, the size increase in electrolyzer and solid oxide fuel cell will improve system total energy efficiency by 13.635 % and 2.194 %, but promote annual total cost by 4.042 × 104 $ and 2.389 × 103 $, respectively. Besides, sensitivity analysis indicates that the electrolyzer size prioritizes other design parameters in techno-economic performance. Optimal sizes of battery, electrolyzer and solid oxide fuel cell are in cell number range of 333 – 403, 17 – 20, and 26 – 30, respectively, with corresponding optimal total energy efficiency and annual total cost at 70.861 % – 72.147 % and 6.723 × 104 $ – 7.325 × 104 $, respectively. The research results can provide guidance on hydrogen-based cooling, heating and power system design and operation with techno-economic feasibility for low-carbon district energy transition.