Multiple Criteria Decision-making Model Research Articles

A Novel Hybrid Fuzzy Multiple-Criteria Decision-Making Model for the Selection of the Most Suitable Land Reclamation Variant at Open-Pit Coal Mines

The expansion of the open-pit exploitation of mineral raw materials, and especially the energy resources of fossil fuels, makes open-pit coal mines spatially dominant objects of large mining basins. Exploitation activities are accompanied by negative ecological impacts on the environment, which requires the integral planning, revitalization, reclamation, and rehabilitation of the disturbed area for human use in the post-exploitation period. The post-exploitation remediation and rehabilitation of open-pit mining areas and disposal sites, i.e., space disturbed by mining activities and accompanying facilities, are complex synthetic multidisciplinary multiphase engineering project tasks. In this paper, a hybrid fuzzy MCDM model (Multiple-Criteria Decision-Making) was developed for the selection of a reclamation solution for the Tamnava-West Field open-pit mine. IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) was applied to define the weights of 12 criteria of different structures used in the evaluation of reclamation solutions. The Fuzzy ROV (Range of Value) method was applied to select the reclamation solution from a total of 11 solutions previously obtained using a process approach. The results of the hybrid IMF SWARA—Fuzzy ROV model show that forestry is the best solution for the Tamnava-West Field open-pit mine. After the results had been obtained, verification analyses of the proposed model were performed and the best stable proposed reclamation solution was determined.

A Novel Hybrid Gray MCDM Model for Resilient Supplier Selection Problem

The current business climate has generated considerable uncertainty and disrupted supply chain processes. Suppliers have frequently been identified as the primary source of hazards responsible for supply chain disruptions. Using a strategic approach to supplier selection that prioritizes providers with resilience features, mitigating the risk exposure inherent in supply chains is possible. This study proposes a comprehensive gray multiple-criteria decision making (MCDM) method incorporating resilience attributes to supplier selection. To determine criteria weights, the gray PSI and gray BWM methodologies were used, and to evaluate and prioritize resilient providers, the gray MCRAT and gray COBRA methodologies were applied. According to the results obtained by the suggested methodology, the supplier that demonstrated the greatest degree of resilience was determined to be the provider categorized as SPIR 4. The sequential sequence of the SPIR numbers is as follows: SPIR 5, SPIR 1, SPIR 3, SPIR 2, and SPIR 6. The data demonstrate that the developed approach produced accurate results.

Z-number based fuzzy MCDM models for analyzing non-traditional security threats to finance supply chains: A case study from Vietnam

Non-traditional security (NTS) threats have a vast and profound impact on many aspects of economic, political, social, and many other areas, especially supply chain finance (SCF), particularly in countries like Vietnam, which potentially affects the economic efficiency of businesses' supply chain financial, thereby affecting the general economy of the country and the world. In order to prevent and minimize the negative impacts caused by NTS threats to SCF, this study was conducted to identify NTS threats affecting SCF in Vietnam, at the same time calculate the weight of the impact level and find out the cause and effect relationship between them. Solution strategies are also proposed and ranked, thereby serving as a reference basis for relevant parties to choose appropriate response solutions. Due to the multi-criteria nature of NTS threats, the multiple criteria decision-making (MCDM) method is used in combination with the Z-number concept and Fuzzy set theory to approach the problem of certainty and increase the accuracy of study. The NTS threats are first identified through a literature review and then validated for suitability using the DELPHI technique (DELPHI). Suitable threats will be determined by relationship, weighted by Decision Making Trial And Evaluation Laboratory (DEMATEL) method. Proposed strategies are ranked using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The results indicate that there are 19 NTS factors affecting SCF in Vietnam, and the global economic downturn, pandemic and health crisis, financial crisis and cybersecurity risk are the four root cause factors with the most decisive influence. Businesses and concerns need to prioritize addressing these four threats because they not only have a strong impact but also entail many other threats. The two strategies considered to be the most effective are a sustainable practice and a risk-hedging strategy. Businesses, governments, and stakeholders also should pay attention to the macroeconomic environment, technology, and environment and build sustainable businesses, regularly monitoring economic fluctuations and creating plans to prevent risks.

Performance Assessment of Different Sustainable Energy Systems Using Multiple-Criteria Decision-Making Model and Self-Organizing Maps

The surging demand for electricity, propelled by the widespread adoption of intelligent grids and heightened consumer interaction with electricity demand and pricing, underscores the imperative for precise prognostication of optimal power plant utilization. To confront this challenge, a dataset centered on issue-centric power plans is meticulously crafted. This dataset encapsulates pivotal facets indispensable for attaining sustainable power generation, including meager gas emissions, installation cost, low maintenance cost, elevated power generation, and copious resource availability. The selection of an optimal power plant entails a multifaceted decision-making process, demanding a systematic approach. Our research advocates the amalgamation of multiple-criteria decision-making (MCDM) models with self-organizing maps to gauge the efficacy of diverse sustainable energy systems. The examination discerns solar energy as the preeminent MCDM criterion, securing the apex position with a score of 83.4%, attributable to its ample resource availability, considerable energy generation, nil greenhouse gas emissions, and commendable efficiency. Wind and hydroelectric power closely trail, registering scores of 75.3% and 74.5%, respectively, along with other energy sources. The analysis underscores the supremacy of the renewable energy sources, particularly solar and wind, in fulfilling sustainability objectives and scrutinizing factors such as cost, resource availability, and the environmental impact. The proposed methodology empowers stakeholders to make judicious decisions, accentuating facets that are required for more sustainable and resilient power infrastructure.

ASSESSING THE DIFFUSION OF FINTECH INNOVATION IN FINANCIAL INDUSTRY: USING THE ROUGH MCDM MODEL

We develop a conceptual structure to explore how financial technology (FinTech) innovation is being implemented to deal with vague, inconsistent and ambiguous knowledge in actual world. The structure of this study is built upon the technology, organization, environment (TOE) context, which one uses the concept of multi-criteria estimation to measure the significance of FinTech innovation. We develop an integrated MCDM (multiple criteria decision-making) model through rough set theory help administrators obtain a strategic influence relation map for enhancing performance approaching towards the aspiration value. This model involves three steps: primary, we apply this rough number to define group views which reflect upon experts’ real experiences; second, we use the rough DEMATEL-based ANP-(RDANP) to acquire the rough influential weights and rough influential network relationship map (RINRM) based on this TOE structure and its corresponding attributes; finally, we utilize the rough modified VIKOR with the influence to analyze the gap between the performance value and the aspirated level. The empirical case was originated from financial industry in Taiwan. According to the weighting results the expected benefits, technology integration, and competitive pressure were the most important criteria. Our results also illustrate how FinTech innovation can be used for promoting financial services.

Optimizing health service location in a highly urbanized city: Multi criteria decision making and P-Median problem models for public hospitals in Jeddah City, KSA.

Rapid urbanization and population growth have increased the need for optimizing the location of health services in highly urbanized countries like Kingdom of Saudi Arabia (KSA). This study employs a multiple-criteria decision making (MCDM) approach, e.g., fuzzy overlay technique by combining the P-Median location-allocation model, for optimizing health services. First, a geodatabase, containing public hospitals, road networks and population districts, was prepared. Next, we investigated the location and services of five public hospitals in Jeddah city of KSA, by using a MCDM model that included a fuzzy overlay technique with a location-allocation model. The results showed that the allocated five hospitals served 94 out of 110 districts in the study area. Our results suggested additional hospitals must be added to ensure that the entire city is covered with timely hospital services. To improve the existing situation, we prioritized demand locations using the maximize coverage (MC) location problem model. We then used the P-Median function to find the optimal locations of hospitals, and then combined these two methods to create the MC-P-Median optimizer. This optimizer eliminated any unallocated or redundant information. Health planners can use this model to determine the best locations for public hospitals in Jeddah city and similar settings.

Application of fermatean fuzzy weight operators and MCDM model DIBR-DIBR II-NWBM-BM for efficiency-based selection of a complex combat system

The use of complex automated combat systems is a basic feature of every modern combat operation, and all modern armies strive to implement such systems in their units. This paper investigates the choice of a complex combat system based on the efficiency criteria that condition it. The Defining Interrelationships Between Ranked criteria (DIBR) and the Defining Interrelationships Between Ranked criteria II (DIBR II) methods were used to determine the weight coefficients of the criteria, which proved to be effective in assessing the importance of each criterion in the context of making complex decisions. Aggregation of expert opinions, for each of the methods, was performed using the Normalized Weighted Bonferroni Mean (NWBM) operator. After the weight coefficients for both methods were obtained, the aggregation of the obtained values was performed using the Bonferroni Mean (BM) operator, which resulted in the final values of the weight coefficients of the criteria. In order to choose the optimal alternative, the Fermatean Fuzzy Weight Operators (FFWO) and BM operator were used. These operators contributed to the precise evaluation and ranking of alternatives, taking into account their characteristics and specificities. Furthermore, the paper analyzed the sensitivity of the output results to changes in the weight coefficients of the criteria. This is important in order to assess the stability of the Multiple Criteria Decision Making (MCDM) model and ensure its reliability in different scenarios. This work represents a significant contribution to the field of decision-making in this context and provides a useful framework for the selection of complex combat systems, and the combination of different methods and operators enables a comprehensive analysis and optimization of the research problem.

Identifying ESG investment key indicators and selecting investment trust companies by using a Z-fuzzy-based decision-making model

The rising regulatory and public pressure to align investments with sustainability causes an increasing need to analyze how investment institutions conduct and execute investment strategies related to their environmental, social, and corporate governance (ESG) funds. A thorough literature review shows that multiple criteria decision-making models are rarely used to discuss the relationships and weights of ESG fund indices. Furthermore, no ESG investment strategy evaluation framework has been created to deal with such funds. Therefore, a comprehensive framework is developed in this study to help select appropriate investment trust companies (ITCs). Z-numbers are incorporated into the proposed decision-making model in order to reflect information uncertainty and to assess the confidence of expert evaluations. First, a Z-based decision-making trial and evaluation laboratory approach is used to obtain the relationships and weights of indices to generate a causality diagram that allows decision-makers to quickly recognize the critical influencing factors in their evaluation systems. Then, a Z-based reference ideal method is applied to integrate the performance of the ITCs. The empirical application provides a demonstration of the usefulness of the proposed approach for selecting ITCs, which can aid to formulate appropriate strategies.

A rankability-based fuzzy decision making procedure for oil supplier selection

Multiple-criteria decision-making (MCDM) explicitly assesses several conflicting criteria for our daily lives in selecting products, vehicles, techniques, etc. Weighting on criteria is a critical step in MCDM as the invalid weight of criteria will lead to a wrong decision. The proposed method addresses some drawbacks of the entropy-based weighting method commonly used in MCDM. The proposed new weighting method considers multiple evaluation factors, including the performance of the decision-maker, the edge weight basis of a digraph, and dominance relationships in the data. By incorporating these factors, the proposed method overcomes the limitations of the entropy-based method and reduces the total computation required. We conducted experiments using sustainable transportation data and comprehensively analyzed the results. We also propose a fuzzy MCDM model incorporating the proposed weighting method and Dempster–Shafer theory. Our model aims to handle uncertainty and imprecision in decision-making. Finally, the correctness and effectiveness of the proposed model were tested on real-life applications. The results of these tests demonstrated that the proposed method provides a practical and effective approach to decision-making in various domains. Overall, the work introduces a new weighting method based on rankability in MCDM, addresses the limitations of the entropy-based method, and presents a fuzzy MCDM model for handling uncertainty. The experimental results suggest that the proposed approach is promising and offers valuable insights for decision-makers.

A hybrid MCDM model with Monte Carlo simulation to improve decision-making stability and reliability

Employing an appropriate method to achieve a reliable decision remains a challenge for decision-makers (DMs) in the multiple-criteria decision-making (MCDM) process owing to its inherent model-related complexity, sensitivity, and uncertainty. In this context, this study proposes an innovative hybrid MCDM model that integrates criteria importance through intercriteria correlation (CRITIC), multi-attributive border approximation area comparison (MABAC), and k-means with Monte Carlo simulation (i.e., CRITIC–MABAC–Kmeans with Monte Carlo simulation), aiming to address MCDM problems with substantial stability and reliability. Specifically, MABAC attests to the stability of this method, as it is less affected by normalization and weighting schemes. In addition, the challenge of conflicting k-means clustering outcomes, owing to diverse initial centroid selections, is mitigated by a Monte Carlo simulation, which identifies the most probable type of result and compensates for small-sample size bias. The model performance is tested using a case study of observing transport safety accomplishments in the ASEAN region. Enhanced multiple comparisons of the experimental results verify the quality, efficiency, and adaptability of the proposed model, indicating its feasibility for DMs, policymakers, and practitioners as a practical tool for handling real-life MCDM activities in various domains under compounded sensitivity and uncertainty.

Urban design strategies fostering creative workers' sense of identity in creative and cultural districts in East Asia: An integrated knowledge-driven approach

In recent years, many cultural districts have become increasingly unsustainable due to the neglect of physical environment quality. Hence, researchers have thoroughly investigated the close relationship between the physical environment quality of cultural districts and creative workers' sense of identity in creative and cultural districts (CCDs). However, few studies have clarified the complex relationship between physical environment factors and creative workers' sense of identity from a systematic perspective. Therefore, this study aims to establish a hybrid multiple-criteria decision-making (MCDM) model to identify the relationship between physical environment factors and creative workers' sense of identity using an integrated knowledge-driven approach that combines the rough set approach (RSA) and the decision-making trial and evaluation laboratory (DEMATEL) method. A total of 81 CCD cases in East Asia were selected to validate the proposed hybrid MCDM model. As result, through RSA, four core attributes that can help creative workers to form a sense of identity in CCDs were extracted, they are the quality of public art, the order and cleanliness of the building facade, the open spaces supporting social activities and the green beautification degree and characteristics. Then, 23 rules (If…Then…) were established. Additionally, through DEMATEL, the dominant influence relationships among these attributes were clarified based on expert knowledge. A “decision path map” driven by the “integrated knowledge” was developed, which can contribute to formulating and implementing strategies for systematically improving the physical environments of CCDs and consequently fostering creative workers' sense of identity. Specifically, our results show that project managers and designers pay attention to maintaining the authenticity of historical buildings in CCDs and focus on creating appropriate color environments and personalized private spaces based on the audience's aesthetic preferences and private habits to evoke a sense of local identity and belonging in creative workers.

A Combined AHP-PROMETHEE Approach for Portfolio Performance Comparison

Comparing portfolio performance is complex due to the fact that each model is dominant in its own risk space. Since there is no single dominant performance measure, the research problem is how to incorporate several different measures into a performance evaluation model that allows portfolios to be ranked. In this regard, the objective of this study was to develop a new comprehensive method for comparing portfolio performance based on multiple-criteria decision-making (MCDM). This paper proposes an integrated approach for stock market decision making that combines the Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), which allow hierarchical evaluation of a finite number of alternatives according to different criteria. This hybrid approach is especially advantageous, utilizing the strengths of both individual methods. AHP enables the decomposition of a complex problem into its constituent parts and the determination of weights for criteria, while the PROMETHEE method allows the investor to determine the preference function, complete ranking, and analysis of the robustness of the results. For the MCDM model in this study, different dimensions of performance measures are considered criteria: return measures, risk measures, stability measures, and predictability measures. The methodology has been applied in comparing real portfolios selected on the basis of different risk measures. For this purpose, weekly return data were used for a sample of stocks that are components of the STOXX Europe 600 Index for the period 2000–2020. In addition, a sensitivity analysis is performed to investigate the strength of the results of this method. It suggests that the simultaneous consideration of different performance measures and the investor’s attitude towards the importance of these measures are notably important in the portfolio efficiency estimation process.

Key Factors for Enhancing Home Care Workers’ Intention to Stay by Multiple-Criteria Decision Analysis

The ageing population is increasing rapidly in Taiwan, where the ageing rate exceeds even that of Japan, the United States and France. The increase in the disabled population and the impact of the COVID-19 pandemic have resulted in an increase in the demand for long-term professional care, and the shortage of home care workers is one of the most important issues in the development of such care. This study explores the key factors that promote the retention of home care workers through multiple-criteria decision making (MCDM) to help managers of long-term care institutions retain home care talent. A hybrid model of multiple-criteria decision analysis (MCDA) combining Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the analytic network process (ANP) was employed for relative analysis. Through literature discussion and interviews with experts, all factors that promote the retention and desire of home care workers were collected, and a hierarchical MCDM structure was constructed. Then, the hybrid MCDM model of DEMATEL and the ANP was used to analyze the questionnaire data of seven experts to evaluate the factor weights. According to the study results, the key direct factors are improving job satisfaction, supervisor leadership ability and respect, while salary and benefits are the indirect factor. This study uses the MCDA research method and establishes a framework by analyzing the facets and criteria of different factors to promote the retention of home care workers. The results will enable institutions to formulate relevant approaches to the key factors that promote the retention of domestic service personnel and to strengthen the intention of Taiwan's home care workers to stay in the long-term care industry.

Selection of Materials in Construction Industry with Multi-Criteria Decision Making Models

Construction companies play a central role in the development of a country. The construction products stimulate the growth of private physical structures and public infrastructure for many productive activities such as utilities, commerce, and services. A good material selection is an essential part that needs to be studied in order to come out with a product that is of high quality and safe to be used. As a result, materials selection in the field of construction has also become an interesting topic for many researchers. Based on the previous literature, several multiple criteria decision making (MCDM) models are proposed to choose the best materials for a particular application in recent years. With multiple decision criteria, material selection is treated as an MCDM problem. According to past studies, the researchers have emphasized the integration of MCDM models to tackle complicated decision making problems. MCDM model is a good tool and has been widely proposed in various fields as MCDM models are capable to tackle the decision making problems that are taking multiple decision criteria into consideration. The intended purpose of this paper aims to provide a literature review on the material selection in the field of construction with MCDM models.

A hybrid multiple-criteria decision-making model for podcaster selection from the perspective of Taiwanese mattress brands

<abstract> <p>The year 2020 has been called the first year of the podcast in Taiwan. In recent years, Taiwan's podcast industry has witnessed gradual growth in the number of listeners, as well as in programme variety. Podcast subscribers can choose their favourite content, receive new content, and can choose what they want to listen to and download it through different hosting platforms. Additionally, a growing number of companies are attempting to use podcasts to market their brands, achieve targeted brand effects, and build their customer base, especially among the lead generation. This study developed a hybrid multiple-criteria decision-making (MCDM) model to assist Taiwanese mattress brand executives in selecting podcasters to market their brands. This is a major issue that has not yet been explored in the literature. First, 12 sub-criteria (SC) were selected using the fuzzy Delphi method (FDM) and categorised to establish the hierarchical structure. Then, a combination of decision making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and technique for order preference by similarity to ideal solution (TOPSIS) was adopted to assist the case company's executives in selecting the best podcaster. The integrated operations performed in this study are logically coherent, practical, and functional. This hybrid MCDM model allows companies and decision-makers to make objective, efficient, and accurate decisions.</p> </abstract>

A multiple criteria decision analysis based approach to remove uncertainty in SMP models

Software has to be updated frequently to match the customer needs. If software maintainability is not given priority, it affects the software development life cycle and maintenance expenses, which deplete organizational assets. Before releasing software, maintainability must be estimated, as the impact of bugs and errors can affect the cost and reputation of the organization after deployment. Regardless of the programming paradigm, it’s important to assess software maintainability. Many software maintainability prediction models’ compatibilities with new programming paradigms are criticized because their limited applicability over heterogeneous datasets. Due this challenge small and medium-sized organizations may even skip the maintainability assessment, resulting in huge lose to such organizations. Motivated by this fact, we used Genetic Algorithm optimized Random Forest technique (GA) for software maintainability prediction models over heterogeneous datasets. To find optimal model for software maintainability prediction, the Technique for Order preference by Similarity to Ideal Solution (TOPSIS), a popular multiple-criteria decision-making model, is adopted. From the results, it is concluded that the GA is optimal for predicting maintainability of software developed in various paradigms.

MEREC-COBRA APPROACH IN E-COMMERCE DEVELOPMENT STRATEGY SELECTION

The research objective of the paper is to propose a model, based on the Multiple-Criteria Decision-Making (MCDM) methods, that facilitates a selection process of an adequate strategy directed to the development of e-commerce. For that aim, the MEthod based on the Removal Effects of Criteria (MEREC) is applied for defining the criteria weights. The recently proposed COmprehensive Distance Based RAnking (COBRA) method is used for the final assessment and ranking of the considered alternatives. The applicability of the proposed model is tested by using an example borrowed from the literature. Three alternative development strategies are assessed against five evaluation criteria. The final results proved the applicability and reliability of the proposed MCDM model.

Analysis of Innovation Drivers of New and Old Kinetic Energy Conversion Using a Hybrid Multiple-Criteria Decision-Making Model in the Post-COVID-19 Era: A Chinese Case

To overcome the continuous decline in its gross domestic product growth rate, China has advocated new and old kinetic energy conversion (NOKEC) as a policy for sustainable economic development in the post-COVID-19 era. The innovation drivers of NOKEC are the key to promoting sustainable economic development. However, the innovation drivers have various orientations, and their selection requires multiple-criteria decision-making (MCDM). This study proposes a modified Delphi method combined with the best–worst method (BWM) as a research framework for selecting and ranking innovation drivers. Our results show the validity of this integrated research framework on a case based in China in the post-COVID-19 era. The results reveal 21 innovation-driven factors of NOKEC with varying levels of relative importance. These results may provide a basis for policymakers and researchers with a useful further understanding of the importance and prioritizing of innovation drivers. In this study, BWM uses 4% fewer pairwise comparisons than AHP, and the consistency ratio is in the range of 0.00 to 0.24.

Assessment of Conductivity-Temperature-Depth via multi-criteria approach: Regret theory based model on the pythagorean fuzzy environment

Sustainable development of marine economy requires strengthening the marine environmental monitoring and management. Conductivity-Temperature-Depth (CTD) realises multi-dimensional monitoring of marine environment by measuring the traits of seawater. To further promote real-time online marine monitoring, conducting a robust assessment of CTD equipment is a primary task for capacity building. In this paper, to evaluate several equipment options, a multiple-criteria decision-making model, based on regret theory and squared-confidence Pythagorean fuzzy hybrid weighted arithmetic and geometric aggregation (SCPFHWAGA) operator is proposed. The SCPFHWAGA operator takes into account the confidence level, which enhances the comprehensiveness of information aggregation. In addition, considering the differences in subjective confidence in equipment use, social network analysis and regret theory are introduced. The evaluation criteria are then designed with the concerns raised before, during and after using the equipment, which reflects the whole process of CTD equipment evaluation more objectively. The case of Zhejiang (China) is analysed. The robustness and validity of the proposed framework are verified by comparing the results with those obtained using other methods.

The Hybrid Multiple-Criteria Decision-Making Model for Home Healthcare Nurses' Job Satisfaction Evaluation and Improvement.

Objectives: To investigate and evaluate the key factors related to job satisfaction performance of home healthcare nurses (HHNs). Methods: A total of 31 HHNs from three community hospitals in Zhejiang province were invited to participate in the study. They completed a questionnaire survey based on the home healthcare nurse job satisfaction scale (HHNJS) from February to March 2022. Consistent fuzzy preference relation (CFPR) methods and important-performance analysis (IPA) were used to obtain the attribute weights and performance for HHNs job satisfaction. Results: The results showed that the attributes of C 13, C 14, C 15, C 23, C 24, C 42, C 51, and C 52 were key factors influencing HHNs job satisfaction. Conclusion: The hybrid multiple-criteria decision-making (MCDM) model can help home-healthcare-agency administrators better understand the key factors related to HHNs job satisfaction and establish reasonable improvement strategies.