Multi-criteria Decision-making Framework Research Articles

Exploring the dynamic impact of the Northern Sea Route on China-Europe multimodal transportation competition in the context of the Belt and Road Initiative

As Arctic sea ice continues to melt, the Northern Sea Route (NSR) is anticipated to become the third major China-Europe transportation corridor, alongside the Suez Canal Route (SCR) and China Railway Express (CRE). This study investigates the integration of the NSR into the Belt and Road multimodal transportation network and analyzes its dynamic competition with traditional corridors across heterogeneous regions and markets in China. To achieve this, we developed a novel hybrid multi-criteria decision-making (MCDM) framework to evaluate the NSR's potential as an alternative China-Europe trade route. This framework introduces a new criteria utility function (CUF) and integrates the Bayesian best-worst method (BBWM) and measurement of alternatives and ranking according to compromise solution (MARCOS) method. This approach evaluates and prioritizes routes based on cost, time, reliability, safety, sustainability, and convenience, thereby detailing the NSR's market advantages and business prospects. The findings suggest that simply increasing the frequency of transportation services is insufficient to attract Chinese customers to the NSR. Instead, the route's competitiveness is primarily determined by transportation costs. In future scenarios, the NSR will offer additional route options for China-Europe containerized intermodal transport, potentially serving as a complementary corridor to the SCR and CRE under certain conditions. Coastal and sub-coastal regions north of Fujian Province are potential markets for the NSR, while inland regions far from seaports are better suited for connections via the CRE. Notably, time-insensitive containerized cargoes, such as industrial accessories, mechanical equipment, and metal crafts, exhibit higher potential demand for the NSR. In contrast, time-sensitive commodities like fashion apparel, electronics, and medical devices are more likely to be transported by cross-border railroads due to their urgency. Stakeholders, including government agencies, shipping companies, and exporters, can utilize these insights to optimize China-Europe transport organization and effectively commercialize the NSR.

Renewable Energy Technology Selection for Hotel Buildings: A Systematic Approach Based on AHP and VIKOR Methods

This study provides an assessment of renewable energy technology utilization in hotel buildings, which are significant structures in terms of energy consumption. The aim of the study is to determine suitable renewable energy technologies (RETs) for hotel buildings by defining criteria for evaluating RETs, assessing the relative importance of these criteria, and proposing a multi-criteria decision-making framework to solve the problem of selecting the most appropriate RETs during the design stage. The alternatives for RETs and the criteria for their evaluation are gathered through a literature review and expert consultations. Eight fundamental RETs used in hotel buildings (such as heat pumps, solar panels, biomass boilers, etc.) are examined, and nine selection criteria are analyzed. According to the weights determined by the Analytic Hierarchy Process (AHP) method, the initial investment cost is the most influential decision criterion, with a weight of 0.314. As a result of applying the AHP and VIKOR (Multi-Criteria Optimization and Compromise Solution) methods for technology selection, photovoltaic panels emerge as the top-ranked choice. This comprehensive evaluation provides stakeholders in the building production process of hotel buildings with detailed analyses and multi-criteria decision-making methods for selecting RETs.

A Dempster–Shafer Enhanced Framework for Urban Road Planning Using a Model-Based Digital Twin and MCDM Techniques

Rapid urbanization in developing countries presents a critical challenge in the need for extensive and appropriate road expansion, which in turn contributes to traffic congestion and air pollution. Urban areas are economic engines, but their efficiency and livability rely on well-designed road networks. This study proposes a novel approach to urban road planning that leverages the power of several innovative techniques. The cornerstone of this approach is a digital twin model of the urban environment. This digital twin model facilitates the evaluation and comparison of road development proposals. To support informed decision-making, a multi-criteria decision-making (MCDM) framework is used, enabling planners to consider various factors such as traffic flow, environmental impact, and economic considerations. Spatial data and 3D visualizations are also provided to enrich the analysis. Finally, the Dempster–Shafer theory (DST) provides a robust mathematical framework to address uncertainties inherent in the weighting process. The proposed approach was applied to planning for both new road constructions and existing road expansions. By combining these elements, the model offers a sustainable and knowledge-based approach to optimize urban road planning. Results from integrating weights obtained through two weighting methods, the Analytic Hierarchy Process (AHP) and the Bayesian best–worst Method (B-BWM), showed a very high weight for the “worn-out urban texture” criterion and a meager weight for “noise pollution”. Finally, the cost path algorithm was used to evaluate the results from all three methods (AHP, B-BWM, and DST). The high degree of similarity in the results from these methods suggests a stable outcome for the proposed approach. Analysis of the study area revealed the following significant challenge for road planning: 35% of the area was deemed unsuitable, with only a tiny portion (4%) being suitable for road development based on the selected criteria. This highlights the need to explore alternative approaches or significantly adjust the current planning process.

Prioritization of renewable energy for offshore ship charging stations based on intuitionistic fuzzy GLDS method: A case of China

Offshore ship charging station (OSCS) projects can help to address the current demand for electric ships for ocean voyages to a large extent. The proper selection of the energy source for power generation is a key part of the OSCS project. To select the optimal renewable energy for OSCS with many difficulties such as the ambiguity of the decision-making environment, the differences in group assessment information, and the conflict and compensation between criteria, this paper proposed a fuzzy multi-criteria decision-making (MCDM) framework. First, a comprehensive criteria system was constructed. Second, the intuitionistic fuzzy set (IFS) was introduced to express experts’ fuzzy cognition. Third, based on the quality of evaluation, a novel expert weighting method was proposed, and the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator was used to aggregate the individual evaluations. Fourth, the criteria importance through intercriteria correlation (CRITIC) method, and the stepwise weight assessment ratio analysis II (SWARA II) method were introduced to determine the criteria weights. Fifth, considering criteria compensation, and individual and group ranks, the gained and lost dominance score (GLDS) method were used for ranking. Finally, to verify the applicability and reliability of the framework, a case study was conducted in Pingtan Island, Fujian Province. The results show that wind energy was the best alternative, followed by solar, wave and nuclear energy.

Modelling the present and future scenario of urban green space vulnerability using PSR based AHP and MLP models in a Metropolitan city Kolkata Municipal Corporation

ABSTRACT Urban green spaces, despite their recognized importance for sustainable urban development, face significant challenges to their resilience and viability due to rapid urbanization and various associated pressures. The scientific problem addressed in this paper is the vulnerability of urban green spaces to various stressors, including proximity to settlement, land use and land cover changes, and proximity to roads, among others. Despite their importance, there is a lack of comprehensive assessments and frameworks to systematically evaluate and address this vulnerability. Therefore, this study aims to fill this gap by employing a multi-criteria decision-making framework, namely the Analytic Hierarchy Process (AHP), and the Pressure-State-Response (PSR) framework. By analyzing 10 indicators related to urban green space vulnerability through a pairwise comparison matrix, the study identifies critical factors influencing vulnerability and categorizes urban green spaces into different susceptibility levels. Additionally, the research extends its analysis to future scenario development for the years 2030 and 2040, using a Multi-Layer Perceptron (MLP) based Markov Chain model, incorporating various parameters such as distances from built-up areas, roads, rails, streams, slopes, and elevation. Through these methodologies, the study aims to provide valuable insights to urban planners, policymakers, and environmental practitioners, enabling them to make informed decisions to enhance the resilience and sustainability of urban green spaces and urban areas as a whole.

New robust multi-criteria decision-making framework for thermal insulation of buildings under conflicting stakeholder interests

The choice of thermal insulation technology for existing building retrofit can be a complex multi-criteria decision-making (MCDM) problem characterized by multiple stakeholders with conflicting interests. The conflicting interests of the building owners and the policymakers could negatively influence the adoption of low-carbon energy retrofit measures by building possessors, slowing down the progress toward sustainable development. The investigation of this issue is, therefore, crucial to support more informed decisions and offer policymakers useful insights to develop future economic incentives for energy refurbishment of buildings. Most of the existing MCDM studies on the subject do not emphasize the differences among different stakeholders nor consider the outcomes' robustness. To overcome this gap, this paper integrates multi-stakeholder analyses and robustness assessments to provide broad-spectrum and reliable results. An innovative robust MCDM framework for building thermal insulation under conflicting stakeholder interests is proposed and tested on a real case study building (located in Southern Italy) using building dynamic energy simulation. Several alternatives of thermal insulation are evaluated under environmental, energy, and economic key performance indicators (KPIs). The Analytic Hierarchy Process is used to define criteria weights for conflicting decision-makers representing collectivity (policymakers) and private interests. TOPSIS, VIKOR, WASPAS, and MULTIMOORA methods are integrated to perform initial rankings of the alternatives. A rank similarity analysis is performed to evaluate the consensus between MCDM methods, and an ensemble ranking is obtained for each decision-maker using an approach based on the half-quadratic theory. A Confidence Index and a Trust Level are used to evaluate the agreement among the MCDM approaches, verifying the reliability of the final ensemble ranking. The robustness of the framework is attained by complying with well-established literature guidance. The hybrid MCDM analysis showed that porous materials like expanded clay and expanded perlite lead to better results under the KPIs investigated. The worst performances are attained by vacuum insulation panels and aerogel, mainly due to high values of embodied CO2 emissions and long payback periods. Insights upon the performance of different thermal insulating alternatives are also highlighted by the study and a stakeholder comparative analysis demonstrates that global rankings obtained for the different decision-makers show some similarities, but also important deviations, and a full compromise solution is not achieved.

Socially Sustainable Mobility as a Service (MaaS): A practical MCDM framework to evaluate accessibility and inclusivity with application

Promoting socially sustainable mobility services for all social groups is one of the key elements for sustainable development according to the UN's sustainable development goals. Mobility as a Service (MaaS), a rapidly growing smart mobility concept, has the potential to achieve this goal. However, measuring societal impacts of MaaS considering vulnerable social groups' (elderly, disabled, low-income people) needs is still a question for scholars, practitioners, and policy makers. This paper presents a practical 3-stage framework to evaluate the accessibility and inclusion of MaaS systems based on a range of indicators, and a multi-criteria decision making (MCDM) method. A worldwide experts' view survey was conducted among transportation academics and practitioners with 105 valid responses to prioritise the main and sub-criteria in proving an accessible and inclusive MaaS system. The results of MCDM analysis show that accessible transport services, accessible MaaS platforms, and accessibility data collection have a share of 51 %, 29 %, and 20 %, respectively. The functionality of the proposed framework has been illustrated on a real-world multi-city MaaS implementation within the Solent area, in particular Portsmouth city located in Southeast England (UK). The policy recommendations proposed in this study shed light on guiding stakeholders and policymakers to select and implement an accessible and inclusive MaaS system.

Sustainability prioritization of CCUS systems with consideration of criteria interaction: a fuzzy multi-criteria decision-making framework based on 2-additive fuzzy measure and Choquet integral method

Sustainability prioritization of CCUS systems with consideration of criteria interaction: a fuzzy multi-criteria decision-making framework based on 2-additive fuzzy measure and Choquet integral method

A comparative evaluation of multi-criteria decision-making framework for armed unmanned aerial vehicle

A comparative evaluation of multi-criteria decision-making framework for armed unmanned aerial vehicle

Life cycle sustainability assessment of waste-to-electricity plants for 2030 power generation development scenarios in western Lombok, Indonesia under multi-criteria decision-making approach

Globally, the dual challenges of power shortages and waste management underscore the critical need for innovative and sustainable energy solutions. Addressing these issues is essential for achieving environmental sustainability, economic development, and social well-being. This paper proposes an integrated life cycle sustainability assessment (LCSA) of waste-to-electricity (WtE) technologies for the future energy mix in Western Lombok, Indonesia which strongly faces power shortage and waste management challenges. The proposed LCSA assesses three WtE technologies, including gasification, incineration, and landfill gas (LFG) with 5 environmental, 3 social, and 4 economic indicators to estimate the final sustainability score under a multi-criteria decision-making framework. Also, a comparative analysis from the LCSA perspective for different WtE technologies, along with coal, and diesel power plants is conducted as a decision support tool in the 2030 power capacity development scenarios. To indicate the effectiveness and flexibility of the proposed model, the robust decision-making analysis using different important weights on sustainability aspects is investigated. Results show LFG is the most sustainable technology among all options scoring around 0.78. However, coal with a levelized cost of 0.42 $/MWh, and diesel with a payback period of 1.75 years are much more economical options for western Lombok. Sensitivity analysis proves that electricity price is a key parameter strongly affecting the final sustainability score. If supportive policies are adopted associated with electricity produced from WtE, this technology can experience a 16.2 % increase, making it an economic option for Lombok in the future.

Application of TOPSIS-LP and New Routing Models for the Multi-Criteria Tourist Route Problem: The Case Study of Nong Khai, Thailand

This study investigates the application of a new mathematical routing model, integrated with the TOPSIS Linear Programming (TOPSIS-LP) approach, to optimize tourist routes in Nong Khai, Thailand, within a Multi-Criteria Decision-Making (MCDM) framework. The research demonstrates the efficacy of TOPSIS-LP by consistently ranking the same alternative as the optimal route, achieving the highest rankings across various Multi-Attribute Decision Making (MADM) methods, including MOORA, WASPAS, and ARAS. These methods displayed significant consistency in outcome evaluation, with Spearman Correlation Coefficients (SCC) of 0.952 for MOORA WASPAS, and ARAS, indicating the influence of diverse weighting and aggregation strategies in route optimization. Moreover, the study confirmed a perfect alignment (SCC of 1.00) between TOPSIS-LP and the traditional TOPSIS method, affirming that the enhancements to the LP components maintained the integrity of the original model. The findings provide invaluable insights for tourism planners aiming to improve tourist satisfaction and operational efficiency and contribute to the academic discourse by highlighting the practical utility of sophisticated mathematical models in real-world scenarios. This research not only advances the methodological practices in tourist route optimization, but also sets a benchmark for future research aimed at enhancing the effectiveness, robustness, and adaptability of MADM methods in the tourism sector.

Spatial Analysis for Bridge and Road Site Selection: A Multi-Criteria Decision Framework for North and Northeast Thailand

Spatial Analysis for Bridge and Road Site Selection: A Multi-Criteria Decision Framework for North and Northeast Thailand

Evaluation of suitable sites for concentrated solar power desalination systems: case study of Mauritania

It is imperative to address the critical problem of water scarcity in sub-Saharan Africa, especially in light of the aggravating effects of climate change brought on by the extraction of fossil fuels. In order to ensure the availability of drinkable water in these places, this research proposes integrating concentrated solar power (CSP) with desalination systems (DS). Present research is focused on identifying and evaluating potential locations for DS/CSP implementation within Mauritania by employing a comprehensive, multi-criteria decision-making framework. This framework synthesizes mathematical approaches from multi-criteria analysis with geospatial analysis techniques, considering a range of factors including environmental impact, economic viability, demographic demands, and climatic conditions. Research findings reveal that 10% of the Mauritanian, approximately 103070 km2, presents optimal conditions for the deployment of DS/CSP facilities. The study delineates the coastal regions as prime candidates for seawater desalination plants, while the densely populated southeastern areas are identified as suitable for brackish water desalination systems. Conversely, the less inhabited northern territories hold the potential for decentralized brackish water desalination plants. Hence this study provides a holistic approach for DS/CSP systems installation to manage water scarcity as well as energy security issues in Mauritania. And also provides basis for formulating future policies in the region.

A Practice-Oriented Approach for Seismic and Energy Performance Upgrading of Existing Buildings

ABSTRACT Recent studies have focused on identifying the optimal seismic and energy upgrading interventions, among different alternatives, for poorly performing existing buildings. Most of such studies use detailed methodologies to assess the seismic and energy performance of buildings, which are time and computationally demanding, making it nearly unfeasible for practitioners and decision-makers to use them, rendering practice-oriented approaches extremely needed. To overcome the current limitations of more research-oriented methods, this study employs different available simplified approaches, namely SPO2FRAG, cloud-based capacity spectrum method and the Italian seismic risk classification guidelines (Sismabonus), to estimate some of the variables considered when using a multi-criteria decision-making (MCDM) framework to select the optimal combined retrofitting strategy, among a set of different options. To this end, a case-study school building was selected and four seismic retrofitting solutions, combined with three energy-based interventions, were identified. The performance of the building, in its as-built and retrofitted conditions, was analysed considering both simplified and detailed approaches for the estimation of two of the seismic-related variables, namely annual probability of failure and expected annual losses. Such simplified and refined estimates were used as MCDM input, comparing the ranking of retrofitting combined alternatives obtained in both cases, demonstrating the suitability of the proposed simplified methodology for engineering practice.

Assessment of Metaverse wearable technologies for smart livestock farming through a neuro quantum spherical fuzzy decision-making model

Livestock wearable technologies are innovations designed to ensure livestock health management. However, the user aspect of these devices from farmers’ perspective is still questionable. Additionally, livestock wearables are still in progress compared to the other wearables. Thus, this research aims to identify key design features regarding wearable smart collars (WSCs) and rank the alternative WSC prototypes within Metaverse, allowing farmers to select the best wearable device. To this end, an integrated neuro quantum spherical fuzzy multi-criteria decision-making (MCDM) framework is introduced via facial expressions to obtain the priority weights of WSC criteria with the improved decision-making trial and evaluation laboratory (DEMATEL) approach and to rank the WSC alternatives in Metaverse through the improved multi-objective optimization based on ratio analysis (MOORA) model. The novelties of this research are: (1) to build and introduce a novel decision support tool based on facial expressions, expert recommendations, and the quantum spherical fuzzy sets, (2) to guide industrial designers about the essential features of WSCs, whereas they are designing these devices, and (3) to help smallholder farmers to decide on the best WSC to enhance animal welfare and efficiency of animal production. Concerning the findings, “sound and stress analyzer” is the most significant feature, followed by “disease detection” and “price.” Moreover, Prototype 3 is the best WSC for farmers to adopt for livestock health management. Some essential implications are further presented.

Promoting inclusivity in education amid the post-COVID-19 challenges: An interval-valued fuzzy model for pedagogy method selection

The advent of the COVID-19 pandemic has brought about unprecedented disruptions in the field of education, necessitating a reevaluation of pedagogical approaches in the post-COVID era. This research paper introduces a novel interval-valued fuzzy simple additive weighting (SAW), weighted product model (WPM), and weighted aggregates sum product assessment (WASPAS) multi-criteria decision-making (MCDM) framework to address the challenge of promoting inclusivity in education amidst the post-COVID era. Leveraging the uncertainty inherent in the post-pandemic educational landscape, the proposed method offers a comprehensive pedagogy selection approach incorporating interval-valued fuzzy sets to account for imprecise and ambiguous data. By integrating the principles of inclusivity and diversity, the method evaluates various pedagogical approaches and their effectiveness in fostering an inclusive learning environment for diverse student populations. The study showcases the application of pedagogy selection in real-world educational scenarios, demonstrating its potential to inform policy decisions and enable educational institutions to adapt and cater to the evolving needs of learners in the aftermath of the COVID-19 pandemic.

Enhancing Decision-Making Processes in the Complex Landscape of the Taiwanese Electronics Manufacturing Industry through a Fuzzy MCDM Approach

This research proposes a hybrid multi-criteria decision-making (MCDM) framework for workforce recruitment in Taiwan’s electronics manufacturing companies, an area with limited research. First, a comprehensive review of existing literature and interviews with industry experts were conducted to compile a list of decision-making criteria and sub-criteria relevant to workforce selection in Taiwan’s electronics industry. The Fuzzy Delphi Method (FDM) was then applied to identify and retain the most critical criteria while eliminating the less important ones. Next, Interpretive Structural Modelling (ISM) was used to calculate the interdependencies among the identified factors. Finally, based on these relationships, the Fuzzy Analytic Network Process (FANP) was employed to calculate the relative importance weights of the criteria and sub-criteria. These weights were then used to rank the criteria, identifying the most important ones and aiding in decision-making. The findings indicate that the proposed method provides a structured and assessable model for making informed decisions in workforce recruitment, particularly in the challenging environment of Taiwan’s electronics manufacturing industry, which faces a shortage of skilled labor. The research presents three primary contributions: the development of a systematic criteria selection technique using FDM, the establishment of consistent criteria relations for decision-makers using ISM, and the proposal of an application model employing the FANP method to identify and rank appropriate criteria for hiring new employees. The study highlights work attitude, adaptability to the environment, and work ability as major criteria. It also emphasizes the importance of discipline compliance, a positive attitude, and adherence to health and safety protocols as the top sub-criteria for workforce selection.

Site selection and prediction of urban emergency shelter based on VGAE-RF model

As urban development accelerates and natural disasters occur more frequently, the urgency of developing effective emergency shelter planning strategies intensifies. The shelter location selection method under the traditional multi-criteria decision-making framework suffers from issues such as strong subjectivity and insufficient data support. Artificial intelligence offers a robust data-driven approach for site selection; however, many methods neglect the spatial relationships of site selection targets within geographical space. This paper introduces an emergency shelter site selection model that combines a variational graph autoencoder (VGAE) with a random forest (RF), namely VGAE-RF. In the constructed urban spatial topological graph, based on network geographic information, this model captures both the latent features of geographic unit coupling and integrates explicit and latent features to forecast the likelihood of emergency shelters in the construction area. This study takes Beijing, China, as the experimental area and evaluates the reliability of different model methods using a confusion matrix, Receiver Operating Characteristic (ROC) curve, and Imbalance Index of spatial distribution as evaluation indicators. The experimental results indicate that the proposed VGAE-RF model method, which considers spatial semantic associations, displays the best reliability.

Embracing Sustainability in Green Supplier Evaluation: A Novel Integrated Multi-Criteria Decision-Making Framework

Nowadays, industrial production is a critical issue for companies as public consciousness is rising because of environmental impacts. Like most other industries, the ready-made garment (RMG) industry faces immense pressure to be more competitive in this increasingly competitive market and proactively reduce waste and ecological footprint impacts. Besides, suppliers’ environmental performance and image have far more effect on a corporation’s environmental sustainability than the corporation’s internal environmental efforts. Moreover, there is a shortage of environmental consciousness in emerging economies; thus, green supply chain practices are lagging. Therefore, the green supplier evaluation criteria should be identified and focused on during sustainable procurement. This study analyses quantitative and qualitative factors and explores the relationship between green supplier preference principles using the combination of the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS). Initially, the supplier’s green performance was measured by identifying the criteria from relevant literature. After that, the Fuzzy DEMATEL technique measures the related criteria weights, and the Fuzzy TOPSIS method utilizes these criteria weights to rank suppliers among alternative suppliers. The findings will guide policymakers to improve supplier quality, increase profitability through improved brand recognition, and attract consumers who prioritize environmentally friendly products.

A Collaborative Multi-Criteria Decision-Making Framework for the Adaptive Reuse Design of Disused Railways

With an increasing focus on sustainable development and the reuse of disused infrastructure, there is a pressing need for effective decision-making methods. This study explored the use of the PROMETHEE method within a collaborative decision-making framework to assess the adaptive reuse options for a disused railway in Sicily, Italy. Using a participatory paradigm, the research actively involves stakeholders in the assessment process. The collaborative PROMETHEE approach acts as a tool for multi-criteria decision analysis, facilitating the comparison and prioritization of different reuse alternatives based on predefined criteria. The research highlights the effectiveness of PROMETHEE in streamlining decision-making processes for adaptive reuse efforts, particularly in the context of integrated conversions of abandoned railways. By combining multi-criteria decision analysis with collaborative methods, the study contributed to the formulation of sustainable and socially responsible strategies for the valorization of disused railway infrastructure. The results underline the importance of collaborative decision-making processes and the instrumental role of PROMETHEE in assisting stakeholders in evaluating and selecting adaptive reuse options for disused railways, thereby, in turn, promoting the sustainable development of these sites.