Search Results (63)

This research determines how to integrate factors related to evacuation in emergency preparedness using techniques for Multicriteria Decision-Making (MCDM). A distinctive MCDM technique that incorporates human behavior into evacuation models enhances decision-making and safety during emergencies, especially in vulnerable populations. For this purpose, a hybrid combination of MCDM methods—CRiteria Importance Through Intercriteria Correlation (CRITIC), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Weighted Aggregated Sum Product Assessment (WASPAS)—is used to rank the vulnerability of Chilean regions by considering various factors. First, the related factors are ranked by CRITIC, and the result is that the “psychosocial problem” factor has the highest priority and weight. Then, according to the hybrid methods and CRITIC, all regions of Chile are ranked first with TOPSIS, WASPAS, and a combination of them to determine which one has the highest priority. The results show that the Santiago Metropolitan Region has the highest priority for vulnerability in all three methods. Full article

This paper addresses the challenges in extracting content words within the domains of natural language processing (NLP) and artificial intelligence (AI), using sustainable development goals (SDGs) corpora as verification examples. Traditional corpus-based methods and the term frequency-inverse document frequency (TF-IDF) method face limitations, including the inability to automatically eliminate function words, effectively extract the relevant parameters’ quantitative data, simultaneously consider frequency and range parameters to evaluate the terms’ overall importance, and sort content words at the corpus level. To overcome these limitations, this paper proposes a novel method based on a weighted aggregated sum product assessment (WASPAS) technique. This NLP method integrates the function word elimination method, an NLP machine, and the WASPAS technique to improve the extraction and ranking of content words. The proposed method efficiently extracts quantitative data, simultaneously considers frequency and range parameters to evaluate terms’ substantial importance, and ranks content words at the corpus level, providing a comprehensive overview of term significance. This study employed a target corpus from the Web of Science (WOS), comprising 35 highly cited SDG-related research articles. Compared to competing methods, the results demonstrate that the proposed method outperforms traditional methods in extracting and ranking content words. Full article

Background: Nowadays, traditional supply chain management (SCM) processes are undergoing a profound transformation enabled by advanced technologies derived from Industry 4.0. The rapid adoption of these technologies has led to the emergence of smart SCM, which integrates modern technologies in sourcing, production, distribution, and sales. Supplier evaluation and selection (SES) in smart SCM is a strategic decision impacting the entire supply chain. Organizations must also incorporate sustainability principles into their strategic decisions alongside smart production and efficiency. Methods: The main objective of this study is to develop a multi-criteria decision-making (MCDM) approach under uncertainty to address sustainable–smart supplier evaluation and selection problems. The approach integrates polygonal fuzzy numbers (POFNs), Z-numbers, expected interval (EI), and expected value (EV) to develop methods such as the logarithmic methodology of additive weights (LMAW) and the weighted aggregated sum product assessment (WASPAS), which are used to prioritize criteria and rank suppliers. Furthermore, novel approaches are introduced for calculating membership functions, $\mathcal{a}$-cut formulations, and the crispification process in POFNs. Results: A real case study in the home appliance industry revealed that cost reduction through smart technologies, green and smart logistics and manufacturing, and smart working environments are the most critical evaluation criteria. Suppliers three and four, excelling in these areas, were identified as top suppliers. Conclusions: The proposed approaches effectively addressed hybrid uncertainty in SES problems within smart SCM. Finally, sensitivity and comparative analysis confirmed their robustness and reliability. Full article

Supplier selection in the shipbuilding industry is a typical multicriteria group decision-making (MCGDM) problem, often characterized by significant uncertainty and fuzziness. To address this issue effectively, this paper proposes a novel integrated approach for supplier selection in shipbuilding enterprises by combining intuitionistic fuzzy sets (IFSs) with the weighted aggregated sum product assessment (WASPAS) method. The proposed method utilizes IFS operators alongside an innovative process for evaluating indicator weights. Initially, an intuitionistic fuzzy number approach is employed to obtain indicator data, which effectively captures the uncertainty of linguistic variables and ensures accurate reflection of real-world conditions. Subsequently, the indicator weights are evaluated by integrating subjective weights, derived through the best–worst method, with objective weights, calculated using an entropy-based approach, resulting in more balanced and realistic weight assignments. Subsequently, the WASPAS method is used to prioritize alternative suppliers, and a shipbuilding enterprise in Shanghai is taken as an example to verify the effectiveness of the model. In addition, to evaluate the stability of the proposed method, sensitivity analyses were performed for varying attribute values. The results demonstrate that the combination of subjective and objective weights enhances the stability of the method under varying attribute weights. Finally, a comparison with various existing methods based on intuitionistic fuzzy information proves that the proposed method exhibits certain advantages in solving the MCGDM problem under uncertain environments. Full article

With rapid urbanization and industrialization, Vietnam is facing many challenges in solid waste management and increasing energy demand. In this context, the development of renewable energy from solid waste not only solves the problem of environmental pollution but also makes an important contribution to energy security and sustainable economic development. Solid waste to energy is a system of solid waste reatment by thermal methods, in which the heat generated from this treatment process is recovered and utilized to produce energy. Site selection is one of the biggest challenges for renewable energy projects. In addition to technical factors, this decision must also consider environmental impacts, including protecting ecosystems, minimizing noise, and limiting impacts on public health. To solve this problem, multi-criteria decision making (MCDM) methods combined with fuzzy numbers are often used. These methods allow planners to evaluate and balance competing factors, thereby determining the most optimal location for the project. In this study, the authors proposed a Spherical Fuzzy Multi-Criteria Decision-making Model (SFMCDM) for site selection in solid waste-to-energy projects. In the first stage, all criteria affecting the decision-making process are defined based on literature review, experts and triple bottom line model (social, environmental, and economic), and analytic hierarchy process (AHP), and fuzzy theory is applied for calculating the weights in the second stage. The weighted aggregated sum product assessment (WASPAS) method is utilized for ranking four potential locations in the final stage. The contribution of the proposed process is its structured, systematic, and innovative approach to solving the location selection problem for renewable energy projects. Choosing the right location not only ensures the success of the project but also contributes to the sustainable development of renewable energy. Full article

This study introduces a multi-criteria decision-making (MCDM) framework for optimizing multi-energy network scheduling (MENS). As energy systems become more complex, the need for adaptable solutions that balance consumer demand with environmental sustainability grows. The proposed approach integrates conventional and alternative energy sources, addressing uncertainties through fermatean fuzzy sets (FFS), which enhances decision-making flexibility and resilience. A key component of the framework is the use of stochastic optimization and cooperative game theory (CGT) to ensure efficiency and reliability in energy systems. To evaluate the importance of various scheduling criteria, the study applies the logarithmic percentage change-driven objective weighing (LOPCOW) method, offering a systematic way to assign weights. The weighted aggregated sum product assessment (WASPAS) method is then used to rank potential solutions. The hybrid scheduling alternative, combining distributed and centralized solutions, stands out as the best alternative, significantly improving resource optimization and system resilience. While implementation costs may increase, the hybrid approach balances flexibility and rigidity, optimizing resource use and ensuring system adaptability. This work provides a comprehensive framework that enhances the efficiency and sustainability of energy systems, helping decision-makers address fluctuating demands and renewable energy integration challenges. Full article

Selecting optimal design solutions is inherently complex due to multiple criteria encompassing users’ uncertain needs, experiences, and costs. This process must manage uncertainty and ambiguity, making developing a scientific, rational, and efficient guidance method imperative. Bipolar T-spherical fuzzy sets (BTSFS), a hybrid of bipolar fuzzy sets and T-spherical fuzzy sets, effectively handle the bipolarity inherent in all elements. In this work, we propose a Weighted Aggregated Sum Product Assessment (WASPAS) method based on BTSFS and the Aczel–Alsina T-norm (AATN) and T-conorm (AATCN) to address the problem of selecting conceptual design solutions. We first establish operational rules for BTSFS using AATN and AATCN and introduce weighted aggregation operators (BTSFAAWA) and geometric aggregation operators (BTSFAAWG) while examining fundamental properties, such as idempotency, boundedness, and monotonicity. Subsequently, we propose a two-stage BTSFS-based WASPAS method; criterion weights are calculated using the BTSFAAWA operator, and final rankings are obtained through comprehensive calculations using both the weighted sum method (WSM) based on BTSFAAWA and the weighted product method (WPM) based on BTSFAAWG. Finally, we validate the effectiveness of our method through a case study of the selection of cultural and creative products. Sensitivity and comparative analyses are conducted to demonstrate the advantages of our approach. Full article

The development of materials and the products made from them should respond to new challenges posed by market changes and also by climate change. Therefore, the objective of this investigation was to develop a method that supports the sustainable development of materials and the products made from them based on an aggregated indicator of quality and environmental load in the life cycle (QLCA). The testing and illustration of the QLCA method included a passenger car tyre and nine prototypes. These prototypes were described using eight quality criteria: season, class, size of the load index, speed index, rolling, adhesion, and external noise. Then, customer expectations regarding the importance of the criteria and satisfaction with the indicators in the current and modified states were obtained. Based on the customer assessment, the quality indicators of the prototypes were assessed. This assessment was supported by the weighted sum model (WSM) and the entropy method. Then, life cycle assessment for the reference tyre was performed using the Ecoinvent database in the OpenLCA program. LCA indicators were modelled for other prototypes, taking into account quality changes. As a result of the verification of the method, an aggregated QLCA indicator was estimated, based on which it was possible to select the most favourable (qualitatively and environmentally) prototype out of nine. This was the P4 prototype (QLCA = 0.57). The next position in the ranking was taken by P7 (QLCA = 0.43). The QLCA method can be used to determine the direction of development of materials and products in terms of their sustainable development. Full article

The purpose of this research paper is to present a framework for selecting a biogas plant for the Indian rural community, considering performability factors such as reliability, quality, maintainability, safety, and sustainability. This will ensure that the plant operates reliably, efficiently, and safely over its entire life cycle and can play a significant role as a decision-support tool for decision-makers (e.g., managers, engineers, stakeholders). The proposed framework integrates the Analytic Hierarchy Process (AHP), and the Weighted Aggregated Sum Product Assessment (WASPAS) to optimally evaluate and prioritize the best alternative based on performability factors. The findings show that the suitable biogas plant in the context of the Indian rural population is a fixed-dome-type plant. The decision-making process in selecting the best biogas plant can be effectively aided by using this suggested tool. Currently, there are no proper tools or methods for selecting biogas plants for rural areas due to a lack of data or relevant literature on operational issues. The proposed method uses performability factors for the selection, which has not been researched so far. Moreover, the AHP–WASPAS approach offers a robust method for selecting biogas plants, ensuring efficient and sustainable energy production. The proposed method will help policymakers and stakeholders to choose the best biogas plant in the context of Indian rural application. Full article

Building façades represent one of the most critical elements affecting a city’s quality of life, and they impact the country’s economic income by attracting visitors. However, performance data on façades are limited or incomplete, making it challenging for designers to evaluate their effectiveness in energy efficiency, thermal performance, durability, and other key performance metrics. This paper presents a comprehensive framework for evaluating and prioritizing material selection criteria in building cladding, establishing the relationship with available alternatives, and integrating decision-making processes with Building Information Modeling (BIM) to automate the Value Engineering (VE) concept. The material selection criteria from the literature and international standard manual were identified, and their criteria weight was then evaluated using SWARA (stepwise weight assessment ratio analysis). Additionally, WASPAS (weighted aggregated sum product assessment) was utilized to evaluate the alternative cladding materials based on the defined criteria and their associated quality weight (QW). The life cycle cost (LCC) of the alternatives was computed. The VE was computed and then ranked based on the QW and LCC of the alternatives. The procedure was connected to the BIM model to automate the assessment, specifying the necessary parameters and the BIM computation. A case study of an office building façade was conducted to validate the proposed framework. In this study, the significant criteria were durability, wind load resistance, and thermal insulation. This approach enables executives to evaluate cladding selection, ensuring efficient decision-making processes. The proposed method and its results were subjected to expert testing, and the satisfaction rate exceeded 80%, confirming the framework’s reliability in evaluating alternatives. This paper enhances the understanding of material selection methodologies and provides a valuable contribution to the field of construction management. Full article

The green supplier selection presents numerous challenges, from initial assessment to final selection, which specialists in this field of supplier management often encounter. Among the techniques that aim to meet these challenges that are continually progressing is the creation and development of high-quality decision-making tools. In this study, the assessment of suppliers hinges on both traditional and environmental factors. A framework of multi-criteria decision-making (MCDM) is announced in order to appraise green supplier selection. This framework integrates Fuzzy Analytical Hierarchy Process (AHP) combined with two additional methods: WASPAS (“Weighted Aggregated Sum-Product Assessment”) and TOPSIS (“Technique for Order Preference by Similarity to Ideal Solution”). At the outset, there were five environmental criteria for green supplier selection: “Health and Safety”, “Sustainable Product Design”, “ISO 14001 Certification”, “Investment Recovery”, and “Green Packaging”, along with three conventional criteria including quality, price, and delivery, were pinpointed through a review of literature and expert input to facilitate the MCDM approach. As part of demonstrating the pertinency of the recommended framework, a practical case study of the automotive industry in Morocco is discussed. The results demonstrate that the utilized Fuzzy hybrid methods yield consistent rankings for green suppliers. Additionally, among the four green suppliers, number three obtained the best ranking, which indicates the robustness and performance of the chosen models. Furthermore, this study offers a unified platform for selecting green suppliers under a Fuzzy environment. Therefore, the chosen strategy and its analysis provide relevant data and information to decision-makers for the choice and selection of suppliers. It can also provide and help procurement departments and decision-makers to choose and select the efficient ecological supplier in the global market supply chain. Full article

With the advancement of information technology, the Internet is pivotal in today’s society, serving as a global connectivity platform. Leveraging Internet technology within an enterprise can improve operational efficiency and curtail costs. However, traditional Internet platform selection methods cannot simultaneously handle quantitative and qualitative information, fuzzy semantics, and incomplete expert-provided information. To address these limitations, this study integrated the fuzzy analytic hierarchy process (FAHP) and the weighted aggregated sum product assessment (WASPAS) approaches to tackle Internet platform selection problems within an incomplete information environment. To demonstrate the validity of this research approach, this study utilized a construction industry Internet platform selection case to confirm the efficacy of the proposed novel fuzzy analytic hierarchy process-based method. Comparative analysis against the weighted sum model (WSM), weighted product model (WPM), FAHP, and typical WASPAS approaches was conducted with numerical verification, revealing that the proposed method in this study effectively manages comprehensive information and yields more rational outcomes for construction industry Internet platforms. Full article

This study presents an in-depth analysis of the selection process for primary sectors impacting the economic activity in Romania, employing an interval-valued fuzzy (IVF) approach combined with multi-criteria decision-making (MCDM) methodologies. This research aims to identify eight key criteria influencing the selection of Romanian primary sectors, including technology adaptation, infrastructure development and investment, gross domestic product (GDP), sustainability, employment generation, market demand, risk management and government policies. The current analysis evaluates eight primary sector performances against these eight criteria through the application of three MCDM methods, namely, Simple Additive Weighting (SAW), Weighted Product Model (WPM), and Weighted Aggregated Sum Product Assessment (WASPAS). Ten economic experts comprising a committee have been invited to provide their views on the criteria’s importance and the alternatives’ performance. Based on the decision-maker’s qualitative judgement, GDP acquires the highest weightage, followed by environmental impact and sustainability, thus indicating the most critical factors among the group. The IVF-MCDM hybrid model indicates the energy sector as Romanian primary sector with the most potential, followed by the agriculture and forestry sector among the list of eight alternatives. It also explores the robustness of results by considering sensitivity analysis and the potential impacts of political and international factors, such as pandemics or armed conflicts, on sector selection. The findings indicate consistency in sector rankings across the different methodologies employed, underscoring the importance of methodological choice and criteria weighting. Additionally, this study sheds light on the potential influence of political and international dynamics on sector prioritization, emphasizing the need for comprehensive decision-making frameworks in economic planning processes. Full article

The capacity of transport infrastructure is one of the very important tasks in transport engineering, which depends mostly on the geometric characteristics of road and headway analysis. In this paper, we have considered 14 road sections and determined their efficiency based on headway analysis. We have developed a novel interval fuzzy-rough-number decision-making model consisting of DEA (data envelopment analysis), IFRN SWARA (interval-valued fuzzy-rough-number stepwise weight-assessment-ratio analysis), and IFRN WASPAS (interval-valued fuzzy-rough-number weighted-aggregate sum–product assessment) methods. The main contribution of this study is a new extension of WASPAS method with interval fuzzy rough numbers. Firstly, the DEA model was applied to determine the efficiency of 14 road sections according to seven input–output parameters. Seven out of the fourteen alternatives showed full efficiency and were implemented further in the model. After that, the IFRN SWARA method was used for the calculation of the final weights, while IFRN WASPAS was applied for ranking seven of the road sections. The results show that two sections are very similar and have almost equal efficiency, while the other results are very stable. According to the results obtained, the best-ranked is a measuring segment of the Ivanjska–Šargovac section, with a road gradient = −5.5%, which has low deviating values of headways according to the measurement classes from PC-PC to AT-PC, which shows balanced and continuous traffic flow. Finally, verification tests such as changing the criteria weights, comparative analysis, changing the λ parameter, and reverse rank analysis have been performed. Full article

The combination of the lightweight nature and mechanical properties of AZ91 makes it a suitable material for defense, aerospace, and automotive components. The study of the friction and wear properties of AZ91 contributes to the understanding of interactions of surfaces in relative motion. Hybrid ceramic reinforced composites can be tailored to offer enhanced mechanical and tribological properties. The present study highlights the development of AZ91-based hybrid composites reinforced with nano hBN and micron-sized TiB₂ ceramic particles. The hBN is used as a hybridizing agent in the perspective of improving the friction and wear behavior of the composites. The Taguchi L16 orthogonal array was used to prepare the experimental plan. The normal load, sliding speed, and sliding distance were considered as influencing factors in the experiments against the responses, wear rate, and coefficient of friction. Multi-Criteria Decision-Making methods such as Additive Ratio Assessment System (ARAS) and Weighted Aggregated Sum Product Assessment (WASPAS) were employed to optimize the experiments. The presence of hBN decreased the wear rate and coefficient of friction of the hybrid composites. The adhesive mode of wear mechanism was found to be operative in the composites. Full article