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A stable and accurate forecast of water quality parameters is crucial for planning and managing future investment programs. A well-known water quality indicator is the total dissolved solids (TDS), which measures the number of metals, minerals, and salts dissolved in a particular volume of water. This paper introduces an innovative approach, which combines the kernel extreme learning machine (KELM) with the robust weight mean of vectors (INFO) algorithm. The proposed technique stands out for its strategic integration of Boruta-XGBoost (B-XGB) as an exceptional feature selection method, along with variational mode decomposition (VMD) to decompose the input variables and enhance the accuracy of forecasting. The integrated model is termed V-KELM-INFO. INFO is an essential component that incorporates a complex exploration process. It utilizes a weighted mean of vectors, a vector combining operator to enhance population variety, and a localized search operator to expedite convergence. INFO, a potent tool, is included in the V-KELM training stage. It efficiently extracts optimum parameters and dramatically improves the accuracy of monthly TDS predictions at the Idenak station in southwest Iran. Also, the multi-criteria decision-making (MCDM) approach of weighted aggregated sum product assessment (WASPAS) is used to rank models. The V-KELM-INFO model was chosen based on the WASPAS results and statistical metrics (R = 0.962, RMSE = 57.84, WHD = 7.01, and U95 = 160.74) as the best model to forecast the TDS. In addition, the superiority of the proposed model was assessed against the metaheuristic-based V-KELM models, comprising the V-KELM grey wolf optimizer (V-KELM-GWO), V-KELM slime mold algorithm (V-KELM-SMA), and V-KELM equilibrium optimizer (V-KELM-EO). This work provided insight into improving the accuracy of modeling-based methodologies and spurred water quality modeling technology to develop sustainable and clean practices. • A novel machine learning (KELM-INFO) approach for forecasting TDS is created. • The KELM-INFO is validated by four well-known machine learning methods. • The V-KELM-INFO model is assessed against the metaheuristic-based V-KELM models. • This work provides insight into improving the accuracy of model-based methods. [ABSTRACT FROM AUTHOR]

Selecting sustainable suppliers is crucial to achieving sustainability objectives within the supply chain. However, the inherent complexity and uncertainty associated with evaluating supplier performance necessitate the use of fuzzy decision-making tools. This paper proposes the use of cubic intuitionistic fuzzy sets (CIFS) with four aggregation operators (AOs) based on Schweizer-Sklar (SS) t-norm and t-conorm: cubic intuitionistic fuzzy softmax Schweizer-Sklar weighted average (CIFSSSWA), cubic intuitionistic fuzzy softmax Schweizer-Sklar ordered weighted average (CIFSSSOWA), cubic intuitionistic fuzzy softmax Schweizer-Sklar weighted geometric (CIFSSSWG), and cubic intuitionistic fuzzy softmax Schweizer-Sklar ordered weighted geometric (CIFSSSOWG). The inclusion of pseudocodes increases practical application. An exhaustive sensitivity analysis assesses the influence of different parameters on the effectiveness of these techniques. The advantages of precision, efficiency, and execution time are underscored in the comparative analysis, which offers valuable insights that can inform more informed decision-making regarding the selection of green suppliers. The results demonstrate the efficacy of all the proposed approaches in providing a comprehensive and accurate evaluation of green suppliers. This study helps green supplier selection practices get better and gives practical advice to supply chain professionals who want to make their operations more sustainable. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP), a subfield of artificial intelligence (AI), has progressively influenced corpus-based methods, with keyword extraction often relying on complex NLP algorithms or models as an integral technique within corpus-based methods. With growing concern for sustainability issues, keyword extraction impacts the information acquisition in decision-making and policy development. However, traditional corpus-based keyword extraction methods involve limitations, such as the inability to automatically exclude meaningless words, evaluate the relative importance of keyword parameters, and integrate parameters for comprehensively keyword evaluation. To address these limitations, this paper proposes an entropy-based corpus method. The proposed method first optimizes the keyword list by excluding function and generic words using a machine-based technique (word types decrease by 5.76%; total words decrease by 72.2%). Second, it calculates the objective weights of log-likelihood (0.5518), frequency (0.4048), and range (0.0433) parameters to define their relative importance, facilitating parameter integration before evaluating keyword importance. Then, it calculates the aggregated value of each keyword to assess its level of importance. As a result, it streamlines the manual word selection process and comprehensively evaluates the importance of keywords. Compared to the four traditional methods, the keyword extraction results of the proposed method, which accounts for only 1.77% of the original list, better reflects the linguistic patterns of the target corpus, potentially facilitating future corpus-based keyword analysis research. [Display omitted] [ABSTRACT FROM AUTHOR]

Natural language processing (NLP), a subfield of artificial intelligence (AI), has progressively influenced corpus-based methods, with keyword extraction often relying on complex NLP algorithms or models as an integral technique within corpus-based methods. With growing concern for sustainability issues, keyword extraction impacts the information acquisition in decision-making and policy development. However, traditional corpus-based keyword extraction methods involve limitations, such as the inability to automatically exclude meaningless words, evaluate the relative importance of keyword parameters, and integrate parameters for comprehensively keyword evaluation. To address these limitations, this paper proposes an entropy-based corpus method. The proposed method first optimizes the keyword list by excluding function and generic words using a machine-based technique (word types decrease by 5.76%; total words decrease by 72.2%). Second, it calculates the objective weights of log-likelihood (0.5518), frequency (0.4048), and range (0.0433) parameters to define their relative importance, facilitating parameter integration before evaluating keyword importance. Then, it calculates the aggregated value of each keyword to assess its level of importance. As a result, it streamlines the manual word selection process and comprehensively evaluates the importance of keywords. Compared to the four traditional methods, the keyword extraction results of the proposed method, which accounts for only 1.77% of the original list, better reflects the linguistic patterns of the target corpus, potentially facilitating future corpus-based keyword analysis research. [Display omitted] [ABSTRACT FROM AUTHOR]

Recycling waste is one of the main methods of sustainable design concept, which can reduce resource consumption and thus create eco-friendly products. Traditional green design has focused on the recycling of waste and the extension of the life cycle of product materials. Some scholars also consider emotional Customer Requirements (CRs) in eco-friendly products, but prioritize appearance over environmental function innovation. The rapid development of internet technology has enabled the proliferation of online product reviews, which can be analyzed to inform eco-innovative product design that meets CRs. Based on the CRs extracted from online reviews, this paper proposes an eco-innovative product design method that aims to recycle waste and meet emotional CRs. Firstly, online reviews are extracted using Latent Dirichlet Allocation (LDA), and CRs for environmental protection functions, appearance, human-computer interaction, and service experience are summarized by integrating sustainable design concepts and emotional design theories. Secondly, the Theory of Inventive Problem Solving (TRIZ) is utilized to suggest targeted solutions for core CRs, innovating product function modules and usage. Thirdly, the combination of Kansei Engineering (KE) and the Interactive Genetic Algorithm (IGA) is utilized for appearance design. Lastly, the research process is explained using the proposed method, with the example of a coffee maker's design, and its effectiveness and practicality are verified. The proposed method aims to recycle waste, optimize resource utilization, achieve environmental protection goals, and provide innovative approaches for advancing the circular economy. Additionally, this method prioritizes environmental sustainability and considers emotional CRs. [ABSTRACT FROM AUTHOR]

One of the most important areas of infrastructure projects that can contribute to the goals of sustainable development is the energy sector. Forming the right set of energy projects can lead to developing a sustainable environment. Another important aspect is the resilience of energy projects. This paper introduces a novel approach that simultaneously considers resilience and sustainability in forming energy project portfolios. In the first part, the relative importance of each project is computed under a triangular neutrosophic (TN) environment. This approach keeps the vagueness of data while evaluating projects versus resiliency and sustainability criteria. In the second part, an interval mathematical model is presented to maximize the total value of the selected portfolio while addressing bi-level budgeting and skill utilization. In this study, uncertainty is expressed in a hybrid way which enhances the flexibility of computations in addition to providing experts with more freedom in expressing their opinions. To present the application of this method, data from an existing case study has been used, and the process has been implemented step by step. This application demonstrates the importance of addressing resilience and sustainability in energy projects. Moreover, it shows the benefits of applying a hybrid solution approach in presenting the vagueness of energy projects in addition to assigning the budget in a bi-level process. • Introducing a new energy project evaluation approach under neutrosophic fuzzy sets. • Considering resilience and sustainability in energy project portfolio evaluation. • Presenting a new model by bi-level budgeting and skill utilization. • Providing project manager assignments through skill improvements and declines. • Using data from a case study to demonstrate the applicability of proposed approach. [ABSTRACT FROM AUTHOR]

For the first time, the critical problem of selecting the most sustainable strategy for urban parcel delivery is addressed and solved in this paper. It aims to help not only postal and logistics companies but also regulatory agencies and city authorities find an environmentally friendly, socially responsible, and economically viable solution. Three alternatives for organizing urban parcel delivery are defined, which are traditional, united consolidation center, and inner-city hubs. According to the three pillars of sustainability, an assessment framework for practitioners is proposed. This paper introduces a linguistic q-rung orthopair fuzzy Choquet integral approach to evaluate sustainable strategies for urban parcel delivery. The linguistic q-rung orthopair fuzzy Choquet integral averaging and linguistic q-rung orthopair fuzzy Choquet integral geometric operators have been constructed for the first time for this purpose. The operator-related qualities are demonstrated. The operators are used to create a multi-criteria group decision-making approach. The case study of the city of Belgrade illustrates the practical applicability of the linguistic q-rung orthopair fuzzy Choquet integral approach. This research findings show that the united consolidation center is the most sustainable strategy for organizing urban parcel delivery. [ABSTRACT FROM AUTHOR]

Recycling spent lithium-ion batteries is a significant way to achieve life-cycle management and a green circular economy, helping to achieve carbon neutrality. The form of battery pack disassembly has gradually moved away from manual manipulation to a human–robot collaborative process. However, there is no established methodology for assessing spent lithium-ion battery disassembly between the environmental benefits, technical feasibility, and economic viability of human–robot collaborative disassembly (HRCD). To ensure the efficiency and security of HRCD processes, this paper develops a resilience assessment model for HRCD using stability, redundancy, efficiency and adaptation metrics. The resilience assessment theory is used to obtain the internal relations among the resilience indicators of the system. In this work, we evaluate the resilience of HRCD for lithium-ion battery disassembly by integrating fuzzy Bayesian fusion with an analytical network process unfolding cloud. The 2017 Chevrolet Bolt driver demonstrated the feasibility and effectiveness of the proposed approach, which allows greater flexibility in complex tasks performed by humans and robots. In case study, the weights values of stability, redundancy, efficiency and adaptability are 0.340533, 0.161384, 0.203835 and 0.294248, respectively. IR2&HO1 and IR3&HO2 have an assessment value of 93 and 95, respectively, with eigenvalues of 1.28 and 1.23, which are level I. RI3&HO1 and RI4&HO3 belong to level IV, with an evaluation value of 38 and 45, respectively, and a characteristic value of 2.27 and 2.55, respectively. The integrated methodology proposed in this paper could be effectively used by disassembly management, to evaluate the HRCD disassembly scheme resilient strategies for better managing production. [ABSTRACT FROM AUTHOR]

The recycling of the used products collected by the manufacturing companies for recycling from their customers and their inclusion in the production system for remanufacturing has become an essential topic regarding sustainable production systems. Using parts and equipment in the remanufacturing system, especially in electronic products and products in the automotive sector, is preferred because of the opportunities to reduce costs and increase energy and workforce efficiency. In this article, we analyzed the management of a hybrid production system that processes new or returned materials in the manufacturing system. An intuitionistic fuzzy linear mathematical model is developed to demonstrate financial benefit and make decisions about using used parts in remanufacturing systems. Our study contributes to the literature by first adapting the magnitude function to the hybrid manufacturing and remanufacturing process optimization problem. Also, the innovative scope of the paper is expanded by applying IVITFN to the remanufacturing process optimization problem for the first time using comparative analysis. We consider relevant costs, the uncertainty about the number of returned products, demand, unit costs and unit production times, and capacity limitation in determining the optimal operation condition. The proposed model results show smaller cost values against other models in the literature. [ABSTRACT FROM AUTHOR]

With the predicted depletion of natural resources and alarming environmental issues, sustainable development has become a popular as well as a much-needed concept in modern process industries. Hence, manufacturers are quite keen on adopting novel process monitoring techniques to enhance product quality and process efficiency while minimizing possible adverse environmental impacts. Hardware sensors are employed in process industries to aid process monitoring and control, but they are associated with many limitations such as disturbances to the process flow, measurement delays, frequent need for maintenance, and high capital costs. As a result, soft sensors have become an attractive alternative for predicting quality-related parameters that are 'hard-to-measure' using hardware sensors. Due to their promising features over hardware counterparts, they have been employed across different process industries. This article attempts to explore the state-of-the-art artificial intelligence (Al)-driven soft sensors designed for process industries and their role in achieving the goal of sustainable development. First, a general introduction is given to soft sensors, their applications in different process industries, and their significance in achieving sustainable development goals. AI-based soft sensing algorithms are then introduced. Next, a discussion on how AI-driven soft sensors contribute toward different sustainable manufacturing strategies of process industries is provided. This is followed by a critical review of the most recent state-of-the-art AI-based soft sensors reported in the literature. Here, the use of powerful AI-based algorithms for addressing the limitations of traditional algorithms, that restrict the soft sensor performance is discussed. Finally, the challenges and limitations associated with the current soft sensor design, application, and maintenance aspects are discussed with possible future directions for designing more intelligent and smart soft sensing technologies to cater the future industrial needs. • This paper provides a detailed description on state-of-the-art of soft sensors. • This work discusses how the industry can be sustainable via advanced monitoring. • This provides a good overview on soft sensing of different industries. • This identifies the current challenges in soft sensing to the manufacturing industry. • This work provides some possible directions for improving soft sensors. [ABSTRACT FROM AUTHOR]

This paper proposes a novel integrated framework for nesting (i.e., part orientation selection and two-dimensional packing) and scheduling parts assigned to batches (or jobs) on additive manufacturing machines. For the first time, a tri-objective optimization model is designed that interprets profit, energy utilization of machines, and goodwill losses (i.e., tardiness, negotiation, and increasing due date) as three sustainability criteria. Besides, considered negotiation plans may reduce the prices of ordered parts for a possible increase in the initial due date set by customers. The model's scalability is supported by tailoring three algorithms: robust improved ɛ -constraint method, non-dominated sorting genetic algorithm (NSGA-II), and multi-objective grey wolf optimizer (MOGWO). Several insights are derived by analyzing the model's sensitivity to its key parameters and validating its applicability by a case study of Amazon's last-mile delivery process. The results confirm the conflicting objectives, suggested action plans, and proposed algorithms. [Display omitted] • Developing a flexible/faster nesting method in additive manufacturing (AM) systems. • Considering sustainability criteria for nesting and scheduling in AM systems. • Proposing a framework to set negotiation plans for scheduling and nesting decisions. • Developing a representation method for meta-heuristic algorithms. • Deriving insights for a last-mile delivery system in AM by a real-world case study. [ABSTRACT FROM AUTHOR]