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The significance of sustainability and environmental conservation is on the rise, garnering increasing attention from customers, businesses, industries, and regulatory authorities. To foster sustainable manufacturing practices, this research paper presents a comprehensive framework that integrates synergistic elements such as rework strategies, green technology, and carbon control policies. Imperfect quality items are being considered in the model which are proposed to be dealt with a dual approach: either salvaging or reworking them. The study also investigates the impact of carbon control policies on the manufacturing industry, focusing on sustainability and the equilibrium between environmental protection and economic growth. These policies aim to regulate carbon emissions by imposing penalties or incentives, thus encouraging companies to adopt practices that reduce their carbon footprint. Furthermore, investing in green technology offers environmental benefits, cost savings, a competitive advantage, and regulatory compliance. It helps in carbon reduction through which organizations can assess their cleaner practices and environmental efforts. The objective of the developed model is to maximize the expected profit by optimizing selling price, green investment, and production quantity. Analysis of the numerical results of the proposed study is being provided to demonstrate its validity and reliability. Results demonstrated that the utilization of the rework process yields superior outcomes compared to the scenario without rework. A closer evaluation reveals that the limited carbon policy with the rework process leads to significantly diminished levels of carbon emissions and augmented profitability in contrast to alternative policies. Importantly, this approach exhibits a remarkable 2.13% higher economic efficiency and an impressive 37.10% greater adherence to environmentally sustainable practices, thus highly favoring the adoption of such strategies. Consequently, the manufacturing industry can reduce greenhouse gas emissions while promoting economic growth and profitability through the implementation of carbon control policies and investment in green technology, particularly in conjunction with rework processes. [ABSTRACT FROM AUTHOR]

• A multi-attribute group decision method based on the interval-valued intuitionistic fuzzy set is proposed. • A sustainability evaluation of an agricultural water-land-energy-carbon nexus is provided. • Multidimensional indicators are combined through stability, coordinativity and connectivity. • The water, land, energy and carbon subsystems of the agricultural system are quantified based on dual uncertainty. • The model is shown to solve real-world relationship management problems. The interactions between agricultural subsystems, resource scarcity and ecological degradation increase the need for agricultural sustainability assessments in relation to water, land, energy and carbon emissions. In addition, uncertainties caused by natural factors and human activities complicate sustainability assessments. Therefore, this study proposes a regional agricultural sustainability assessment method based on the water-land-energy-carbon (WLEC) nexus system under high uncertainty. This method couples the interval-valued intuitionistic fuzzy set (IVIFS) and multi-attribute group decision making (MAGDM) methods to quantitatively characterise and assess element management and corresponding relationships within the agricultural WLEC nexus system in a multidimensional manner. The paper is able to (1) clarify the role of the coupled agricultural "water-land-energy-carbon" nexus and (2) address the double ambiguity and uncertainty in the indicators and more objectively reflect the interval fluctuations of natural resources and social activities in both positive and negative scenarios. (3) Sustainability management of agricultural resources is carried out according to different regional conditions. The method was applied to Heilongjiang Province in northeast China, and the coupling of agricultural WLEC nexus system in 13 administrative regions of the study area was quantitatively analysed. The degree of agroecological sustainability from a multidimensional perspective is higher in Daxinganling and lower in the provincial capital, Harbin. The recommended range of groundwater use in the study area is [16%,28%] to ensure ecological sustainability when total agricultural output fluctuates between 285,395.18 and 404,495.33 billion yuan. This methodology helps policy makers understand the overall dynamics of agricultural sustainability in an environment of multiple uncertainties and gives suggestions for better assessing management. [ABSTRACT FROM AUTHOR]

This paper applies computational linguistics learning methods to the banking industry and climate change fields. We introduce our data-driven framework, climateBUG, with the aim of detecting latent information about how banks discuss their activities related to climate change using natural language processing (NLP). This framework consists of an ingestion pipeline, a configurable database, and a set of API's. In addition, climateBUG offers two standalone components, namely a unique annotated corpus of approximately 1.1M statements from EU banks' annual and sustainability reporting and a deep learning model adapted to the semantics of the corpus. When benchmarking on classification performance, our model outperforms other models with similar scopes due to its stronger domain relevance. We also provide examples of how the framework can be applied from a user perspective. • A data-driven framework to detect qualitative information in banks' annual reporting. • Natural language processing is applied to non-financial reporting and sustainability. • Framework developed with an interdisciplinary approach and relevant domain knowledge. • Framework includes annotated data and learning model as standalone components. [ABSTRACT FROM AUTHOR]

The evaluation of urban energy internet (UEI) is crucial for the establishment of low-carbon cities. This paper aims to present a hybrid decision-making framework to address UEI evaluation problems from a sustainability perspective. First, the Fermatean fuzzy Aczel-Alsina power aggregation operators are proposed to aggregate evaluation information, and the remarkable properties of these operators are explored. Then, the combined weight of the criteria is determined through the decision-making trial and evaluation laboratory method and the Shannon entropy-based power weighted method within the Fermatean fuzzy circumstance. Moreover, the classic multiobjective optimization by ratio analysis plus the full multiplicative form method is extended using the proposed aggregation operators. Finally, an index system was constructed from a sustainability perspective, and the level of development of the four UEI projects is assessed using the proposed method. The experimental results demonstrate that the proposed method can provide beneficial guidelines for the construction and development of the UEI. Furthermore, the reliability and effectiveness of the proposed decision-making framework are validated through sensitivity analysis and a comparative study with existing MCDM methods. [ABSTRACT FROM AUTHOR]

Aquaculture net pen defects such as biofouling, vegetation, and holes are key challenges to efficient and sustainable fish production in aquaculture. These defects must be monitored to avoid problems with aquaculture net safety as well as fish growth. Recently, deep learning methods have been adopted to solve detection and classification problems in different applications. However, the conventional methods are challenging to meet the demands of high precision and real-time detection of aquaculture net defects in a complex marine environment. Towards this end, this paper proposes an autonomous net pen defect detection system that contains a novel multi-scale semantic segmentation topology for detecting the biofouling, vegetation, and hole problems in the aquaculture environment. In particular, we emphasize fusing the attention maps obtained across different decomposition levels of the network to generate rich feature distributions that enable the accurate extraction of biofouling, vegetation, and holes within aquaculture nets. Moreover, the proposed model is thoroughly tested on two private datasets and two public datasets which were acquired from a real-field fish farms. Across all four datasets, the proposed framework showed remarkable biofouling, vegetation, and hole detection performance, where it outperformed state-of-the-art methods by 6.58%, 3.69%, 6.44%, and 4.78% in terms of mean average precision across LABUST, KU, NDv1, and NDv2 datasets, respectively. • Detection of aquaculture net defects such as biofouling, vegetation, and net holes. • A novel multi-scale encoder–decoder topology. • A new loss function, dubbed L t , can constrain the segmentation networks. • A release of the two highly annotated aquaculture datasets. [ABSTRACT FROM AUTHOR]

Population aging and a shortage of medical resources are driving the growth of home health care (HHC). Many HHC studies have focused on developing an optimal routing plan to reduce operation costs; however, few have addressed the attendant long-term sustainability concerns, such as HHC center locations and carbon emissions from transportation. This paper proposes a home health care location-routing problem with a mixed fleet of electric and conventional vehicles that considers battery swapping stations. It aims to simultaneously determine the locations of HHC centers, the scheduling of caregivers with respect to skill requirements, and a routing plan for a mixed fleet under specific time windows, load capacities, synchronized visits, and driving ranges. To address this problem, the paper proposes a novel competitive simulated annealing (CSA) algorithm in which a series of problem-specific effective local search operators expand the solution space of the CSA algorithm, with a competitive mechanism to adaptively adjust these operators to accelerate convergence speed and improve exploration ability. To enhance the exploitation ability, it employs a modified simulated annealing algorithm with a heating strategy and variable neighborhood descent. Comparison experiments are conducted to verify the superior performance of the CSA algorithm in comparison to existing algorithms and the Gurobi solver. Finally, sensitivity analyses are performed on key factors and provide some new managerial insights. [ABSTRACT FROM AUTHOR]

Along with future sustainability, factors such as environmental protection, co-prosperity, and ethical management are also gaining attention, resulting in the increased significance of the non-financial performance of companies. Accordingly, many institutions are evaluating the non-financial activities of companies using environmental, social, and governance (ESG) as evaluation indicators. However, the use of the current ESG rating as a reliable indicator has a limitation as the criteria are different for each evaluation institution and the ESG rating for the same company may differ when provided by different evaluation institutions. Accordingly, it is important to directly extract non-financial information generated by various media to provide objective ESG information, but the studies that focus on the need for automatic and direct extraction and classification of ESG information are limited. Therefore, this paper proposes a classifier that can discriminate ESG information. To train the ESG classifier, a dataset was constructed by manually labeling ESG data, and the ESG classifier recorded a classification accuracy of 86.66% for the 4-class classification problem of the constructed dataset. In addition, three application experiments were conducted to verify the usability of the proposed model. First, the results confirmed that the ESG classifier showed a significant performance of 83.96% accuracy for sectors not included in the training data. Second, the results qualitatively confirmed that the proposed model properly extracts ESG-related information from multi-source text data. Finally, the ESG dataset was additionally constructed through the pseudo-labeling technique, and the performance improvement of the data augmentation and classifier was verified. The ESG classifier proposed in this paper will provide appropriate ESG information to stakeholders. In future studies, we intend to extract and classify ESG-related information from more diverse types of data through an advanced ESG classifier. Further, we will use the information for investigating methods for reflecting the non-financial performance of companies in ESG ratings. • ESG is an indicator of a company's nonfinancial performance. • Develop a model for extracting ESG-related information. • Building a dataset for ESG information extraction and classification. • Applying ESG-classifier to other sectors and multisource text data. • Proposal of additional construction of ESG dataset using pseudo labeling technique. [ABSTRACT FROM AUTHOR]

To reduce energy consumption (EC) in the wastewater treatment process (WWTP) for achieving energy conservation and emission reduction, multi-objective evolutionary algorithm (MOEA) applied in WWTP needs to have better convergence and diversity. In this paper, a non-dominated sorting genetic algorithm III based on utopian point improvement (NSGAIII-UP) is proposed, which overcomes the disadvantage that multi-objective evolutionary algorithm based on utopian point decomposition (MOEA/D-UP) cannot handle the multi-segment discontinuous Pareto front (PF) problem well. By comparing the proposed NSGAIII-UP with 8 advanced algorithms by 2 metrics on 22 benchmark functions, the effectiveness of the proposed algorithm in dealing with the complex PF problems is proved. Moreover, a multivariable model predictive controller (MMPC) is used to control the dissolved oxygen and nitrate nitrogen concentrations in WWTP, in which NSGAIII-UP is applied to the upper EC optimization of WWTP. The simulation results show that compared with the closed-loop control, the EC in the WWTP is reduced by 7.395 %, and the lowest EC is achieved compared with the other 8 algorithms. It helps to reduce energy waste in WWTP and promotes ecological sustainability. The source code of NSGAIII-UP is publicly available at https://github.com/sut16/NSGAIII-UP. [ABSTRACT FROM AUTHOR]

Sustainable Supply Chain Network Design (SCND) poses a complex challenge, as it often requires trade-offs in decision-making. The problem becomes even more challenging when there is a need to integrate risk management into the design process. It is steered by this challenge that the purpose of the paper is twofold. First, it proposes a Simulation-Based Decision Support System (SBDSS) for a real case study of a Portuguese wine SCND problem. Second, it analyzes different wine Supply Chain (SC) configurations under risk situations and sustainability objectives. The main contributions of this research are as follows. First, it concerns the method that was employed, which, contrarily to traditional approaches in literature (e.g., stochastic or genetic programming), consists of a simulation approach, in which agents and their interactions in an environment with uncertainty are modeled. Second, for the generation of non-dominated solutions, a simheuristic was employed, which combines multi-objective optimization with simulation. Finally, the type of industry that was considered, due to the lack of cases focusing on it in literature. The results suggest that the proposed approach determines a set of Pareto-front solutions that consider the sustainability dimensions of SCND, as well as risk situations. Furthermore, the implications that several parameters have on the sustainability of the designed SCs were assessed, as well as the impacts that the considered risks have on the performance of SCs. In particular, it was found that transportation risks impacted considerably more the performance of the SC than the risks on the prices of energy and raw materials. [ABSTRACT FROM AUTHOR]

Pavement management systems (PMS) were typically developed to manage interurban networks. This paper presents an urban pavement management system (UM-PMS) that integrates all the modules of a PMS considering the particularities of the urban context. The tool uses a Geographic Information System (GIS) to import, analyze, and manage the data of the urban road network. The inspection data is obtained by an automatic equipment composed by a video camera installed on a vehicle. Images are analyzed by deep learning techniques based on Convolutional Neural Networks. Further, appropriate decisions on maintenance treatments are made by integrating multi-objective optimization and multi-criteria decision-making methods to plan efficient maintenance strategies considering economic, environmental, social, and performance objectives. Finally, the tool is applied to an urban case study to illustrate its applicability. Outcomes indicate that the proposed framework can obtain a sustainable short-term plan without losing sight of the long-term efficiency. [ABSTRACT FROM AUTHOR]

Power Plants (PPs) is considered as critical facilities in each region because of essential role through energy generation processes. These facilities are also depended to water availability especially in water stress areas. Due to the critical water shortage in many areas around the world, it is necessary to make an optimal condition among water consumption and the increasing demand for electricity to prevent any further conflict of interests between industry, householders and the environmental goals. There are different techniques for controlling Water Consumption (WC) in these industries. This paper develops a smart Decision Support System (DSS) for monitoring, prediction and control sections based on Artificial Intelligent (AI) and integration of the PESTEL matrix and Multi Criteria Decision Making (MCDM) methods. Monitoring section comprises Fuel Consumption (FC), Atmospheric Temperature (AT), Power Plant Temperature (PPT) and Power Plant Efficiency (PPE), in which FC has the most influence on WC based on ANOVA evaluations in both cold and warm seasons. The prediction results have illustrated that Adaptive Neuro Fuzzy Inference System model is more efficient for the WC estimation with a correlation coefficient over 0.99. Ordered Weighted Averaging (OWA) also demonstrated that in the optimistic and pessimistic states, the most priority is linked to E3 (Establishment of evaporation control systems by contractor companies and concluding a guaranteed purchase contract with a power plant worth one and a half times the current amount of water price). In the last step of technical approaches, the smart controlling system is added for execution of water-energy nexus in the PP based on proportional–integral–derivative controller system. Finally, the performance of the DSS is approved with more than 80% agreement of experts and more than 90% precision in prediction procedure through this investigation. Application of this DSS can also be helpful for developing countries to achieve the UN Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Since the different matching schemes between manufacturing tasks and services have a great influence on the life cycle sustainability of complex product system (CoPS), the strategy used to find cost-effective, environmentally friendly, and socially acceptable matching results is essential for sustainable development. This paper proposed a novel two-sided matching model for CoPS based on life-cycle sustainability assessment (TSMM-CoPS-LCSA). First, the two-sided matching model based on the life-cycle sustainability assessment of manufacturing tasks and services for CoPS is presented. Then, a bi-level decision-making method is proposed, where learning and synergy effect are considered to ensure stability in the matching process. Sequentially, to facilitate the optimization solution, the Karush-Kuhn-Tucker (KKT) conditions are adopted to convert the bi-level model to a single model which aims to minimize the total cost, environmental impact, and social impact. Subsequently, for solving this nonlinear and complex multi-objective decision-making model, an improved hybrid algorithm that integrated Tabu Search and non-dominated sorting genetic algorithm II (TS-NSGA-II) is applied to solve the above model. Finally, a case study of CoPS manufacturing project is illustrated to demonstrate the effectiveness and superiority of the proposed TSMM-CoPS-LCSA model. The results indicate that the proposed method can find a better services scheme in line with actual demand, which provides a guiding significance for promoting the sustainable development of the manufacturing industry. [ABSTRACT FROM AUTHOR]

• A sustainable supply chain (SSC) under the cap-and-trade regulation is studied. • Double-level sustainability effort is proposed in the SSC. • Effects of double-level sustainability effort on the SSC performance are explored. • Optimal levels of sustainability, quality, and green effort are determined. • A compensation-based wholesale price contract is developed to coordinate the SSC. In recent decades, due to the growing sustainable development concerns, the supply chains members, such as manufacturers and retailers, are interested in investing in environmental sustainability and social sustainability issues to gain more market share in today's competitive business environment. This paper contributes to the literature by proposing double-level sustainability effort in a sustainable supply chain consists of one manufacturer and one retailer under the cap-and-trade regulation. The manufacturer invests in the technology to reduce carbon emissions and improve the quality of products. On the other hand, the retailer invests in green effort while decides on its periodic review order-up-to-level replenishment policy. Under such a case, both the manufacturer's and the retailer's decisions not only affect their individual profitability but also impact on the whole supply chain performance and sustainability level. Using game-theoretic mathematical modeling, the double-level sustainability effort is first analyzed under the decentralized and centralized structures. Then, a compensation-based wholesale price contract is introduced to coordinate the sustainable supply chain under the cap-and-trade regulation. The surplus profit obtained under the coordination plan is shared between the manufacturer and retailer depending on their bargaining power. The data generated based on the assumptions of a pharmaceutical case study is applied to indicate the applicability of developed models. The results illustrate that the developed coordinated system simultaneously enhances the whole sustainable supply chain performance from the social, environmental, and economic points of view. [ABSTRACT FROM AUTHOR]

• AVs scheduling problem is transformed to a NWFPJSP problem. • A new AV scheduling approach on Bidirectional paths (IFSB) is presented. • IFSB enables bidirectional path more efficient than unidirectional path. A robotic mobile fulfillment system (RMFS) is a "parts-to-picker" system employing a fleet of autonomous vehicles (AV), which transport pods between a storage area and picking stations. In this paper, an AV interference-free scheduling on bidirectional paths (IFSB) approach is studied and shows better working efficiency than unidirectional paths. In order to model AV scheduling on bidirectional path with interference-free constraints, a no-wait flexible process job shop scheduling problem (NWFPJSP) is employed to evaluate total completion time. A mathematical model aiming to minimize the total completion time is presented with as constraint interference-free operation. An A* algorithm is modified for path planning and a simulated annealing algorithm (SA) is employed for scheduling AVs and their paths. Based on a case study with a small RMFS, the IFSB approach results in a 40% increase of the storage capacity and a 22% decrease of the total completion time when compared to a unidirectional approach. Meanwhile, the number of AVs stop-starts are decreased by 40% and the paths length for all AV are decreased by 36%. This indicates the great potential of bidirectional paths in future logistic applications, which may increase the storage capacity, working efficiency, technical health of the AVs, and environmental sustainability. [ABSTRACT FROM AUTHOR]