An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls.

This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision-making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices.

Application and planning of an energy-oriented stochastic disassembly line balancing problem.

End-of-life (EOL) products are getting more and more attention as a result of the rapid decline in environmental resources and the dramatic rise in population at the moment. Disassembly is a crucial step in the reuse of EOL products. However, the disassembly process for EOL products is highly uncertain, and the disassembly planning method may not produce the anticipated outcomes in actual implementation. Based on the physical nature of the product disassembly process with multiple uncertain variables, certainty disassembly cannot adequately characterize the uncertain variables effectively. Uncertainty disassembly takes into account the changes in parts caused by product use, such as wear and corrosion, which can better coordinate the arrangement of disassembly tasks and better match the actual remanufacturing process. After analysis, it was found that most of studies on uncertain disassembly focus on the economic efficiency perspective and lack of energy consumption considerations. For the gaps in the current study, this paper proposes a stochastic energy consumption disassembly line balance problem (SEDLBP) and constructs a mathematical model of SEDLBP based on the disassembly of spatial interference matrix, In this model, the energy consumption generated by the disassembly operation and workstation standby is not a constant value but is generated stochastically in a uniformly distributed interval. In addition, an improved social engineering optimization algorithm that incorporates stochastic simulation (SSEO) is proposed in this paper to effectively address the issue. The incorporation of swap operators and swap sequences in SSEO makes it possible to solve discrete optimization problems efficiently. A comparison of a case study with some well-tested intelligent algorithms demonstrates the efficacy of the solutions produced by the proposed SSEO.

Wildlife resource conservation and utilization for achieving sustainable development in China: main barriers and problem identification.

The rapid growth of the industrial economy has affected the survival of wildlife, and the decline in wildlife resources will in turn have some negative impact on the industrial economy. For the sustainable development of the industrial economy, human beings began to reflect on traditional development thinking and strive to find a development strategy that harmonizes industrial economic development and resource protection, and wildlife protection gradually attracted people's attention. "Protecting wild animals, maintaining ecological balance, and promoting economic development" has become a hot topic in the new century. Wildlife resources are valuable natural resources and play an important role in the ecosystem, which is related to the well-being and future of human beings. In recent years, China has made great progress in wildlife protection, while protecting and expanding wildlife habitats, introducing relevant laws and regulations, and other measures which have been implemented recently. However, there are still shortcomings in the protection of wildlife in China. Over-utilization, habitat loss and degradation, environmental pollution, climate change, weak legal awareness, and indiscriminate hunting all pose serious threats to wildlife in China. In this regard, this paper summarizes the main problems and barriers to wildlife resource conservation and utilization in China. Based on the analytic hierarchy process (AHP), the main technology factors influencing wildlife resource conservation and utilization in China are identified. Finally, the future research development direction of wildlife conservation is discussed based on the critical factors. This can provide some guidance for developing wildlife resource conservation and utilization for a sustainable ecosystem in China.

A survey of multi-criteria decision-making techniques for green logistics and low-carbon transportation systems.

With the increasing severity of environmental problems, low-carbon development has become an inevitable choice. Nowadays, low-carbon green sustainable development is influenced by a variety of factors such as social, environmental, technological, and economic development levels, making its development complex, which in turn imposes challenges on decision-makers. In this context, the application of multi-criteria decision-making (MCDM) in different areas of sustainable development engineering has become a hot topic. Although many reviews of MCDM techniques already exist, there is a lack of holistic review efforts on MCDM in the field of low-carbon transport and green logistics. Considering these shortcomings in the state of the art, this paper systematically reviews more than 190 papers from 2010 to 2022, constructs a general structure of MCDM techniques for this research topic, provides a comprehensive review and analysis of it, and clarifies the current practices. Furthermore, future directions for the development of MCDM techniques for green logistics and low-carbon transportation systems are presented as well.

Hybrid evolutionary algorithm for stochastic multiobjective disassembly line balancing problem in remanufacturing.

With the development of the industrial economy and the accelerated renewal of products, many end-of-life products (EOL) have been generated to pollute our environment. This fact highlights the importance of recycling and remanufacturing EOL products as an active research topic. An efficient disassembly line is one solution for improving the remanufacturing and recycling processes of EOL products while reducing the environmental pollution. Although many optimization models and intelligent algorithms were developed to address the disassembly line balancing problem (DLBP), uncertainty was ignored by them. To alleviate the drawbacks of uncertainty for the disassembly operation, this study proposes a stochastic multi-objective optimization model for the DLBP minimizing the disassembly idle rate, smoothness, and energy consumption generated during the operation under uncertain operation time. Another novelty of this paper is to present an improved version of the northern goshawk optimization algorithm using a stochastic simulation method to solve the proposed model. The feasibility of the proposed model and the applicability of the developed algorithm are shown by two extensive examples. Finally, the performance of the proposed algorithm is revealed by a comparison with recent and state-of-the-art algorithms from the literature.

Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies.

The COVID-19 pandemic has made a significant impact on various supply chains (SCs). All around the world, the COVID-19 pandemic affects different dimensions of SCs, including but not limited to finance, lead time, demand changes, and production performance. There is an urgent need to respond to this grand challenge. The catastrophic impact of the COVID-19 pandemic prompted scholars to develop innovative SC disruption management strategies and disseminate them via numerous scientific articles. However, there is still a lack of systematic literature survey studies that aim to identify promising SC disruption management strategies through the bibliometric, network, and thematic analyses. In order to address this drawback, this study presents a set of up-to-date bibliometric, network, and thematic analyses to identify the influential contributors, main research streams, and disruption management strategies related to the SC performance under the COVID-19 settings. The conducted analyses reveal that resilience and sustainability are the primary SC topics. Furthermore, the major research themes are found to be food, health-related SCs, and technology-aided tools (e.g., artificial intelligence (AI), internet of things (IoT), and blockchains). Various disruption management strategies focusing on resilience and sustainability themes are extracted from the most influential studies that were identified as a part of this work. In addition, we draw some managerial insights to ensure a resilient and sustainable supply of critical products in the event of a pandemic, such as personal protective equipment (PPE) and vaccines.

Capacitated multi-objective disassembly scheduling with fuzzy processing time via a fruit fly optimization algorithm.

This work proposes a capacitated fuzzy disassembly scheduling model with cycle time and environmental cost as parameters, which has broad applications in remanufacturing and many other production systems. Disassembly scheduling is not always given accurately as a time quota in a production system, particularly in the obsolete product remanufacturing process. It is important to study novel models and algorithms based on uncertainty processing time to solve uncertainty disassembly scheduling problems. In this paper, a mixed-integer mathematical programming model is proposed to minimize the cycle time and environmental cost, whilst a metaheuristic approach based on a fruit fly optimization algorithm (FOA) is developed to find a fuzzy disassembly scheduling scheme. To estimate the effectiveness of the proposed method, the proposed algorithm is tested with different size cases of product disassembly scheduling. Furthermore, experiments are conducted to compare with other multi-objective optimization algorithms. The computational results demonstrate the proposed algorithm outperforms other algorithms on computational efficiency and applicability to different problems. Finally, a case study is described to illustrate the proposed method. The main contribution of this current work shows the proposed algorithm to solve the problem of disassembly scheduling in an uncertain environment practically and efficiently.

Evaluation of traffic noise pollution using geographic information system and descriptive statistical method: a case study in Mashhad, Iran.

Environmental consequences and the epidemiologic results of noise pollution have chronic effects leading to widespread complications in the long run. As far as we know, there are a few studies for pollution monitoring and control systems in comparison with other environmental pollutants. One of the largest metropolitan cities located in Iran is Mashhad city as known as one of the biggest religious cities in the world. Different properties of this city including historical, industrial, and religious draw thousands of visitors to Mashhad, yearly. This fact motivates us to contribute to the concept of noise pollution in streets and sidewalks around the Holy Shrine, namely, Imam Reza. In this regard, different measurements using geographic information system (GIS) and descriptive statistical methods were conducted for our case study in Mashhad, Iran. All measurements and records were done during the peak of morning crowd (10-12 AM) and evening crowd (4-6 PM) on both sidewalks of each street around the Holy Shrine. This study showed that the pollution in the evening time span (4-6 PM) has the maximum level of noise. Among all streets in our case study in Mashhad, Iran, Tabarsi street has the most amount of noise pollution with a mean of 78 dB(A) for the mean intensity for each point, and Imam Reza street has the minimum amount of pollution with a mean of 72.75 dB(A). Our findings from the temporal perspective analysis confirm that the noise pollution peaks in the evening, when weather conditions are favorable. From the spatial perspective analysis, the most intensive noise pollution was observed around residential and accommodation land uses, which have the highest number of arterial routes towards the Holy Shrine.

A novel viewpoint to the green city concept based on vegetation area changes and contributions to healthy days: a case study of Mashhad, Iran.

One of the significant challenges in urbanization is the air pollution. This highlights the need of the green city concept with reconsideration of houses, factories, and traffic in a green viewpoint. The literature review confirms that this reconsideration for green space has a positive effect on the air quality of large cities and to reduce the air pollution. The purpose of this study is to evaluate the annual vegetation changes in the green space of Mashhad, Iran as a very populated city in the middle east to study the air pollution. To investigate the relationship between the air pollution and vegetation, the Landsat 8 satellite images for summer seasons of 2013-2019 were used to extract changes in vegetation by calculating the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the optimized soil adjusted vegetation index (OSAVI). The main contribution in comparison with the relevant studies is to study the relationship between clean, healthy, and unhealthy days with the green space area for the first time in Mashhad, Iran. The results show that the implementation of green city concept in Mashhad, Iran, has been increased by 64, 81, and 53% by NDVI, EVI, and OSAVI, respectively, during the study period. The vegetation area of this city is positively correlated to clean and healthy days and has a negative correlation to unhealthy days, in which the greatest values for NDVI, EVI and OSAVI are 0.33, 0.52, and -0.53, respectively.

An integrated approach for a sustainable supplier selection based on Industry 4.0 concept.

The recent advances in sustainable supply chain management are integrated with Industry 4.0 concepts. This study develops a new integrated model to consider the sustainability and Industry 4.0 criteria for the supplier selection management. The proposed approach consists of the fuzzy best worst method (FBWM) and the two-stage fuzzy inference system (FIS) to assess the selection of suppliers. Firstly, this study determines a comprehensive list of Industry 4.0 and sustainability criteria along with their definitions. Then, the importance weight of each criterion is computed by the FBWM. Subsequently, a two-stage FIS is devoted to nominate the suppliers' performance with regard to the sustainability and Industry 4.0 criteria. To show the applicability of our integrated model, a case study for a textile company in Iran is provided. Finally, some sensitivity analyses are done to assess the efficiency of the proposed integrated approach. One finding is to establish a decision-making framework to evaluate suppliers separately, rather than relatively in a fuzzy environment using Industry 4.0 and sustainability criteria.

Preventive maintenance for the flexible flowshop scheduling under uncertainty: a waste-to-energy system.

Nowadays, an efficient and robust plan for maintenance activities can reduce the total cost significantly in the equipment-driven industry. Maintenance activities are directly associated with the impact on the plant output, production quality, production cost, safety, and the environmental performance. To address this challenge more broadly, this paper presents an optimization model for the problem of flexible flowshop scheduling in a series-parallel waste-to-energy (WTE) system. To this end, a preventive maintenance (PM) policy is proposed to find an optimal sequence for processing tasks and minimize the delays. To deal with the uncertainty of the flexible flowshop scheduling of waste-to-energy in practice, the work processing time is modeled to be uncertain in this study. Therefore, a robust optimization model is applied to address the proposed problem. Due to the computational complexity of this model, a novel scenario-based genetic algorithm is proposed to solve it. The applicability of this research is shown by a real-life case study for a WTE system in Iran. The proposed algorithm is compared against an exact optimization method and a canonical genetic algorithm. The findings confirm a competitive performance of the proposed method in terms of time savings that will ultimately save the cost of the proposed PM policy.

A hybrid computational intelligence approach for bioremediation of amoxicillin based on fungus activities from soil resources and aflatoxin B1 controls.

Nowadays, releasing the Emerging Pollutants (EPs) in the nature is one of the main reasons for many health and environmental disasters. Amoxicillin as an antibiotic is one of the EPs and categorized as the Endocrine Disrupting Compounds (EDCs) in hazardous materials. Accumulation of amoxicillin in the soil bulk increases the cancer risk, drug resistances and other epidemiological diseases. Hence, the soil bioremediation of antibiotics can be a solution for this problem which is more environmental-friendly system. This study technically creates a bio-engine setup in soil bulk for remediation of amoxicillin based on Aspergillus Flavus (AF) activities and Removal Percentage (RP) of amoxicillin with Aflatoxin B1 Generation (AG) controls. The main novelty is to propose a hybrid computational intelligence approach to do optimization for mechanical and biological aspects and to predict the behavior of bio-engine's effective mechanical and biological features in an intelligent way. The optimization model is formulated by the Central Composite Design (CCD) which is set by the Response Surface Methodology (RSM). The prediction model is formulated by the Random Forest (RF), Adaptive Neuro Fuzzy Inference System (ANFIS) and Random Tree (RT) algorithms. According to the experimental practices from real soil samples in different times and places, concentration of amoxicillin and Aflatoxin B1 are set equal to 25 mg/L (ppm) and 15 µg/L (ppb). Likewise, the outcomes of experiments in CCD-RSM computations are evaluated by curve fitting comparisons between linear, 2FI, quadratic and cubic polynomial equations with considering to regression coefficient and predicted regression coefficient values, ANOVA and optimization by sequential differentiation. Based on the results of CCD-RSM, the RP performance in the optimum conditions is measured around 86% and in 25 days after runtime, the RP and AG are balanced in the safe mode. The proposed hybrid model achieves the 0.99 accuracy. The applicability of the research is done using real field evaluations from drug industrial park in Mashhad city in Iran. Finally, a broad analysis is done and managerial insights are concluded. The main findings of the present research are: (I) with application of bioremediation from fungus activities, amoxicillin amounts can be control in soil resources with minimum AG, (II) ANFIS model has the best accuracy for smart monitoring of amoxicillin bioremediation in soil environments and (III) based on the statistical assessments Aeration Intensity and AF/Biological Waste ratio are most effective on the amoxicillin removal percentage.

Production scheduling of off-site prefabricated construction components considering sequence dependent due dates.

The growing environmental concerns, excessive utilization of natural resources, and high energy consumption have put a severe pressure on the construction industry to adopt new methods in response to these challenges. As a remedy, prefabricated construction methods are broadly utilized to enhance the productivity of the construction activities. However, challenges associated with on-time production of precast components have not adequately been addressed. Thus, this study proposes a new insight to the theory of scheduling dealing with sequence-dependent due dates, in which total weighted earliness and tardiness are minimized. To tackle uncertainties and complexities associated with the stochastic nature of production problems, three integrated simulation-optimization algorithms, which employ simulation methods inside a metaheuristic framework, are proposed. Three metaheuristic algorithms, including genetic algorithm (GA), differential evolution (DE), and imperialist competitive algorithm (ICA), are employed to minimize objective function. A series of computational tests are conducted to investigate the performance of these approaches. Results indicate that integrated DE-simulation approach can provide better results in comparison with other approaches.

Blockchain in supply chain management: a review, bibliometric, and network analysis.

Blockchain is a distributed ledger technology that has attracted both practitioners and academics attention in recent years. Several conceptual and few empirical studies have been published focusing on addressing current issues and recommending the future research directions of supply chain management. To identify how blockchain can contribute to supply chain management, this paper conducts a systematic review through bibliometric and network analysis. We determined the key authors, significant studies, and the collaboration patterns that were not considered by the previous publications on this angel of supply chain management. Using citation and co-citation analysis, key supply chain areas that blockchain could contribute are pinpointed as supply chain management, finance, logistics, and security. Furthermore, it revealed that Internet of Things (IoT) and smart contracts are the leading emerging technologies in this field. The results of highly cited and co-cited articles demonstrate that blockchain could enhance transparency, traceability, efficiency, and information security in supply chain management. The analysis also revealed that empirical research is scarce in this field. Therefore, implementing blockchain in the real-world supply chain is a considerable future research opportunity.