Sustainable Network Research Articles

Using supervised machine learning algorithms in pavement degradation monitoring

• Dynamic pavement monitoring system using vibration-based method. • Noise cancelling and vibration data smoothing. • Degradation detection and classification using machine learning. Pavement monitoring plays a vital role in maintaining sustainable road network conditions and provides road users with satisfactory comfort riding. Regular and routine monitoring provide clear information on road conditions and the level of damage to pavement surfaces. In this study, a vibration-based method was used as a monitoring technique to evaluate the pavement surface conditions on local roads. Pre-processing techniques were used to cancel noise and prepare the data for feature extraction and prediction. Two multi-classification Machine Learning (ML) models were used, including Random Forest (RF) and Decision Tree (DT), for the automated classification and detection of different types of pavement distresses. In addition, a Support Vector Machine (SVM) technique was used to develop a binary ML model for the same classification and detection purposes. The results showed that the developed ML models provide high accuracy in predicting the road degradation classification with about 93% accuracy using the RF and 90% accuracy using the DT. Using the SVM model, the overall average accuracy of detection and classification of pavement defects was about 96%.

SAVIOR: A Sustainable Network of Vehicles with Near-Perpetual Mobility

Switching to Battery Electric Vehicles (BEV) can have a significant positive impact on our environment. However, the adoption of BEVs is vastly impeded by battery-related concerns, such as limited travel range, long charging time, high purchasing cost (battery-induced) and lack of charging stations. Additionally, it is very expensive to build a large infrastructure of fast charging stations that can cater to a full-scale BEV fleet. Alternative solutions, such as charging from the road and BEV-to-BEV stationary charge sharing, have been proposed to counteract range anxiety, but they are mostly ineffective and suffer from scalability issues. In this article, we present SAVIOR, an innovative Internet-of-Things (IoT) framework for replenishing BEV batteries on-the-go with the help of unmanned aerial vehicles (UAVs) and mobile charging stations (MoCS). This will allow rapid BEV battery replenishment, eliminating the need for BEVs to make prolonged and pre-planned halts for re-charging. We also observe that package delivery UAVs can utilize this framework to make long-distance trips with the help of mobile charging stations and BEVs. We quantitatively analyze the effectiveness of such a framework through a simulation platform that we have developed. There is a drastic improvement in the mobility of BEVs and UAVs. Through statistical analysis, we also observe that greenhouse gas emissions (even for BEVs and UAVs) can be significantly reduced by SAVIOR if the MoCS are powered by renewable energy sources (e.g., solar).

Exploring Digitalization within the Tourism Supply Chain Network

Purpose: Digitalisation among SMMEs has been slow due to the constraints such as a lack of infrastructure, networks, technologies, and related skills to fast-track digital transition. These binding constraints make it harder for Small Micro and Medium Enterprises (SMMEs) in developing countries to undertake this digital transition. The purpose of this study is to explore the state of digitalisation within the Tourism Supply Chain Network (TSCN) among SMMEs in Gauteng.
 Design/ Methodology/ Approach: An ethnographic qualitative methodology was adopted to have a better understanding of the complexity and idiosyncrasies of digitalisation within the TSCN among the SMMEs in Gauteng where interviews were conducted from twelve participants until saturation.
 Findings: The results revealed that apart from the lack of digital skills (data analytics software and video editing technology) and limited understanding of knowledge management especially for the legal systems within the supply network among the selected SMMEs remains a challenge.
 Practical Implication: Formulating integrated business ventures and approaching big projects as consortiums not as individual small micro entities is critical for more purpose-driven tourism networks, especially for emerging businesses. SMMEs hardly work with other businesses in the area to develop sustainable networks; hence, there is a need for an effective travel and tourism data Management Company connecting both international and domestic suppliers for effective and efficient service to the businesses and the customers.
 Paper type: Research paper

Sustainable hierarchical multi-modal hub network design problem: bi-objective formulations and solution algorithms

This paper presents a bi-objective model for the design and optimization of a sustainable hierarchical multi-modal hub network. The proposed model focuses on sustainability by considering economic, environmental, and social aspects of the decisions in a hierarchical network. A case of Turkish network for freight transportation is used to validate the proposed model. To solve the small-sized problems, the augmented epsilon constraint method version 2 (AUGMECON2) is applied. It can be inferred from the Pareto-optimal set obtained by AUGMECON2 that the effect of increasing the number of hubs after a threshold is marginal. The current contribution proposes two multi-objective genetic algorithms (NSGA-II and NRGA), which incorporate LP solving and Dijkstra algorithm. The results show the superiority of NRGA compared to NSGA-II in terms of solution time. Also, we present an alternative, more efficient formulation to the problem. Based on the alternative formulation, in addition to AUGMECON2, we use two exact methods, including Torabi and Hassini (TH) method and augmented weighted Tchebycheff procedure (AWTP), to find Pareto-optimal solutions for small, medium, and large-sized problems (including the case study). The performance of the proposed solution methods is measured using some multi-objective indicators. The results show the superiority of AUGMECON2.

A Bi-objective two-stage stochastic optimization model for sustainable reverse supply chain network design under carbon tax policy and government subsidy considering product quality

ABSTRACT There is a growing concern over environmental pollution resulting from the production. Moreover, the expansion of various industries has led to an increased demand for raw materials. To address these challenges, this paper aims to investigate the bi-objective optimization of a sustainable reverse supply chain network while considering two key sources of uncertainty in the returned product quality and the remanufacturing capacity. Additionally, the study considers the effects of carbon tax policies and government subsidies on remanufactured products, while also focusing on three important sustainability aspects - economic, social, and environmental. The study uses two quality thresholds at inspection centers to sort products, and the epsilon constraint and NSGA-II are applied to solve the model. Through numerical analysis, the research demonstrates that objective functions are sensitive to uncertain parameters and minimum acceptable quality levels. Furthermore, the study reveals that government subsidies can offset the negative effects of carbon tax policies.

Learning from serosurveillance for SARS-CoV-2 to inform pandemic preparedness and response

Learning from serosurveillance for SARS-CoV-2 to inform pandemic preparedness and response

Sustainable network analysis and coordinated development simulation of urban agglomerations from multiple perspectives

Sustainable and coordinated development plays a crucial role in urban planning. However, previous studies have not fully explored the complex interplay between different factors that affect coordinated development within urban agglomerations. To address this gap, our study proposes a novel network-based approach that combines qualitative analysis and quantitative verification to assess the coordinated development of urban agglomerations. In this study, we focused on the Yangtze River Delta Urban Agglomeration (YRDUA) as our study area. Firstly, we constructed a network for sustainable development in urban agglomerations based on Sustainable Development Goals (SDG) data. Next, we analyzed the spatial patterns of intercity resource exchange flows to gain a more comprehensive and diversified perspective on regional coordinated development. By integrating qualitative and quantitative analysis, we then used the Exponential Random Graph Model (ERGM) to identify the driving factors of coordinated development. Finally, we optimized the development score of urban subgroups by using a greedy algorithm and determined the direction for coordinated development among different cities in the YRDUA. The findings of our study reveal that the development potential and optimal agglomeration pattern of urban subgroups in the YRDUA are closely linked to intercity cooperation, science and technology industries, foreign investment, and multiple interaction flows. Furthermore, our results suggest that local governments should focus on building a multi-core network flow pattern and fully utilizing the distinctive advantages of urban subgroups according to local conditions and time. These insights can guide policymakers and urban planners in designing more coordinated and efficient urban agglomerations.

RETRACTED ARTICLE: Sustainable and optimal design of Chinese herbal medicine supply chain network based on risk dynamic regulation mechanism

We propose a robust fuzzy design model for a sustainable closed-loop supply chain network. The model is based on a risk dynamic regulation mechanism. In this way, we can solve the problem of sudden disruptions and uncertain demand in the supply chain of Chinese herbal medicines. We also develop a hybrid algorithm solution to solve the model and design a resilient supply chain network. The specific steps are as follows: (1) The risk dynamic regulation mechanism is created with strong risk resistance by considering the information sharing platform, facility defense, drying station scheduling, safety stock, and shared inventory. (2) Based on the dynamic risk regulation mechanism, we establish a sustainable Chinese herbal medicine supply chain network design model. Then, we use the robust fuzzy method and the epsilon constraint to deal with the uncertainty and integrate the model. (3) We introduce opposition-based learning, cosine convergence factor, and levy flight to the original Whale and Grey wolf algorithms to obtain the Hybrid algorithm, which is used to solve the processed model. The results show the model and algorithm proposed in this paper have strong applicability and advantages in designing closed-loop supply chain networks for Chinese herbal medicine and provide references for relevant decision-makers.

A multi-energy multi-microgrid system planning model for decarbonisation and decontamination of isolated systems

Decarbonising and decontaminating remote regions in the world presents several challenges. Many of these regions feature isolation, dispersed demand in large areas, and a lack of economic resources that impede the development of robust and sustainable networks. Furthermore, isolated systems in the developing world are mostly based on diesel generation for electricity, and firewood and liquefied petroleum gas for heating, as these options do not require a significant infrastructure cost. In this context, we present a stochastic multi-energy multi-microgrid system planning model that integrates electricity, heat and hydrogen networks in isolated systems. The model is stochastic to capture uncertainty in renewable generation outputs, particularly hydro and wind, and thus design a multi-energy system proved secured against such uncertainty. The model also features two distinct constraints to limit the emissions of CO2 (for decarbonisation) and particulate matter (for decontamination), and incorporates firewood as a heating source. Moreover, given that the focus is on low-voltage networks, we introduce a fully linear AC power flow equations set, allowing the planning model to remain tractable. The model is applied to a real-world case study to design a multi-energy multi-microgrid system in an isolated region in Chilean Patagonia. In a case with a zero limit over direct CO2 emissions, the total system’s cost increases by 34% with respect to an unconstrained case. In a case with a zero limit over particulate matter emissions, the total system’s cost increases by 189%. Finally, although an absolute zero limit over both, particulate matter and direct CO2 emissions, leads to a total system’s cost increase of 650%, important benefits in terms of decarbonisation and decontamination can be achieved at marginal cost increments.

Heat Waves: Physical Understanding and Scientific Challenges

AbstractHeat waves (HWs) can cause large socioeconomic and environmental impacts. The observed increases in their frequency, intensity and duration are projected to continue with global warming. This review synthesizes the state of knowledge and scientific challenges. It discusses different aspects related to the definition, triggering mechanisms, observed changes and future projections of HWs, as well as emerging research lines on subseasonal forecasts and specific types of HWs. We also identify gaps that limit progress and delineate priorities for future research. Overall, the physical drivers of HWs are not well understood, partly due to difficulties in the quantification of their interactions and responses to climate change. Influential factors convey processes at different spatio‐temporal scales, from global warming and the large‐scale atmospheric circulation to regional and local factors in the affected area and upwind regions. Although some thermodynamic processes have been identified, there is a lack of understanding of dynamical aspects, regional forcings and feedbacks, and their future changes. This hampers the attribution of regional trends and individual events, and reduces the ability to provide accurate forecasts and regional projections. Sustained observational networks, models of diverse complexity, narrative‐based methodological approaches and artificial intelligence offer new opportunities toward process‐based understanding and interdisciplinary research.

Leagile sustainable supply chain network design considering disruption risk by C-Var approach: a case study

Leagile sustainable supply chain network design considering disruption risk by C-Var approach: a case study

Sustainable fixed wireless access with blockchain secured software defined network

Recently, there has been a tremendous increase in the number of service providers offering Fixed Wireless Access (FWA). The main goal of service providers is to offer guaranteed service (GB/month) for all contents by meeting sustainability Key Performance Indicators (KPIs): cost-efficiency and security. To minimize the risk of physical deployment and handle partial recovery, network slicing between service providers is considered by using the same FWA as a service by joint consideration of Infrastructure as a Service (IaaS) and Software as a Service (SaaS). This can only be handled by Software-Defined Network (SDN) controlled OpenFlow-based FWA. However, current OpenFlow switches in the market are used only as IaaS which does not meet cost-efficiency requirement alone. The software-defined OpenFlow tables can be increased by network administrator, which is not recommended because of having no authorization limitation and poses configuration risks on the device. Here, the research question is how can the service providers keep their own network sustainable by using infrastructure in a secure, cost-efficient, and conforming to the standard in a fair way? Therefore, Sustainable Fixed Wireless Access (SFWA) is newly proposed by OpenFlow-based blockchain in this paper. To handle low computational work and fair scheduling, Proof of Stake (PoS) consensus mechanism and Weighted Fair Content Scheduling algorithms are proposed for the validation process of the blocks that stores dynamic configuration details of service providers. At the end of the whole procedure, the valid blockchain is stored in the Secure Policy Database in OpenFlow-based FWA switches. According to performance evaluation, eMBB and URLLC contents can be served by 40% and 53% discount on cost per user with guaranteed service in FWA. The service provider offers a more sustainable network by blockchain-based secured SFWA than the conventional centralized authority-based FWA.

Sustainable Wireless Sensor Networks for Railway Systems Powered by Energy Harvesting from Vibration

The railroad is a particularly efficient means of transporting passengers and goods. In this field, safety, reliability, and punctuality are of great importance, despite harsh environmental conditions and variable loads. Predictive maintenance of train wagons, rails, and stations is becoming increasingly essential [1] and requires massive use of sensors for measuring relevant quantities, such as acceleration, temperature, pressure, strain, and changes in railroad tracks and train wheels.

Supply chain dynamics beyond optimization: Metabolism of regional inter-firm networks

Sustainable supply chain networks are critical to the survival of companies in interconnected business ecosystems. Today's rapidly changing market conditions require firms to restructure their network resources flexibly. In this study, we quantitatively investigated how firms' ability to adapt to the turbulent market depends on the stable maintenance and flexible recombination of inter-firm relationships. Using the proposed quantitative index “metabolism,” we measured the micro-level dynamics of the supply chain, which represents each firm's average replacement rate of business partners. We applied this index to longitudinal data on the annual transactions of about 10,000 firms from 2007 to 2016 in the Tohoku region, which was affected by the 2011 earthquake and tsunami. The distribution of metabolism values differed across regions and industries, indicating differences in the adaptive capacity of the corresponding firms. We also found the typical balance between supply chain flexibility and stability for successful companies that have survived in the market for a long time. In other words, the relationship between metabolism and duration was not linear but U-shaped, indicating an appropriate metabolism value for survival. These findings provide a deeper understanding of supply chain strategies for adapting to regional market dynamics.

Optimal multi-market scheduling of a sustainable photovoltaic-oriented distribution network hosting hydrogen vehicles and energy storages: A regret assessment optimization

One of the goals of the smart-grids is to increase the penetration of renewable energy sources like photovoltaics (PVs) and hydrogen-based resources in a controllable manner. To fully utilize these resources in smart cities, dealing with a significant amount of generation and demand uncertainty may be necessary. This article proposed a two-stage optimization procedure smart distribution grid at the city level with a high penetration of solar and hydrogen-powered cars considering the real-time and day-ahead markets. In addition to renewable resources, the proposed network also considers energy storage (ESs), generator units (GUs), and demand response programs (DRPs). In this paper, uncertainties involved in the system are modeled using a scenario-based method. Then, a regression index, so-called maximum relative regret (MRR) derived from the stochastic p-robust optimization (SPRO) method, is utilized to model the imposed regret on uncertainties. Uncertainties in the proposed system include PV generation, electricity and hydrogen demands, real-time market prices, and hydrogen-powered car behavior. The IEEE 33 bus test system has chosen to evaluate the proposed regret-based framework. The findings show that the proposed approach is well-suited to the conservative SPRO technique for the employed system, in which the projected cost of the objective function increases by just 3.40 % for a 45.42 % decrease in the MRR.

An integrated machine learning and quantitative optimization method for designing sustainable bioethanol supply chain networks

Increasing demand for energy is pushing decision-makers in the Bioethanol Supply Chain Network (BSCN) to adopt second-generation biomass feedstocks to meet sustainability criteria. This study proposes an integrated Machine Learning (ML) and quantitative optimization model to design a Sustainable Bioethanol Supply Chain Network (SBSCN). We use ML methods, such as Random Forest (RF), Extreme Gradient Boosting Method (XGBoost), and Ensemble learning algorithm (Bagging), to project the bioethanol demand. We select the RF method as a superior method to forecast the bioethanol demand as inputs to the model by comparing the performance criteria for these three methods. We then propose a Mixed-Integer Linear Programming (MILP) model to meet the sustainability criteria defined by three objective functions. We present a case study to demonstrate the applicability of the proposed approach. The sensitivity analysis confirms that the costs of establishing preprocessing and biorefinery centers constitute 37% of the total costs of the network. More importantly, we find that the square bale harvest method is among the methods that utilized the most switchgrass land area. More interestingly, our model determined that the square bale harvest method led to 18,450 tons of switchgrass loss in the case study. Finally, our results can be utilized by policymakers and investors to develop efficient SBSCNs.

A multi-objective optimization for a closed-loop sustainable pharmaceutical supply chain network design: a case study

PurposeThis paper deals with the combined management and design of a sustainable pharmaceutical supply chain network with considering recycling.Design/methodology/approachThis paper first utilizes the analytical hierarchy process to select and rank green manufacturers. Second, the authors proposed a multi-objective nonlinear mathematical model to design a sustainable pharmaceutical supply chain network. The proposed model has been linearized and solved using the LP-metric method using GAMS software.FindingsA real case study has been conducted in Iran. The results show that environmental and social issues can be improved while minimizing total costs.Originality/valueGiven the criticality and importance of drugs in human health and the importance of recycling in today's world, proper management and design of a sustainable drug supply chain are necessary. This study pays special attention to environmental issues by utilizing multi-criteria decision approaches and customer satisfaction.

Should I stay or should I go? Understanding stakeholder dis/engagement for deforestation‐free palm oil

AbstractAddressing tropical deforestation in the palm oil sector involves a diverse range of stakeholders who engage or disengage with each other. Palm oil global value chain (GVC) firms (plantation companies, traders and processors, and consumer goods manufacturers and retailers), as well as nongovernmental organisations, financial institutions, consultancies and certification bodies, pursue their respective organisations' agendas through engagement practices, including through coalitions, in a palm oil sustainability network (POSN). Building on interviews with different stakeholder groups, this qualitative study characterises and critically analyses ‘stakeholder engagement’ by examining (1) the priority targets for engagement among different POSN stakeholders, (2) how mechanisms and tools are used in POSN stakeholder engagement or disengagement for addressing deforestation, and (3) the implications of stakeholder engagement or disengagement for addressing deforestation. Engagement and disengagement practices are shaped by and reshape GVC governance, with powerful stakeholders emerging as knowledge brokers and norm setters, raising important challenges for how deforestation is addressed.

Digital Twin Simulation Tools, Spatial Cognition Algorithms, and Multi-Sensor Fusion Technology in Sustainable Urban Governance Networks

Relevant research has investigated how predictive modeling algorithms, deep-learning-based sensing technologies, and big urban data configure immersive hyperconnected virtual spaces in digital twin cities: digital twin modeling tools, monitoring and sensing technologies, and Internet-of-Things-based decision support systems articulate big-data-driven urban geopolitics. This systematic review aims to inspect the recently published literature on digital twin simulation tools, spatial cognition algorithms, and multi-sensor fusion technology in sustainable urban governance networks. We integrate research developing on how blockchain-based digital twins, smart infrastructure sensors, and real-time Internet of Things data assist urban computing technologies. The research problems are whether: data-driven smart sustainable urbanism requires visual recognition tools, monitoring and sensing technologies, and simulation-based digital twins; deep-learning-based sensing technologies, spatial cognition algorithms, and environment perception mechanisms configure digital twin cities; and digital twin simulation modeling, deep-learning-based sensing technologies, and urban data fusion optimize Internet-of-Things-based smart city environments. Our analyses particularly prove that virtual navigation tools, geospatial mapping technologies, and Internet of Things connected sensors enable smart urban governance. Digital twin simulation, data visualization tools, and ambient sound recognition software configure sustainable urban governance networks. Virtual simulation algorithms, deep learning neural network architectures, and cyber-physical cognitive systems articulate networked smart cities. Throughout January and March 2023, a quantitative literature review was carried out across the ProQuest, Scopus, and Web of Science databases, with search terms comprising “sustainable urban governance networks” + “digital twin simulation tools”, “spatial cognition algorithms”, and “multi-sensor fusion technology”. A Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow diagram was generated using a Shiny App. AXIS (Appraisal tool for Cross-Sectional Studies), Dedoose, MMAT (Mixed Methods Appraisal Tool), and the Systematic Review Data Repository (SRDR) were used to assess the quality of the identified scholarly sources. Dimensions and VOSviewer were employed for bibliometric mapping through spatial and data layout algorithms. The findings gathered from our analyses clarify that Internet-of-Things-based smart city environments integrate 3D virtual simulation technology, intelligent sensing devices, and digital twin modeling.

Designing a Scenario-Based Fuzzy Model for Sustainable Closed-Loop Supply Chain Network considering Statistical Reliability: A New Hybrid Metaheuristic Algorithm

In this study, a new nonlinear mathematical programming model of mixed integer was presented to formulate the problem of designing a sustainable closed loop supply chain, in which the three aspects of sustainability, i.e., social effect such as job creation, customer satisfaction, and distributors, environmental effects such as reducing air pollution, and economic effects such as reducing supply chain costs, increasing supply chain reliability, quality of returned products by customers, and product routing were considered. In order to solve the proposed model, a new hybrid metaheuristic algorithm based on the distinctive features of gray wolf algorithm and genetic algorithm was proposed in addition to MOPSO and NSGA-II algorithms. After tuning their parameters by the Taguchi method, their performance in problems with different dimensions was tested and evaluated by MID, DM, and SM criteria. The results of statistical analysis of indices indicated that no significant difference between the performance of the three algorithms at 5% error level. In general, GW-NS, NSGA-II and MOPSO algorithms had better performance in terms of MID index, respectively. In addition, GW-NS, NSGA-II, and MOPSO algorithms performed better in terms of DM index. NSGA-II, MOPSO, and GW-NS algorithms performed better in terms of SM index, respectively. In addition, the variability of DM index in all three algorithms was almost the same, but in MID index, GW-NS algorithm, and in SM index, MOPSO algorithm had the highest change and less sustainability.