Network Design Approach Research Articles

Three-phase artificial intelligence-geographic information systems-based biomass network design approach: A case study in Denizli

Renewable energy sources are of great importance in protecting the environment by reducing greenhouse gas emissions and global warming. Recently, more efficient conversion technologies and different types of resources have been introduced to generate energy in a sustainable way. Biomass-to-energy systems need to be designed efficiently due to high volumes of raw material flows and transportation costs. This study proposes a novel integrated approach for the solution of biomass supply chain network design using artificial intelligence, geographic information systems, multi criteria decision making and mathematical modelling. First, the five-year forecasts of biomass raw materials including animal waste, agricultural residues, and municipal solid waste were made using support vector regression. Alternative biogas facility locations were determined spatially based on various criteria using geographic data and a decision-making technique. Then, considering annual net present value streams, the bioenergy system has been configured using a mixed integer linear programming model. The proposed methodology was applied on a real case in the city of Denizli, Turkey. The results showed that nine conversion facilities could be opened with 2000 kWh capacity each, and approximately 83.2% of net income could came from electricity sales, with the remainder from fertilizer sales. Furthermore, a sensitivity analysis was carried out to see the variations in model parameters such as biomass purchase cost, transportation cost, fertilizer sales prices and discount rate. It was found that the most dominant factor affecting net present value was fertilizer sales price, which was followed by discount rate.

Quality factor based transducer power gain expression

PurposeIn the literature, while designing broadband matching networks, transducer power gain (TPG) is used to measure the transferred power. Generally, in TPG expressions, load and back-end impedances of the matching network are used. This study aims to derive a new quality factor-based TPG expression.Design/methodology/approachIn deriving the new expression, narrowband L type-matching network design approach is used and the new expression in terms of back-end quality factor, load quality factor and output port quality factor is obtained. Then, a broadband-matching network design approach using the derived TPG expression is proposed.FindingsTwo broadband double-matching networks are designed by using the proposed design approach using the derived TPG expression. Performances of the designed-matching networks are compared with the performances of the matching networks designed by means of simplified real frequency technique which is a well-known technique in the literature, and it is shown that they are nearly the same.Originality/valueIn broadband-matching problems, generally an impedance-based TPG expression is used, and it must be satisfied by the designed broadband-matching networks. But, in the literature, there is no quality factor-based TPG expression that can be used in broadband-matching problems. So, this gap in the literature has been filled by this paper.

Designing and learning a lightweight network for infrared small target detection via dilated pyramid and semantic distillation

Large-scale transformer networks can achieve state-of-the-art infrared small target detection accuracy, but the high computational resource consumption makes their inference speed unsatisfactory. Existing lightweight networks can certainly achieve real-time detection of infrared small targets, but no matter how carefully designed the lightweight strategy is, there is still a gap in the accuracy of these networks compared to large-scale networks. To address these problems, in this paper, we propose a lightweight infrared small target detection network capable of high-speed inference and a knowledge distillation method for learning higher-order semantic information from the transformer network. Specifically, based on depthwise separable dilated convolution, we design each stage in the detection network, called multi-scale dilated pyramid network (MDPNet), as a multi-branch parallel pyramid. This design can enlarge the receptive field and enhance the ability to extract contextual features of the network. Furthermore, we utilize knowledge distillation to bridge the detection performance gap with the transformer. Based on the self-attention mechanism, a semantic distillation sub-network is constructed between the teacher transformer and student convolution network, which enables a more efficient cross-model transfer of knowledge about higher-order semantic feature extraction between networks with different mechanisms and increases the detection accuracy without computation load. We demonstrate the rationality and effectiveness of the overall network design and learning approach through exhaustive experiments. On the widely accepted public datasets SIRST and NUDT-SIRST, nIoU reaches 74.88 and 75.10, and PD reaches 99.08 and 97.88, outperforming other networks. With the number of parameters of only 0.23 M, the network achieves an inference speed of 137 FPS for 320 × 320 infrared images.

Can I Really Protect My Broadcast Plant From a Cybersecurity Attack?

Internet Protocol (IP) networks are fundamental building blocks of the broadcast technical plant. Integration with the legacy broadcast infrastructure can range from minimal system control functions to an end-to-end IP-based content transport facility. Regardless of the level of integration, cybersecurity cannot be ignored to ensure a reliable, secure, and protected system. Industry cybersecurity principles are commonly based on a complex framework of principles from the National Institute of Standards and Technology (NIST). The NIST Framework is often a complex and daunting task to implement within a broadcast facility. An effective cybersecurity program begins with the design of the network. This article provides an understanding of practical implementable cybersecurity precautions based on the NIST Framework for the broadcast station. Specific topics to be addressed will include an overall understanding of cybersecurity principles and a step-by-step infrastructure implementation guide for the broadcast facility whether large or small. The guide will be focused on a secure network design approach, the use of Ethernet switch security capabilities, the use of packet filtering and firewalls, and an understanding of implementing common cybersecurity threat mitigation techniques.

A Reinforcement Learning approach for bus network design and frequency setting optimisation

This paper proposes a new approach to solve the problem of bus network design and frequency setting (BNDFS). Transit network design must satisfy the needs of both service users and transit operators. Numerous optimisation techniques have been proposed for BNDFS in the literature. Previous approaches tend to adopt a sequential optimisation strategy that conducts network routing and service frequency setting in two separate steps. To address the limitation of sequential optimisation, our new algorithm uses Reinforcement Learning for a simultaneous optimisation of three key components of BNDFS: the number of bus routes, the route design and service frequencies. The algorithm can design the best set of bus routes without defining the total number of bus routes in advance, which can reduce the overall computational time. The proposed algorithm was tested on the benchmark Mandl Swiss network. The algorithm is further extended to the routing of express services. The validation includes additional test scenarios which modify the transit demand level on the Mandl network. The new algorithm can be useful to assist transit agencies and planners in improving existing routing and service frequency to cope with changing demand conditions.

BRT network design for transit cost reduction in Cape Town, South Africa

Car dependency in Cape Town, South Africa, like in many modern cities is high but comes at a high cost. One of these is significant network congestion, especially during peak commuting periods. A strategy adopted by the City’s transport development authority to alleviate this situation is the phased development of an integrated public transport network. A bus rapid transit service known as the MyCiTi BRT is proposed to be the backbone of the integrated network. However, an investigation of the existing service reveals a pattern of low ridership and high operating costs which is not economically sustainability. The main objective of this work is therefore to propose solutions to improve the existing service particularly in terms of increasing ridership and reducing operational cost. To achieve this, a network design approach known as simulation-based optimisation that combines simulation with optimisation algorithms is proposed. The results are compared with the baseline MyCiTi network. The outcome shows that the proposed methods are capable of improving the MyCiTi network and thereby its service.

A liquid-free conducting ionoelastomer for 3D printable multifunctional self-healing electronic skin with tactile sensing capabilities.

Conductive elastomers with both softness and conductivity are widely used in the field of flexible electronics. Nonetheless, conductive elastomers typically exhibit prominent problems such as solvent volatilization and leakage, and poor mechanical and conductive properties, which limit their applications in electronic skin (e-skin). In this work, a liquid-free conductive ionogel (LFCIg) with excellent performance was fabricated by utilizing the innovative double network design approach based on a deep eutectic solvent (DES). The double-network LFCIg is cross-linked by dynamic non-covalent bonds, which exhibit excellent mechanical properties (2100% strain while sustaining a fracture strength of 1.23 MPa) and >90% self-healing efficiency, and a superb electrical conductivity of 23.3 mS m-1 and 3D printability. Moreover, the conductive elastomer based on LFCIg has been developed into a stretchable strain sensor that achieves accurate response recognition, classification, and identification of different robot gestures. More impressively, an e-skin with tactile sensing functions is produced by in situ 3D printing of sensor arrays on flexible electrodes to detect light weight objects and recognize the resulting spatial pressure variations. Collectively, the results demonstrate that the designed LFCIg has unparalleled advantages and presents wide application potential in flexible robotics, e-skin and physiological signal monitoring.

Cross-Layer MAC/Routing Protocol for Reliability Improvement of the Internet of Things

The IEEE 802.15.4 standard is recognized as one of the most successful for short-range low-rate wireless communications and is used in Internet of Things (IoT) applications. To improve the performance of wireless networks, interest in protocols that rely on interaction between different layers has increased. Cross-layer design has become an issue in wireless communication systems as it can improve the capacity of wireless networks by optimizing cooperation between multiple layers that constitute network systems. Power efficiency and network scalability must be addressed to spread IoT. In multi-hop networks, many devices share wireless media and are geographically distributed; consequently, efficient medium access control (MAC) and routing protocols are required to mitigate interference and improve reliability. Cross-layer design is a novel network design approach to support flexible layer techniques in IoT. We propose a cross-layer protocol for the MAC layer and routing layer to satisfy the requirements of various networks. The proposed scheme enables scalable and reliable mesh networking using the IEEE 802.15.4 standard and provides robust connectivity and efficient path discovery procedures. It also proposes a novel address-allocation technique to improve address-allocation methods that cannot support large sensor networks. Simulation results indicate that the proposed scheme could improve reliability and reduce end-to-end delay.

Locally produced food for restaurants: a theoretical approach for the supply chain network design

PurposeThis paper evaluates the feasibility and benefits of a local food distribution system, which connects farmers and restaurant owners from a logistics perspective. This paper considers a platform to improve operations and investigates various schemes for delivering locally produced food to restaurants using a food hub.Design/methodology/approachTo compare distribution scenarios and derive managerial implications, a simulation model has been developed and executed in Matlab 2019a©. The model evaluates various settings of business connections between farmers and restaurateurs.FindingsResults of computational experiments highlight great potentials of such a system, particularly to reduce travel distances. To obtain these positive externalities, the local system requires specific attention during the design of logistical aspects and needs to be planned following a specific structure.Practical implicationsThe developed simulation model can be used to improve understanding of related short food supply chains by analyzing specific cases where the main actors involved differ in terms of type, number, and location.Originality/valueThe paper analyzes the feasibility and the effects of a new distribution system that can connect supply chain actors directly. The analyses focus on logistics aspects, a topic that is often neglected in sustainable consumption research. Furthermore, the paper does not focus of a single case study but develops a customizable model to be used in various settings.

A hybrid modeling approach for resilient agri-supply network design in emerging countries: Colombian coffee supply chain

Sustainability and resilience in Agri-Food Supply Chains is a challenging topic of current interest in the research community. Resilience for Agri-Food Supply Chain (AFSC) is the capability of the supply network to manage and mitigate disruptions due to global warming and natural phenomena such as landslides and floods of crops, among others caused by humans. A significant challenge is to design efficient and resilient AFSCs in emerging countries while perishability constraints are considered. A methodology to design an AFSC for emerging countries is addressed in this research. The phenomena that aid in identifying critical aspects of the AFSC affecting their resilience are identified. The former approach combines optimization and simulation schemes by considering resilience metrics related to availability and connectivity. Indeed, the solution approach addresses the uncertainty by using simulation of disruptive events and finding resilient designs using mathematical programming. The proposed framework has been evaluated in a Colombian coffee supply chain. The obtained results show the efficiency of the proposed scheme to design AFSCs and allow the practitioners to measure, predict, compare, and improve the level of resilience of their supply chains (SCs).

Optimal Communication Network Design of Microgrids Considering Cyber-Attacks and Time-Delays

Distributed secondary control stands out for its flexibility and expandability in microgrids (MGs) control, in where communication network plays a fundamental and critical role. The communication topology and link-weights have significant impact on the attack-resilience and dynamic performance of MGs, which should be appropriately designed. However, the joint optimization problem of communication topology and link-weights in the existing literature of consensus-based secondary voltage control of MGs is usually neglected. To bridge this gap, we propose a novel two-stage optimization approach for the communication network design, which jointly optimizes the topological structure to enhance the structural survivability and link weights to improve the dynamic performance (including the speed of convergence and robustness to time-delay). The first stage problem is formulated into a mixed-integer semi-define programming (MISDP) model based on convex relaxation technique, which is then converted equivalently into an integer quadratic programming (IQP) problem and then a MISDP feasibility problem to facilitate the solution. The second stage problem is formulated into a bi-objective SDP model to compromise between the convergence performance and robustness to time-delay. Simulations based on a microgrid with 10 distributed generation (DG) units under different scenarios are implemented to verify the effectiveness of the proposed method.

Ranking community detection algorithms for complex social networks using multilayer network design approach

Purpose Community detection is a significant research field in the study of social networks and analysis because of its tremendous applicability in multiple domains such as recommendation systems, link prediction and information diffusion. The majority of the present community detection methods considers either node information only or edge information only, but not both, which can result in loss of important information regarding network structures. In real-world social networks such as Facebook and Twitter, there are many heterogeneous aspects of the entities that connect them together such as different type of interactions occurring, which are difficult to study with the help of homogeneous network structures. The purpose of this study is to explore multilayer network design to capture these heterogeneous aspects by combining different modalities of interactions in single network. Design/methodology/approach In this work, multilayer network model is designed while taking into account node information as well as edge information. Existing community detection algorithms are applied on the designed multilayer network to find the densely connected nodes. Community scoring functions and partition comparison are used to further analyze the community structures. In addition to this, analytic hierarchical processing-technique for order preference by similarity to ideal solution (AHP-TOPSIS)-based framework is proposed for selection of an optimal community detection algorithm. Findings In the absence of reliable ground-truth communities, it becomes hard to perform evaluation of generated network communities. To overcome this problem, in this paper, various community scoring functions are computed and studied for different community detection methods. Research limitations/implications In this study, evaluation criteria are considered to be independent. The authors observed that the criteria used are having some interdependencies, which could not be captured by the AHP method. Therefore, in future, analytic network process may be explored to capture these interdependencies among the decision attributes. Practical implications Proposed ranking can be used to improve the search strategy of algorithms to decrease the search time of the best fitting one according to the case study. The suggested study ranks existing community detection algorithms to find the most appropriate one. Social implications Community detection is useful in many applications such as recommendation systems, health care, politics, economics, e-commerce, social media and communication network. Originality/value Ranking of the community detection algorithms is performed using community scoring functions as well as AHP-TOPSIS methods.

A new scenario-based robust optimization approach for organ transplantation network design with queue condition and blood compatibility under climate change

Organ transplantation network design is a critical issue for continuing human life. This paper presents a new tri-objective robust optimization model for the design of transplantation networks that are adaptable to disruption scenarios. This paper presents a new mixed-integer non-linear programming (MINLP) model to construct a scenario-based transplantation supply chain. In this regard, objective functions of the proposed scenario-based model are developed to minimize the delivery time of the organs after disruption and maximize the quality of organs and the compatibility of blood types. Afterward, the queue of people in health facilities with limiting capacity may reduce the surgeries and lead to death risk. Therefore, it is necessary to examine a queue system that reflects the situation and changes over time. The risk of climate change over the network can be created from the queue and affects the quality of organs. When the risk of climate change makes delay receiving the organs by transplant centers, the compatibility of blood types is a helpful method to handle surgery rates and decrease the mortality rate. Moreover, one of the other issues that have an impact on the decisions of the managers in the transplant process is related to making an appropriate decision by respecting uncertain conditions. For this reason, this paper has proposed a new hybrid solution approach based on the scenario-based robust optimization approach and the compromise solution method to tackle different uncertainties in an optimization model. In addition, the proposed model is NP-hard, and this paper utilizes two metaheuristic optimization algorithms to solve the problem, including genetic algorithm (GA) and black widow optimization (BWO). The empirical examples are provided to validate the performance of the proposed model, and these represent the advantage of BWO over GA. After introducing essential analyses, the comparative analysis and managerial insights are explained.

A pareto-based multi-objective network design approach for mitigating the risk of hazardous materials transportation

Hazardous materials (HazMat) transportation is one of the indispensable links in the chemical process industry and is closely related to safety, security, and environmental concerns. HazMat transportation routing is a major proactive risk management measure. Most related studies focus on diminishing the transportation risk for one single HazMat shipment. This work addresses a global perspective on the problem where the correlations of multiple simultaneous HazMat shipments are considered. A multi-objective transportation network design model is defined to optimize the objectives of the transportation risk and the transportation cost. A well-designed Pareto-based hybrid heuristic algorithm is proposed for model solving, which integrates a linear decomposition procedure for initial population construction, a Pareto-based scatter search procedure for population evolution, and a variable neighborhood search procedure for solution improvement. Computational results indicate that the proposed algorithm is competitive in solving large-scale instances since it is able to strike a great balance between effectiveness and efficiency. The detailed solution analysis shows that the proposed approach can effectively coordinate multiple simultaneous HazMat transportation processes. Moreover, we find that performing multiple short-distance shipments would significantly reduce the total transportation risk, but results in the higher transportation cost. This work can provide a set of Pareto optimal transportation networks for different industrial application scenarios to achieve the trade-off between economic and safety.

A simulation-based optimization approach for network design: The circular economy perspective

The Circular Economy (CE) is identified as a solution to address negative impacts due to conventional ‘take-make-consume-dispose’ practices. We posit that a new business model termed Lease & Sell, which integrates the product-service-system (PSS) strategy with product sale for ownership, could enhance opportunities to promote circularity. This paper presents a simulation-based optimization approach for closed-loop network design to make location and allocation decisions when implementing a Lease & Sell strategy. The proposed approach is applied to the case of appliance manufacturer to compare performance when the company only sells product for ownership vs. when a Lease & Sell strategy is applied. Results show that while profitability increases with Lease & Sell when more End of Life (EoL) strategies are introduced (i.e., reuse, remanufacture, and recycling), the selling price for products in secondary markets has a higher impact on overall benefits. Findings also show that trade-offs exist between profitability, lead time, and social objectives.

An optimization approach for disaster relief network design under uncertainty and disruption with sustainability considerations

Human-made, natural, and unexpected disasters always cause human and financial losses to communities. Disaster management is a framework with proven performance to reduce the damage caused by disaster and supply chain disruptions. Transferring the injured people from affected areas to hospitals at the minimum possible time is a crucial goal in times of disaster. This paper develops a two-stage stochastic programming model to transport the injured people from affected areas to hospitals in the incidence of multiple disruptions at transportation links and facilities under uncertainties. Herein, economic, social, and environmental aspects of sustainability are considered, while simultaneous disruptions are managed to minimize the adverse impacts of the disasters. We aim to determine optimal locations to establish transfer points and flows between the relief network nodes with sustainability considerations. Ultimately, a case study in District 12 of Tehran, Iran is conducted to ensure the proposed model’s validity and performance. Various sensitivity analyses are also implemented to ensure the model’s effectiveness. The results indicate that disruptions in facilities and transportation links lead to increased relief time, hence has the most significant negative impact on relief operations.

Regulating Hazardous Material Transportation: A Scenario-Based Network Design Approach with Integrated Risk-Mitigation Mechanisms

Regulating Hazardous Material Transportation: A Scenario-Based Network Design Approach with Integrated Risk-Mitigation Mechanisms

Regulating Hazardous Material Transportation: A Scenario-Based Network Design Approach with Integrated Risk-Mitigation Mechanisms

Regulating Hazardous Material Transportation: A Scenario-Based Network Design Approach with Integrated Risk-Mitigation Mechanisms

상수관로 고유속 관망 설계 적용에 따른 효과분석

Traditional water distribution network (WDN) design approaches have primarily focused on fulfilling hydraulic conditions such as water pressure and demand while placing less emphasis on water quality issues. Owing to a large number of documented water quality failures, there has been an increasing interest in water quality management throughout the design or operational phases of WDNs. This paper presents a novel approach for designing high-velocity WDNs to improve water quality security and satisfy the hydraulics of the network. The following is a summary of the procedures: (1) define the hydraulic and water quality factors, (2) determine the network layout using the modified branched index, and (3) determine the pipe diameter in the network to achieve self-cleaning velocity. The proposed approach was applied to a portion of the CW city network. As a result, the network layout was transformed to a branched network to reduce the number of pipes that did not meet the self-cleaning velocity requirements. If the pipe did not achieve self-cleaning velocity after transforming the layout, additional pipe diameter adjustments were made to satisfy the self-cleaning velocity requirements. Owing to this effort, the length of the network was reduced, and 78.8% of the pipes achieved self-cleaning velocity. In addition, water age at dead-ends and changes in water age were minimized, allowing for efficient water quality management of the network.

A possibilistic programming approach for biomass supply chain network design under hesitant fuzzy membership function estimation

The recognition of membership function by knowledge acquisition from experts is an important factor for many fuzzy mathematical programming models. Thus, the hesitant fuzzy membership function (HFMF) estimation could help users of the mathematical programming approaches to provide a powerful solution in continuous space problems. Therefore, this study proposes a possibilistic programming approach based on Bezier curve mechanism for estimating the HFMF. In the process of possibilistic programming approach, an optimization model is presented to tune the primary parameters of Bezier curve by the goal of minimizing the sum of the squared errors (SSE) between the empirical data and fitted HFMF. After that, the efficiency and applicability of the proposed approach is checked by proposing a novel mathematical model for biomass supply chain network design problem. In this case, the bio-products demand is declared as an imprecise parameter that follows from the estimated HFMF to increase the accuracy of the obtained results by addressing the uncertainty and unreliability of the information. Finally, a computational experiment and validation procedure about the biomass supply chain network design is provided to peruse the verification and validation of the proposed approaches.