Network Design Tool Research Articles

Designing a stochastic supply chain network: An error-bound-based heuristic

Designing a stochastic supply chain is challenging because demand is often partially observable or unknown in advance. This paper presents an alternative approach to address a three-echelon stochastic supply chain network design with insufficient demand information. We formulate a stochastic mixed-integer program with chance constraints. We identify a lower and upper bound on the value at risk to deal with chance constraints, thus inducing two mixed-integer linear approximations to the stochastic program. We develop a branch-and-price scheme to solve the approximations and a primal heuristic algorithm for initial columns. We then create a heuristic algorithm for solving the original problem by implementing sensitivity analysis and error bound on the approximations. Besides network size, computational experiments find that network design performance depends on aggregate service levels and maximum demand variations. The aggregate behavior of service levels has an inverse impact on the network design performance, while maximum coefficients of demand variations present a positive effect. Computational results reveal that our proposed algorithms perform well compared to normal distributions and a robust optimization formulation. Our models and algorithms suggest a feasible network design tool for handling uncertainties in supply chains.

Dynamical modelling and experimental validation of a fast and accurate district heating thermo-hydraulic modular simulation tool

This paper presents a new thermo-hydraulic model for district heating systems simulations, which aims at being a fast and accurate tool to simulate highly renewable networks characterized by fluctuating energy profiles. The main novel aspect of the tool lies in the heat transmission modelling over long pipes based on a Lagrangian numerical approach. In comparison to other existing models, this approach significantly reduces computational time and it increases results' accuracy. The elaborated method avoids numerical diffusion in the results and consequently allows proper prediction of temperature propagation, especially in case of fast changes of fluctuating profiles. The tool is built following a modular procedural programming approach in order to facilitate the simulation of multicomponent system. Thanks to its modular structure, every components of the system is built with the same structure that is differently declined according to each component's requirements. In this way, new additional elements' models easily fit the existing ones.The model is validated under real operating conditions with hourly monitoring data of an Italian district heating network.The results show good correspondence also in the most peripheral nodes of the network, where the largest deviations are normally encountered, thus making the model a reliable and fast simulation tool for district heating network design and operational control.

Tools and Techniques for Teaching and Research in Network Design and Simulation

Computer networking is taught for some time at universities. A network is a complex mix of applications, communications protocols and link technologies, traffic flows and routing algorithms. Teaching the networking concept, the network design process is a challenging task, requiring designers to balance user performance expectations with costs and capacities. One of the obvious approaches employed to deal with its complexity is through the use of modelling and simulation techniques. This paper examines three best educational tools that can be used in the Computer Networking course at Cardiff Metropolitan University or any University for educational purpose. The paper demonstrates that a successful teaching programme needs to combine a range of teaching tools to achieve its aims. Analysing various aspects of the course provides evaluation of the overall success of the tools. Many network design and simulation tools have been studied. Based on our research and experiment, the best educational tools were selected that could be used for the purpose of teaching in the area of computer communications networks. The wide area network modelling tool Delite, the network simulator ns-3, the topology generation tool Brite can be used together to teach students the networking concepts. These tools can not only help students to understand computer network principles but also improve their network practical skills. Currently there are various tools available that are created by different companies and groups of researchers in academia and that are intended for use as practical and/or educational tools for network design including security issues. To make this task easy, different users, researchers and companies have developed different network modelling and simulation (MS) tools. Very often, such tools are addressing different issues in the network design process which are complementary to each other. It would be useful for learning and improving the network design process by allowing the use of a variety of tools.

Variational Quantum Optimization of Nonlocality in Noisy Quantum Networks

The noise and complexity inherent to quantum communication networks leads to technical challenges in designing quantum network protocols using classical methods. We address this issue with a hybrid variational quantum optimization framework (VQO) that simulates quantum networks on quantum hardware and optimizes the simulation using differential programming. We maximize nonlocality in noisy quantum networks to showcase our VQO framework. Using a classical simulator we investigate the noise robustness of quantum nonlocality. Our VQO methods reproduce known results and uncover novel phenomena. We find that maximally entangled states maximize nonlocality in the presence of unital qubit channels, while nonmaximally entangled states can maximize nonlocality in the presence of nonunital qubit channels. Thus, we show VQO to be a practical design tool for quantum networks even when run on a classical simulator. Finally, using IBM quantum computers we demonstrate that our VQO framework can maximize nonlocality on noisy quantum hardware. In the long-term, our VQO techniques show promise of scaling beyond classical approaches and can be deployed on quantum network hardware to optimize network protocols against their inherent noise.

PRO-Simat: Protein network simulation and design tool

PRO-Simat is a simulation tool for analysing protein interaction networks, their dynamic change and pathway engineering. It provides GO enrichment, KEGG pathway analyses, and network visualisation from an integrated database of more than 8 million protein-protein interactions across 32 model organisms and the human proteome. We integrated dynamical network simulation using the Jimena framework, which quickly and efficiently simulates Boolean genetic regulatory networks. It enables simulation outputs with in-depth analysis of the type, strength, duration and pathway of the protein interactions on the website. Furthermore, the user can efficiently edit and analyse the effect of network modifications and engineering experiments. In case studies, applications of PRO-Simat are demonstrated: (i) understanding mutually exclusive differentiation pathways in Bacillus subtilis, (ii) making Vaccinia virus oncolytic by switching on its viral replication mainly in cancer cells and triggering cancer cell apoptosis and (iii) optogenetic control of nucleotide processing protein networks to operate DNA storage. Multilevel communication between components is critical for efficient network switching, as demonstrated by a general census on prokaryotic and eukaryotic networks and comparing design with synthetic networks using PRO-Simat. The tool is available at https://prosimat.heinzelab.de/ as a web-based query server.

Observation distribution modelling and closed-from precision estimation of scanned 2D geometric features for network design

Geometric features such as cylinders and planes are important objects of interest in terrestrial laser scanner surveys of complex scenes. The quality of the objects modelled from the laser scanner data is a function of many variables and geometric network design plays a key role in maximizing precision. The expected precision can be predicted at the planning stage from simulations of the environment to be scanned. However, this practice can incur a high computational load, even if performed in 2D rather than in 3D. In this paper, a closed-form solution to estimate geometric object precision is proposed as an efficient first order network design tool. It models the laser scanner measurement process with an observation distribution function that is introduced into the least-squares normal equations. Parameter precision is evaluated directly by solving a few (three to six) integrals and inverting the normal equations matrix. The method is presented for two cases of a circle lying in the horizontal plane and a 2D line scanned from a single location. Both a simplified circle model and a more general circle model are explored. The method is then extended using the summation of normals method to allow precision estimation from the combination of multiple scans from different locations. Results from many real datasets, 95 circles and 30 lines, show that the distributions of the range observations and derived Cartesian coordinates follow model predictions. Moreover, results demonstrate that the method can predict circle parameter standard deviations within 4%–6% of the experimental values. The agreement is at the 10% level for a very specific case due to inherent high parameter correlation. The agreement of line parameter standard deviations is much greater, approximately 0.1%. The results show the method can be a valuable tool to predict feature quality with minimal computational requirements. The method is beneficial to not only laser scanner network design but could also be to instantaneous 2D map construction performed for SLAM-based surveys.

Investigation of a coherent dual-polarized 16-QAM 16-channel WDM FSO gamma–gamma fading system under various atmospheric losses

This work presents the dual-polarized quadrature amplitude modulated (QAM) signals transmitted via a sixteen-channel wavelength division multiplexing (WDM) and free-space optics (FSO) communication link. Through an FSO link up to 98 km, it achieves a transmission rate of 120 Gbps per channel. The system's performance is evaluated by calculating atmospheric attenuations in clear, hazy, rainy, and fog situations. Optisystem, an optical network design tool, examined the proposed link. Bit error rate, received signal constellation, error vector magnitude, and geometric loss for various FSO channel lengths were calculated. According to the results, the system can successfully transmit a data rate of 1.92 Tbps over the 98 km range of the FSO link under clear weather, 18.5–3.8 km under haze, 6.25–2.3 km in the rain, and 4.55–2.15 km in fog conditions. Comparing results to previous reports showed that the proposed system was found to be better in dynamic atmospheric circumstances.

Stepwise slime mould growth as a template for urban design

The true slime mould, Physarum polycephalum, develops as a vascular network of protoplasm, connecting node-like sources of food in an effort to solve multi-objective transport problems. The organism first establishes a dense and continuous mesh, reinforcing optimal pathways over time through constructive feedbacks of protoplasmic streaming. Resolved vascular morphologies are the result of an evolutionarily-refined mechanism of computation, which can serve as a versatile biological model for network design at the urban scale. Existing digital Physarum models typically use positive reinforcement mechanisms to capture meshing and refinement behaviours simultaneously. While these automations generate accurate descriptions of sensory and constructive feedback, they limit stepwise design control, reducing flexibility and applicability. A model that decouples the two “phases” of Physarum behaviour would enable multistage control over network growth. Here we introduce such a system, first by producing a site-responsive mesh from a population of nutrient-attracted agents, and then by independently calculating from it a flexible, proximity-defined shortest-walk to produce a final network. We develop and map networks within existing urban environments that perform similarly to those biologically grown, establishing a versatile tool for bio-inspired urban network design.

Optical network design and analysis tools: A test of time

Telecom operators' infrastructure is sustained by optical communication networks that provide the means for exchanging large amounts of information, which is essential for many modern society needs. Optical networks are characterized by rapid breakthroughs in a variety of technologies. Relevantly, the last decade encompassed remarkable advances in optical networks’ subfields of signal processing, electronics, photonics, communications, protocols, and control-plane architectures. Hence, these advancements unlocked unprecedented transmission capacities, reconfigurability and programmability, entailing an evolution in the way which networks were designed, planned, and analyzed. In this paper, we review the historical status of optical planning and design tools by focusing on the major enabling technologies and relevant landmarks of the last decade(s). We begin by pinpointing the major breakthroughs in the optical data plane, estimation models capturing the transmission medium behavior and the control plane. We then distil the implications that these advancements entail in the landscape of optical network design and analysis tools, which commonly sit “on top” of the control plane or as a fully separated entity. Then, we speculate with our view for the future, in which automatic validation of optical network operations and dimensioning jointly with learning/artificial intelligence mechanisms will permit zero-touch optical networking: i.e. updating, provisioning, and upgrading network capacities, by means of automation with minimal human intervention. We conclude with a proposal of an architecture that encompasses data and control planes in a comprehensive manner for paving the way towards zero-touch optical networking.

Design of profitable networks of biogas plants in Chile

Locations for profitable biogas plant networks for the generation of electric power were investigated. A population-based location algorithm was developed. Using information regarding biogas power and plants cost found in the literature, together with data on the location and number of dairy cows of 572 farms in southern Chile, economic analises were made. Each farm was evaluated as a potencial location for a biogas plant, both alone and as part of different combinations. The algorithm starts with a population of one-plant combinations and generates new iterations by inserting additional biogas plants as long as the NPV increases. In each iteration, only a diverse subset of the combinations is selected. This approach is compared to the application of the ArcGIS Network Analyst (NA) function for the set cover problem, usually employed in the literature for biogas plant network location problems. Using the technical parameters for the operation of biogas plants found in the literature, we show that the proposed algorithm gives better results than the NA algorithm in terms of profit. Biogas plant networks were obtained for different scenarios of biomass availability (40–80%), energy sale prices (90–100 USD/MWh) and transport costs (0.3–0.4 USD/tkm). The results indicate that 18 of the 572 farms would be good candidates to site a biogas plant in at least one of the scenarios. A maximum of eight farms appear in a scenario with 80% of biomass availability and a transport cost of 0.3 USD/tkm. This solution reaches a NPV of USD 3,538,394, which exceeds by more than 70% the USD 2,075,057 obtained with the solution retrieved by the NA function. As the algorithm presented obtained better results than the ArcGIS network analyst, it can be used as an appropriate design tool for biogas plant networks.

Tools for quantum network design

Quantum networks will enable the implementation of communication tasks with qualitative advantages with respect to the communication networks known today. While it is expected that the first demonstrations of small scale quantum networks will take place in the near term, many challenges remain to scale them. To compare different solutions, optimize over parameter space, and inform experiments, it is necessary to evaluate the performance of concrete quantum network scenarios. Here, the authors review the state-of-the-art of tools for evaluating the performance of quantum networks. The authors present them from three different angles: information-theoretic benchmarks, analytical tools, and simulation.

Detection of minute defects using transfer learning-based CNN models

In this paper, a design and training tool for convolutional neural networks (CNNs) is introduced, which facilitates to construct transfer learning-based CNNs based on a series-type network such as AlexNet, VGG16 and VGG19 or a directed acyclic graph (DAG)-type network such as GoogleNet, Inception-v3 and IncResNetV2. Minute defect detection systems are developed for resin-molded articles by transfer learning of AlexNet. AlexNet has the shallowest layer structure and the smallest number of weights within the six powerful networks, so that it is selected as the first CNN for evaluation. In the transfer learning process, after the last fully connected layers are replaced according to the number of categories needed for new tasks, an additional fine training is conducted using training images including small typical defects. In experiments, transfer learning-based AlexNet $$\_$$ 6 and AlexNet $$\_$$ 2 are obtained to deal with six and binary classification tasks, respectively. Then, our originally designed 15 layers CNNs named sssNet $$\_$$ 6 and sssNet $$\_$$ 2 are also prepared and trained for comparison. Finally, AlexNet $$\_$$ 6 and sssNet $$\_$$ 6, AlexNet $$\_$$ 2 and sssNet $$\_$$ 2 are quantitatively compared and evaluated through classification experiments, respectively.

TRANSVERSAL COMPETENCIES IN ICT STUDY PROGRAMS: AN INTRODUCTION TO CURRENT RESEARCH

The research is carried out within the framework of the doctoral thesis “Methodology and tools for sensor network design”, where transversal competencies and their assessment are related to ICT study programs, such as “Mechatronics” implemented by Rezekne Academy of Technologies and based on Smart Specialization Strategy (RIS3) ICT and smart materials, technologies and engineering systems.Today's labour market needs competitive employees who are able to analyze situations with systematic and design thinking. The education system needs to change in line with the requirements of the Fourth Industrial Revolution, so prospective workers need to have knowledge of interdisciplinary and multidisciplinary processes.The aim of this research is to determine the transversal competencies of ICT study programs. The monographic and descriptive method has been applied for studying scientific literature. The research looks at the competencies and skills that are needed for ICT graduates, such as digital, innovative, entrepreneurship, research, communication, critical thinking, human relation, leadership, planning, problem solving.

Designing recycling networks for construction and demolition waste based on reserve logistics research field

Construction and demolition waste (CDW) has a significant effect on the environment. A well-designed recycling network is a prerequisite of CDW recycling, which is favourable for improving the recycling rate. However, the lack of quantitative tools to measure the characteristics of the construction industry has a serious effect on the effective design of CDW recycling network. Extensive studies on reverse logistics network design over the past decades have provided a solid foundation for recycling network design. Thus, in this study, reverse logistics research is explored to determine possible solutions to the issue of CDW recycling network design. This study aims to provide a model for CDW treatment plants location planning and capacity expansion planning based on the reverse logistics research in the construction industry. In our model, the conflicts between the objectives of different stakeholders and the dynamic decision-making environment in the construction industry were considered. Then, a multi-period multi-objective mixed-integer linear programming model was developed to minimize the CDW disposal costs of contractors and to maximize the profits of recycling firms and recycling rate of CDW. Chongqing, China was selected as the case to validate the proposed model and derive managerial insights. Results show our model provides an efficient tool for CDW recycling network design.

Linear Power Flow Method Improved With Numerical Analysis Techniques Applied to a Very Large Network

In this paper, we propose a fast linear power flow method using a constant impedance load model to simulate both the entire Low Voltage (LV) and Medium Voltage (MV) networks in a single simulation. Accuracy and efficiency of this linear approach are validated by comparing it with the Newton power flow algorithm and a commercial network design tool Vision on various distribution networks including real network data. Results show that our method can be as accurate as classical Nonlinear Power Flow (NPF) methods using a constant power load model and additionally, it is much faster than NPF computations. In our research, it is shown that voltage problems can be identified more efficiently when MV and LV are integrally evaluated. Moreover, Numerical Analysis (NA) techniques are applied to the Large Linear Power Flow (LLPF) problem with 27 million nonzeros in order to improve the computation time by studying the properties of the linear system. Finally, the original computation times of LLPF problems with real and complex components are reduced by 2.8 times and 5.7 times, respectively.

A heat- and mass-integrated design of hydrothermal liquefaction process co-located with a Kraft pulp mill

This paper aims to establish a new standard process for heat and mass integration of hydrothermal liquefaction, co-located with an existing Kraft pulp mill, to produce bio-crude. Hydrothermal liquefaction is an energy-intensive process that operates at high temperature and pressure and produces a biocrude similar to conventional crude oil. The key advantages of installing hydrothermal liquefaction in proximity with a Kraft Mills enables the use of black liquor as a feed to hydrothermal liquefaction, asset repurposing and optimisation, as well as supply chain and logistics integration. This work follows a design process to increase the energy efficiency of the hydrothermal liquefaction process by using an iterative mass and heat integration procedure to optimise mass and energy flows and assets of the hydrothermal liquefaction process. The method uses process simulation tools, Pinch Analysis, heat exchanger network design tool, and the understanding of the process constraints to develop a heat exchanger network for the hydrothermal liquefaction process with maximum energy recovery, minimum number of units and enhanced mass integration. The number of heat exchangers in the network reduced from 14 to 7 when the proposed method was applied. Substituting bio-crude from the new integration hydrothermal liquefaction process for conventional fuels has the potential to decarbonise transport fuels by 11.3 kg CO2-e/GJ of fuel.

Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber

We have developed a convolutional neural network (CNN) that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a $\nu_\mu$ charged current neutral pion data samples.

Strategic Decision-Making in Global Supply Chain Network Design – How a Decision Support System Combining MILP and AHP on a Pareto Front Can Alleviate Decision-Making

Integrating quantitative and qualitative data is a challenge in global supply chain decision making. Decision support tools should be easier to use and focus on providing transparency and supporting human judgement. A strong emphasis on multi-objective models and trade-offs is needed. We investigate methods to solve strategic global supply chain network design problems and develop a solution framework, comprised of mixed-integer linear programming, the Analytical Hierarchy Process and the Pareto front. It is applied in a case study in the med-tech industry. Results show that the framework is easy to use for practitioners, accommodates qualitative as well as quantitative criteria and provides transparency over the entire range of efficient solutions. Managers can use it to discuss and select the configuration best fitting to their overall strategy. We believe the framework and application results can contribute to the development of more effective and user-friendly decision support tools for strategic global network design.

Numerical Investigations on District Heating Pipelines Under Combined Axial And Lateral Loading

Within the design process of district heating networks, the soil resistances in axial and lateral pipeline direction are commonly treated independently as friction resistance and bedding pressure. However, at curved segments or near ellbows, these resistances occur simultaneously and affect each other. The state of knowledge regarding this topic is summarized, and it is shown that only limited information exists for this case of loading.Therefore, a three-dimensional finite element model was developed, using the sophisticated concept of hypoplasticity as an advanced constitutive model for the bedding material. This soil model is able to account for dilatancy, barotropy and pycnotropy of granular soils. Subsequently, variations of the loading direction were performed for a reference system. The investigations give a good insight into the behaviour of district heating pipelines under combined loading, showing the interdependency of skin friction resistance and bedding pressure. We present a design approach which incorporates interaction terms, derived from the presented investigations. Results gained from these investigation are then transferred to the academic district heating network design tool IGtH-Heat, to evaluate in which manner the incorporation of coupling terms between bedding and friction resistance influences the pipe-soil interaction. Additionally, a temperature dependent formulation of maximum friction resistance is adopted to incorporate the effects of radial pipe displacement. Thereby we demonstrate that the predicted pipeline’s displacement significantly change when these effects are taken into account. Using this new formulation, model predictions are compared to data from full scale field measurements.

Design of customised products and manufacturing networks: towards frugal innovation

ABSTRACTAs manufacturing companies move towards frugal innovation, more and more tools that take into account regional customer requirements and regionalisation practices are emerging. Information and communication technologies (ICT) are considered as a means to integrate customers into product design phase. Moreover, digitalised solutions that provide seamless decision-support tools that consider multi-conflicting criteria have emerged and are integrated in existing business models to provide holistic solutions. This paper presents a unified framework of an augmented reality product customisation application, which supports realistic product visualisation so as to extract end users’ requirement that provides input to a web-based supplier selection and the network design tool that considers frugal criteria. The two tools are connected and supported by a product lifecycle management (PLM) tool. The proposed framework is applied in a white-good industry case study and meaningful performance indicators provided by the industry are used to evaluate the proposed framework.