Network Design Solutions Research Articles

Temporal Dynamics and Physical Priori Multimodal Network for Rehabilitation Physical Training Evaluation.

Sensor-based rehabilitation physical training assessment methods have attracted significant attention in refined evaluation scenarios. A refined rehabilitation evaluation method combines the expertise of clinicians with advanced sensor-based technology to capture and analyze subtle movement variations often unobserved by traditional subjective methods. Current approaches center on either body postures or muscle strength, which lack more sophisticated analysis features of muscle activation and coordination, thereby hindering analysis efficacy in deep rehabilitation feature exploration. To address this issue, we present a multimodal network algorithm that integrates surface electromyography (sEMG) and stress distribution signals. The algorithm considers the physical knowledge a priori to interpret the current rehabilitation stage and efficiently handles temporal dynamics arising from diverse user profiles in an online setting. Besides, we verified the performance of this model using a learned-nonuse phenomenon assessment task in 24 subjects, achieving an accuracy of 94.7%. Our results surpass those of conventional feature-based, distance-based, and ensemble baseline models, highlighting the advantages of incorporating multimodal information rather than relying solely on unimodal data. Moreover, the proposed model presents a network design solution for rehabilitation physical training that requires deep bioinformatic features and can potentially assist real-time and home-based physical training work.

Optimizing energy efficiency in intelligent vehicle-oriented road network design: A novel traffic assignment method for sustainable transportation

In light of the development of intelligent vehicles and the persistent energy crisis, improving the predictive accuracy of macroscopic traffic flow and formulating energy-efficient road network design solutions are essential to promote sustainable development in road transportation. This study introduces a dual-objective impedance function that incorporates considerations of both travel time and energy consumption. The function is developed utilizing the principles of intelligent vehicle energy flow and traffic flow parameters. Then, an enhanced user equilibrium traffic assignment (E-UE) model is formulated. The effectiveness of the E-UE function was validated through road experimental data. The performance of E-UE was analyzed using a planned road network consisting of 28 sections. In comparison with the classical UE model, the E-UE model promotes uniform traffic distribution and alleviates congestion, leading to a 1.1 % reduction in average road energy consumption. This highlights the E-UE model’s effectiveness in accurately capturing the impacts on energy consumption with enhanced predictive precision. Strategies to mitigate potential energy consumption include implementing the E-UE model with realistic weights, adjusting weights for specific roads, employing a design speed of 50 km/h, and utilizing 6-lane roads. This study contributes to the advancement of energy-saving technologies in sustainable transportation.

Dependability of local power systems based on autonomous generation units

Aim. The paper aims to create and verify methods for predicting the dependability of autonomous power supply to local low-power consumers. Methods. The work uses simulation tools, i.e., computational experiment for predicting the dependability and safe service life of autonomous cluster distributed energy networks that include various power generation installations, power transmission lines and an automated self-regulation system for the purpose of diversifying the risks of failure. Results. Using methods of probabilistic analysis of technical system safety, the dependability of power supply is predicted in terms of possible energy network failures and threats to the life of consumers. The problem can be solved by simulating and predicting the moment of failure of a power network, i.e., critical violation of design conditions, accident, etc. This scenario allows – for long periods of operation – simulating failure situations due to degradation and destruction of elements, units, power lines, as well as failures under multidimensional uncertainty of prediction of dependability and quality of power supply. The key output of simulation is a set of recommendations to ensure energy security. The paper presents simulated dependability indicators, analysis of the effect of structural design solutions underlying a distributed energy network on the dependability and energy security. Conclusion. The use of computational experiments and results of power supply dependability simulation as part of predicting probabilistic indicators of failures of equipment and elements of cluster energy networks, identifying their consequences for the energy security of sparsely populated and remote locations lays the foundation for quality management of energy networks. Additionally, in the future, additional causes and initial events may be included in the model for the purpose of studying the facts of failures. For instance, such causes as uneven generation using renewable sources, finite fuel supply for conventional generation sources, reliability of the power grid personnel. The proposed approach allows verifying promising new energy network design solutions, for example, including energy storage systems into a distributed energy network that – at various points in time – may be either “generators” or “consumers”. Simulation of local energy networks is of practical interest and is important for improving their consumer quality, resistance to hazardous environmental effects.

Routing and scheduling co-design for holistic software-defined deterministic network

As a result of the growing complexity of industrial Internet applications, traditional hardware-based network designs are encountering challenges in terms of programmability and dynamic adaptability as they struggle to meet the real-time, high-reliability transmission requirements for the vast quantities of data generated in industrial environments. This paper proposes a holistic software-defined deterministic network (HSDDN) design solution. This solution uses a centralized controller to implement a comprehensive software definition, ranging from the network layer down to the physical layer. Within the wireless access domain, we decouple the standard radio-frequency modules from baseband processing to realize a software-defined physical layer, which then allows us to adjust the data transmission cycles and tag the trigger rates to meet demand for low-power, high-concurrency transmission. Within the wired network domain, we integrate software-defined networking with time-sensitive networking and propose a coordinated design strategy to address routing and the deterministic scheduling problem. We define a set of constraints to ensure collaborative transmission of the periodic and aperiodic data flows. To guarantee load balancing across all paths and timeslots, we introduce the Jain’s fairness index as the optimization objective and then construct a nondeterministic polynomial-time (NP)-hard joint optimization problem. Furthermore, an algorithm called Tabu search for routing and scheduling with dual-stages (TSRS-DS) is proposed. Simulation experiments demonstrate the effectiveness of the proposed HSDDN architecture.

A simulation-based optimization approach for designing transit networks

Public transport network design deals with finding efficient network solution(s) from a set of alternatives that best satisfies the often-conflicting objectives of stakeholders like passengers and operators. This work presents a simulation-based optimization (SBO) model for designing public transport networks. The work’s novelty is in developing such a network design model that fully accounts for the stochastic behavior of commuters on the transit network. The SBO discipline solves decision-based problems like the transit network design problem (TNDP) by combining simulation and optimization models. The proposed model integrates a disaggregated activity-based travel demand simulation with a multi-objective network optimization algorithm. Trip-based travel demand models are commonly used to represent traveler behavior in the literature. The approach limits its ability to accommodate the stochastic realities of traveler behavior in a transit network design solution. Using activity-based simulation instead makes it possible to account for a more realistic traveler behavior, especially real-time decisions made in response to changing network dynamics which ultimately affect the distribution of demand over time on the network. The proposed model is applied to the improved design of the integrated public transport network in the City of Cape Town, South Africa. The results show SBO can design efficient network solutions that reflect the objectives of network stakeholders.

Development of a blood supply chain network design model by considering lateral transshipment and blood substitution

blood supply chain network design is an approach to design an efficient blood supply chain network. This study developed a model for determining the best Blood Supply Chain Network Design solution that considering costs and social sustainability. The developed model was divided into four echelons: blood donation centers, blood banks, regional hospitals, and demand points. The objective of this research was to minimize total supply chain costs while maximize job opportunities. This study considered the concept of lateral transshipment and blood substitution at the third echelon, namely the regional hospital, to ensure that demand was met. The concept of lateral transshipment enabled regional hospitals that having excess of blood unit to satisfy blood supplies to other regional hospitals. Furthermore, blood substitution was a response to emergency conditions, specifically a shortage of appropriate blood product units. Blood substitution scheme is allowed in this research, that is, each type of blood product with a certain group can not only meet its own demand, but can also be used as a replacement for the same product with other compatible groups. a multi-objective possibilistic mixed integer linear programming model was developed. According to the obtained results, the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created can be discovered by the model.

Neural Predictor-Based Low Switching Frequency FCS-MPC for MMC With Online Weighting Factors Tuning

In this article, we propose a novel predictive control framework for modular multilevel converter, which makes use of neural predictor-based low switching frequency finite control-set model predictive control methodology with respect to online weighting factors tuning subject to robustness characteristics. The main objectives of this article are to maintain a low switching frequency operation and to enhance the system robustness. First, a modified cost function together with an online weighting factors auto-tuning technique, which has good potential to fulfill the low frequency operation, is presented under different operating conditions. Next, we provide a framework to explicitly address parameter mismatches and unknown nonlinear system dynamics in this proposal by introducing the neural network predictor control technique. The novelty of contribution of this article lies not only in incorporating the proposed cost function with a predictor-based neural network design solution for satisfying the requirement of the low switching frequency and robustness but also in the utilization of the auto-tuning technique for online determining the suitable weighting factors. Finally, we demonstrate the applicability of the proposed predictive control framework in comparison with the state-of-the-art finite control-set model predictive control approaches under various operating conditions.

Energy trilemma in active distribution network design: Balancing affordability, sustainability and security in optimization-based decision-making

A significant reduction in greenhouse gas emissions entails complex trade-offs among economic, environmental and security objectives of the energy trilemma. The associated proliferation of distributed energy resources in power distribution networks requires optimized planning to ensure economic efficiency and security, where security is defined as the ability to withstand outages (reliability) and imbalances between generation and load (flexibility). This paper investigates the trade-offs among active distribution network design solutions in the energy trilemma. Storage, renewable and dispatchable generators are installed to minimize cost, interruptions and emissions via epsilon-constraint mixed integer linear program. The ramping capability of distributed energy resources is harnessed to provide flexible capacity to the surrounding grid, and to enable the transition from active distribution networks into microgrids with islanding capability. Decision-making strategies for selecting the best design from a Pareto-optimal set are contrasted based on the economic, sustainability and security of the design and operations. Applying these strategies to a Californian distribution network shows nonlinear trade-offs between the trilemma objectives, reducing both emissions and interruption durations by up to 64%. Remarkably, energy supply is diversified with up to 47% of local generation entailing a strong reduction in operating expenditures. While the increase in local generation can reduce the peak imports from the surrounding grid by 56% compared to a network without distributed energy resources, enforcing strict emission limits can lead to renewable exports that exceed the peak load by 130%. Sensitivity analyses identify the performance indicators which strongly influence the selection of the best design.

A two-stage stochastic model for co-firing biomass supply chain networks

A sustainable alternative to fossil fuels is biomass. The design of cost-efficient biomass networks is necessary to compete with nonrenewable resources. The creation of integrated biomass supply chain (BSC) network design models and solution procedures can contribute to the achievement of this goal. In particular, the emerging bioenergy industry must take advantage of the economies of scale in transportation to minimize the final product’s cost. Hub-and-spoke networks have been proposed as a modeling approach to design large scale BSCs. The majority of these models are deterministic and do not consider the inherent variability in the biomass feedstock, such as physical and chemical properties that affect transportation, the effects of future climate on the biomass supply, overall cost, and production operations. Levels of ash and moisture are directly related to the quality of the feedstock, which negatively affects production of biofuels, increasing transportation and handling costs as well as the burden on the BSC’s efficiency. Varying weather affects biomass yield, which creates a fluctuation in the incoming supply into the network, creating a complex large scale Newsvendors Problem. In this paper, a stochastic hub-and-spoke network model and an efficient solution are proposed to minimize logistical costs by finding an optimal production and distribution network as well as optimal transportation while reducing computational burden by using metaheuristics, such as Simulated Annealing, when solving large NP-hard instances. The proposed solution procedure was compared to recent benchmark models, i.e. Bender’s Decomposition, yielding an overall solution difference of less than 7.71% and average reduction time difference of 91.4%. Case studies with several scenarios based on varying weather conditions were created using realistic data from the northeast region of the U.S.

An easy MS Excel software to use for water distribution system design: A real case distribution network design solution

Development of an MS Excel Visual Basic for Applications (VBA) program for water distribution network (WDN) design is presented in this paper. The tool solves a given closed-loop WDN by Hardy-Cross algorithm. Both VBA programming and MS Excel’s data validation modules are employed to provide easy-to-use environment for engineers and also in the education of engineers, and moreover, software solutions in the market are expensive and hard to obtain. A step by step solution to a part of Uskudar which is a county of Istanbul network is also provided. The color-coded instructions for the correction of the network system will be helping for the users. The tool is open source and can be used for the design of WDNs by engineers and students.

Design/upgrade of a transparent optical network topology resilient to the simultaneous failure of its critical nodes

AbstractThis paper addresses two related problems in the context of transparent optical networks. In the network design problem, the aim is to identify a set of fiber links to connect a given set of nodes. In the network upgrade problem, the aim is to identify a set of new fiber links to add to a given network topology. For a given fiber length budget, the aim in both problems is to maximize the network resilience to the simultaneous failure of its critical nodes. The resilience is evaluated by the average 2‐terminal reliability (A2TR) against a set of critical node failures and the critical nodes are the ones that minimize the A2TR of the network. So, the design/upgrade problem is a bi‐level max‐min optimization problem. Recently, a multi‐start greedy randomized heuristic was proposed for both problems. Here, we propose an alternative method based on a greedy deterministic algorithm and we provide computational results showing that the new method obtains better solutions. The results show that the resiliency difference between existing network topologies and the best network design solutions is very high but this difference can be significantly reduced by network upgrades with small fiber length budgets.

A network design problem formulation and solution procedure for intercity transit services

This study presents a network design problem formulation and solution procedure for an intercity transit service that has multiple routes and serves multiple terminals in the origin and destination cities. The proposed solution procedure consists of three steps: (1) a set of candidate terminals are selected; (2) candidate routes are generated between those terminals using a k-shortest-path algorithm; (3) a mixed integer linear programing model finds the optimal routes, terminals, frequencies, fleet size and depot locations, given a set of constraints and the objective of minimizing total passenger travel time and vehicle deadheading time. The solution procedure was implemented for a newly conceived transit service between Tucson and Phoenix in Arizona, USA. The final routes found by the model look reasonable, and a sensitivity analysis showed that the objective function is most sensitive to changes in the demand satisfaction ratio, and then to the maximum number of routes and fleet size.

Greenhouse gas observation network design for Africa

An optimal network design was carried out to prioritise the installation or refurbishment of greenhouse gas (GHG) monitoring stations around Africa. The network was optimised to reduce the uncertainty in emissions across three of the most important GHGs: CO2, CH4, and N2O. Optimal networks were derived using incremental optimisation of the percentage uncertainty reduction achieved by a Gaussian Bayesian atmospheric inversion. The solution for CO2 was driven by seasonality in net primary productivity. The solution for N2O was driven by activity in a small number of soil flux hotspots. The optimal solution for CH4 was consistent over different seasons. All solutions for CO2 and N2O placed sites in central Africa at places such as Kisangani, Kinshasa and Bunia (Democratic Republic of Congo), Dundo and Lubango (Angola), Zoétélé (Cameroon), Am Timan (Chad), and En Nahud (Sudan). Many of these sites appeared in the CH4 solutions, but with a few sites in southern Africa as well, such as Amersfoort (South Africa). The multi-species optimal network design solutions tended to have sites more evenly spread-out, but concentrated the placement of new tall-tower stations in Africa between 10ºN and 25ºS. The uncertainty reduction achieved by the multi-species network of twelve stations reached 47.8% for CO2, 34.3% for CH4, and 32.5% for N2O. The gains in uncertainty reduction diminished as stations were added to the solution, with an expected maximum of less than 60%. A reduction in the absolute uncertainty in African GHG emissions requires these additional measurement stations, as well as additional constraint from an integrated GHG observatory and a reduction in uncertainty in the prior biogenic fluxes in tropical Africa.

Designing a sustainable municipal solid waste management system in Pathum Thani, Thailand

In most developing countries, the disposal and treatment of solid waste in urban areas has proved to be a problem for the social acceptance of local residents and long-term sustainable development. This study proposes a multi-objective optimisation methodology to investigate sustainable network designs for solid waste management system. A case of Pathum Thani province, Thailand, is used to illustrate the application of the methodology. The social impact, defined as the number of people affected by the unpleasant sanitary conditions of nearby solid waste management facilities, is estimated by using GIS tools. The sustainability performances are normalised to a satisfaction scale, allowing decision makers to select network design solutions based on their sustainability target preferences. The results suggest that the facility location can significantly alter the sustainability performances, and it is shown that the locations of waste management facilities can be arranged to simultaneously improve all of the aspects of sustainability.

Smart Metering Wireless Networks at 169 MHz

Intelligent metering systems are being rolled-out on a large-scale worldwide, enabling consumer to make informed choices about consumption patterns and energy saving, while supporting the development of new retail services and products. Unfortunately, the lack of established and shared international standards represents a serious hindrance to be overcome for a complete development of a profitable market. The identification of suitable communication protocols and cost-effective network architectures represent a challenging aspect. In this framework, different network design solutions for wireless smart metering systems at 169 MHz are considered and investigated in this paper, aiming at cost efficient deployment based on extensive re-use of existing infrastructures in urban scenarios, namely, macro-cellular and lighting networks. Coverage assessment and frequency planning issues are addressed, together with an ad hoc measurement campaign carried out to fill the gap in the knowledge of urban propagation in the 169 MHz band. Results show that cost-effective deployment of the intelligent metering network is achievable. Notably, a spatial reuse factor larger than the overall number of available frequency channels might be necessary, thus meaning that the spectral resources shall be also allocated according to a time division scheme, where the hubs are switched off at turn. Anyway, this requirement should not affect the overall reading rate in practical applications.

Designing a sustainable municipal solid waste management system in Pathum Thani, Thailand

In most developing countries, the disposal and treatment of solid waste in urban areas has proved to be a problem for the social acceptance of local residents and long-term sustainable development. This study proposes a multi-objective optimisation methodology to investigate sustainable network designs for solid waste management system. A case of Pathum Thani province, Thailand, is used to illustrate the application of the methodology. The social impact, defined as the number of people affected by the unpleasant sanitary conditions of nearby solid waste management facilities, is estimated by using GIS tools. The sustainability performances are normalised to a satisfaction scale, allowing decision makers to select network design solutions based on their sustainability target preferences. The results suggest that the facility location can significantly alter the sustainability performances, and it is shown that the locations of waste management facilities can be arranged to simultaneously improve all of the aspects of sustainability.

A scheme for determining vehicle routes based on Arc-based service network design

ABSTRACTIn freight transportation, less-than-truckload carriers often need to assign each vehicle a cyclic route so that drivers can come back home after a certain period of time. However, the Node-Arc model for service network design addresses decisions on each arc and does not determine routes directly, although the vehicle balancing constraint ensures that the number of outgoing vehicles equals the number of incoming vehicles at each node. How to transform the optimized service network into a set of vehicle routes remains an important problem that has not yet been studied. In this paper, we propose a three-phase scheme to address this problem. In the first stage, we present an algorithm based on the depth-first search to find all of the different cyclic routes in a service network design solution. In the second stage, we propose to prune poor cyclic routes using real-life constraints so that a collection of acceptable vehicle routes can be obtained before route assignment. Some of the pruning can also be done in the first stage to speed up the proposed algorithm. In the third stage, we formulate the problem of selecting a set of cyclic routes to cover the entire network as a weighted set covering problem. The resulting model is formulated as an integer program and solved with IBM ILOG CPLEX solver. Experimental results on benchmark instances for service network design indicate the effectiveness of the proposed scheme which gives high-quality solutions in an efficient way.

Industrial wastewater treatment network based on recycling and rerouting strategies for retrofit design schemes

The advent of complex industrial water/wastewater management problems points to a need for effective systematic design for a sustainable solution. The objective of this work is to extend the research in the area of systematic design of water/wastewater management by further developing and extending a generic model-based synthesis and design framework for retrofit wastewater treatment networks (WWTN) of an existing industrial process. The developed approach is suitable for grassroots and retrofit systems and adaptable to a wide range of wastewater treatment problems. A sequential solution procedure is employed to solve a network superstructure-based optimization problem formulated as Mixed Integer Linear and/or Non-Linear Programming (MILP/MINLP). Data from a petroleum refinery effluent treatment plant together with special design constraints are employed to formulate different design schemes based on recycling and rerouting strategies focusing on completely splitting system and zero liquid discharge (ZLD) opportunity. The base case design of the existing process has been verified against the refinery data, while the grassroots and the retrofit options are generated and compared with the existing process. The network design solutions obtained with effectively computational time from the case study shows an improvement in the reduction of a total annualized cost (TAC) and wastewater discharge rate (WWDR) as a result of water recycling and rerouting options. Pareto plot (trade-off solution graph) for the analysis of such optimal solutions has been applied to implicitly verify the optimality of the solution based on all possible scenarios. Superior retrofit alternatives have been identified based on their performance including cost and environmental impacts and can be used as efficient design guidelines for the future development of the existing wastewater treatment process.

Time-dependent transportation network design that considers health cost

This paper proposes a bi-level optimisation framework for time-dependent discrete road network design that considers health impacts. A general health cost function is proposed and captured in the framework. The function simultaneously considers the health impacts of road traffic emissions, noise, and accidents due to network expansion. To solve the problem, the artificial bee colony (ABC) algorithm is proposed to search the network design solutions of the upper-level problem, while the method of successive averages and the Frank–Wolfe algorithm are adopted to solve the lower-level time-dependent land-use transportation problem. A repairing procedure is proposed to remedy infeasible solutions. A numerical study is set up to illustrate the conflict between maximising consumer surplus and minimising the health cost. This paper also reveals a paradox phenomenon that with an increasing amount of emissions, the health cost decreases. Moreover, the existence of a health inequity between different residential zones is demonstrated. A modified Sioux Falls network is adopted to show the performance of the solution algorithm as well as the effectiveness of the proposed repairing procedure.

On-Shore Wind Farm Cable Network Optimisation Utilising a Multiobjective Genetic Algorithm

A wind farm is a collection of large scale (usually > 1MW) wind turbines generally located across wide and uneven terrain in order to capture sufficient wind resources to generate a source of electrical energy. The electric power networks of such farms serve to electrically connect all the turbines in the farm back to a central substation, which is in turn connected to a load, often via an existing electricity distribution or transmission network. While optimisation methods currently exist for the design of cable networks in off-shore wind farms, which primarily aim to reduce installation cost and energy loss, the design for onshore farms is usually achieved manually and iteratively, and can often result in a suboptimal design. This paper offers a Genetic Algorithm based optimisation method for onshore applications, and demonstrates how an optimal wind farm cable network design solution can be reached in terms of minimum cost, minimum power losses and maximum reliability. The algorithm developed performs the required calculations and demonstrates that an optimised solution has been reached. It is demonstrated that this method provides faster calculations than the manual method and can be used for any standard on-shore wind farm layout design, utilising components as desired by the user such as underground or overhead cables and single or triple-core cables. Index Terms — Energy networks, genetic algorithms, multi-objective optimization, wind energy.