Optimal Network Design Research Articles

Optimal Design of a Surface Precipitation Network in Canada

Abstract The surface precipitation network in Canada suffers from large data gaps due to the challenge of covering a large country with a low population density. A proof-of-concept for an optimal network design is proposed to more efficiently estimate precipitation in Canada with the design goal of minimizing the interpolation uncertainty. The network design is based on a statistical model of precipitation that accounts for intermittency and non-Gaussianity of precipitation. Our results indicate that the greatest needs for new stations are in British Columbia, where coastal and mountain climate leads to more uncertainty in precipitation amounts, while the Prairie Provinces (Alberta, Saskatchewan, and Manitoba) could gain efficiencies by reducing their network size. Despite the current low density of stations in the territories north of Canada, these drier and colder regions only have a moderate need for more stations, mostly in the mountainous regions of Yukon. However, from a spatially varying wind undercatch measurement error model, it is shown that these northern regions have greatest need for higher-accuracy measurements. Significance Statement The proposed methodology can guide in the optimal placement of precipitation gauges across a large country such as Canada, which will provide value for money in how rain and snow are monitored.

Reliability, economic, and environmental analysis of fuel-cell-based hybrid renewable energy networks for residential communities

A fuel cell system has received considerable interest as an effective combined heat and power (CHP) generation system. In particular, a polymer electrolyte membrane fuel cell (PEMFC) has been regarded as suitable for residential buildings as their low-temperature heat generation can be used to meet high heat demand. This study aims to optimize renewable energy networks on the basis of PEMFC, considering both different system mixes in an energy network and different system sizes. This study scrutinizes the overall performance of different hybrid energy networks based on PEMFC in terms of the reliability, economic, and environmental perspectives when both the heat and power generation from PEMFC are used to meet the energy need of residential communities. A hybrid renewable energy network for a residential community with 12 households is modeled and simulated using TRNSYS to evaluate the performance of different hybrid energy network scenarios. Four scenarios are created on the basis of the base case with a gas-based fuel cell (FC) system and heat tank by incrementally adding renewable energy systems—a ground source heat pump (GSHP) system, photovoltaic (PV) system, battery, electrolyzer, and hydrogen storage tank. First, the impacts of individual design variables are quantified using variance-based sensitivity analysis for identification of key design variables. Second, the cost optimization of hybrid energy network is performed under the four scenarios of system mixes. Last, the effects of the key design variables for each scenario are further scrutinized through heat maps. The analysis results of the case study confirms the feasibility of a FC system as part of renewable energy network for residential buildings. Furthermore, Scenario 3 comprising a FC system, GSHP, PV system, and battery is found to be the optimal design in terms of all the three aspects. This result suggests that the whole performance of hybrid energy network can be improved when other renewable systems are included in the network to complement the FC system. In contrast, Scenario 4 using green hydrogen has poorer performance than Scenario 3 using gas as fuel due to high unit prices of hydrogen systems and low efficiency of green hydrogen production. Furthermore, external factors such as utility prices and system unit prices are considered in further analysis and found to have great influence on the optimal design of energy networks.

Optimal Design of an Eco-Friendly Transportation Network under Uncertain Parameters

The choice of attributes in the multi-attribute decision-making process becomes frequently uncertain because of the diverse degree of preference for alternatives. These are assessed utilizing human decisions and linguistic terms that can be utilized for a more adaptable and delicate assessment. The present article illustrates a multi-attribute decision-making (MADM) process, named the exponential technique for order of preference by similarity to an ideal solution (Exp-TOPSIS), considering the selection of attributes with existing uncertainty. Another three notable multi-attribute decision-making (MADM) processes, termed as multi-attribute utility theory (MAUT), elimination and choice expressing reality method (ELECTRE), and the technique for order of preference by similarity to an ideal solution (TOPSIS) are utilized to present a comparison with the proposed methodology by proposing a mathematical model for a solid transportation problem intending to minimize carbon emissions under an uncertain environment. The uncertainty theory, which depends on human conviction degree, is utilized to define the uncertain parameters of the model related to the problem. Applying the proposed one and the other three multi-attribute decision-making processes, the best emission factors are observed to mitigate the carbon emissions from the transport sectors. In this context, the proposed method has some advantages over the existing techniques in selecting the emission factors. All four MADM approaches with different weights have been tested to choose the best five attributes among nine options to be utilized in the mathematical model to minimize the total carbon emission ejection from transportation. In every case, the obtained result states that the proposed Exp-TOPSIS gives the minimum carbon emissions in a range of 2100–2500 units. LINGO 13.0 solver is used to address the deterministic solid transportation problem, and finally, this study presents some investigations on the selection of carbon emission factors and future utilization of the proposed multi-attribute decision-making process.

Optimal design of water reuse networks in cities through decision support tool development and testing

Water scarcity and droughts are an increasing issue in many parts of the world. In the context of urban water systems, the transition to circularity may imply wastewater treatment and reuse. Planning and assessment of water reuse projects require decision-makers evaluating the cost and benefits of alternative scenarios. Manual or semi-automatic approaches are still common practice for planning both drinking and reclaimed water distribution networks. This work illustrates a decision support tool that, based on open data sources and graph theory coupled to greedy optimization algorithms, is able to automatically compute the optimal reclaimed water network for a given scenario. The tool provides not only the maximum amount of served reclaimed water per unit of invested cost, but also the length and diameters of the pipes required, the location and size of storage tanks, the population served, and the construction costs, i.e., everything under the same architecture. The usefulness of the tool is illustrated in two different but complementary cities in terms of size, density, and topography. The construction cost of the optimal water reclaimed network for a city of approximately 100,000 inhabitants is estimated to be in the range of €0.17–0.22/m3 (for a payback period of 30 years).

Optimal Design of Water Distribution Network Using Improved Artificial Bee Colony Algorithm

Optimal Design of Water Distribution Network Using Improved Artificial Bee Colony Algorithm

Optimal design and cost analysis of water distribution networks based on pressure-dependent leakage using NSGA-II

Leakage from water distribution networks (WDNs) is inevitable. Therefore, during design a WDN, engineers add a percentage of each nodal water demand as leakage discharge to total node demand. The amount of leakage depends on the pressure, which is not known at the design stage. Considering a constant percentage of node demand in lieu of its leakage makes the problem worse. In this study, the effect of leakage on the optimal WDN design was investigated by developing the matrix form of the gradient algorithm while accounting for leakage using the pressure-dependent model. Non-dominated genetic algorithm version II (NSGA-II) was used as the optimization engine with two objectives which includes minimizing the network construction cost and minimizing the total network pressure deficiency. Two well-known two- and three-loop WDNs in literature were studied. The results indicated that the pressure-dependent leakage varies between 12.9 and 29.44% of the node demand while the network construction cost stays the same if compared with the fixed percentage leakage model, and the construction cost would increase by 17–31%, if leakage is not accounted for. This is expected the optimized diameters and hydraulic characteristics of the networks being affected by the leakage calculation method.

Evaluating distribution network optimal structure with respect to solar hosting capacity

Optimal design of distribution networks has become an important topic for analysis due to the growing share of photovoltaics (PV). Low and medium voltage networks should undergo structural changes to accommodate widespread PV generation and optimal operation. In this paper, the impact of the network structure on the solar hosting capacity (HC) is analyzed with respect to the role of low and medium voltage networks in power delivery. A given set of load nodes is simulated with multiple feeding substations and varying peak power and number of PV plants. The slime mold algorithm is utilized for numerous topology generations and measured load time series represent regions ranging from rural to urban. The results reveal that networks should go through significant structural changes to cope with larger PV generation and even more so to increase the HC. On the other hand, voltage control measures, such as on-load tap changers, PV reactive power control and curtailment, provide a competitive solution to varying size of distribution networks in hosting solar power. Finally, the analysis in this study provides evidence to a possible need in the change of residential PV policies in order to sustain the current pace of adopting PV plants in Finland.

Facilities Location in the Supply Chain Network Using an Iterated Local Search Algorithm

The supply chain design problem is one of the important problems in supply chain management. In this problem, the goal is to locate several facilities for distribution centers in different geographical areas to cover customer demands. In the model presented in this paper, customer demand is met through distribution centers, while these distribution centers supply products from manufacturing plants. In this paper, a mathematical model of integer linear programming is presented that, in addition to the optimal supply chain network design, considers the facility's location. This model aims to minimize the costs of sending products from the factory to distribution centers, from distribution centers to customers, and establishing distribution centers. To evaluate the proposed model, different network samples were created randomly on small, medium and large scales for experiments. In order to perform the experiments, a linear programming solver (LP Solver) and an iterated local search algorithm were used to compare the performance of each of these methods to find the optimal solution. These experiments show the superiority of the iterated local search algorithm compared to LP Solver in achieving the optimal solution in the shortest runtime for all instances.

Optimal reliable design of energy-efficient Wireless Body Area Networks

A Wireless Body Area Network (WBAN) is a network of wireless devices that are used to monitor the health of individuals or provide them with information pertinent to their personal health. A WBAN typically consists of multiple sensors and a central sink and it enables the tracking of multiple body signals without the need for cables or other wired connections, allowing for a more comfortable user experience. The used sensors are extremely small and lightweight, making them well-suited for long-term medical monitoring. In this paper, we consider the joint problem of device positioning and data routing in WBANs leveraging relay-based multi-hop communication to address network energy consumption and reliable data transmission, which are two fundamental challenges in the WBAN design. The deployment of a WBAN is the most important factor that impacts both the network lifetime and reliability. On the other side, routing in multi-hop WBANs is of paramount importance for efficient communications between sensors and the centralized hub. For this reason, we define and assign to each relay a concave and non-increasing function (called reliability function) which permits to decrease consistently congestion occurrence in the network by balancing relays loads. In this work, we consider the problem of designing an energy-aware routing on WBANs while ensuring that all data are delivered to the sink with a sufficiently large probability. This problem is formulated as a non-convex mixed-integer non-linear programming problem and proven to be NP-hard. We first present an efficient linearization technique for the problem and then propose lower- and upper-bounding schemes for the problem, which can be applied within a cutting plane algorithm-based heuristic to solve the problem. Finally, we evaluate the proposed model with a real topology scenario and compare its performance with the most notable methods presented in the literature. Numerical results show that our model, independently of patient body movement, can transfer data through reliable paths from biosensors to the sink while further offering these options: (1) the probability that each biosensor successfully transfers its data to the sink is adjustable according to the desired application; (2) congestion is limited through a careful balancing of incoming traffic to relays, and (3) a small number of relays is installed, while ensuring an energy-efficient routing of data.

Energy transport and optimal design of noisy Platonic quantum networks

Optimal energy transport is one of the primary goals for designing efficient quantum networks. In this work, the maximum energy transport is investigated for three-dimensional quantum networks with Platonic geometries affected by dephasing and dissipative Markovian noise. The network and the environmental characteristics corresponding the optimal design are obtained and investigated for five Platonic networks with 4, 6, 8, 12, and 20 number of sites that one of the sites is connected to a sink site through a Markovian dissipative process. Such optimal designs could have various applications like switching and multiplexing in quantum circuits.

Robust estimation of shear wave velocity in a carbonate oil reservoir from conventional well logging data using machine learning algorithms

In recent decades, various applications of shear wave velocity have been reported in oil and gas projects. These applications include the determination of lithology, estimation of geomechanical parameters, reservoir fluid detection, etc. The shear wave velocity is usually measured or recorded by DSI (Dipole Shear Sonic imager) instruments. This log is not usually available in all wells of a hydrocarbon field because of the high cost of running this log compared to other conventional logs. However, it is tried to estimate the shear wave velocity from other related logs.The literature contains many empirical rock physics models used to estimate shear wave velocity. Most of them were developed for sandstone lithologies and didn't applicable in carbonate reservoir because of existing some vertical and horizontally complex fracture networks in the carbonate reservoirs that cause a quite different behavior for the shear wave velocity.The main objective of this research is to concentrate on the effect of lithology in the estimation of the target log for a carbonate reservoir. At the earliest step, preprocessing is performed and 455 data points of several conventional well logs, i.g., acoustic, density, neutron porosity, resistivity, gamma-ray, calcite volume, dolomite volume, and water saturation logs have been applied to generate the synthetic shear wave velocity log in a carbonate oil reservoir.First, original data are divided into training, validation, and testing subsets. Different data-driven predictive models were built: Multiple Linear Regression (MLR), Ensemble Learning Method (ELM), and three architectures of Artificial Neural Networks (ANN). The performance of all techniques was compared with each other. Finally, the feed-forward neural network shows the highest accuracy with R-values of 0.99 and 0.96 for the training and testing datasets. Moreover, the network design and tuning parameters of the model are adjusted using the grid search optimization technique to obtain the optimal design of the artificial neural network for estimating the shear wave velocity log. Hence, a deep artificial neural network, i.e., [8–11–15-1] network with bayesian regularization training algorithm and hyperbolic tangent sigmoid transfer function, is proposed to predict the target response in other wells.

An optimal neural network design for fractional deep learning of logistic growth

An optimal neural network design for fractional deep learning of logistic growth

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.

Optimal Design of Reverse Logistics Recycling Network for Express Packaging Considering Carbon Emissions

With the development of China’s express delivery industry, the number of express packaging has proliferated, leading to many problems such as environmental pollution and resource waste. In this paper, the process of reverse logistics network design for express packaging recycling is given as an example in the M region, and a four-level network containing primary recycling nodes, recycling centers, processing centers, and terminals is established. A candidate node selection model based on the K-means algorithm is constructed to cluster by distance from 535 courier outlets to select 15 candidate nodes of recycling centers and processing centers. A node selection model based on the NSGA-II algorithm is constructed to identify recycling centers and processing centers from 15 candidate nodes with minimizing total cost and carbon emission as the objective function, and a set of Pareto solution sets containing 43 solutions is obtained. According to the distribution of the solution set, the 43 solutions are classified into I, II, and III categories. The results indicate that the solutions corresponding to Class I and Class II solutions can be selected when the recycling system gives priority to cost, Class II and Class III solutions can be selected when the recycling system gives priority to environmental benefits, and Class III solutions can be selected when the society-wide recycling system has developed to a certain extent. In addition, this paper also randomly selects a sample solution from each of the three types of solution sets, conducts coding interpretation for site selection, vehicle selection, and treatment technology selection, and gives an example design scheme.

An optimization approach for food waste management system based on technical integration under different Water/Grease proportions

Large quantities of food waste challenge sustainable development and there is significant value in the proper treatment of such waste. There are several food waste treatment technologies available, and food waste valorization requires planning for the collection, transportation, pre-treatment, treatment, and product sales. Managers need comprehensive system management objectives and strategies to promote the circular economy. To determine the optimal network design for food waste management systems, this paper develops a multi-objective mixed integer non-linear model from economic, energy, environmental, and social perspectives that integrates different treatment technologies into the three-phase separation pre-treatment. The proposed model was applied to a food waste management system in Chongqing, China, and the effects of different decision preferences of the managers, the water and grease proportion in the food waste on the optimization results were analyzed and discussed. It was found that different grease and water proportions influenced the optimal application of the technologies and system management decisions. With a 10% to 15% grease proportion, composting was seen to be more suitable than incineration or anaerobic digestion treatments, and the simultaneous application of multiple technologies was seen to be beneficial for food waste management systems.

Probabilistic warm solutions-based multi-objective optimization algorithm, application in optimal design of water distribution networks

Single objective optimization of water distribution networks (WDNs) based on construction costs reduces system resiliency. Thus, researchers have developed various optimization frameworks to maximize system resilience while minimizing construction costs during the last decade. They generally utilized population-based meta-heuristic algorithms. These algorithms start with random populations (initial cold solutions), so they need high computational time to converge to the optimal solutions. This paper aims to reduce search space by introducing prebaked initial or warm solutions based on the hydraulic properties of WDNs which are discharge variance and dissipated power function. After generating initial warm solutions, several probabilistic models are used to bring them closer to the global optimal Pareto front and increase their diversity. The proposed approach is used to optimize the design of two well-known benchmarks, Alprovits and Hanoi, and a real-world large-scale WDN. Results show that using warm solutions as the initial population reduces time, the number of iterations, and fitness evaluations to reach a certain accuracy level, more than two times on average for the benchmarks. Also, results show that the proposed method reduces the average construction costs by 45% compared to the traditional optimization method in the same execution time for real-world WDN.

Incorporating travel time means and standard deviations into transportation network design problem: a hybrid method based on column generation and Lagrangian relaxation

ABSTRACT Travel times in real-world transportation networks are affected by many disruptions. When we conduct the network design optimization, the traffic condition and its resulting travel time variability should be taken into account. However, most of the previous network design optimizations adopted the lengths or expected travel times of links. Based on travel time means and standard deviations, we develop an arc-based model that is a nonlinear and concave integer program. By the Dantzig-Wolfe reformulation, we transform it into an equivalent column-based model that is an integer linear program with a large number of variables. Based on the column-based model, we develop a hybrid method based on column generation and Lagrangian relaxation. The restricted master problem can be settled by the linear programming solvers. The pricing subproblems incorporate independent reliable shortest path problems and a knapsack problem. In numerical experiments, the proposed method can generate feasible solutions with good integrality gaps.

Predicting future well performance for environmental remediation design using deep learning

In this study, we developed a deep learning (DL) framework with a multi-channel three-dimensional convolutional neural network (MC3D-CNN) to predict well performance and thereby assist future environmental remediation design. Such prediction of extraction well performance at designated locations is critical for configuring pump-and-treat (P&T) well network design and operation, setting reasonable target closure dates for overall remedying, and estimating remedy costs. The framework is developed with operational and monitoring data routinely collected during P&T remedy operations, including well extraction and injection rates as well as in situ contaminant concentrations. Traditionally, the collected data were rarely used for purposes other than assessing past well performance and the accuracy of the conceptual site model. However, recent advances in data-driven computational approaches enable better use of the large datasets to inform future well performance, enhance site characterization, and improve remediation planning. In this study, we established a DL framework to integrate transient three-dimensional contaminant plumes and multiple aquifer properties (e.g., hydraulic conductivity and hydrostratigraphic maps) to identify characteristic patterns controlling and representing extraction well mass recovery, aiming at providing future mass recovery estimates for existing wells and candidate wells at any proposed locations. We evaluated our framework by using a realistic synthetic dataset generated from a well-calibrated flow and transport model used in the 200 West Area of the U.S. Department of Energy’s Hanford Site in southeastern Washington state. The multi-channel feature in our framework allows integration of various types and temporal densities of training datasets for DL model development. Overall, we found that the trained DL model achieved an accuracy of over 90% in ranking extraction well performance in validation datasets, and over 80% in predicting high-performance-ranking well locations. This data-informed approach provides a flexible tool to support adaptive site management, streamline decision-making, and potentially reduce remediation time and costs. Our DL framework can be used as a filtering tool to improve the current P&T network optimization design by reducing the number of candidate well locations.

Comparative Synthesis: Learning Near-Optimal Network Designs by Query

When managing wide-area networks, network architects must decide how to balance multiple conflicting metrics, and ensure fair allocations to competing traffic while prioritizing critical traffic. The state of practice poses challenges since architects must precisely encode their intent into formal optimization models using abstract notions such as utility functions, and ad-hoc manually tuned knobs. In this paper, we present the first effort to synthesize optimal network designs with indeterminate objectives using an interactive program-synthesis-based approach. We make three contributions. First, we present comparative synthesis, an interactive synthesis framework which produces near-optimal programs (network designs) through two kinds of queries (Validate and Compare), without an objective explicitly given. Second, we develop the first learning algorithm for comparative synthesis in which a voting-guided learner picks the most informative query in each iteration. We present theoretical analysis of the convergence rate of the algorithm. Third, we implemented Net10Q, a system based on our approach, and demonstrate its effectiveness on four real-world network case studies using black-box oracles and simulation experiments, as well as a pilot user study comprising network researchers and practitioners. Both theoretical and experimental results show the promise of our approach.

Optimal design of intelligent sensors in improving the safety of steel structures

Instrument health monitoring can identify the behavioral characteristics and detect the possible failure of structures during their lifetime. Structural health monitoring is a process in which various issues are addressed, including the diagnosis and location of damage to structures. A structural health monitoring system receives information about the instrument's behavior from the sensor network. Because installing sensors, receiving information from them, recording and processing data, and concluding is an essential process in monitoring, lack of data leads to incorrect or insufficient results, and additional data confuses the analysis. And data processing. Also, increasing the number of sensors increases the cost of monitoring the structure. Therefore, the optimal design of the sensors network to correctly use sensors in terms of type, number, and critical location is essential.