Optimal Network Design Research Articles

Two-stage stochastic programming model to locate capacitated EV-charging stations in urban areas under demand uncertainty

Abstract Due to the dangerous effects of fossil fuels, policymakers tend to substitute fossil-fuel-based vehicles with electric ones. Thus, the optimal design of a charging station network providing convenient access for the users is of great importance. This paper presents a two-stage stochastic programming model for the problem of locating charging stations in urban areas. Parking lots around the buildings which may be visited by people during the day are considered as potential locations for charger installation. The model determines the parking lots that should be equipped with chargers and the number as well as the type of chargers that must be placed in each parking lot considering the demand as an uncertain parameter. The proposed model is examined on the dataset of a midtown area, taken from the literature, and an efficient heuristic algorithm based on Benders decomposition is utilized to solve the model. The results indicate that the heuristic method can find a near-optimal solution (with the optimality gap of at most 0.05%) in a short time.

Optimal design and operation of an islanded water-energy network including a combined electrodialysis-reverse osmosis desalination unit

This paper proposes an optimization framework to simultaneously optimize the saline and freshwater water sources as well as decentralized renewable and conventional energy sources, which consequently results in an economic and efficient water-energy system operation. The inextricable links between water and energy systems are taken into considerations, and a mixed integer nonlinear programming formulation is used to solve the proposed optimization model. A hybrid electrodialysis-reverse osmosis is integrated in the water distribution system and its electricity consumption is minimized. Also, the day-ahead economic dispatch problem of the energy system incorporates the random behavior of solar and wind generation units, along with charging and discharging modes of battery storage systems. The outcomes of the proposed models highlight: 1) integrating the hybrid desalination module in the water-energy system will reduce peak demands by more than 30% if the water and energy systems are solely supplied by saline water and renewable energy sources, respectively; 2) presence of an extra water storage tank also decreases peak demands by 13%. The impact of key elements of the combined system such as pumps’ speeds, water tank constraints, and presence of renewable sources on the output cost, generated energy, and hydraulic parameters is investigated through case studies.

Redundant binary codes in genetic algorithms: multi-objective design optimization of water distribution networks

Abstract Genetic algorithms have been shown to be highly effective for optimization problems in various disciplines, and binary coding is generally adopted as it is straightforward to implement and lends itself to problems with discrete-valued decision variables. However, a difficulty associated with binary coding is the existence of redundant codes that do not correspond to any element in the finite discrete set that the encoded parameter belongs to. A common technique used to address redundant binary codes is to discard the chromosomes in which they occur. Effective alternatives to the outright removal of redundant codes are lacking in the literature. This article presents illustrative examples based on the problem of optimizing the design of water distribution networks. Two benchmark networks in the literature and two different multi-objective design optimization models were considered. Different fixed mapping schemes gave significantly different solutions in the search space. The main inference from the results is that mapping schemes that improved diversity in the population of solutions achieved better results, which may pave the way for the development of practical and effective mapping schemes.

Discontinuous transition to loop formation in optimal supply networks

The structure and design of optimal supply networks is an important topic in complex networks research. A fundamental trait of natural and man-made networks is the emergence of loops and the trade-off governing their formation: adding redundant edges to supply networks is costly, yet beneficial for resilience. Loops typically form when costs for new edges are small or inputs uncertain. Here, we shed further light on the transition to loop formation. We demonstrate that loops emerge discontinuously when decreasing the costs for new edges for both an edge-damage model and a fluctuating sink model. Mathematically, new loops are shown to form through a saddle-node bifurcation. Our analysis allows to heuristically predict the location and cost where the first loop emerges. Finally, we unveil an intimate relationship among betweenness measures and optimal tree networks. Our results can be used to understand the evolution of loop formation in real-world biological networks.

Optimal design of water pipeline networks for the development of shale gas resources

AbstractOne of the major concerns in shale gas production is water management. Millions of gallons of water are injected to fracture each well and a significant amount returns to the surface as flowback. Operators are increasingly reusing flowback to reduce freshwater consumption and impaired water disposal. Because of this, networks of water pipelines in U.S. shales are growing fast. This work is aimed at addressing the optimal planning of shale gas operations in multiple wellpads together with the design of water distribution networks (WDN). We propose a multiperiod mixed‐integer linear programming model to solve the challenging stay‐or‐mobilize trade‐off. The proposed model permits to schedule operations at a detailed level, accounting for the WDN required to maximize the reuse of impaired water. We present illustrative examples involving up to 20 pads, 4 frac‐crews, and 100 wells developed over 1 year, showing that the design of the WDN can be effectively optimized.

Optimal Design of Deep Residual Network Based on Image Classification of Fashion-MNIST Dataset

With the increasing number of layers of deep neural network, the performance of the model tends to be saturated gradually. As a very deep network architecture, the deep residual network shows beneficial characteristics in precision and convergence. This paper deeply studied the essence of deep residual network and intended to design an optimal model for classification in Fashion-MNIST dataset. Due to the significant effects of the hyper-parameters in neural network models, the method of designing model structure and optimizing training process were investigated. Next, the performances of the training models under different conditions were compared, and finally an optimal training model whose accuracy was up to 96.21% was obtained. The experimental result shows that the performance of the model can be improved by partly widening the network and selecting more advanced training process.

Preconditioning Water Distribution Network Optimization with Head Loss–Based Design Method

AbstractThis paper develops a new domain knowledge–based initial design method for the optimization of water distribution network design. The new initial water distribution network design method, t...

Optimal artificial neural network design for simultaneous modeling of multicomponent adsorption

In this work, ultrasound-modified chitin (UM-chitin) was used to adsorb cobalt (Co2+), methylene blue (MB), and nickel (Ni2+) in single, binary and ternary systems, at different temperatures. The single adsorption isotherms showed that the affinity of the UM-chitin towards the metals was higher than that of the dye. Antagonist adsorption was found for the binary adsorption of Co2+ and MB; however, the presence of Ni2+ in the ternary system promoted the adsorption of MB and inhibited the adsorption of Co2+. Therefore, an optimal artificial neural network was developed to predict these synergistic and antagonistic interactions simultaneously. Different ANN configurations were tested using the initial concentration of each adsorbate (0–650 mg L−1) and the solution temperature (298–328 K) as input variables. The optimal ANN structure, which uses 2 hidden layers with 5 and 10 neurons, tangent sigmoid activation function (tansig) at both hidden and output layers, and Bayesian Regulation as its backpropagation algorithm demonstrated suitable ability in simultaneously predict the adsorption capacity for each adsorbate (MSE < 0.0003 and R > 0.9995).

Sustainable sugarcane-to-bioethanol supply chain network design: A robust possibilistic programming model

Issues such as high fossil-fuel price fluctuations, considerable air pollution, and the realization that fossil sources are not unlimited threaten the global economy. Hence, researchers have been attracted by biomass, especially sugarcane, as a source of renewable energy to produce bioethanol and other biofuels. This research proposes a three-phase robust supply chain network design optimization model to produce bioethanol from sugarcane. Fuzzy integrated data envelopment analysis method is employed to select suitable cultivation lands as supply potential points. The model considers sustainability in its implementation so that the objectives are to maximize the profit, minimize environmental effects, and maximize social performance. Since some parameters are naturally uncertain, a robust possibilistic programming model is proposed, considering the possibility of transportation disruptions. The model performance has been illustrated through a case study in Iran. Finally, the results of the mathematical model have shown that the recommended supply chain design is justifiable. Practical strategic insights include recommendations for locating refineries in seven provinces (Gilan, Kermanshah, Hamedan, Semnan, Khorasan Razavi, Khouzestan, Kihkooliye). These results have been validated using a simulation-based approach, which demonstrates that the proposed model recommends decisions that more suitable than the deterministic model in terms of average and standard deviation of objective values.

Global performance of metaheuristic optimization tools for water distribution networks

Numerous metaheuristic optimization algorithms are used for optimal design of water distribution networks. Each algorithm shows dissimilar characteristics depending on the network properties and the sensitivity analysis of the algorithm control variables. New performance metrics of metaheuristic optimization methods are proposed using simple but robust refined metrics and were applied to the available literature data for different algorithms which have previously been used for three popular benchmark water distribution networks. In general, recent performance metrics are devoted to measure effectiveness, efficiency, and reliability in a separate manner, which made some confusion, which is the best?. In the present work, the proposed metrics are used to calculate both of best global and average global performance of different optimization algorithms. The results show that the present metrics have a good distinctive performance between different algorithms. The Fittest individual referenced Differential Evolution is found to be the best algorithm.

Development and Sensitivity Analysis of an Empirical Equation for Calculating the Amplitude of Pressure Head Loss of Oscillating Water Flow in Different Types of Pipe

Low pressure oscillating water flow can reduce the investment and energy consumption of irrigation. It is also effective in reducing the clogging of an emitter and improving the spraying quality of sprinklers. In order to overcome the problem of the complex process in calculating the amplitude of the pressure head loss of oscillating water flow in different types of pipes, in this study, an empirical equation for the amplitude of the pressure head loss of oscillating water flow in different types of pipe has been developed. Further, validation experiments have been conducted to verify the accuracy of the calculated amplitudes of the pressure head loss by the empirical equation. The results show that average relative error between the measured and the calculated amplitudes of the pressure head loss by the empirical equation is 10.77%. Since the relative errors are small, it is an indication that the amplitudes of the pressure head loss calculated by the empirical equation are accurate. For the empirical equation developed in this study, the sensitivity of the model parameters has been analyzed. The results show that the amplitude of velocity, the internal pipe diameter, and the length of pipe are classified as highly sensitive. The average velocity, the period of oscillating water flow, and the modulus of elasticity of the pipe material are classified as sensitive. The thickness of the pipe wall is classified as medium sensitive. Compared with the calculation models of the existing researches, the empirical equation reduces the number of parameters required to be calculated, by which many complicated calculations are avoided, which greatly improves the computing efficiency. This is conducive to the efficient operation and management of oscillating water flow in irrigation pipe networks and also provides help for the optimal design of irrigation pipe networks.

Optimal design of exchange networks with blind inputs and its application to Eco-industrial parks

Motivated by the design and optimization of the water exchange networks in Eco-Industrial Parks (EIP), we investigate the abstract Blind-Input model for general exchange networks. This abstract model is based on a Game Theory approach, formulating it as a Single-Leader-Multi-Follower (SLMF) game: at the upper level, there is an authority (leader) that aims to minimize the consumption of natural resources, while, at the lower level, agents (followers) try to minimize their operating costs. We introduce the notion of Blind-Input contract, which is an economic contract between the authority and the agents in order to ensure the participation of the latter ones in the exchange networks. More precisely, when participating in the exchange network, each agent accepts to have a blind input in the sense that she controls only her output fluxes, and the authority commits to guarantee a minimal relative improvement in comparison with the agent’s stand-alone operation. The SLMF game is equivalently transformed into a single mixed-integer optimization problem. Thanks to this reformulation, examples of EIP of realistic size are then studied numerically.

Novel resilient-sustainable strategies for second-generation biofuel network design considering Neem and Eruca Sativa under hybrid stochastic fuzzy robust approach

Nowadays, concerns about fossil fuel depletion, ecological crisis, and energy security lead to renewable energy generation. The present study provides a multi-objective, multi-period, multi-product integrated sustainable-resilient mixed-integer linear programming model for the optimal design of second-generation biofuel supply chain networks under uncertainty. A novel hybrid stochastic fuzzy-robust approach was utilized to tackle the uncertainty of parameters, and the weighted sum method was applied to deal with the multi-objective problem. Several resiliency measures are introduced to enhance the resiliency of the network and resist against any disruption. Furthermore, strategies to fulfill social impacts are proposed to promote sustainable development of the considered network. Then, a real case study is implemented to investigate the validation of the proposed model. The results indicated that simultaneously considering three measures, including resilience, social impacts, and cost and setting a trade-off between them by decision-makers can boost biofuel supply chain network design in an optimal manner.

Sensor network design based on system-wide reliability criteria. Part I: Objectives

Reliability based criteria are quite popular for optimal sensor network design. We present a modified definition of system reliability for sensor network design for two applications: reliable estimation of variables in a steady state linear flow process, and reliable fault detection and diagnosis for any process. Unlike the weakest-link based definition of system reliability in the literature, the proposed definition considers the entire system and is consistent with the reliability concept used in classical reliability literature. For each application, dual approaches for defining system reliability are proposed, and their analogy with the reliability problem in the classical reliability literature is established. Using examples and stochastic simulations, the advantage of using the proposed system reliability in contrast to the existing definition is illustrated. Part II of this series of articles presents methods for efficient generation of the system reliability function and its use in optimization-based approaches for designing optimal sensor networks.

Compensation Network Optimal Design Based on Evolutionary Algorithm for Inductive Power Transfer System

Conventional design and optimization of passive compensation network (PCN) for inductive power transfer (IPT) system are based on specific topologies. The demerits of this design method are: i) The topology is mostly chosen by experience; ii) The design parameters are not multi-objective optimal. Aiming at these issues, this paper proposes an optimal PCN design scheme based on evolutionary algorithm (EA) to synchronously optimize the topology and parameters of PCN for IPT system. Firstly, a unified mathematical model of the PCN is presented and derived by transmission matrix. Then, according to the mathematical model, the multi-objective functions (such as output fluctuation and efficiency) as well as the constraints (such as load and coupling coefficient) for the optimal PCN design are established. The EA based multi-objective optimal PCN design algorithm is further constructed. Six optimal results are obtained using the algorithm, and one optimized PCN having minimum output current fluctuation and high-efficiency is chosen to validate the effectiveness of the proposed design scheme in experiment. For the given IPT system with the optimized PCN, the maximum fluctuation of output current is no more than 11%, within 200% of load variation and about 77% of coupling variation.

Design optimization of water distribution networks: real-world case study with penalty-free multi-objective genetic algorithm using pressure-driven simulation

Water distribution systems are an integral part of the economic infrastructure of modern-day societies. However, previous research on the design optimization of water distribution systems generally involved few decision variables and consequently small solution spaces; piecemeal-solution methods based on pre-processing and search space reduction; and/or combinations of techniques working in concert. The present investigation was motivated by the desire to address the above-mentioned issues including those associated with the lack of high-performance computing (HPC) expertise and limited access in developing countries. More specifically, the article’s aims are, firstly, to solve a practical water distribution network design optimization problem and, secondly, to develop and demonstrate a generic multi-objective genetic algorithm capable of achieving optimal and near-optimal solutions on complex real-world design optimization problems reliably and quickly. A multi-objective genetic algorithm was developed that applies sustained and extensive exploration of the active constraint boundaries. The computational efficiency was demonstrated by the small fraction of 10-245 function evaluations relative to the size of the solution space. Highly competitive solutions were achieved consistently, including a new best solution. The water utility’s detailed distribution network model in EPANET 2 was used for the hydraulic simulations. Therefore, with some additional improvements, the optimization algorithm developed could assist practitioners in day-to-day planning and design.

The study on optimal design of river monitoring network using modified approaching degree model: a case study of the Liaohe River, Northeast China.

This paper proposes a quantitative method to optimize the existing river monitoring network based on a modified approaching degree model, T test, and Euclidean distance. In this study, the Liaohe River located in Liaoning province, China, was taken as a research object. Samples were collected from 8 sampling sites throughout the monitoring network, and water quality parameters were analyzed every 2months from January 2009 to December 2010. The results show that the average concentrations of the ammonia nitrogen (NH4+-N) and chemical oxygen demand (COD) were beyond grade III of the Environmental Quality Standards for Surface Water of China (GB3838-2002), and they were the main water quality parameters. After optimization, the number of monitoring sections along the Liaohe River was reduced to five from the original eight, thus saving 37.5% of the monitoring cost; meanwhile, there is no significant difference between the un-optimized and optimized monitoring networks, and the optimized monitoring network remains to be able to perform as good as the original one. In addition, the total data attainment rate was improved greatly, and the duplicate setting degree of monitoring points decreased significantly compared with other optimal methods. The optimized monitoring network proves to be more efficient, reasonable, and economically feasible, so this quantitative method can help optimize the changing orderly river monitoring networks.

Tree-Type Irrigation Pipe Network Planning and Design Method Using ICSO-ASV

Research on tree-type irrigation pipe networks is an important component of agricultural water-saving projects. The optimal design of tree-type irrigation pipe networks is a key aspect regarding the profitability of irrigated agriculture. Meanwhile, swarm intelligence optimization algorithms have good computational ability and can be applied to solve many optimization problems in agricultural engineering. To identify the lowest investment cost for a pipe network, this study defined the concept of an upper water node to ensure the connectivity of tree-type irrigation pipe networks, and therefore, improve the pipe network planning model without using preliminary network connection diagrams. In addition, this study proposed an improved chicken swarm optimization algorithm (Improved Chicken Swarm Optimization using Adaptive Search and Variation, ICSO-ASV), which was applied to solve 12 test functions of different dimensions. The test results show that, compared to the traditional chicken swarm algorithm and other algorithms in the control group, the ICSO-ASV algorithm could effectively improve the global search capability. Finally, the ICSO-ASV algorithm was used to plan and design 15-node and 40-node pipe networks. The calculation results show that the average investment costs of the two pipe networks generated by the ICSO-ASV algorithm were 42.20% and 31.09% lower than those generated by the traditional chicken swarm algorithm, which further verified the feasibility of applying ICSO-ASV to design tree-type irrigation pipe networks. Thus, the design method proposed in this study can solve the optimal problems of tree-type irrigation pipe networks with varying topologies. The optimal solutions can be generated automatically using the ICSO-ASV algorithm if essential parameters of the pipe network planning model are provided.

Optimization of water distribution networks using developed binary genetic algorithm and hydraulic model software

The optimal design of urban water distribution networks is a significant issue that has been of critical interest in the water industry for many years. The optimal design of the distribution network aims to find the best solution for transferring water from the reservoir to consumers at the lowest cost. In this study, optimization of the water distribution network (ZONE 1 of Ilam city, Iran) was performed using the fast messy genetic algorithms (FMGA) tool in the hydraulic model for three different pipe networks. Also, these networks were optimized by using a combination of EPANET and an in-house developed binary genetic algorithm in MATLAB. The costs of the optimized hydraulic networks of polyethylene and polypropylene pipes were lower, respectively, by 20.56 % and 52.94 % compared to the consulting company's original designs, while for the glass fiber reinforced plastic pipe (GRP) pipe network the cost increased by 12.61 %. Also, the results of a developed algorithm for polyethylene pipe decreased by 22.13 %.

Optimal sensor network design for multi-scale, time-varying differential algebraic equation systems: Application to an entrained-flow gasifier refractory brick

An algorithm for optimal sensor network design for multi-scale, time-varying differential algebraic equation systems with non-separable dynamics is presented. As the process is time-varying, an integral normalized posterior error covariance of a multi-scale filter is minimized to obtain the optimal sensor locations. For reducing the computational cost, an adaptive sampling rate approach is considered for the slowly-varying variables. The algorithm is applied to a smart refractory brick with embedded sensors as part of an entrained-flow gasifier. Thermistors and interdigital capacitors are considered as candidate measurement technologies for estimating temperature and slag penetration profile along the gasifier wall. When the optimal set of sensors obtained from the algorithm is used for estimating temperature and slag penetration profiles in a multi-scale Kalman filter framework, satisfactory estimates are obtained despite high measurement noise and model mismatch.