Network Planning Model Research Articles

Managing water-energy-carbon nexus in integrated regional water network planning through graph theory-based bi-level programming

The water-energy-carbon nexus (WECN) has raised the attentions in the past years due to the shortages of water and energy resources, climate change and their close connection. It is critical to study WECN in water network systems due to their energy- and carbon-intensive characteristics. In optimization of WECN in water network planning, decision makers with conflictive objectives may have different decision-making power levels. In this study, an integrated Graph Theory-Based Bi-Level Water Network Planning Model, named GraBiL is developed, which represents a methodological contribution to the challenge of quantitative interrelationships of WECN and hierarchical decision-making problem in regional-scale water network planning. The GraBiL model has improved upon the existing bi-level programming and graph theory-based method for solving spatial layout optimization in two-level decision-making problem. Two-level conflictive objectives including minimizing total system cost and maximizing energy saving are considered. Fuzzy uncertainties associated with water loads are quantified. The impacts of carbon emission control on total system economic costs, energy consumption and the optimal water network planning are effectively addressed. The results from a hypothetical case study indicate that enhanced overall satisfaction for meeting the two-level objectives can be achieved with the proposed GraBiL model. Optimal water network planning schemes including spatial layout under four representative carbon emission control scenarios are compared. The proposed model has provided insight into quantitative interrelationships of WECN, hierarchical decision-making, optimization of water network spatial layout and quantification of fuzzy uncertainty associated with water loads in regional-scale water network planning.

A DNN-Based Channel Model for Network Planning in Train Control Systems

With the increasing demand for rail transit, wireless communication technologies are playing a growing significant role in train control systems, which enables the railway systems to provide a higher capacity and more efficient services. However, due to the nature of radio frequency propagation, the quality of the train-to-ground wireless connections is highly dependent on a well-planned deployment of the wayside access points. To improve both the accuracy and the efficiency in railway network planning, in this paper, a deep learning technology is exploited to model the wireless propagation, which was very difficult to deterministically predict at a fast speed in our previous research due to the high computation demanding. In this proposed wireless propagation model, Kalman filter is utilized to update the neural network parameters online, which makes this model can meet the variation of the environment. The numeric evaluation result shows that the deep neural network based wireless channel model can precisely predict the outage probability with a very low computational cost.

Regional active distribution network planning study based on robust optimization

With the increasing installed capacity of new energy generation, the randomness and volatility of new energy generation has seriously affected the stable operation of the grid. And the traditional generation side dispatching method can no longer meet the demand of the grid to maintain stable operation, which greatly affects the consumption of new energy. In order to improve the rate of new energy consumption and economic efficiency, this paper proposed a regional active distribution network economic evaluation index that takes construction costs, operating costs, and new energy subsidies into account. What is more, the paper summarized the physical constraints of each equipment in the regional active distribution network. Combining the economic evaluation index and the physical constraints, the paper raised a regional active distribution network planning and scheduling model based on robust optimization. Finally, the regional active distribution network planning model based on robust optimization is validated in terms of the new energy consumption rate and the economic benefits by optimizing the examples of load-based regional active distribution network.

Intelligent Cognitive Radio Ad-Hoc Network: Planning, Learning and Dynamic Configuration

Cognitive radio (CR) is an adaptive radio technology that can automatically detect available channels in a wireless spectrum and change transmission parameters to improve the radio operating behavior. A CR ad-hoc network (CRAHN) should be able to coexist with primary user (PU) systems and other CR secondary systems without causing harmful interference to licensed PUs as well as dynamically configure autonomous and decentralized networks. Therefore, an intelligent system structure is required for efficient spectrum use. In this paper, we present a learning-based distributed autonomous CRAHN network system model for network planning, learning, and dynamic configuration. Based on the system model, we propose machine learning-based optimization algorithms for spectrum sensing, cluster-based ad-hoc network configuration, and context-aware signal classification. Using the sensing engine and the cognitive engine, the surrounding spectrum usage and the neighbor network operation status can be analyzed. The proposed policy engine can create network operation policies for the dynamically changing surrounding wireless environment, detect policy conflicts, and infer the optimal policy for the current situation. The decision engine finally determines and configures the optimal CRAHN configuration parameters through cooperation with a learning engine, in which we implement the proposed machine-learning algorithms. The simulation results show that the proposed machine-learning CRAHN algorithms can construct CR cluster networks that have a long network lifetime and high spectrum utility. Additionally, with high signal context recognition performance, we can ensure coexistence with neighboring systems.

Channel Path-Loss Measurement and Modeling in Wireless Data Network (IEEE 802.11n) Using Artificial Neural Network

Careful network planning has become increasingly critical with the rising deployment, coverage, and congestion of wireless local area networks (WLANs).This paper investigates and determine the Path-loss exponent value for the ubiquitous wireless local area network at the Federal University Oye-Ekiti for the line of sight and non-line of sight (N-LOS). Aside this, the paper also models the wireless network using artificial neural network (ANN) technology by training some neurons based on data collected from a drive-test.
 The proposed ANN model performed with accuracy and is offered as a simple, yet strong predictive model for network planning – having both speed and accuracy. Results show, that for the area under study, Oye Campus has a higher standard deviation of 5.76dBm as against ikole Campus with 1.44dBm, this is because of dense vegetation at Oye Campus.
 In view of this, the paper provides a predictive site survey for rapid wireless Access point deployment.

Establishing a Network Planning Model of Urban-Rural Logistics Based on Hub-and-Spoke Network

Establishing a Network Planning Model of Urban-Rural Logistics Based on Hub-and-Spoke Network

Accounting for price endogeneity in airline itinerary choice models: An application to Continental U.S. markets

Network planning models, which forecast the profitability of airline schedules, support many critical decisions, including equipment purchase decisions. Network planning models include an itinerary choice model that is used to allocate air total demand in a city pair to different itineraries. Multinomial logit (MNL) models are commonly used in practice and capture how individuals make trade-offs among different itinerary attributes; however, none that we are aware of account for price endogeneity. This study formulates an itinerary choice model that is consistent with those used by industry and corrects for price endogeneity using a control function that uses several types of instrumental variables. We estimate our model using a database of more than 10million passenger trips provided by the Airlines Reporting Corporation. Results based on Continental U.S. markets for May 2013 departures show that models that fail to account for price endogeneity overestimate customers’ value of time and result in biased price estimates and incorrect pricing recommendations. The size and comprehensiveness of our database allows us to estimate highly refined departure time of day preference curves that account for distance, direction of travel, number of time zones traversed, departure day of week and itinerary type (outbound, inbound or one-way). These time of day preference curves can be used by airlines, researchers, and government organizations in the evaluation of different policies such as congestion pricing.

Accounting for Price Endogeneity in Airline Itinerary Choice Models: An Application to Continental U.S. Markets

Network planning models, which forecast the profitability of airline schedules, support many critical decisions, including equipment purchase decisions. Network planning models include an itinerary choice model which is used to allocate air total demand in a city pair to different itineraries. Multinomial logit (MNL) models are commonly used in practice and capture how individuals make trade-offs among different itinerary attributes; however, none that we are aware of account for price endogeneity. This study formulates an itinerary choice model that is consistent with those used by industry and corrects for price endogeneity using a control function that uses several types of instrumental variables. We estimate our models using database of more than 3 million tickets provided by the Airlines Reporting Corporation. Results based on Continental U.S. markets for May 2013 departures show that models that fail to account for price endogeneity overestimate customers’ value of time and result in biased price estimates and incorrect pricing recommendations. The size and comprehensiveness of our database allows us to estimate highly refined departure time of day preference curves that account for distance, direction of travel, the number of time zones traversed, departure day of week and itinerary type (outbound, inbound or one-way). These time of day preference curves can be used by airlines, researchers, and government organizations in the evaluation of different policies such as congestion pricing.

Accounting for Price Endogeneity in Airline Itinerary Choice Models: An Application to Continental U.S. Markets

Network planning models, which forecast the profitability of airline schedules, support many critical decisions, including equipment purchase decisions. Network planning models include an itinerary choice model that is used to allocate air total demand in a city pair to different itineraries. Multinomial logit (MNL) models are commonly used in practice and capture how individuals make trade-offs among different itinerary attributes; however, none that we are aware of account for price endogeneity. This study formulates an itinerary choice model that is consistent with those used by industry and corrects for price endogeneity using a control function that uses several types of instrumental variables. We estimate our model using a database of more than 3 million tickets provided by the Airlines Reporting Corporation. Results based on Continental U.S. markets for May 2013 departures show that models that fail to account for price endogeneity overestimate customers’ value of time and result in biased price estimates and incorrect pricing recommendations. The size and comprehensiveness of our database allows us to estimate highly refined departure time of day preference curves that account for distance, direction of travel, number of time zones traversed, departure day of week and itinerary type (outbound, inbound or one-way). These time of day preference curves can be used by airlines, researchers, and government organizations in the evaluation of different policies such as congestion pricing.

FiWi Network Planning for Smart Metering Based on Multistage Stochastic Programming

This article presents a novel model of mixed integer linear programming (MILP) to conduct network planning (Fiber‑Wireless‑FiWi) that along a timeline space facilitates growth, expansion and scalability of resources involved in communications network, traditionally used in cellular telephony, and is possible that extends as a multiservice network to provide smart metering, as is the case in Advanced Metering Infrastructure (AMI) in Smart Grid, thus optimizing their infrastructure and minimizing investment costs. This paper proposes a hybrid architecture (fiber optic-wireless networks) with realistic approaches to planning and scalability considerations under scenarios that present uncertainty in the evolution of consumer demand, both for primary users of cell phone and smart metering as a secondary user. We propose a model for network planning based on Multistage Stochastic Programming (MSP) with a growth in demand for stochastic users and FiWi networks scalability through expected maximum benefit for their investment. The approach of this article shows that it is possible to design real FiWi large‑scale networks under a cost-effective evolution considering a scheme appropriate growth in the timeline.

Research on Integration of Urban and Rural Bus Line Network Planning Model

The advance of urbanization makes existing bus line network can not meet the travel needs of the increasing urban and rural residents, but reasonable integration of urban and rural bus line network planning is able to effectively solve this problem. In this paper, as a starting point to establish bi-level programming model of integration of urban and rural bus line network planning, the goal of upper model is minimum total investment in the construction of urban and rural public bus line network to the transport sector and the target of lower model is network resistance minimum. Then, based on the genetic algorithm to solve the model and put forward steps and flowchart. Established bi-level programming model by analyzing the studies can provide a theoretical reference for the planning of urban and rural bus line network.

Regional policy, social networks and informal structures

This paper presents a study on regional development and innovation systems. The theoretical points of departure are gender theory and two perspectives from human geography and sociology: the theory of network governance for regional development and the theory of homosocial networks. The regional policy of the EU today is characterized by a strongly emphasized governance model: i.e., an orientation towards networks and cluster initiatives. The 2004–2007 regional development programme in Värmland, a Swedish county, is a template for this policy. Its basic principles included partnership engagements, networking, EU-specific linguistic usage and superstar rhetoric. However, this seemingly innovative regional policy has roots in traditional industrial society (ironworks and paper mills). The network-planning model allows informal social structures to re-enter the arena of regional planning. Gender equality in regional government policy was challenged by the presence of a homosocial shadow (seamy-side) structure, such as secret networks and clubs on the outside of the official organizations. Networks were also important in the reproduction of traditional power structures, male dominance and hegemony. One conclusion we reach is that networking can be used mutually as a progressive force and as a conservative tool among actors in the innovation of policy.

IP/MPLS-over-OTN-over-DWDM Multilayer Networks: An Integrated Three-Layer Capacity Optimization Model, a Heuristic, and a Study

Multilayer network design has received significant attention in current literature. Despite this, the explicit modeling of IP/MPLS over OTN over DWDM in which the OTN layer is specifically considered has not been addressed before. This architecture has been identified as promising that bridges integration and interaction between the IP and optical layers. In this paper, we present an integrated capacity optimization model for network planning of such multilayer networks that consider the OTN layer as a distinct layer with its unique technological sublayer constraints. We develop a heuristic algorithm to solve this model for large networks. Finally, we provide a detailed numeric study that considers various cost parameter values of each layer in the network. We analyze the impact of each layer's cost parameter values on neighboring layers and overall network cost.

The Provincial Network Planning Model Study Based on 0-1 Mixed Integer for Programming Tobacco Commercial Enterprise

In order to adapt to the trend of modern logistics, coordinate the planning of tobacco logistics across the province, and save logistics resource, lower logistics cost and promote the core competitiveness of tobacco logistics, we use the 0-1integer programming method to build the network planning model for tobacco business. To revise the objective function and limitation factor of general location model for tobacco industry, and to make overall planning for the distribution system across the province, and make it more cost competitiveness.

Network planning model for 3G network based on 3D ray tracing technology

According to the requirements of 3G network planning,based on ray tracing theory,a method for predicting the strength of wave propagating was proposed.In this paper,it designed and built the 3D database based on GIS to provide the data for prediction,and used the bounding box and octree technologies to accelerate the algorithm.Finally,the result was displayed in 3D visualization.The simulation results show that the method can provide support data for 3G network planning sufficiently,and the visualization effect is good.

Hybrid Wireless-Optical Broadband Access Network (WOBAN): Network Planning Using Lagrangean Relaxation

The concept of a hybrid wireless-optical broadband access network (WOBAN) is a very attractive one. This is because it may be costly in several situations to run fiber to every home (or equivalent end-user premises) from the telecom central office (CO); also, providing wireless access from the CO to every end user may not be possible because of limited spectrum. Thus, running fiber as far as possible from the CO toward the end user and then having wireless access technologies take over may be an excellent compromise. How far should fiber penetrate before wireless takes over is an interesting engineering design and optimization problem, which we address in this paper. We propose and investigate the characteristics of an analytical model for network planning, namely optimum placements of base stations (BSs) and optical network units (ONUs) in a WOBAN (called the primal model, or PM). We develop several constraints to be satisfied: BS and ONU installation constraints, user assignment constraints, channel assignment constraints, capacity constraints, and signal-quality and interference constraints. To solve this PM with reasonable accuracy, we use ldquoLagrangean relaxationrdquo to obtain the corresponding ldquoLagrangean dualrdquo model. We solve this dual problem to obtain a lower bound (LB) of the primal problem. We also develop an algorithm (called the primal algorithm) to solve the PM to obtain an upper bound (UB). Via simulation, we compare this PM to a placement heuristic (called the cellular heuristic) and verify that the placement problem is quite sensitive to a set of chosen metrics.

A case study of urban ecological networks and a sustainable city: Tehran’s metropolitan area

The aim of this study is to propose the application of landscape ecology in planning urban ecological networks to conserve nature in urban landscapes and to develop a sustainable use of urban lands. Compared to the classical planning approach based on socio-economic land suitability, the principles of landscape ecology are helpful simultaneously conserving the ecological processes of landscapes and their steady changes. This approach could help in defining sustainable landscape development, aiming for a balance between both physical and natural systems in urban areas. This research is focused on the ecological networks in Tehran’s metropolitan area as a case study to provide a model for network planning in other urban areas, where urbanization seriously threats the natural environment. In concluding this research, the spatial structure and function of the area are studied and categorized based on the patch-corridor-matrix model. The paper concludes with methods of intervention and suggestions for the structural and functional improvement of urban landscapes towards achieving a more sustainable form of land use planning.

Analysis of effect factors-based stochastic network planning model

Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependence among activity durations when more than one activity is possibly affected by the same indeterminate factors. On this basis of analysis of indeterminate effect factors of durations, the effect factors-based stochastic network planning (EFBSNP) model is proposed, which emphasizes on the effects of not only logistic and organizational relationships, but also the dependent relationships, due to indeterminate factors among activity durations on the project period. By virtue of indeterminate factor analysis the model extracts and describes the quantitatively indeterminate effect factors, and then takes into account the indeterminate factors effect schedule by using the Monte Carlo simulation technique. The method is flexible enough to deal with effect factors and is coincident with practice. A software has been developed to simplify the model-based calculation, in VisualStudio.NET language. Finally, a case study is included to demonstrate the applicability of the proposed model and comparison is made with some advantages over the existing models.

An integrated multi-depot hub-location vehicle routing model for network planning of parcel service

The paper describes why optimal design of depot and hub transportation networks for parcel service providers makes it necessary to develop a generalized hub location and vehicle routing model (VRM). Analogous problems occur for postal, parcel and piece goods service providers. A generalized hub location and VRM is developed which encompasses the determination of the number, locations of hubs and depots and their assigned service areas as well as the routes between the demand points and consolidation points (depots, hubs). Because this task leads to a very complex transport design problem, a heuristic solution concept has to be developed. The applicability to a case study is demonstrated; the case of completely new system configuration for Austria is considered. Management must simultaneously decide the number, location, service areas and routes from demand points to depots and vice versa (the pickup and delivery structure) as well as the number and locations of hubs and the routes of depot–hub and hub–hub transports (the hub location or line haul structure), The developed management support decision model leads for real cases to a tremendous number of some million binary variables as well as continuous variables and million of constraints.

Telephone speech quality prediction: Towards network planning and monitoring models for modern network scenarios

This paper addresses the problem of predicting the quality of telephone speech. Starting from a definition of quality, which takes communicative as well as service-related factors into account, a new classification scheme for prediction models is proposed. It considers input and output parameters, the network components and application area the model is used for, as well as the psychoacoustic and judgment-related bases. According to this scheme, quality prediction models can be classified into signal-based comparative measures, network planning models and monitoring models. Whereas signal-based approaches have been described extensively in literature, this paper discusses the latter two approaches in detail. The underlying psychoacoustic properties of two network planning models, the E-model and the SUBMOD model, are analyzed, and combined approaches for monitoring models are developed. Quality predictions obtained from the models are compared to the results of auditory test data, and weaknesses as well as network elements that remain uncovered are identified. Possible future extensions to the models are pointed out, including wide-band scenarios and speech sound quality, non-stationary impairments as well as speech technology devices.