A Network Flow Model Approach to Determining Optimal Intervention Programs for Railway Infrastructure Networks

The determination of the optimal interventions to execute on rail infrastructure networks is a challenging task, due to the many types of objects (e.g., bridges, tracks, and switches), how the objects work together to provide service, and the possible reductions in costs and service disruptions as obtained by grouping interventions. Although railway infrastructure managers are using computer systems to help them determine intervention programs, there are none that result in the highest net benefits while taking into consideration all of these aspects. This paper presents a network flow model approach that allows for determining the optimal intervention programs for railway infrastructure networks while taking into considerations different types of objects, how the objects work together to provide service, and object and object-traffic dependencies. The network flow models are formulated as mixed integer linear programs, where the optimal intervention program is found by using the simplex and branch and bound algorithms. The modelling approach is illustrated by using it to determine the optimal intervention program for a 2200 m multi-track railway line consisting of 11 track sections, 23 switches, and 39 bridges. It is shown that the proposed constrained network flow model can be used to determine the optimal intervention program within a reasonable amount of time, when compared to more traditional models and search algorithms.

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AUTOMATIC MODELING OF SIGNALING PATHWAYS BY NETWORK FLOW MODEL

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720009004138 ◽

2009 ◽

Vol 07 (02) ◽

pp. 309-322 ◽

Cited By ~ 18

Author(s):

XING-MING ZHAO ◽

RUI-SHENG WANG ◽

LUONAN CHEN ◽

KAZUYUKI AIHARA

Keyword(s):

Signal Transduction ◽

Signaling Pathways ◽

Protein Interaction ◽

Network Flow ◽

Computational Models ◽

Flow Model ◽

Mapk Signaling ◽

Mixed Integer ◽

Network Flow Model ◽

Ppi Networks

Signal transduction is an important process that controls cell proliferation, metabolism, differentiation, and so on. Effective computational models which unravel such a process by taking advantage of high-throughput genomic and proteomic data are highly demanded to understand the essential mechanisms underlying signal transduction. Since protein–protein interaction (PPI) plays an important role in signal transduction, in this paper, we present a novel method for modeling signaling pathways from PPI networks automatically. Given an undirected weighted protein interaction network, finding signaling pathways is treated as searching for optimal subnetworks according to some cost function. To cope with this optimization problem, a network flow model is proposed in this work to extract signaling pathways from protein interaction networks. In particular, the network flow model is formalized and solved as a mixed integer linear programming (MILP) model, which is simple in algorithm and efficient in computation. The numerical results on two known yeast MAPK signaling pathways demonstrate the efficiency and effectiveness of the proposed method.

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A sparse solution to the bounded subset selection problem: a network flow model approach

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing ◽

10.1109/icassp.2004.1327054 ◽

2004 ◽

Cited By ~ 6

Author(s):

M. Alghoniemy ◽

A.H. Tewfik

Keyword(s):

Network Flow ◽

Flow Model ◽

Subset Selection ◽

Selection Problem ◽

Bounded Subset ◽

Sparse Solution ◽

Network Flow Model ◽

Model Approach

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Modelling the Complex Relationship between Interventions, Interventions Costs and the Service Provided When Evaluating Intervention Programs on Railway Infrastructure Networks

Infrastructures ◽

10.3390/infrastructures5120113 ◽

2020 ◽

Vol 5 (12) ◽

pp. 113

Author(s):

Marcel Burkhalter ◽

Bryan T. Adey

Keyword(s):

Mathematical Formulation ◽

Intervention Programs ◽

System Model ◽

Mixed Integer ◽

Infrastructure Networks ◽

System Models ◽

Railway Infrastructure ◽

Complex Relationships ◽

Intervention Costs ◽

The Relationship

Determining the interventions, e.g., maintenance, renewal, improvement and extension, to be included in an infrastructure program requires the consideration of the asset, intervention, traffic, and network characteristics. This, in turn, requires the development of an appropriate system model enabling the construction of straightforward optimisation models. Although there are already a considerable number of such system models in the literature, improved modelling of the complex relationships between interventions, intervention costs and the service provided by the infrastructure network is possible—especially in the trade-off between the accuracy of considering the complex relationships and the simplicity of the mathematical formulation. This paper explains how to build system models for railway infrastructure networks that capture the complex relationships in a system model that can then be used to construct mixed integer linear optimisation models. The proposed type of system model includes how both intervention costs and impacts on service vary as a function of the type, time and location of the interventions included in intervention programs. The system models of this type consist of a graph that is used to model the relationship between the interventions and intervention costs on the asset level, and the relationship between the interventions and the service provided on the network level. The algorithm uses systematic intervention classification and a hierarchical network state structure to build the system model. For illustration purposes, a system model for a railway network consisting of five track segments, seven switches, a bridge, a tunnel and the power supply system is developed using the algorithm.

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Operation of a CCHP System Using an Optimal Energy Dispatch Algorithm

ASME 2008 2nd International Conference on Energy Sustainability, Volume 1 ◽

10.1115/es2008-54096 ◽

2008 ◽

Cited By ~ 4

Author(s):

Heejin Cho ◽

Sandra D. Eksioglu ◽

Rogelio Luck ◽

Louay M. Chamra

Keyword(s):

Energy Efficiency ◽

Energy Cost ◽

Network Flow ◽

Flow Model ◽

Energy Requirement ◽

Optimal Operation ◽

Operational Conditions ◽

Optimal Energy ◽

Network Flow Model ◽

The Cost

The Combined Cooling, Heating, and Power (CCHP) systems have been widely recognized as a key alternative for thermal and electric energy generation because of the outstanding energy efficiency, reduced environmental emissions, and relative independence from centralized power grids. Nevertheless, the total energy cost of CCHP systems can be highly dependent on the operation of individual components and load balancing. The latter refers to the process of fulfilling the thermal and electrical demand by partitioning or “balancing” the energy requirement between the available sources of energy supply. The energy cost can be optimized through an energy dispatch algorithm which provides operational/control signals for the optimal operation of the equipment. The algorithm provides optimal solutions on decisions regarding generating power locally or buying power from the grid. This paper presents an initial study on developing an optimal energy dispatch algorithm that minimizes the cost of energy (i.e., cost of electricity from the grid and cost of natural gas into the engine and boiler) based on energy efficiency constrains for each component. A deterministic network flow model of a typical CCHP system is developed as part of the algorithm. The advantage of using a network flow model is that the power flows and efficiency constraints throughout the CCHP components can be readily visualized to facilitate the interpretation of the results. A linear programming formulation of the network flow model is presented. In the algorithm, the inputs include the cost of the electricity and fuel and the constraints include the cooling, heating, and electric load demands and the efficiencies of the CCHP components. This algorithm has been used in simulations of several case studies on the operation of an existing micro-CHP system. Several scenarios with different operational conditions are presented in the paper to demonstrate the economical advantages resulting from optimal operation.

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