Transit Network Optimization

2005 ◽  
Vol 1923 (1) ◽  
pp. 180-188 ◽  
Author(s):  
Fang Zhao ◽  
Ike Ubaka ◽  
Albert Gan
Keyword(s):  
Network Optimization ◽  
Large Scale ◽  
Search Algorithm ◽  
Benchmark Problems ◽  
Dade County ◽  
Transit Network ◽  
Search Spaces ◽  
Route Network ◽  
Global Optimal ◽  
Transit Route

This paper presents a mathematical methodology for transit route network optimization. The goal is to provide an effective computational tool for optimization of a large-scale transit route network. The objectives are to minimize transfers and maximize service coverage. Formulation of the method consists of three parts: representation of transit route network solution search spaces, representation of transit route and network constraints, and a stochastic search scheme capable of finding the expected global optimal result on the basis of an integrated simulated annealing, tabu, and greedy search algorithm. The methodology has been tested with published solutions to benchmark problems and applied to a large-scale realistic network optimization problem in Miami–Dade County, Florida.

Simultaneous Bus Transit Route Network and Frequency Setting Search Algorithm

2019 ◽  
Vol 145 (4) ◽  
pp. 04019011 ◽  
Author(s):  
S. M. Hassan Mahdavi Moghaddam ◽  
K. Ramachandra Rao ◽  
G. Tiwari ◽  
Pravesh Biyani
Keyword(s):  
Search Algorithm ◽  
Bus Transit ◽  
Route Network ◽  
Transit Route

scholarly journals Some Computational Insights on the Optimal Bus Transit Route Network Design Problem

2012 ◽  
Vol 47 (3) ◽  
Author(s):  
Wei (David) Fan ◽  
Randy B. Machemehl
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Large Scale ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Research Experiences ◽  
Bus Transit ◽  
Route Network ◽  
Transit Route ◽  
Demand Aggregation

The objective of this paper is to present some computational insights based on previous extensive research experiences on the optimal bus transit route network design problem (BTRNDP) with zonal demand aggregation and variable transit demand. A multi-objective, nonlinear mixed integer model is developed. A general meta-heuristics-based solution methodology is proposed. Genetic algorithms (GA), simulated annealing (SA), and a combination of the GA and SA are implemented and compared to solve the BTRNDP. Computational results show that zonal demand aggregation is necessary and combining metaheuristic algorithms to solve the large scale BTRNDP is very promising.

Model Order Reduction of Transmission Line Model

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Transmission Line ◽  
Large Scale ◽  
Original System ◽  
Reduced Order Model ◽  
Benchmark Problems ◽  
Transmission Line Model ◽  
Order Model ◽  
Line Model ◽  
Reduced Order ◽  
Numerical Effort

Transmission Line model are an important role in the electrical power supply. Modeling of such system remains a challenge for simulations are necessary for designing and controlling modern power systems.In order to analyze the numerical approach for a benchmark collection Comprehensive of some needful real-world examples, which can be utilized to evaluate and compare mathematical approaches for model reduction. The approach is based on retaining the dominant modes of the system and truncation comparatively the less significant once.as the reduced order model has been derived from retaining the dominate modes of the large-scale stable system, the reduction preserves the stability. The strong demerit of the many MOR methods is that, the steady state values of the reduced order model does not match with the higher order systems. This drawback has been try to eliminated through the Different MOR method using sssMOR tools. This makes it possible for a new assessment of the error system Offered that the Observability Gramian of the original system has as soon as been thought about, an H∞ and H2 error bound can be calculated with minimal numerical effort for any minimized model attributable to The reduced order model (ROM) of a large-scale dynamical system is essential to effortlessness the study of the system utilizing approximation Algorithms. The response evaluation is considered in terms of response constraints and graphical assessments. the application of Approximation methods is offered for arising ROM of the large-scale LTI systems which consist of benchmark problems. The time response of approximated system, assessed by the proposed method, is also shown which is excellent matching of the response of original system when compared to the response of other existing approaches .

Research on Hyperpaths in the Urban Rail Transit Network

2014 ◽  
Vol 488-489 ◽  
pp. 1439-1443
Author(s):  
Jin Hai Li ◽  
Jian Feng Liu
Keyword(s):  
Large Scale ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Network ◽  
Travel Costs ◽  
Urban Rail ◽  
Distinctive Structure ◽  
Feasible Path ◽  
Definition Of ◽  
Urban Rail Transit Network

Hyperpaths enumeration is one of the basic procedures in many traffic planning issues. As a result of its distinctive structure, hyperpaths in Urban Rail Transit Network (URTN) are different from those in road network. Typically, one may never visit a station more than once and would never transfer from one line to another that has been visited in a loopless URTN, meaning that stations a hyperpath traversed cannot be repeated, neither do lines in loopless networks. This paper studies the relationships between feasible path and the shortest path in terms of travel costs. In this paper, a new definition of hyperpath in URTN is proposed and a new algorithm based on the breadth first searching (BFS) method is presented to enumerate the hyperpaths. The algorithm can safely avoid hyperpath omission and can even be applied in networks containing loops as well. The influence of parameters on hyperpaths is studied by experimentally finding hyperpaths in the subway network in Beijing. A group of suggested parameter pairs are then given. Finally, a numerical experiment is used to illustrate the validity of the proposed algorithm. The results imply the significance of the convergence of the BFS algorithm which can be used to search hyperpaths in large scale URTN even with loop.

scholarly journals Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

2021 ◽  
Vol 11 (10) ◽  
pp. 4438
Author(s):  
Satyendra Singh ◽  
Manoj Fozdar ◽  
Hasmat Malik ◽  
Maria del Valle Fernández Moreno ◽  
Fausto Pedro García Márquez
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Methods ◽  
Bidding Strategy ◽  
Wind Speed Profile ◽  
Gravitational Search ◽  
Modified Gravitational Search Algorithm

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

scholarly journals A Matheuristic Approach Combining Local Search and Mathematical Programming

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Carolina Lagos ◽  
Guillermo Guerrero ◽  
Enrique Cabrera ◽  
Stefanie Niklander ◽  
Franklin Johnson ◽  
...  
Keyword(s):  
Mathematical Programming ◽  
Local Search ◽  
Large Scale ◽  
Search Algorithm ◽  
Facility Location Problem ◽  
Specific Information ◽  
Local Search Algorithm ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location ◽  
Installation Cost

A novel matheuristic approach is presented and tested on a well-known optimisation problem, namely, capacitated facility location problem (CFLP). The algorithm combines local search and mathematical programming. While the local search algorithm is used to select a subset of promising facilities, mathematical programming strategies are used to solve the subproblem to optimality. Proposed local search is influenced by instance-specific information such as installation cost and the distance between customers and facilities. The algorithm is tested on large instances of the CFLP, where neither local search nor mathematical programming is able to find good quality solutions within acceptable computational times. Our approach is shown to be a very competitive alternative to solve large-scale instances for the CFLP.

Bilateral bidding strategy in joint day-ahead energy and reserve electricity markets considering techno-economic-environmental measures

2021 ◽  
pp. 0958305X2110148
Author(s):  
Mojtaba Shivaie ◽  
Mohammad Kiani-Moghaddam ◽  
Philip D Weinsier
Keyword(s):  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Power Grid ◽  
Mathematical Formulation ◽  
Test System ◽  
Bidding Strategy ◽  
Level Problem ◽  
Optimal Bidding ◽  
Environmental Measures

In this study, a new bilateral equilibrium model was developed for the optimal bidding strategy of both price-taker generation companies (GenCos) and distribution companies (DisCos) that participate in a joint day-ahead energy and reserve electricity market. This model, from a new perspective, simultaneously takes into account such techno-economic-environmental measures as market power, security constraints, and environmental and loss considerations. The mathematical formulation of this new model, therefore, falls into a nonlinear, two-level optimization problem. The upper-level problem maximizes the quadratic profit functions of the GenCos and DisCos under incomplete information and passes the obtained optimal bidding strategies to the lower-level problem that clears a joint day-ahead energy and reserve electricity market. A locational marginal pricing mechanism was also considered for settling the electricity market. To solve this newly developed model, a competent multi-computational-stage, multi-dimensional, multiple-homogeneous enhanced melody search algorithm (MMM-EMSA), referred to as a symphony orchestra search algorithm (SOSA), was employed. Case studies using the IEEE 118-bus test system—a part of the American electrical power grid in the Midwestern U.S.—are provided in this paper in order to illustrate the effectiveness and capability of the model on a large-scale power grid. According to the simulation results, several conclusions can be drawn when comparing the unilateral bidding strategy: the competition among GenCos and DisCos facilitates; the improved performance of the electricity market; mitigation of the polluting atmospheric emission levels; and, the increase in total profits of the GenCos and DisCos.

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