Using Genetic Algorithms to Optimize Stopping Patterns for Passenger Rail Transportation

Abstract

In a passenger railroad system, the stopping pattern optimization problem determines the train stopping strategy, taking into consideration multiple train classes, station types, and customer origin‐destination (OD) demand, to maximize the profit made by a rail company. The stopping pattern is traditionally decided by rule of thumb, an approach that leaves much room for improvement. In this article, we propose an integer program for this problem and provide a systematic approach to determining the optimal train stopping pattern for a rail company. Commonly used commercial optimization packages cannot solve this complex problem efficiently, especially when problems of realistic size need to be solved. Therefore, we develop two genetic algorithms, namely binary‐coded genetic algorithm (BGA) and integer‐coded genetic algorithm (IGA). In many of the past evolutionary programming studies, the chromosome was coded using the binary alphabet as BGA. The encoding and genetic operators of BGA are straightforward and relatively easy to implement. However, we show that it is difficult for the BGA to converge to feasible solutions for the stopping pattern optimization problem due to the complex solution space. Therefore, we propose an IGA with new encoding mechanism and genetic operators. Numerical results show that the proposed IGA can solve real‐world problems that are beyond the reach of commonly used optimization packages.

Automatic Tuning of a Retina Model for a Cortical Visual Neuroprosthesis Using a Multi-Objective Optimization Genetic Algorithm

“…As a matter of fact, MOOGA has been used to study and improve the reliability of embedded software when diverse hardening techniques are applied 41 , improve Apache server quality metrics when diverse compiler options are used 42,43 , or feature selection for network anomalies 44 . Furthermore, these techniques have been validated by other authors in other areas of knowledge such as integrated engineering or civil engineering [45][46][47][48][49] , production-distribution planning problems 50 , counterrotating compressors 51 , multi-label lazy algorithms 52 , quantitative association rules 53 , design of in-building wireless networks 54 and others [55][56][57][58][59] . Genetic Algorithms (GAs) are methods for solving optimization problems.…”

Optimal TMDS for Improving the Seismic Performance of Historical Buildings

“…Also, combination of GA with fuzzy logic and neural network has shown better performance in solving optimization problem [38][39][40][41][42]. GA has been used for solving the optimization in di erent elds of civil engineering, such as structure optimization [43][44][45][46], space frames design [47], system identi cation [48], and transportation [49,50]. Also in designing structural control systems as instance, neurogenetic algorithm has been used for designing optimal nonlinear active controller for high rise buildings [39], designing optimal active controllers for nonlinear structures [51,52], multiple TMDs [12,13], and MR dampers for structures [53], and optimization of earthquake energy dissipation system for high rise buildings [54].…”

A hybrid optimization approach for sustainable process planning and scheduling

“…Among these algorithms, GA is the most popular one [2,3,15,22,32,55]. It was used to generate the feasible sequences of operations and identify the optimal tool sequence in process planning for machining (e.g., milling).…”

“…In GA, the operators including selection, crossover and mutation are used to improve the populations gradually [32]. The number of generations equals to 1000, the crossover and mutation rates equal to 0.8 and 0.6 respectively.…”