Study on Combinational Scheduling Optimization of Bus Transit Rapid Based on Tabu Search & Genetic Algorithm

2015 ◽  
Vol 744-746 ◽  
pp. 1827-1831
Author(s):  
Cheng Zhi Chang ◽  
Xu Mei Chen ◽  
Meng Wang
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Search Algorithm ◽  
Operating Cost ◽  
Fundamental Principle ◽  
Total System ◽  
Scheduling Optimization ◽  
System Cost ◽  
Scheduling Model ◽  
Mixed Genetic Algorithm

The goal is to minimize the sum of operating cost and passengers’ travel cost, and establish an optimized combinational scheduling model of Bus Rapid Transit (BRT) combined with regular bus, express bus and shuttle bus. A mixed genetic algorithm based on tabu search algorithm (GA-TS) has been designed after analyzing the fundamental principle of genetic algorithm (GA) and tabu search (TS). A case study has been carried out on the combinational scheduling optimization of a selecting BRT line. By adopting the combinational scheduling model, 5.24% of the total system cost can be saved, which is quite prominent. The mixed genetic algorithm based on GA-TS can optimize the BRT scheduling system, shorten the turnaround time of operating BRT vehicles, effectively reduce the total system cost of BRT and improve decision-making efficiency and service quality.

scholarly journals A Tabu-Genetic Hybrid Search Algorithm for Job-shop Scheduling Problem

2019 ◽  
Vol 95 ◽  
pp. 04007
Author(s):  
Yan Ge ◽  
Aimin Wang ◽  
Zijin Zhao ◽  
Jieran Ye
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Search Performance ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Hybrid Search ◽  
Shop Scheduling

To deal with the job-shop scheduling problem (JSP), a tabu-genetic hybrid search algorithm is proposed. The algorithm generates several initial solutions distributed in the whole solution space for tabu search by genetic algorithm, which avoids the over-dependence on the initial solution of tabu search algorithm. With the mechanism mentioned above, the algorithm proposed has both global search performance of genetic algorithm and local search performance of labu search algorithm. Finally, a program was developed with the achral data of FT (10x 10). to verify the feasibility and effectiveness of the algorithm. The result shows that the algorithm achieves satisfactory results in all indexes mentioned above.

Application of Tabu-Genetic Algorithm in Reactive Power Optimization

2014 ◽  
Vol 1006-1007 ◽  
pp. 1021-1025
Author(s):  
Song Tao Zhang ◽  
Gong Bao Wang ◽  
Hui Bo Wang
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Local Search ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Reactive Power Optimization ◽  
Simple Genetic Algorithm ◽  
Better Than

By using tabu search algorithm which has strong local search ability as mutation operator of genetic algorithm, the tabu-genetic algorithm is designed for reactive power optimization in this paper, the strong global search ability of genetic algorithm and strong local search ability of tabu search algorithm is combined, the disadvantage of weak local search ability of genetic algorithm is conquered. Otherwise, the over limit of population is recorded and filtered, to ensure the final individual is under limit and effective. The tabu-genetic algorithm and simple genetic algorithm are used for simulation of IEEE 14-bus system 500 times, the results indicate that the performance of the tabu-genetic algorithm is much better than the simple genetic algorithm, its local search ability is improved obviously, and the active power loss is reduced more.

Method of Improving Production Scheduling Based on the Genetic Algorithm

2011 ◽  
Vol 411 ◽  
pp. 415-418
Author(s):  
Yong Gao ◽  
Ming Yu Li ◽  
Jian Ping Wang
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Model Simulation ◽  
Optimization Method ◽  
Production Model ◽  
Forward Simulation ◽  
Production Schedule ◽  
Scheduling Optimization ◽  
Simulation Data ◽  
Scheduling Model

In order to improve the inventory control efficiency and quality in manufacturing company, one production scheduling optimization method is put forward. Simulation of production model is firstly constructed, such as description of the production model, simulation data, machine processes and scheduling model. Moreover, Genetic Algorithm is applied to generate a production schedule for efficient running of machine. The simulation result is analyzed to verify the method by comparing product simulation with actual production.

scholarly journals Course scheduling optimization using genetic algorithm and tabu search

2021 ◽  
Vol 9 (3) ◽  
pp. 157-166
Author(s):  
Arif Amrulloh ◽  
Enny Itje Sela
Keyword(s):  
Higher Education ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Tabu Search ◽  
Search Method ◽  
Scheduling Optimization ◽  
Taboo Search ◽  
Course Scheduling ◽  
Study Programs ◽  
Tabu Search Method

Scheduling courses in higher education often face problems, such as the clashes of teachers' schedules, rooms, and students' schedules. This study proposes course scheduling optimization using genetic algorithms and taboo search. The genetic algorithm produces the best generation of chromosomes composed of lecturer, day, and hour genes. The Tabu search method is used for the lecture rooms division. Scheduling is carried out for the Informatics faculty with four study programs, 65 lecturers, 93 courses, 265 lecturer assignments, and 65 classes. The process of generating 265 schedules took 561 seconds without any scheduling clashes. The genetic algorithms and taboo searches can process quite many course schedules faster than the manual method.

Entropy-Enhanced Genetic Algorithm with Tabu Search for Job Shop Scheduling Problems

2012 ◽  
Vol 590 ◽  
pp. 557-562 ◽  
Author(s):  
Ying Jie Huang ◽  
Xi Fan Yao ◽  
Dong Yuan Ge ◽  
Yong Xiang Li
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Traditional Scheduling ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Benchmark Instances

By combining Genetic algorithm with Tabu search algorithm and adjusting crossover rate and mutation rate based on information entropy, a hybrid genetic algorithm was proposed for larger-scale job shop scheduling problems, and the benchmark instances were used to verify the algorithm with simulation. Simulation results show that the proposed algorithm can solve larger-scale job shop scheduling problems, and it has obvious advantages over traditional scheduling algorithms.

Public assistance intensification of wind-integrated restructured endowment system using thyristor-controlled series compensator peerless location and gauging using the hybrid Genetic Algorithm-Gravitational Search Algorithm

2019 ◽  
pp. 002072091989420
Author(s):  
J Rajakumar ◽  
Sujatha Balaraman
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Operating Cost ◽  
Hybrid Genetic Algorithm ◽  
Market Model ◽  
Wind Generator ◽  
Gravitational Search ◽  
Thyristor Controlled Series Compensator ◽  
Bus System

In a deregulated electricity market, it may at times become challenging to swift all the essential power which are obligatory to move along the transmission line due to congestion. This paper primly waltz up the finest allotment of thyristor-controlled series compensator in deregulated capacity setup with wind generator by considering the maximization of social welfare cost as objective function. In this work, hybrid market model has been considered and the hybrid algorithm is used as a tool, in which Gravitational Search Algorithm is used for attaining optimal location of thyristor-controlled series compensator as major issue, though Genetic Algorithm-based top-notch outflow of power minimizes operating cost after incorporating thyristor-controlled series compensator and Wind Generator as sub-optimization problem. The coherence of this prospective has been tested and analyzed on modified IEEE 14-bus system and modified IEEE 118-bus system at different loading conditions. The influences on the locational marginal pricing and system voltage have been also investigated in this work and the obtained results are compared with other globally accepted techniques reported in the literary texts.

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