Research on Hoisting Sequence Optimization for Prefabricated Components Based on Genetic Algorithm

ICCREM 2017 ◽  
2017 ◽  
Author(s):  
Gaoyu Yue ◽  
Kailang Huang ◽  
Jingjing Yang
Keyword(s):  
Genetic Algorithm ◽  
Sequence Optimization

scholarly journals Modular sequence optimization with hybrid genetic algorithm

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 51-56
Author(s):  
B. Denkena ◽  
M.-A. Dittrich ◽  
S. Wilmsmeier ◽  
S.J. Settnik
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Sequence Optimization

Sequence Optimization for Timetabling High Frequency Passenger Trains

2012 ◽  
Author(s):  
A. Abbas-Turki ◽  
O. Grunder ◽  
A. El Moudni ◽  
E. Zaremba
Keyword(s):  
Genetic Algorithm ◽  
Resource Sharing ◽  
High Frequency ◽  
Elementary Model ◽  
Sequence Optimization ◽  
Train Timetable ◽  
Passenger Trains ◽  
Rail Traffic

Recently, Harrod has revealed that the occupancy constraints are not sufficient to timetable rail traffic. This insufficiency occurs when opposite movements of trains share the same route. In this paper, we propose a quick algorithm that considers overtaking and opposite train’s movements. This algorithm builds a train timetable from a proposed sequence of trains. It is based on an elementary model of an invariant resource sharing system. The algorithm is proposed in order to extend the genetic algorithm that calculates the sequence of trains for maximizing the frequency of passenger’s train.

New Hybrid Genetic Algorithm for Pitch Sequence Optimization of CVT Variator Chain

2017 ◽  
Vol 1 (2) ◽  
pp. 137-144 ◽  
Author(s):  
Kenji Tsutsumi ◽  
Yoshitaka Miura ◽  
Yusuke Kageyama ◽  
Arata Miyauchi
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Sequence Optimization

An Elitism Based Genetic Algorithm for Welding Sequence Optimization to Reduce Deformation

2016 ◽  
Vol 121 (1) ◽  
pp. 17-36 ◽  
Author(s):  
Jesus Romero-Hdz ◽  
Sinai Aranda ◽  
Gengis Toledo-Ramirez ◽  
Jose Segura ◽  
Baidya Saha
Keyword(s):  
Genetic Algorithm ◽  
Sequence Optimization ◽  
Welding Sequence

A Methodology for Assembly Sequence Optimization by Hybrid Cuckoo-Search Genetic Algorithm

2018 ◽  
Vol 17 (01) ◽  
pp. 47-59 ◽  
Author(s):  
G. V. S. K. Karthik ◽  
Sankha Deb
Keyword(s):  
Genetic Algorithm ◽  
Cuckoo Search ◽  
Metaheuristic Algorithm ◽  
Assembly Sequence ◽  
Sequence Optimization ◽  
Assembly Sequences ◽  
Ga Algorithm ◽  
The Cost ◽  
The Given ◽  
Optimal Assembly

In this paper, we have proposed and implemented a methodology for assembly sequence optimization by using a nature-inspired metaheuristic algorithm, known as hybrid cuckoo-search genetic algorithm (CSGA). The cost criteria for optimization in the present formulation takes into consideration the total assembly time and the number of reorientations during the assembly process. To demonstrate the application of the CSGA, an example assembly containing 19 parts has been presented and the results have been compared with those of another metaheuristic algorithm, Genetic Algorithm (GA). From the results, it has been observed that for the given problem, the CSGA not only produces optimal assembly sequences with costs comparable to that of GA, but the convergence of CSGA algorithm has been found to be faster than the GA algorithm.

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