A multi-objective genetic algorithm to solve a single machine scheduling problem with setup-times

2019 ◽  
Vol 33 (4) ◽  
pp. 494
Author(s):  
Youssef Harrath ◽  
Amine Mahjoub ◽  
Jihene Kaabi
Keyword(s):  
Genetic Algorithm ◽  
Single Machine ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Setup Times ◽  
Scheduling Problem ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Genetic Algorithm Based on Simulation for Single Machine Scheduling Problem with Setup Times

2011 ◽  
Vol 328-330 ◽  
pp. 404-407
Author(s):  
Mei Hong Liu ◽  
Zhen Hua Li ◽  
Jun Ruo Chen
Keyword(s):  
Genetic Algorithm ◽  
Single Machine ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Setup Times ◽  
Scheduling Problem ◽  
Np Hard ◽  
Mutation Operators ◽  
Hard Problems ◽  
The Traveling Salesman Problem

Single machine scheduling problem with setup times is proved to be an NP-hard problems, and its complexity is equivalent to the traveling salesman problem (TSP) of n cites. Integrating the advantages of simulation and genetic algorithm (GA), this paper proposes a GA based on simulation to solve this NP-hard problem. Then, it introduces how to build the simulation model and how to design chromosome coding and selection, crossover and mutation operators of GA for this special scheduling problem in details. An experiment has been carried out and the result proves that the method is feasible and should be adopted.

Genetic Algorithm for Single Machine Scheduling Problem with Setup Times

2013 ◽  
Vol 457-458 ◽  
pp. 1678-1681 ◽  
Author(s):  
Quan Ouyang ◽  
Hong Yun Xu
Keyword(s):  
Genetic Algorithm ◽  
Single Machine ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Setup Times ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Crossover Operator ◽  
Roulette Wheel ◽  
Selection Of

This paper describes a genetic algorithm to solve the single machine scheduling problem with setup times, which uses the fixed two point crossover operator (F2PX) to produce new offspring chromosomes and uses the roulette wheel method in the selection of the chromosome population. In order to avoid the premature convergence we use a neighborhood based mutation operator to conduct disturbance in our genetic algorithm. Through the application of this genetic algorithm in practical scheduling problems, the effect of the genetic algorithm proposed in this paper is remarkable.

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