Multi-step crossover genetic algorithm for bi-criteria parallel machine scheduling problems

2021 ◽  
Vol 18 (1) ◽  
pp. 71
Author(s):  
Sameer Sharma ◽  
Mehak Chadha ◽  
Harpreet Kaur
Keyword(s):  
Genetic Algorithm ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Scheduling Problems ◽  
Machine Scheduling Problems

A Genetic Algorithm Approach to Parallel Machine Scheduling Problems Under Effects of Position-Dependent Learning and Linear Deterioration

2021 ◽  
Vol 12 (3) ◽  
pp. 195-211
Author(s):  
Oğuzhan Ahmet Arık ◽  
Mehmet Duran Toksarı
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Test Problems ◽  
Scheduling Problems ◽  
Genetic Algorithm Approach ◽  
Dependent Learning ◽  
Machine Scheduling Problems

This paper investigates parallel machine scheduling problems where the objectives are to minimize total completion times under effects of learning and deterioration. The investigated problem is in NP-hard class and solution time for finding optimal solution is extremely high. The authors suggested a genetic algorithm, a well-known and strong metaheuristic algorithm, for the problem and we generated some test problems with learning and deterioration effects. The proposed genetic algorithm is compared with another existing metaheuristic for the problem. Experimental results show that the proposed genetic algorithm yield good solutions in very short execution times and outperforms the existing metaheuristic for the problem.

Parallel Machine Scheduling Problems

2013 ◽  
pp. 189-215
Author(s):  
Alessandro Agnetis ◽  
Jean-Charles Billaut ◽  
Stanisław Gawiejnowicz ◽  
Dario Pacciarelli ◽  
Ameur Soukhal
Keyword(s):  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Scheduling Problems ◽  
Machine Scheduling Problems
Sign in / Sign up

Export Citation Format

Share Document