Title :
A memetic algorithm for Permutation Flow Shop Problems
Author :
Rahman, Humyun F. ; Sarker, Ruhul A. ; Essam, Daryl L.
Author_Institution :
Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, NSW, Australia
Abstract :
The Permutation Flow Shop Scheduling Problem (PFSP) is a well-known combinatorial optimization problem. In this paper, a Genetic Algorithm (GA) based approach has been developed to solve PFSP, with the objective of minimizing the makespan for a set of jobs. Two new priority rules; such as Gap Filling (GF) technique and Job Shifting (JS), have been introduced to enhance the performance of the GA. The algorithm has been used to solve a set of standard benchmark problems and the results have been compared with state-of-the-art algorithms. The comparison demonstrates that the overall performance of the algorithm is quite satisfactory.
Keywords :
combinatorial mathematics; flow shop scheduling; genetic algorithms; GA based approach; GF technique; JS; PFSP; combinatorial optimization problem; gap filling technique; genetic algorithm based approach; job makespan minimization; job shifting; memetic algorithm; permutation flow shop scheduling problem; priority rules; standard benchmark problems; Algorithm design and analysis; Filling; Genetic algorithms; Job shop scheduling; Schedules; Sociology; Statistics; Flow Shop Scheduling; Genetic Algorithm; Makespan; Memetic Algorithm;
Conference_Titel :
Evolutionary Computation (CEC), 2013 IEEE Congress on
Conference_Location :
Cancun
Print_ISBN :
978-1-4799-0453-2
Electronic_ISBN :
978-1-4799-0452-5
DOI :
10.1109/CEC.2013.6557755
