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Somayeh Kalantari
Islamic Azad University
Computer Science & IT Electrical & Electronics Engineering Engineering

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GASA-JOSH: A Hybrid Evolutionary-Annealing Approach for Job-Shop Scheduling Problem Somayeh Kalantari; Mohamad Saniee Abadeh
Bulletin of Electrical Engineering and Informatics Vol 2, No 2: June 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (212.617 KB) | DOI: 10.11591/eei.v2i2.216

Abstract

The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan. In this paper, we develope a hybrid approach for solving JSSPs called GASA-JOSH. In GASA-JOSH, the population is divided in non-cooperative groups. Each group must refer to a method pool and choose genetic algorithm or simulated annealing to solve the problem. The best result of each group is maintained in a solution set, and then the best solution to the whole population is chosen among the elements of the solution set and reported as outcome. The proposed approach have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a large set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 23 benchmark problems and compared results obtained with a number of algorithms established in the literature.
An Effective Multi-Population Based Hybrid Genetic Algorithm for Job Shop Scheduling Problem Somayeh Kalantari; Mohamad SanieeAbadeh
Bulletin of Electrical Engineering and Informatics Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (59.198 KB) | DOI: 10.11591/eei.v2i1.262

Abstract

The job shop scheduling problem is a well known practical planning problem in the manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this paper, a multi-population based hybrid genetic algorithm is developed for solving the JSSP. The population is divided in several groups at first and the hybrid algorithm is applied to the disjoint groups. Then the migration operator is used. The proposed approach, MP-HGA, have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 15 benchmark problems and compared results obtained with a number of algorithms established in the literature. The experimental results show that MP-HGA could gain the best known makespan in 13 out of 15 problems.
GASA-JOSH: A Hybrid Evolutionary-Annealing Approach for Job-Shop Scheduling Problem Somayeh Kalantari; Mohamad Saniee Abadeh
Bulletin of Electrical Engineering and Informatics Vol 2, No 2: June 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v2i2.216

Abstract

The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan. In this paper, we develope a hybrid approach for solving JSSPs called GASA-JOSH. In GASA-JOSH, the population is divided in non-cooperative groups. Each group must refer to a method pool and choose genetic algorithm or simulated annealing to solve the problem. The best result of each group is maintained in a solution set, and then the best solution to the whole population is chosen among the elements of the solution set and reported as outcome. The proposed approach have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a large set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 23 benchmark problems and compared results obtained with a number of algorithms established in the literature.
An Effective Multi-Population Based Hybrid Genetic Algorithm for Job Shop Scheduling Problem Somayeh Kalantari; Mohamad SanieeAbadeh
Bulletin of Electrical Engineering and Informatics Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v2i1.262

Abstract

The job shop scheduling problem is a well known practical planning problem in the manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this paper, a multi-population based hybrid genetic algorithm is developed for solving the JSSP. The population is divided in several groups at first and the hybrid algorithm is applied to the disjoint groups. Then the migration operator is used. The proposed approach, MP-HGA, have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 15 benchmark problems and compared results obtained with a number of algorithms established in the literature. The experimental results show that MP-HGA could gain the best known makespan in 13 out of 15 problems.
GASA-JOSH: A Hybrid Evolutionary-Annealing Approach for Job-Shop Scheduling Problem Somayeh Kalantari; Mohamad Saniee Abadeh
Bulletin of Electrical Engineering and Informatics Vol 2, No 2: June 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (212.617 KB) | DOI: 10.11591/eei.v2i2.216

Abstract

The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan. In this paper, we develope a hybrid approach for solving JSSPs called GASA-JOSH. In GASA-JOSH, the population is divided in non-cooperative groups. Each group must refer to a method pool and choose genetic algorithm or simulated annealing to solve the problem. The best result of each group is maintained in a solution set, and then the best solution to the whole population is chosen among the elements of the solution set and reported as outcome. The proposed approach have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a large set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 23 benchmark problems and compared results obtained with a number of algorithms established in the literature.
An Effective Multi-Population Based Hybrid Genetic Algorithm for Job Shop Scheduling Problem Somayeh Kalantari; Mohamad SanieeAbadeh
Bulletin of Electrical Engineering and Informatics Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (59.198 KB) | DOI: 10.11591/eei.v2i1.262

Abstract

The job shop scheduling problem is a well known practical planning problem in the manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this paper, a multi-population based hybrid genetic algorithm is developed for solving the JSSP. The population is divided in several groups at first and the hybrid algorithm is applied to the disjoint groups. Then the migration operator is used. The proposed approach, MP-HGA, have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 15 benchmark problems and compared results obtained with a number of algorithms established in the literature. The experimental results show that MP-HGA could gain the best known makespan in 13 out of 15 problems.
Co-Authors Mohamad Saniee Abadeh Mohamad SanieeAbadeh