An algorithm for solving the job-shop problem
Management Science
A branch and bound algorithm for the job-shop scheduling problem
Discrete Applied Mathematics - Special volume: viewpoints on optimization
A fast taboo search algorithm for the job shop problem
Management Science
Open Shop Scheduling to Minimize Finish Time
Journal of the ACM (JACM)
The ant colony optimization meta-heuristic
New ideas in optimization
Tabu Search
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
ACO Applied to Group Shop Scheduling: A Case Study on Intensification and Diversification
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
ACO Applied to Group Shop Scheduling: A Case Study on Intensification and Diversification
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
Computers and Operations Research
On the load distribution and performance of meta-computing systems
ISPDC'03 Proceedings of the Second international conference on Parallel and distributed computing
| Hi-index | 0.00 |
The Group Shop Scheduling Problem (GSP) is a generalization of the classical Job Shop and Open Shop Scheduling Problems. In the GSP there are m machines and n jobs. Each job consists of a set of operations, which must be processed on specified machines without preemption. The operations of each job are partitioned into groups on which a total precedence order is given. The problem is to order the operations on the machines and on the groups such that the maximal completion time (makespan) of all operations is minimized. The main goal of this paper is to provide a fair comparison of five metaheuristic approaches (i.e., Ant Colony Optimization, Evolutionary Algorithm, Iterated Local Search, Simulated Annealing, and Tabu Search) to tackle the GSP. We guarantee a fair comparison by a common definition of neighborhood in the search space, by using the same data structure, programming language and compiler, and by running the algorithms on the same hardware.