Convergence of an annealing algorithm
Mathematical Programming: Series A and B
Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Approximation algorithms for scheduling unrelated parallel machines
Mathematical Programming: Series A and B
Computers and Operations Research
Genetic algorithms: foundations and applications
Annals of Operations Research
Heuristics for scheduling unrelated parallel machines
Computers and Operations Research
Job shop scheduling by simulated annealing
Operations Research
Applying tabu search to the job-shop scheduling problem
Annals of Operations Research - Special issue on Tabu search
Genetic local search in combinatorial optimization
CO89 Selected papers of the conference on Combinatorial Optimization
Exact and Approximate Algorithms for Scheduling Nonidentical Processors
Journal of the ACM (JACM)
Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors
Journal of the ACM (JACM)
Algorithms for Scheduling Tasks on Unrelated Processors
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Worst-case analysis of a scheduling algorithm
Operations Research Letters
Scheduling unrelated parallel machines with sequence-dependent setups
Computers and Operations Research
Size-reduction heuristics for the unrelated parallel machines scheduling problem
Computers and Operations Research
Computers and Operations Research
Mathematical and Computer Modelling: An International Journal
Makespan minimization for scheduling unrelated parallel machines with setup times
Journal of Intelligent Manufacturing
An iterated greedy algorithm for the large-scale unrelated parallel machines scheduling problem
Computers and Operations Research
Journal of Intelligent Manufacturing
Electronic Notes in Theoretical Computer Science (ENTCS)
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Simulated annealing and taboo search are well-established local search methods for obtaining approximate solutions to a variety of combinatorial optimization problems. More recently, genetic algorithms have also been applied. However, there are few studies which compare the relative performance of these different methods on the same problem. In this paper, these techniques are applied to the problem of scheduling jobs on unrelated parallel machines to minimize the maximum completion time. Results of extensive computational tests indicate that the quality of solutions generated by a genetic algorithm is poor. However, a hybrid method in which descent is incorporated into the genetic algorithm is comparable in performance with simulated annealing and taboo search.