Scheduling in a sequence dependent setup environment with genetic search
Computers and Operations Research - Special issue on genetic algorithms
Journal of Global Optimization
Theoretical Aspects of Local Search (Monographs in Theoretical Computer Science. An EATCS Series)
Theoretical Aspects of Local Search (Monographs in Theoretical Computer Science. An EATCS Series)
Computers and Industrial Engineering
Minimizing total earliness and tardiness on a single machine using a hybrid heuristic
Computers and Operations Research
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Heuristics for minimizing maximum lateness on a single machine with family-dependent set-up times
Computers and Operations Research
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
A simulated annealing algorithm for single machine scheduling problems with family setups
Computers and Operations Research
A hybrid dual-population genetic algorithm for the single machine maximum lateness problem
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Batch scheduling to minimize maximum lateness
Operations Research Letters
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We consider the problem of scheduling a number of jobs, each job having a release time, a processing time, a due date and a family setup time, on a single machine with the objective of minimizing the maximum lateness. We develop a hybrid genetic algorithm and validate its performance on a newly developed diverse data set. We perform an extensive study of local search algorithms, based on the trade-off between intensification and diversification strategies, taking the characteristics of the problem into account. We combine different local search neighborhood structures in an intelligent manner to further improve the solution quality. We use the hybrid genetic algorithm to perform a comprehensive analysis of the influence of the different problem parameters on the average maximum lateness value and the performance of the algorithm(s).