The shifting bottleneck procedure for job shop scheduling
Management Science
Job shop scheduling by simulated annealing
Operations Research
A genetic algorithm for the job shop problem
Computers and Operations Research - Special issue on genetic algorithms
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
A fast taboo search algorithm for the job shop problem
Management Science
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A hybrid genetic algorithm for the job shop scheduling problems
Computers and Industrial Engineering
An Advanced Tabu Search Algorithm for the Job Shop Problem
Journal of Scheduling
A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem
Computers and Operations Research
Computers and Operations Research
An efficient job-shop scheduling algorithm based on particle swarm optimization
Expert Systems with Applications: An International Journal
A prediction based iterative decomposition algorithm for scheduling large-scale job shops
Mathematical and Computer Modelling: An International Journal
High-multiplicity cyclic job shop scheduling
Operations Research Letters
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This paper describes the application of a metaheuristic to a real problem that arises within the domain of loads' dispatch inside an automatic warehouse. The truck load operations on an automated storage and retrieval system warehouse could be modeled as a job shop scheduling problem with recirculation. The genetic algorithm is based on random key representation, that is very easy to implement and it allows the use of conventional genetic operators for combinatorial optimization problems. This genetic algorithm includes specific knowledge of the problem to improve its efficiency. A constructive algorithm based in Giffler-Thompson's algorithm is used to generate non delay plans. The constructive algorithm reads the chromosome and decides which operation is scheduled next. This option increases the efficiency of the genetic algorithm. The algorithm was tested using some instances of the real problem and computational results are presented.