An algorithm for solving the job-shop problem
Management Science
A practical use of Jackson's preemptive schedule for solving the job shop problem
Annals of Operations Research
A branch and bound algorithm for the job-shop scheduling problem
Discrete Applied Mathematics - Special volume: viewpoints on optimization
A genetic algorithm for the job shop problem
Computers and Operations Research - Special issue on genetic algorithms
Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
A fast taboo search algorithm for the job shop problem
Management Science
An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The hybrid heuristic genetic algorithm for job shop scheduling
Computers and Industrial Engineering
Parallel GRASP with path-relinking for job shop scheduling
Parallel Computing - Special issue: Parallel computing in numerical optimization
A hybrid particle swarm optimization for job shop scheduling problem
Computers and Industrial Engineering
A very fast TS/SA algorithm for the job shop scheduling problem
Computers and Operations Research
Computers and Operations Research
A random key based genetic algorithm for the resource constrained project scheduling problem
Computers and Operations Research
Project scheduling using a competitive genetic algorithm
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
A genetic algorithm for the job shop scheduling with a new local search using Monte Carlo method
AIKED'11 Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
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This paper presents an optimization approach for the job shop scheduling problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The proposed approach is based on a genetic algorithm technique. The scheduling rules such as SPT and MWKR are integrated into the process of genetic evolution. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities and delay times of the operations are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed approach.