Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
PROGENITOR: a genetic algorithm for production scheduling
Wirtschafts Informatik
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Computers and Industrial Engineering
Minmax earliness/tardiness scheduling in identical parallel machine system using genetic algorithms
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
A hybrid GA-SA algorithm for just-in-time scheduling of multi-level assemblies
Computers and Industrial Engineering
Genetic algorithms for flowshop scheduling problems
Computers and Industrial Engineering
On the architecture of object-oriented scheduling systems
On the architecture of object-oriented scheduling systems
A genetic alorithm for multiple objective sequencing problems in mixed model assembly lines
Computers and Operations Research
Scheduling grouped jobs on single machine with genetic algorithm
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Parallel machine scheduling with earliness and tardiness penalties
Computers and Operations Research
A genetic algorithm for multi-level, multi-machine lot sizing and scheduling
Computers and Operations Research
Computers and Industrial Engineering
A modified genetic algorithm for single machine scheduling
Computers and Industrial Engineering
An evolutionary approach to multi-objective scheduling of mixed model assembly lines
Computers and Industrial Engineering
Preemptive scheduling with changeovers: using column generation technique and genetic algorithm
Computers and Industrial Engineering
An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms
The hybrid heuristic genetic algorithm for job shop scheduling
Computers and Industrial Engineering
Managing Genetic Search in Job Shop Scheduling
IEEE Expert: Intelligent Systems and Their Applications
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
Using Genetic Algorithms to Schedule Flow Shop Releases
Proceedings of the 3rd International Conference on Genetic Algorithms
Direct Chromosome Representation and Advanced Genetic Operators for Production Scheduling
Proceedings of the 5th International Conference on Genetic Algorithms
Control of Parallel Population Dynamics by Social-Like Behavior of GA-Individuals
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Precast production scheduling using multi-objective genetic algorithms
Expert Systems with Applications: An International Journal
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Production scheduling is a form of decision-making, which plays a crucial role in manufacturing as well as in service industries. Many approaches are used in practice in order to find good and feasible solutions in the scheduling problems. Approaches based on Genetic Algorithms (GAs) are becoming more and more widespread nowadays. This paper is the result of the author's extensive study on the use of advanced technologies in the field of production planning and scheduling and follows his previous research on the use of expert systems in this field (Metaxiotis et al., 2002). The present paper presents famous GAs known in the literature and current applications, analyses the relative benefits and concludes by sharing thoughts and estimations on GAs future prospects in this area.