A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Scheduling Computer and Manufacturing Processes
Scheduling Computer and Manufacturing Processes
Proceedings of the 5th International Conference on Genetic Algorithms
Scheduling Algorithms
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Production scheduling has attracted the interest of production economics communities for decades, but there is still a gap between academic research, real-world problems, operations research and simulation. Genetic Algorithms (GA) represent a technique that has already been applied to a variety of combinatorial problems. Simulation can be used to find a solution to problems through repetitive simulation runs or to prove a solution computed by an optimization algorithm. We will explain the application of two special GAs for job-shop and resource-constrained project scheduling problems trying to bridge the gap between problem solving by algorithm and by simulation. Possible goals for scheduling problems are to minimize the makespan of a production program or to increase the due-date reliability of jobs or possibly any goal which can be described in a mathematical expression. The approach focuses on integrating a GA into a commercial software product and verifying the results with simulation.