Using cloud computing for solving JSSP
with experimental laboratory results
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Scheduling is the assignment of limited resources to tasks in time, to optimizing one or more objective functions. Therefore it is necessary to find appropriate solutions to this problem. Because of their deterministic and sequential nature, the traditional problems are not able to handle the completely non-linear space of job shop scheduling problem. In order to find an appropriate solution, the genetic algorithm must have the chance of producing a big part of the solution space, in its population. Therefore for a very hard problem such as job shop scheduling, the genetic algorithm population...
Scheduling is the assignment of limited resources to tasks in time, to optimizing one or more objective functions. Therefore it is necessary to find appropriate solutions to this problem. Because of their deterministic and sequential nature, the traditional problems are not able to handle the completely non-linear space of job shop scheduling problem. In order to find an appropriate solution, the genetic algorithm must have the chance of producing a big part of the solution space, in its population. Therefore for a very hard problem such as job shop scheduling, the genetic algorithm population must be as large as possible. However the larger population introduces a big computational time, especially in a single common computer, the genetic algorithm is executed sequentially and therefore it is unable to handle larger populations. In this book, we introduced a cloud computing framework to execute the genetic algorithms with large population sizes and overcome limited resource problem of common single computers.
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