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Solving a One-Machine Scheduling Problem to Minimize Makespan

Solving a One-Machine Scheduling Problem to Minimize Makespanvon Lukas Daberer Sie sparen 13% des UVP sparen 13%
Über Solving a One-Machine Scheduling Problem to Minimize Makespan

The semiconductor industry is a fast growing sector. In order to meet the demand, maximum performance must be provided with the highest quality. Amongst other tasks, process optimization is essential for this. The purpose of this book is to characterize a flow problem in order to show the potential of optimization and to develop optimization algorithms. Therefor it was tried to optimize the order of a job sequence of an existing semiconductor manufacturing equipment, by applying different approaches. One approach is to list all possible permutations, characterize the solution volume by a target function and evaluate an extremum of the target function. Another approach deals with a genetic algorithm which evolves an approximated solution over several iterations. Both, the exact solution and the approximated solution are examined based on timing, resource utilization, and outcome quality.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9786202218214
  • Einband:
  • Taschenbuch
  • Seitenzahl:
  • 116
  • Veröffentlicht:
  • 13. Juni 2019
  • Abmessungen:
  • 150x7x220 mm.
  • Gewicht:
  • 191 g.
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Beschreibung von Solving a One-Machine Scheduling Problem to Minimize Makespan

The semiconductor industry is a fast growing sector. In order to meet the demand, maximum performance must be provided with the highest quality. Amongst other tasks, process optimization is essential for this. The purpose of this book is to characterize a flow problem in order to show the potential of optimization and to develop optimization algorithms. Therefor it was tried to optimize the order of a job sequence of an existing semiconductor manufacturing equipment, by applying different approaches. One approach is to list all possible permutations, characterize the solution volume by a target function and evaluate an extremum of the target function. Another approach deals with a genetic algorithm which evolves an approximated solution over several iterations. Both, the exact solution and the approximated solution are examined based on timing, resource utilization, and outcome quality.

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