A Production Scheduling Problem Using Genetic Algorithm

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A Production Scheduling Problem Using Genetic
Algorithm
R. Knosala, T. Wal Silesian Technical
University, Konarskiego Gliwice, Poland

• Presented by Ken Johnson

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Introduction

• The way of Flexible Manufacturing cell work
  Scheduling with the aid of genetic algorithm and
  draft of code strings,
• Results obtained by computer program have been
  presented.
• In the first case it has been assumed that the
  cell works in optional mode (every operation can
  be done on every machine)
• In the second, each works in sequential mode (the
  first operation is executed on the first machine,
  the second operation on the second, etc)
• The only criterion of evaluation is the time of
  work. (shortest for a finite number of jobs and
  machines).

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Genetic Algorithms

• Search algorithms, based on natural selection
  mechanisms and heredity.
• They join the survival principle of the best
  fitted strings with systematic information
  exchange.
• In every generation the new group of artificial
  organisms, made from the fusion of the best
  fitted representatives fragments of previous
  generation, come into existence.

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Genetics
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Task Parameters (values of function domain) must
be transformed to the code strings.

• 1. they do not directly transform task
  parameters, but their coded form.
• 2. they lead searching, coming out not from one
  point, but from some population of points.
• 3. they use only fitness function, but do not
  use derivative or other auxiliary information.

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Design Principles

• First block defines which jobs are first taken
  into consideration
• Within each job are the operations in order of
  succession when machining

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Program Structure

• Program leads operations of genetic algorithm for
  600 generations (it is constant, assumed number).
  
• There are 30 individuals (code strings) in every
  generation.

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Fitness Function

• Maximizes work time of longest working machine
• Singles out the worst, and gets rid of it
• Takes bottle-necking into account

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Crossover
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Mutation

• Ensures natural selection is following the best
  route
• Occurs in both 1st and 2nd blocks
• In 2nd block, a double mutation occurs

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Models

• Scheduling 3 jobs to 2 machines

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Results

• In the form of Gantt Charts
• For a more complex problem

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Results

• Reached near optimal solution very fast (by
  200 generations)

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Conclusions

• Genetic algorithm has generated correct schedules
• Not sure that the solution is optimal.
• Number of jobs and their operations have not had
  influence on quality of obtained results
• Gained schedules have been correct for all cases,
  that means strings assure right
• Applied structure of code string has assured
  good, but not the best, efficiency of creation
  and propagation of schemes
• Assured high adjustment of strings

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