Upload
flora-carr
View
213
Download
0
Embed Size (px)
Citation preview
A Production Scheduling A Production Scheduling Problem Using Genetic Problem Using Genetic AlgorithmAlgorithm
Presented by: Ken Johnson
R. Knosala, T. WalSilesian Technical University, Konarskiego
Gliwice, Poland
IntroductionIntroductionThe 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).
Genetic AlgorithmsGenetic AlgorithmsSearch 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.
GeneticsGenetics
Task Parameters Task Parameters (values of function (values of function domain) must be transformed to the code domain) must be transformed to the code strings. 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.
Design PrinciplesDesign PrinciplesFirst block defines which jobs are
first taken into consideration Within each job are the
operations in order of succession when machining
Program StructureProgram Structure
Program leads operations of genetic algorithm for 600 generations (it is constant, assumed number).
There are 30 individuals (code strings) in every generation.
Fitness FunctionFitness Function
Maximizes work time of longest working machine
Singles out the worst, and gets rid of it
Takes bottle-necking into account
CrossoverCrossover
MutationMutationEnsures ‘natural selection’ is
following the best routeOccurs in both 1st and 2nd blocksIn 2nd block, a ‘double’ mutation
occurs
ModelsModelsScheduling 3 jobs to 2 machines:
ResultsResultsIn the form of Gantt ChartsFor a more complex problem:
ResultsResultsReached “ near optimal ”
solution very fast (by 200 generations)
ConclusionsConclusionsGenetic algorithm has generated correct
schedulesNot 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