Comparison of Different Approaches to the Cutting Plan Scheduling
Allocation of specific cutting plans and their scheduling to individual cutting machines presents a combinatorial optimization problem. In this respect, various approaches and methods are used to arrive to a viable solution. The paper reports three approaches represented by three discreet optimizati...
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Technical University of Kosice
2011-10-01
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Online Access: | http://www.qip-journal.eu/index.php/QIP/article/view/35 |
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doaj-8d852e3a1e974093aea181c40535bc092020-11-24T23:23:07ZengTechnical University of Kosice Kvalita Inovácia Prosperita1335-17451338-984X2011-10-01151475610.12776/qip.v15i1.3538Comparison of Different Approaches to the Cutting Plan SchedulingPeter Bober0Technical University of KosiceAllocation of specific cutting plans and their scheduling to individual cutting machines presents a combinatorial optimization problem. In this respect, various approaches and methods are used to arrive to a viable solution. The paper reports three approaches represented by three discreet optimization methods. The first one is back-tracing algorithm and serves as a reference to verify functionality of the other two ones. The second method is optimization using genetic algorithms, and the third one presents heuristic approach to optimization based on anticipated properties of an optimal solution. Research results indicate that genetic algorithms are demanding to calculate though not dependant on the selected objective function. Heuristic algorithm is fast but dependant upon anticipated properties of the optimal solution. Hence, at change of the objective function it has to be changed. When the scheduling by genetic algorithms is solvable in a sufficiently short period of time, it is more appropriate from the practical point than the heuristic algorithm. The back-tracing algorithm usually does not provide a result in a feasible period of time.http://www.qip-journal.eu/index.php/QIP/article/view/35optimizationschedulingback-tracinggenetic algorithmsheuristic algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Peter Bober |
spellingShingle |
Peter Bober Comparison of Different Approaches to the Cutting Plan Scheduling Kvalita Inovácia Prosperita optimization scheduling back-tracing genetic algorithms heuristic algorithm |
author_facet |
Peter Bober |
author_sort |
Peter Bober |
title |
Comparison of Different Approaches to the Cutting Plan Scheduling |
title_short |
Comparison of Different Approaches to the Cutting Plan Scheduling |
title_full |
Comparison of Different Approaches to the Cutting Plan Scheduling |
title_fullStr |
Comparison of Different Approaches to the Cutting Plan Scheduling |
title_full_unstemmed |
Comparison of Different Approaches to the Cutting Plan Scheduling |
title_sort |
comparison of different approaches to the cutting plan scheduling |
publisher |
Technical University of Kosice |
series |
Kvalita Inovácia Prosperita |
issn |
1335-1745 1338-984X |
publishDate |
2011-10-01 |
description |
Allocation of specific cutting plans and their scheduling to individual cutting machines presents a combinatorial optimization problem. In this respect, various approaches and methods are used to arrive to a viable solution. The paper reports three approaches represented by three discreet optimization methods. The first one is back-tracing algorithm and serves as a reference to verify functionality of the other two ones. The second method is optimization using genetic algorithms, and the third one presents heuristic approach to optimization based on anticipated properties of an optimal solution. Research results indicate that genetic algorithms are demanding to calculate though not dependant on the selected objective function. Heuristic algorithm is fast but dependant upon anticipated properties of the optimal solution. Hence, at change of the objective function it has to be changed. When the scheduling by genetic algorithms is solvable in a sufficiently short period of time, it is more appropriate from the practical point than the heuristic algorithm. The back-tracing algorithm usually does not provide a result in a feasible period of time. |
topic |
optimization scheduling back-tracing genetic algorithms heuristic algorithm |
url |
http://www.qip-journal.eu/index.php/QIP/article/view/35 |
work_keys_str_mv |
AT peterbober comparisonofdifferentapproachestothecuttingplanscheduling |
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1725565170571280384 |