871. Genetic Programming for Solving Flexible Flow Shop Scheduling Problems with Maximal Time Lags
Invited abstract in session TA-12: Scheduling Approaches for Complex Manufacturing Systems, stream Scheduling and Project Management.
Tuesday, 8:30-10:00Room: Clarendon SR 1.02
Authors (first author is the speaker)
| 1. | Daniel Schorn
|
| Enterprise-wide Software Systems, FernUniversität in Hagen | |
| 2. | Lars Moench
|
| FernUniversität in Hagen |
Abstract
A scheduling problem for a two-stage flexible flow shop with maximal time lags between consecutive operations motivated by semiconductor manufacturing is considered. The jobs have unequal ready times and both initial and inter-stage time lags. The total weighted tardiness is the performance measure. A heuristic scheduling framework using genetic programming (GP) to automatically discover priority indices is proposed. Computational experiments are carried out on randomly generated problem instances. The results are compared with the ones of a reference heuristic based on a biased random key genetic algorithm combined with a backtracking procedure and a MILP-based decomposition approach. The results show that high-quality schedules are obtained in a short amount of computing time using the GP approach.
Keywords
- Scheduling
- Artificial Intelligence
- Metaheuristics
Status: accepted
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