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Abstract: Based on genetic agorithms (GAs), a scheduling approach is
presented, which can be used to address the job shop scheduling problem
in dynamic manufacturing systems constrained by machines, workers and
robots. A new chromosome representation is also presented for batch
process scheduling and its length is variable. In the dynamic
environment, jobs arrive continuously, machines may be broken and
repaired, due date of job may change, a new class job comes up during
processing. Inspired by the rolling horizon optimization method from
predictive control technology, a periodic and event-driven rolling
horizon scheduling is utilized for adaptation to continuous processing
in a changing environment. The algorithm takes into account dispatching
rules with variable weights in the performance function. Simulation
results show that the strategy is more suitable for a dynamic job shop
environment than the static scheduling strategy.
Key words: Multi-resources Dynamic job-shop scheduling Genetic
algorithm
CLC No: F406
国家自然科学基金资助项目(59990470).
Received 20010403, received
in revised form 20010902
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