Online Supervised Learning Approach for Machine Scheduling

Bartosz Sądel,

Bartłomiej Śnieżyński


Due to rapid growth of computational power and demand for faster
and more optimal solution in today's manufacturing, machine learning has lately
caught a lot of attention. Thanks to it's ability to adapt to changing conditions
in dynamic environments it is perfect choice for processes where rules cannot be
explicitly given. In this paper proposes on-line supervised learning approach for
optimal scheduling in manufacturing. Although supervised learning is generally
not recommended for dynamic problems we try to defeat this conviction and
prove it's viable option for this class of problems. Implemented in multi-agent
system algorithm is tested against multi-stage, multi-product ow-shop problem.
More specically we start from dening considered problem. Next we move to
presentation of proposed solution. Later on we show results from conducted
experiments and compare our approach to centralized reinforcement learning to
measure algorithm performance.

Słowa kluczowe: supervised learning, reinforcement learning, scheduling, multi-agent system

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Czasopismo ukazuje się w sposób ciągły on-line.
Pierwotną formą czasopisma jest wersja elektroniczna.

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