Abstract
This research addresses a hybrid dynamic pre-emptive and competitive neural-network approach in solving the multi-objective dispatching problem. It optimises three performance criteria simultaneously, namely: cycle time, slack time, and throughput. A case study is adopted to illustrate the performance of applying the methodology. Thin film transistor-liquid crystal display (TFT-LCD) is a high-technology industry, with a growing market. The manufacturing process is complex. It involves multi-products, sequence-dependent set-ups, random breakdowns, and multiple-objectives, with bias-weighted optimisation problems. To determine appropriate dispatching strategies, under various system conditions, is a non-trivial challenge to control the complex systems. There has been little research on these problems aimed at solving them simultaneously. This paper presents an event-triggered dynamic dispatching system that combines artificial intelligence methods to archive optimum dispatching strategies under diverse shop-floor conditions. Results show this system to be superior to previous researches.
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