A generalised makespan estimation for shop scheduling problems, using visual data and a convolutional neural network
In Shop Scheduling problems, minimising total processing time (makespan) by means of heuristic methods is one of the main goals for throughput optimisation. Furthermore, reliably estimating makespan is critical for new order acceptance and for heuristic method selection. However, heuristic
methods solutions either come without estimates or with very slow ones. Current estimation approaches are limited to either the number of heuristic methods accounted for, or to specific Shop Scheduling subproblems. They are especially limited in generalising over shop layout configurations
and limited to non-visual data input. In order to overcome these two hurdles, a convolutional neural network algorithm for quick and accurate makespan regression is proposed, applicable to a wide variety of Shop Scheduling Problems. This algorithm allows for an information-rich, visual representation
of the problem, that generalises over shop layout configuration. This has not been tried by prior studies, and the authors argue that this is a main contribution of this work. Results are compared to prior approaches in terms of the [Inline formula] value. It is shown that, without compromising
on estimation performance, the proposed algorithm improves upon prior research by allowing for visual input and for a wider variety of problems in terms of Shop Scheduling layout.
Keywords: AI in manufacturing systems; automated manufacturing systems; automation; machine learning; makespan estimation; scheduling
Document Type: Research Article
Affiliations: 1: Graduate School of Engineering, The University of Tokyo, Tokyo, Japan 2: Research into Artifacts Center for Engineering, The University of Tokyo, Tokyo, Japan 3: Yaskawa Electric Corporation, Research & Laboratory Center, Tsukuba, Japan
Publication date: 03 June 2019
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