Inference- and Optimization-based Approximated Solver for Dynamic Job-shop Scheduling Problem
Abstract: The Job-shop Scheduling Problem (JSP) is a well-known combinatorial optimization problem that arranges tasks for efficient processing. It is used in a broad range of industrial applications, such as smart manufacturing and transportation.
We focus on updating a schedule in a situation where the set of jobs varies, and we propose an inference-based model called JSPformer within data-driven scheme.
JSPformer permits a solution inference with a variable set of jobs by encoding input data into a set of job-wise feature vectors and by using a neural network for set-structured data.
Furthermore, for cases where a few minutes of computation is possible, we propose JSPformer+Opt, a hybrid model of JSPformer and a local optimization.
The local optimization is intended to make a more efficient schedule quickly from an inference solution. It uses part of the inference and optimizes the rest to improve the solution quality while reducing the problem size for fast computation.
In numerical experiments, JSPformer+Opt produced better or more competitive solutions for dynamic JSP instances within a minute compared to optimized solutions using an exact solver for over 30 minutes.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Stefano_Teso1
Submission Number: 2387
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