Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep
  Learning Method   The Jobs shop Scheduling Problem (JSP) is a canonical combinatorial
optimization problem that is routinely solved for a variety of industrial
purposes. It models the optimal scheduling of multiple sequences of tasks, each
under a fixed order of operations, in which individual tasks require exclusive
access to a predetermined resource for a specified processing time. The problem
is NP-hard and computationally challenging even for medium-sized instances.
Motivated by the increased stochasticity in production chains, this paper
explores a deep learning approach to deliver efficient and accurate
approximations to the JSP. In particular, this paper proposes the design of a
deep neural network architecture to exploit the problem structure, its
integration with Lagrangian duality to capture the problem constraints, and a
post-processing optimization to guarantee solution feasibility.The resulting
method, called JSP-DNN, is evaluated on hard JSP instances from the JSPLIB
benchmark library. Computational results show that JSP-DNN can produce JSP
approximations of high quality at negligible computational costs.

14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: quant-ph