Can I read Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method on EtoBox?

Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method by Kotary, James; Fioretto, Ferdinando; Van Hentenryck, Pascal is a scholarly article available to read on EtoBox.

What is Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method about?

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.

Author

Kotary, James; Fioretto, Ferdinando; Van Hentenryck, Pascal

Published

2021

Language

EN