A loosely-coupled deep reinforcement learning approach for order acceptance decision of mass-individualized printed circuit board manufacturing in industry 4.0

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Jiewu Leng
  • Guolei Ruan
  • Yuan Song
  • Qiang Liu
  • Yingbin Fu
  • And 2 others
  • Kai Ding
  • Xin Chen

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number124405
Journal / PublicationJournal of Cleaner Production
Volume280
Issue numberPart 2
Online published28 Sep 2020
Publication statusPublished - 20 Jan 2021

Abstract

Printed Circuit Board (PCB) manufacturing is a kind of energy-intensive and pollution-intensive industries. With the increment of individualized requirements, PCB manufacturers face massive customized orders with a variety of specifications. The individualized customization on orders results in large differentials of the profit, energy consumption, and environmental pollution. Making energy-efficient order acceptance decisions can reduce carbon consumption and improve material utilization during the whole manufacturing process. An order acceptance decision model is established based on a loosely-coupled integration of deep learning and reinforcement learning techniques. Firstly, different from a simple assumption of the linear cost function in a small-scale manufacturing system, the deep learning algorithm is presented for accurately predicting the production cost, makespan, and carbon consumption of incoming PCB orders in the large-scale manufacturing system. Secondly, these predicted cleaner production indicators are combined with original order features to perform a reinforcement learning-based order acceptance decision. The proposed loosely-coupled deep reinforcement learning approach is verified with a dataset built based on data collected from a PCB manufacturer in China. This research is expected to provide an environment-friendly order acceptance decision-making approach for sustainable manufacturing in the Industry 4.0 context.

Research Area(s)

  • Big data, Deep learning, Industry 4.0, Order acceptance decision, Reinforcement learning, Sustainable manufacturing

Citation Format(s)

A loosely-coupled deep reinforcement learning approach for order acceptance decision of mass-individualized printed circuit board manufacturing in industry 4.0. / Leng, Jiewu; Ruan, Guolei; Song, Yuan; Liu, Qiang; Fu, Yingbin; Ding, Kai; Chen, Xin.

In: Journal of Cleaner Production, Vol. 280, No. Part 2, 124405, 20.01.2021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review