Lenovo Schedules Laptop Manufacturing Using Deep Reinforcement Learning

Yi Liang (), Zan Sun (), Tianheng Song (), Qiang Chou (), Wei Fan (), Jianping Fan (), Yong Rui (), Qiping Zhou (), Jessie Bai (), Chun Yang () and Peng Bai ()
Additional contact information
Yi Liang: AI Laboratory, Lenovo Research, Beijing 100193, China
Zan Sun: AI Laboratory, Lenovo Research, Beijing 100193, China
Tianheng Song: AI Laboratory, Lenovo Research, Beijing 100193, China
Qiang Chou: AI Laboratory, Lenovo Research, Beijing 100193, China
Wei Fan: AI Laboratory, Lenovo Research, Beijing 100193, China
Jianping Fan: AI Laboratory, Lenovo Research, Beijing 100193, China
Yong Rui: AI Laboratory, Lenovo Research, Beijing 100193, China
Qiping Zhou: LCFC, Lenovo, Hefei 230601, China
Jessie Bai: LCFC, Lenovo, Hefei 230601, China
Chun Yang: LCFC, Lenovo, Hefei 230601, China
Peng Bai: LCFC, Lenovo, Hefei 230601, China

Interfaces, 2022, vol. 52, issue 1, 56-68

Abstract: Lenovo Research teamed with members of the factory operations group at Lenovo’s largest laptop manufacturing facility, LCFC, to replace a manual production scheduling system with a decision-making platform built on a deep reinforcement learning architecture. The system schedules production orders at all LCFC’s 43 assembly manufacturing lines, balancing the relative priorities of production volume, changeover cost, and order fulfillment. The multiobjective optimization scheduling problem is solved using a deep reinforcement learning model. The approach combines high computing efficiency with a novel masking mechanism that enforces operational constraints to ensure that the machine-learning model does not waste time exploring infeasible solutions. The use of the new model transformed the production management process enabling a 20% reduction in the backlog of production orders and a 23% improvement in the fulfillment rate. It also reduced the entire scheduling process from six hours to 30 minutes while it retained multiobjective flexibility to allow LCFC to adjust quickly to changing objectives. The work led to increased revenue of US $1.91 billion in 2019 and US $2.69 billion in 2020 for LCFC. The methodology can be applied to other scenarios in the industry.

Keywords: production scheduling; deep reinforcement learning; multiobjective optimization; combinatorial optimization; Edelman Award (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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