Self-growth learning-based machine scheduler to minimize setup time and tardiness in OLED display semiconductor manufacturing

Lee, Donghun; Kim, Kwanho

doi:10.1016/j.asoc.2023.110600

Cited by 5 publications

(1 citation statement)

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“…They designed a simulation optimization approach, in which the first stage uses a GA to generate initial solutions with great potential and the second stage uses an optimal computing budget allocation (OCBA)-based simulation to accurately evaluate the solutions. Lee et al proposed a deep Q-learning (DQL) for a R m p j , s lk , M j , PM F l ∑ s lk , ∑ T j [117].…”

Section: Non-batch Machine Scheduling Problemsmentioning

confidence: 99%

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Fang,

Cheang,

Lim

2023

Sustainability

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Machine scheduling problems associated with semiconductor manufacturing operations (SMOs) are one of the major research topics in the scheduling literature. Lots of papers have dealt with different variants of SMOs’ scheduling problems, which are generally difficult to tackle theoretically and computationally. In this paper, the single machine, parallel machines, flow shops, and job shops scheduling problems from SMOs have been reviewed, based on different processing constraints, e.g., batch processing, auxiliary resources, incompatible job families, and reentrant flow, etc., with the cycle time, flow time, and throughput-related performance measures. Given the vast and diverse nature of the current literature, it is urgently needed to make a systematic survey in order to identify the important research problems, research trends, and the progress of the related solution methods, as well as clarify future research perspectives. We hope the findings and observations could provide some insights to the researchers and practitioners in this domain.

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Section: Non-batch Machine Scheduling Problemsmentioning

confidence: 99%