Multi-subpopulation parallel computing genetic algorithm for the semiconductor packaging scheduling problem with auxiliary resource constraints

Wang, Hung-Kai; Lin, Yu-Chun; Liang, Che-Jung; Wang, Ya-Han

doi:10.1016/j.asoc.2023.110349

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…Fu et al (2011) developed a new mixed-integerlinear-programming (MILP) model for the batch production scheduling of a semiconductor back-end facility with serial production stages. Wang et al (2023) presented a multisubpopulation parallel computing genetic algorithm for the semiconductor packaging scheduling problem with auxiliary resource constraints. Our paper is most related to Lin and Chen (2015), who presented a simulation-optimization approach for a hybrid semiconductor back-end manufacturing process scheduling problem by considering job splitting and merging.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(2011) developed a new mixed-integer-linear-programming (MILP) model for the batch production scheduling of a semiconductor back-end facility with serial production stages. Wang et al. (2023) presented a multi-subpopulation parallel computing genetic algorithm for the semiconductor packaging scheduling problem with auxiliary resource constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Lin & Chen, 2015; Fu et al. , 2011; Wang, Lin, Liang, & Wang, 2023), the following questions are still unsettled:Development of industry 4.0 technologies (e.g. IIoT, AI and big data analytics) makes real-time scheduling possible (Ghaleb, Zolfagharinia, & Taghipour, 2020; Zhang, Tang, Li, Liu, & Zhang, 2021; Hu, Jia, He, Fu, & Liu, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Despite of the significant research progress in semiconductor back-end shopfloor scheduling (e.g. Lin & Chen, 2015;Fu et al, 2011;Wang, Lin, Liang, & Wang, 2023), the following questions are still unsettled:…”

Section: Introductionmentioning

confidence: 99%

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A real-time production scheduling method for RFID-enabled semiconductor back-end shopfloor environment in industry 4.0

Sun,

Zhang

2023

IJSBI

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PurposeA real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.Design/methodology/approachThe architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.FindingsThe authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.Originality/valueThis paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%