An evolutionary simulation-optimization approach for the problem of order allocation with flexible splitting rule in semiconductor assembly

Chiu, Chun-Chih; Lai, Chyh-Ming; Chen, Chien‐Ming

doi:10.1007/s10489-022-03701-2

Cited by 6 publications

(2 citation statements)

References 65 publications

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“…Hence, the flow time of the order begins from the order releasing and ends when all subsequent jobs are completed. The order splitting and merging makes the orders scheduling be more complex in semiconductor back-end industries (Hung, Liang, & Chen, 2013; Chiu, Lai, & Chen, 2023; Lin & Chen, 2015).…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…Cases with uncertain parameters in the form of probabilistic and fuzzy were constructed in this study to fill the prevalent research gap. Numerous studies have been carried out in dealing with supplier selection problems, indicating the need of decisionmaking support for such problems in many fields such as power management [7][8][9], healthcare [10,11], automotive [12,13], electrical equipment manufacturing [14,15], garment industries [16], mega-construction [17], and others.…”

Section: Introductionmentioning

confidence: 99%

Integrated Supplier Selection and Inventory Management Problems Considering Discounts and Uncertainty: A Fuzzy-Probabilistic Approach

Sutrisno,

Widowati,

Sunarsih

et al. 2023

JESA

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This study addresses the integration of supplier selection and inventory management issues that involve discounts, with a unified decision-making support formulated through a mathematical programming approach. The challenges addressed encompass uncertain parameters such as defective goods rates, late delivery rates, and demand, some of which are treated as probabilistic/random variables under data availability assumptions, while others are managed as fuzzy variables where data is not explicitly required. The joint problems were synthesized into a piecewise fuzzy-probabilistic optimization model with the aim of minimizing total operational costs, and the optimal decision was deduced by solving this model. Further, the model was constructed incorporating multi-period observations, indicating its ability to generate optimal solutions for multiple procurement activity periods. Computational simulations were executed to demonstrate the calculation of the optimal decision and to appraise the proposed model. All calculations were performed in LINGO 19.0 optimization software, leveraging its uncertain programming package. The computational process employed the generalized reduced gradient -a popular method for solving optimization problems due to its requirement of only a differentiable objective function -in conjunction with the branch and bound algorithmrecognized for its simplicity in branching and bounding feasible solutions. The results affirmed that the proposed model successfully delivered the optimal solution for the problem at hand. Therefore, the proposed model is deemed appropriate for implementation by practitioners in manufacturing/retail industries as a decision-making tool to curtail operational costs associated with their procurement and warehousing operations.

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ICCP: A heuristic process planning method for personalized product configuration design

Hu,

Qiu,

Zhang

et al. 2023

Appl Intell

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An evolutionary simulation-optimization approach for the problem of order allocation with flexible splitting rule in semiconductor assembly

Cited by 6 publications

References 65 publications

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

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

Integrated Supplier Selection and Inventory Management Problems Considering Discounts and Uncertainty: A Fuzzy-Probabilistic Approach

ICCP: A heuristic process planning method for personalized product configuration design

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