A self-organized approach for scheduling semiconductor manufacturing systems

Yu, Qingyun; Yang, Haolin; Lin, Kwei-Jay; Li, Li

doi:10.1007/s10845-020-01678-8

Cited by 13 publications

(10 citation statements)

References 38 publications

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“…New dispatching rules [138,165,166,[169][170][171][172]175] Adaptive rules [110,149,177,178,180,188,190,191,193] Maintain multiple rules [60,[182][183][184][185] Closed-loop control [109,139,176,[194][195][196][197]…”

Section: Methods Referencesmentioning

confidence: 99%

“…Lee et al used a combination of multiple dispatching rules to schedule jobs in the photolithography and decomposition operations, in which the weights of dispatching rules are adjusted by a sequential search method [179]. Yu et al proposed a dynamic dispatching rule based on self-organization [180].…”

Section: Stochastic and Dynamic Schedulingmentioning

confidence: 99%

“…Operations Main Characteristics Objectives Approach/Result [34] burn-in oven p-batch, 𝑎 , B 𝐶 WRB, CACB, 𝑂(𝑛 ) [36] burn-in oven p-batch, 𝑎 , B 𝐶 WRB, decomposition-based MILP heuristics [37] cleaning p-batch, 2D packing, 𝑎 , B 𝐶 MILP, constructive heuristics, BRKGA, HBL [38] burn-in oven p-batch, 𝑎 , B ∑𝐶 MILP, HMMAS, 𝑂(𝑡 max 𝑛 𝑛 ) [40] burn-in oven p-batch, 𝑟 , 𝑎 , B 𝐶 ACO, DP [41] burn-in oven p-batch, 𝑟 , 𝑎 , B 𝐶 MILP, WIS, FFWIS-ERT, ACO [42] burn-in oven p-batch, 𝑟 , 𝑎 , B 𝐶 job distances, FRS, MDS, UD [43] burn Appendix B GRASP-based dispatching rules [175] photolitho-graphy r j , M j , aux machine utilization, on-time delivery dedication load-based dispatching rules [176] wafer fabrication p-batch, recrc wafer movement, utilization, throughput closed-loop control based on load balancing [177] wafer fabrication hot jobs, p-batch WIP, bottleneck utilization ADR, BPNN, PSO [178] wafer fabrication hot jobs, p-batch WIP movement ADR, linear regression, GA [179] wafer fabrication p-batch, aux, recrc throughput, TSC dispatching rules [180] wafer fabrication r j , s lk , etc.…”

Section: Referencesmentioning

confidence: 99%

See 2 more Smart Citations

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: Methods Referencesmentioning

confidence: 99%

Section: Stochastic and Dynamic Schedulingmentioning

confidence: 99%

Section: Referencesmentioning

confidence: 99%

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

Fang,

Cheang,

Lim

2023

Sustainability

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“…If yes, go to step 2; Otherwise, put the workpiece into the scrap area. Step 2: Calculate the process constraint urgency 𝑃 1 of the workpiece according to equation (20). Formula ( 20) means that at time 𝑡 , for the workpiece with process constraints, the greater the ratio between the theoretical value and the actual value of the remaining processing time of the process constraints, the greater the probability that the process constraints will not be satisfied in the Step 3, and the easier it is to be selected by the equipment for priority processing; If the theoretical process constraint remaining processing time multiplied by the time constant 𝑂 is greater than the actual process constraint remaining processing time, then the probability of the workpiece not meeting the process constraint is exceptionally high.…”

Section: Composite Dispatch Rulesmentioning

confidence: 99%

A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems

Zhang

Zhao

et al. 2023

Preprint

Self Cite

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A robust optimal scheduling method driven by multi-objects is proposed for the collaborative optimization problem between dynamic scheduling, preventive maintenance of equipment, and robustness of scheduling schemes in a complex manufacturing system. Firstly, the equipment maintenance task is mapped to the process level, and composite dispatching rules with weight parameters are designed, which flexibly consider equipment maintenance and system processing status. Secondly, the performance-driven ideology is analyzed through two models based on the IWOA-MLP algorithm. Thirdly, the feedback mechanism ideology facilitates adaptive closed-loop optimizations. Finally, a series of experiments were carried out on the simulation platform of a semiconductor manufacturing enterprise in Shanghai. The experimental results show that the proposed robust optimal scheduling system can effectively deal with mixed uncertainty, improve production performances, and maintain highly robust measures.

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“…These methods mostly used fixed weights and static dispatching rules, which were difficult to meet the demand of multi-objective optimal scheduling of the interbay system. Aiming at the real-time production scheduling problem in the semiconductor manufacturing industry, a self-organizing dynamic scheduling rule is proposed to improve the scheduling efficiency, and the optimal scheduling scheme is automatically generated through interactive methods [9]. Based on system status, a neural network was used to adjust attribute weights.…”

Section: Real-time Production Scheduling Problemmentioning

confidence: 99%

A Dynamic Dispatching Method for Large-Scale Interbay Material Handling Systems of Semiconductor FAB

et al. 2022

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Interbay Automated Material Handling Systems (AMHS) are widely adopted especially in Semiconductor Wafer Fabrication Systems (SWFS). The dispatching method plays a major role in the control of AMHS. This paper proposes an efficient multi-objective dynamic dispatching method which will dynamically adjust vehicle-load assignments according to the real-time situation of the system. A multi-objective cost function with variable weights is established, taking into account various performance indices (i.e., transport time, throughput, cycle time, vehicle utilization, movement, and waiting time), and the corresponding mathematical model is formulated. Then, in order to obtain the suitable weights according to the real-time condition, an advanced method is developed based on fuzzy theory. After that, a Hungarian algorithm is adopted to solve the model. Finally, simulations are conducted to validate the proposed method. The results demonstrate that it has better comprehensive performance compared to the previous dispatching methods.

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A self-organized approach for scheduling semiconductor manufacturing systems

Cited by 13 publications

References 38 publications

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

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

A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems

A Dynamic Dispatching Method for Large-Scale Interbay Material Handling Systems of Semiconductor FAB

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