A fuzzy hierarchical reinforcement learning based scheduling method for semiconductor wafer manufacturing systems

2021 ◽  
Vol 61 ◽  
pp. 239-248
Author(s):  
Junliang Wang ◽  
Pengjie Gao ◽  
Peng Zheng ◽  
Jie Zhang ◽  
W.H. Ip
Keyword(s):  
Reinforcement Learning ◽  
Manufacturing Systems ◽  
Semiconductor Wafer ◽  
Hierarchical Reinforcement Learning ◽  
Scheduling Method ◽  
Wafer Manufacturing

Elman Neural Network–Based Dynamic Scheduling of Wafer Photolithography Process

2011 ◽  
Vol 186 ◽  
pp. 36-40 ◽  
Author(s):  
Bing Hai Zhou
Keyword(s):  
Neural Network ◽  
Manufacturing System ◽  
Dynamic Scheduling ◽  
Learning Algorithm ◽  
Semiconductor Wafer ◽  
Simulation Experiments ◽  
Elman Neural Network ◽  
Scheduling Method ◽  
Photolithography Process ◽  
Wafer Manufacturing

Photolithography area is usually a bottleneck area in a semiconductor wafer manufacturing system (SWMS). It is difficult to schedule photolithography area on real-time optimally. Here, an Elman neural network (ENN)-based dynamic scheduling method is proposed. An ENN-based sample learning algorithm is proposed for selecting best combination of scheduling rules. To illustrate the feasibility and practicality of the presented method, the simulation experiment is developed. A numerical example is use to evaluate the proposed method. Results of simulation experiments show that the proposed method is effective to schedule a complex wafer photolithography process.

Weighted Cost Rate-Based Heuristic Scheduling Method for Semiconductor Furnaces

2010 ◽  
Vol 44-47 ◽  
pp. 2186-2190
Author(s):  
Zhi Qiang Lu ◽  
Bing Hai Zhou
Keyword(s):  
Dynamic Scheduling ◽  
Scheduling Algorithm ◽  
Due Dates ◽  
Semiconductor Wafer ◽  
Cost Rate ◽  
Heuristic Scheduling ◽  
Scheduling Method ◽  
Waiting Cost ◽  
Objective Value ◽  
Wafer Manufacturing

In semiconductor wafer manufacturing, furnaces are mainly used for diffusion and deposition operations. During operations, wherever a furnace becomes available, scheduling the next batch deals with decisions on a batch which operation to process next and how many wafer lots to group the batch. To resolve the mentioned scheduling problem of the furnace where existing multi-products and different due dates, a heuristic dynamic scheduling algorithm called WCRHA (weighted cost rate heuristic algorithm) is presented to minimize the objective value of the waiting cost per unit time based on due date constraints. Simulation results show that the proposed algorithm is valid and feasible. Compared with the previous dynamic scheduling algorithms, it is more efficient in ensuring delivery and reducing completion time.

A new scheduling method for manufacturing systems

1990 ◽  
Vol 19 (1-4) ◽  
pp. 22-26 ◽  
Author(s):  
Wilfred V. Huang ◽  
Jianxin Tang
Keyword(s):  
Manufacturing Systems ◽  
Scheduling Method

Hierarchical Reinforcement Learning

2021 ◽  
Vol 54 (5) ◽  
pp. 1-35
Author(s):  
Shubham Pateria ◽  
Budhitama Subagdja ◽  
Ah-hwee Tan ◽  
Chai Quek
Keyword(s):  
Reinforcement Learning ◽  
Future Research ◽  
Comprehensive Overview ◽  
Open Problems ◽  
Practical Applications ◽  
Hierarchical Reinforcement Learning ◽  
The Past ◽  
Agent Learning ◽  
Multi Agent ◽  
Supplementary Material

Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future research in HRL. Furthermore, we outline a few suitable task domains for evaluating the HRL approaches and a few interesting examples of the practical applications of HRL in the Supplementary Material.

Research of Flexible Dynamic Scheduling Problem Based on Genetic Algorithm

2009 ◽  
Vol 16-19 ◽  
pp. 743-747
Author(s):  
Yu Wu ◽  
Xin Cun Zhuang ◽  
Cong Xin Li
Keyword(s):  
Genetic Algorithm ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Dynamic Scheduling ◽  
Scheduling Problem ◽  
Static Scheduling ◽  
Coding Method ◽  
Scheduling Method ◽  
Distribution Principle ◽  
Working Procedure

Solve the flexible dynamic scheduling problem by using “dynamic management & static scheduling” method. Aim at the property of flexible Manufacturing systems, the dynamic scheduling methods are analyzed and a coding method based on working procedure is improved in this paper. Thus it can be efficiently solve the problem of multiple working routes selection under the active distribution principle. On the other hand, the self-adaptive gene is provided and the parameters of the genetic algorithm are defined. In such a solution, the scheduling is confirmed to be simple and efficient.

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