應用於儲料槽分配問題的元優化架構

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姓名

陳宏霈(Hong-Pei Chen) 查詢紙本館藏

畢業系所

軟體工程研究所

論文名稱

應用於儲料槽分配問題的元優化架構
(A Meta-optimization Framework for Feeder Assignment)

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摘要(中)

置件機優化問題討論已久，但大多數的研究只能適用於特定置件機型號或簡化問
題。而且在目前工業製造領域相對封閉的狀況下，優化所需的重要參數如儲料槽位置、
儲料槽間隔大小、材料移動速度限制或相機位置等只有置件機製造商能夠取得，因此
許多置件機通常會搭配專用程式，提供操作與優化功能。然而專用程式通常會有非常
多的限制，難以對優化的目標進行調整，也無法細部調整置件動作。因此本論文提出
一元優化架構，針對置件機優化中的儲料槽分配問題，使用基因演算法將專用程式的
優化成果再優化，並以 Panasonic NPM-W2 與專用程式 NPM-DGS 為例，設計客製化適
應函數與主觀函數以達到不同的優化目標。實驗成果顯示我們的架構除了達到額外優
化目標外，還能再改善 DGS 的優化結果 2.2% 至 38% 不等。

摘要(英)

Mounter optimization has been discussed for a long time, while most of these works are
aimed at certain mounter type or simplify the problem. Besides, in situation of that industrial
manufacturing is relatively self-enclosed, most of these critical arguments required in optimization such as the position of feeder slots, the interval of nozzles in beams, the limited moving
speed of each component and the position of the camera are held in the mounter manufacturer.
Therefore, many mounters come with a dedicated program that provides operation and optimization function. However, there are many limitations to the dedicated program, which makes
it hard to adjust the optimization target or modified mounting motion in detail. As a result, we
propose a meta-optimization framework that focuses on feeder assignment problem which is a
subproblem of mounter optimization. Also, we take Panasonic NPM-W2 along with dedicated
NPM-DGS software as an example to demonstrate how to design a custom fitness/subjective
function in order to fulfill different optimization targets. The experiment result shows that not
only addition optimization targets are achieved, but also improved the result of DGS by decreasing total cycle time vary from 2.2% to 38%.

關鍵字(中)

★ 儲料槽分配
★ 基因演算法
★ 元優化

關鍵字(英)

★ Feeder Assignment
★ Genetic Algorithm
★ Meta-Optimization

論文目次

摘要 v
Abstract vi
Contents vii
List of Figures ix
List of Tables x
1 Introduction 1
2 Research Background 3
2.1 Background Knowledge 3
2.1.1 Optimization of placement mounter 3
2.1.2 Genetic Algorithm 5
2.1.3 Meta-optimization 6
2.2 Related work 7
2.3 Motivation 8
3 Formal definition 10
3.1 Problem definition 10
3.2 Custom Fitness function and Subjective function 11
3.2.1 Fitness function 12
3.2.2 Subjective function 13
4 System Design 15
4.1 System Architecture 15
4.2 System Components 16
4.2.1 Job Scheduler 16
4.2.2 GA Solver 17
4.2.3 Dispatcher and agent 18
4.3 Dedicated Driver and RPA 22
4.3.1 File Inspection 23
4.3.2 Robotic Process Automation 23
5 Evaluation 24
5.1 Experiment environment 24
5.2 Experiment design and Goal 25
5.3 Experiment Result 26
6 Conclusion and Future Work 29
Reference 31

參考文獻

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指導教授

王尉任(Wei-Jen Wang)

審核日期

2019-6-21

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