本研究利用模擬最佳化求解行李搬運系統(BHS)的控制因子，暫存區時間設定、主要暫存道開啟數量之暫存機制設定問題，以最小化無法進暫存區行李量與回流行李量、系統壅塞為目標。本研究以桃園國際機場第二航廈之轉盤型的行李搬運系統為例，建構系統之模擬模型。全自動化之行李搬運系統，在現行的控制因子設定下，逐年增加的行李量將使系統超過原先預期之負荷量，有系統壅塞、無法進暫存區行李量過多之問題。
考慮尖峰時刻行李量較多，且不同區域之行李來到情況亦不同，本研究將對不同時段下不同區域的暫存機制分別設定，並考量機場實務需求，如：行李旅行時間(Traveling Time)。無法進暫存區行李量過多使地勤人員必須同時處理許多航班行李，造成人員作業困難，甚至因人為疏失而發生行李送錯或遺失。對行李搬運系統之組成與運作方式先加以瞭解，並找到暫存區時間設定、主要暫存道開啟數量，可減少無法進暫存區行李量，卻增加回流行李量。考量回流行李量過多將影響由報到櫃台進來的行李的輸送過程，行李回堵至報到櫃台會使旅客等待，為機場極不想發生之情形，本研究以最小化無法進暫存區行李量與回流行李量之合併目標、系統壅塞為目標進行求解。
研究結果發現，不同時段下不同區域之暫存機制設定，可使無法進暫存區行李量，比起現行的控制因子設定下約五千件無法進暫存區行李量，減少約15%。在行李來到具隨機性下，透過最佳資源分配法比起平均資源分配法節省五天的模擬時間，而應用模擬最佳化中最佳抽樣分配策略結合啟發式演算法求解，更少耗費五個小時的模擬時間。本研究尋找到無法進暫存區行李量與回流行李量極小之方案，並透過蒐集方案之行李旅行時間，驗證所求得之方案仍符合機場之需求。

This research details control factors of baggage handling system (BHS) with carousel-based unloading zone, and simulation optimization is used to find the best buffer zone mechanism setting of BHS for reducing congestion in the system. Taiwan Taoyuan International Airport Terminal Two (TPE) is taken as case study. With the growing number of tourists recently, insufficient capacity of BHS has been a problem faced by TPE. Reducing the number of manual handling baggage and congestion in the system is an important issue.
The buffer zone mechanism settings in different area and time-period are presented. The baggage arrival in peak-time is not the same as it in non-peak-time, so is it in the different area of the system. The important control factors of BHS are buffer zone mechanisms including time bucket of the each buffer zone and the number of main buffers opened which decreases manual handling baggage, but increases reflux baggage. Manual handling baggage make ground crews sort the baggage with multiple flights which is more labor intensive, and has higher error rates; Much reflux baggage may affect the process of baggage from check-in counter, and make passenger wait. Therefore, the study is to minimize the consolidation goal with the number of manual handling baggage and reflux baggage.
In this research, considering different buffer zone mechanism settings in different area and time-period, the results are superior to that of current control factor setting of BHS in reducing the number of manual handling baggage, 15% on average. Based on the stochastic baggage arrival, optimal computing budget allocation is used for saving five days of simulation time compared with equal resource allocation. By comparing OSAS with OCBA, there is even five hours saving of the simulation time. The study found the best alternative of buffer zone mechanism settings to minimize the number of manual handling baggage and reflux baggage. Furthermore, the traveling time should meet the airport requirement, and the simulation is used to validate the performance measure. At last, the result meets the requirement as well as evaluating the feasibility of the decisions.

摘要 I
目錄 IV
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 4
1.3研究範圍與假設 4
1.4研究架構 6
第二章 文獻回顧 8
2.1行李搬運系統的相關文獻 8
2.2模擬最佳化 12
2.2.1基因演算法(Genetic algorithm, GA) 13
2.2.2最佳資源分配法(Optimal Computing Budget Allocation,OCBA) 16
2.2.3最佳抽樣分配策略(Optimal Sample Allocation Strategy, OSAS) 24
第三章 行李搬運系統問題與分析 27
3.1桃園國際機場行李搬運系統整體架構概述 27
3.2行李於行李搬運系統之行為 28
3.2.1行李送至行李搬運系統之自動行李分揀機 29
3.2.2行李進入卸載道或處理無法進暫存區行李之轉盤 30
3.3行李來到資訊分析 36
3.4桃園國際機場行李搬運系統之關鍵控制因子 39
3.4.1問題描述 40
3.4.2桃園國際機場行李搬運系統之模擬模式建構 41
3.4.3影響系統績效之關鍵控制因子 44
3.4.4全時段相同暫存區時間設定之結果分析 50
3.4.5尖峰時段、不同區域之意義 56
第四章 暫存區時間設定之問題與分析 61
4.1問題定義 61
4.2求解方法-最佳資源分配法 69
4.3實驗設計 71
4.4實驗結果與分析 73
第五章 南北區尖峰時段暫存機制之實驗與分析 92
5.1問題定義 92
5.2求解方法-模擬最佳化 100
5.3實驗設計 104
5.4實驗結果與分析 105
第六章 結論與建議 124
6.1 結論 124
6.2 建議 126
參考文獻 128
附錄 131

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