機場行李搬運系統管控因子之模擬分析

Simulation analysis of control factors in an Airport Baggage Handling System

鍾唯兌(Chung, WeiTui)

指導教授 ： 林則孟

國立清華大學/工學院/工業工程與工程管理學系/碩士(2015年)

摘要

近年來在國際航班需求量逐年增加下，行李運輸系統已超過原本預期之負荷量，此時將關閉部分系統進行改善工程，因此產生暫存產能不足之問題。然而，控制因子皆以系統工程師之主觀意識與經驗進行設定，而此情況並未有效利用系統資源。有鑑於此，若能找出較佳之系統管控因子設定參數，將可提出暫存產能不足之解決方案。
本研究針對桃園國際機場第二航廈行李運輸系統(BHS)之管控因子進行模擬與分析。首先，探討影響行李搬運系統之管控因子，並分析可提升暫存產能利用之對策；此外，為避免行李資訊使評估系統有所偏差，論文中將蒐集多筆數據，並依據各禮拜之航班與行李資訊進行分配配適，產生多組情境使模擬模式在評估方案能更加客觀化；最後，藉由前述探討下，暫存區區間設定為影響系統暫存產能之要因。若區間設定不佳，將發生少數暫存區無早到行李之情況，其將使人工分揀行李量增加且顯示暫存產能閒置。因此，本研究依據模擬最佳化之方法解決行李搬運系統暫存產能不足之問題，其考量全區系統運作與機械故障之行為，以最小化人工分揀行李量為目標，找到合適之暫存區區間設定方案。在方案數眾多與模擬時間長之情況，論文中使用基因演算法進行解空間搜尋，並結合OCBA有效分配模擬資源且節省模擬時間，進而找出較佳之方案組合。
在調整暫存區區間設定與原先設定下比較，每天可減少約200件人工分揀行李量。行李來到資訊在各星期皆有不同航班與行李行為，因此本研究透過模擬模式找出不同之暫存區區間設定，依據各星期之行李特性找出較符合之方案，提供於系統管理者與機場決策者以改善行李運輸系統暫存產能利用率。

關鍵字

行李搬運系統 ； 模擬最佳化 ； 基因演算法 ； OCBA

並列摘要

In recent years, the storage of Baggage Handling System has exceeded its original expected because of increasing demand of flight. The system is going to improve its equipment so that part of system is closed. However, the engineering will reduce the capacity. Usually, the control factors in system is set by engineer’s experience. If the factors is set carefully, the storage could store more baggage for the problem.
The paper used simulation to analyze control factors in an airport baggage handling system. First, we will discuss with what control factor is in system and how to improve the storage. Besides, we collect a lot of data to avoid the special condition make the deviation. According to the analysis, time window setting of each buffer is one of important control factor. It will affect the numbers of baggage with manual operation. Therefore, in order to minimize the numbers of baggage with manual operation we used simulation optimization to solve the storage problem by time window setting of each buffer. Because of lots of solutions and time consuming we combine with genetic algorithm and optimal computing budget allocation to search the good solution.
Compare with original setting, the new setting can reduce almost 200 of baggage with manual operation. The input baggage profile is different from weeks. According to characteristic of each week, we provide different setting of time window setting of each buffer for policymaker to solve the problem.

並列關鍵字

Baggage Handling System ； Simulation Optimization ； Genetic Algorithm ； Optimal Computing Budget Allocation

參考文獻

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