基因演算法應用於休閒產業導覽解說員排班問題之研究-以Y館為例

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2009

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中文摘要 許多遊客服務中心莫不致力於服務品質的提升,安排了導覽解說員的服務,讓遊客能夠在一次旅行中留下深刻的印象,提高遊客的滿意度,隨著也提高了遊客的重遊意願。然而,更多導覽解說員將增加遊客服務中心更多人力資源管理成本。為了要提供合適的導覽解說員人數,同時不降低服務的品質,遊客服務中心需要一個有效的、有系統的方法來安排導覽。 本研究的主要目的,即是運用一個有效的方法來解決導覽解說員排班問題,當導覽解說員人數及導覽時段的數目變得很大時,它變成一個複雜的問題。為解決此問題,本研究首先藉由導覽解說員排班志願表來瞭解導覽解說員排班的志願或偏好,在導覽解說員填好志願表後,將導覽解說員的導覽時段志願或偏好進行彙整。其次,根據遊客服務中心的目標,以及排程的限制進行求解。由於一般遊客服務中心的管理者或是決策者都希望在短時間內即能求得多種可行解,從前人的文獻可以發現,運用啟發式演算法比運用精確演算法更加合適,基因演算法在求解人員排班問題可以得到很好的結果,因此,本研究以基因演算法進行求解。 為驗證基因演算法的有效性,本研究以Y館的情境來進行分析,透過本研究可瞭解基因演算法在求解導覽解說員排程問題的效能。除此之外,也對導覽解說員人數不穩定需求的改變、依據Y館內部對導覽解說員的績效管理轉換成權重,對排班的影響有廣泛的瞭解,不僅在學術上具有相當的貢獻,也在實務上具有很高的應用價值。
Abstract In order to improve tour service quality, many tourist service centers (TSCs) tried to provide tour guides for their visitors. This not only provided the tourists with a background knowledge of the places they have visited but it has also satisfied their demand for good quality of service. Due to an increase demand for tour guides which also resulted in the increase cost of human resources, it is important to develop a system on how to plan a schedule for these tour guides inorder to minimize costs. Consequently, this study was made to develop an efficient scheduling system to avoid complicated problems which will arise when the number of tour guides and also their working hours has increased. First, a survey was done with regards to the choices and preferences of the tour guides. Second, it was done in accordance to the goal of TSCs. In general, most managers or decision makers of TSCs hope they could get as many available solutions as much as possible in a short time frame. We got good results when genetic algorithm was used for solving the scheduling problem. An analysis was made in the scheduling of tour guides for Y museum to prove that genetic algorithm is useful. In this study, we realized that genetic algorithm is very efficient in solving the problem of TSCs. This research have contributed to academic field and has also valuable practical use.

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休閒產業, 基因演算法, 人員排班, 遊客服務中心, 導覽解說員, leisure industry, genetic algorithm, crew scheduling, tourist service centers, tour guides

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