臺灣大學: 工業工程學研究所陳正剛; 吳政鴻馬啟康Ma, Chi-KangChi-KangMa2013-03-292018-06-292013-03-292018-06-292010http://ntur.lib.ntu.edu.tw//handle/246246/254686收益管理研究以最大化企業的收益為目標，探討如何在訂價、產能控制、需求管理等議題中尋找最佳的規劃方案，要以適當的價格在適當的地點銷售給適當的顧客使企業的收益最大化。航空業是最早以收益管理得到成果的產業之一，以動態訂價、艙位控管與超額定位等方式以銷售出最多的艙位，而收益管理研究多以短期的需求當作考量，例如:決定某一航班經濟艙的艙位訂價，是在早已排定的飛機班表下進行短期規劃。在企業實際的營運中，必須要在銷售產品前早一步思考產品的類型與市場定位等不同的產品屬性，作不同時程的相關作業規劃。 對於多屬性的產品需求，該如何訂價與分配產能以最大化收益，是文獻上已知不易求解的問題。訂價必須考量不同產品屬性，而訂價又影響產品需求，產能則必須依產品屬性作最適當的配置以因應需求變化，所以訂價、產能與多屬性產品需求的規劃是不可區分的議題。而企業的階層作業規劃又可分為長、中、短等不同時程的規劃，不同時程之間的規劃又具有繼承性，如何選擇適當的產品屬性於不同時程作規劃，是企業必須面臨更困難的問題。以多屬性航班的長、中、短期規劃為例，有季節、彈性與艙等等不同的航班機票屬性。若航班規劃選擇以季節之淡旺季作為長期規劃之考量，則其中期規劃就必須於淡、旺季之下分別再選擇機票屬性彈性或艙等作規劃。但航班規劃亦可選用彈性或艙等來當作長期考量的產品屬性，一旦某一屬性被選擇作長期規劃，則中期規劃就必須要繼承長期規劃的結果再選擇另一屬性作規劃，短期規劃則須繼承長、中期之規劃結果。因此，多屬性階層規劃必須要選擇適當的產品屬性作不同時程的規劃。 本論文考量企業在長、中、短期的階層規劃架構下，面臨顧客多樣性的需求時，要如何選擇適當的產品屬性來規劃不同時程的需求與產能分配以最大化收益，我們亦同時考量不同屬性產品彈性共享使用產能時所必須付出之相關成本。在給定的訂價策略下，我們使用平均產能比例 (Mean Proportional Capacity, MPC) 與平均銷售價格 (Average Selling Price, ASP) 等兩種不同的產能配置方式來計算長、中期的聚合期望收益 (Aggregated Expected Revenue)，經由適當的產能解析分配後，我們可在某一訂價策略下，決定最佳產品屬性順序作長、中、短期的作業規劃以最大化期望收益結果，我們稱此產品屬性順序為收益最大化之需求規劃層級 (Revenue-based Demand Planning Hierarchy, RDPH)。我們提供兩個案例說明最佳RDPH的決策與其期望收益結果，第一個案例以醫院病床配置為例，病床具有科部(內科、外科)、就診(急診、門診)、給付(健保、非健保)等屬性，利用此案例，我們說明如何比較兩種不同訂價策略下的最佳RDPH，並據以選擇訂價策略以最大化期望收益。第二個案例以住院腹部超音波為例，腹部病症有腸胃/肝膽、癌症與否、及篩檢/追蹤等屬性，我們利用此案例說明醫院如何根據不同屬性病症之需求與健保給付，比較兩種不同腹部超音波產能擴充策略下的最佳腹部超音波RDPH，並據以選擇產能擴充策略以最大化期望收益。Revenue Management (RM), concerned with maximizing a firm’s revenue, is dealing with many issues such as pricing, capacity control and demand management to design the best planning. Maximizing the revenue is to sell the right price at the right place to the right customer. The airlines industry is one of the earliest industries to apply RM and increase revenue by applying dynamic pricing, seat inventory control and overbooking to sell more seats and thus make more revenue. However, most studies of RM focus on short-term demand. For example, deciding a flight’s cabin ticket prices is a short-term planning under the predetermined flight schedule. In practice, firms must consider many product attributes, such as product types and their corresponding market positions, for different stages of planning before product sale. Pricing and capacity allocation for a product demand with multiple attributes to maximize revenue is known to be a hard problem to solve in the literature. Product pricing needs to consider product attributes. The price in turn changes the demand for each product. The capacity allocation then needs to be adjusted to meet the demand for each product type. Therefore, pricing, capacity control and multi-attribute demand planning are issues that can’t be considered separately. The complexity becomes even greater with the hierarchical planning where different planning stages: long-term, mid-term and short-term planning, are required. The shorter-term planning inherits and has to follow the longer-term planning results. It is thus important to choose right product attributes for different stages of planning. For example, a multi-attribute flight schedule consists of three ticket attributes: “season”, “ticket flexibility” and “seat class”, for long-term, mid-term and short-term capacity planning. If “season” is used first for long-term planning consideration, then the mid-term planning, concerning the “ticket flexibility” or “seat class”, has to be performed under busy or slack season. On the other hand, if the flight schedule uses the “ticket flexibility” or “seat class” first for long-term planning, then the mid-term planning with consideration of “season” has to follow whatever is planned in the longer term. This means that once a product attribute is used for longer-term planning then the shorter-term planning has to inherit the longer-term planning results and is allowed to consider only product attributes other than the one used in the longer-term planning. Therefore, right product attributes have to be chosen for different stages of planning for the multi-attribute hierarchical planning problem.. In this thesis, we consider the multi-attribute hierarchical planning problem to maximize the revenue. We also consider the sharing costs for capacity allocated in the longer-term planning and shared by different product groups. With a given pricing strategy, we use two methods, namely, Mean Proportional Capacity (MPC) and Average Selling Price (ASP), to allocate the capacity and compute the aggregated expected revenue for the longer-term product demand. By capacity allocation of different planning stages, we can determine a sequence of product attribute choices for hierarchical planning to maximize the expected revenue. We refer to such a sequence of product attributes as Revenue-based Demand Planning Hierarchy (RDPH). We use two case studies to explain the calculation of aggregated expected revenue and to demonstrate how RDPH helps the decision making. The first case study is the problem of hospital bed allocation. Three attributes, department (internal medicine or surgical), patient type (emergency or outpatient) and payment type (health insurance or not), for hospital bed are considered. We show how to compare two RDPH with different pricing strategies. Based on the RDPH comparison, we can choose a pricing strategy with the maximum revenue. The second case study is the problem of inpatient abdominal ultrasound. Three attributes, gastrointestinal/hepatobiliary, cancer or not and screening/track, are considered. We use this second case study to show that how the hospital allocates the resources of ultrasound examination for demands coming from disease types with different insurance payment plans. The hospital can compare the abdominal ultrasound RDPHs under two capacity expansion plans and choose the plan maximizing the expected revenue.1302493 bytesapplication/pdfen-US多屬性層級規劃收益管理multi-attributehierarchical planningrevenue management[SDGs]SDG3thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/254686/1/ntu-99-R97546030-1.pdf