【摘要】：隨著經(jīng)濟環(huán)境的變動,傳統(tǒng)的定價方法在實際運用中出現(xiàn)越來越多的漏洞,無法準(zhǔn)確預(yù)測消費需求的變化、無法用精準(zhǔn)的挖掘潛在消費需求、無法實現(xiàn)企業(yè)利潤最大化,已經(jīng)成為服務(wù)型企業(yè)收益管理的難題。本文主要在收益管理的情景下對服務(wù)型產(chǎn)品定價問題進行研究,通過研讀和綜述服務(wù)型產(chǎn)品動態(tài)定價的相關(guān)文獻,明確了服務(wù)型產(chǎn)品的特點以及當(dāng)前服務(wù)型產(chǎn)品定價的若干漏洞,并根據(jù)當(dāng)前國內(nèi)外動態(tài)定價的研究成果,歸納了動態(tài)定價影響因素并建立本文動態(tài)定價模型。之后選取當(dāng)前熱門且符合多維度特性的強化學(xué)習(xí)方法對模型求解計算,得到本文模型的最優(yōu)狀態(tài)動作對,形成環(huán)境和行為的最優(yōu)路徑從而得到最大利潤。為了驗證整體模型和最終結(jié)果,本文以服務(wù)型產(chǎn)品是否有空間上的移動為標(biāo)準(zhǔn)分情況做了案例仿真和分析,得到不同背景下的最優(yōu)價格和利潤。在固定服務(wù)型產(chǎn)品案例上,本文選取酒店客房服務(wù)收益管理為背景,建立了酒店客房動態(tài)定價模型,該模型結(jié)合實際狀況將酒店客房剩余數(shù)量、消費者策略行為、預(yù)售和定價聯(lián)合考慮進行價格決策,謀求最大化的企業(yè)利潤。在移動服務(wù)型產(chǎn)品的案例上,本文選取高速鐵路票價問題作為背景,建立高速鐵路客票動態(tài)定價模型,模型結(jié)合高速鐵路站點區(qū)間、剩余座位量、消費者策略行為、提前期和價格等多重因素,保證高上座率的同時追求最大化利潤。在兩個案例尋求最優(yōu)解的過程中,本文利用強化學(xué)習(xí)理論和Q學(xué)習(xí)方法將收益管理的動態(tài)定價問題轉(zhuǎn)化成AGENT遍歷狀態(tài)和動作的過程,通過Q學(xué)習(xí)迭代利潤函數(shù)得到最優(yōu)的狀態(tài)動作對,直接指導(dǎo)企業(yè)定價行為。之后將本文的動態(tài)定價模型結(jié)果與傳統(tǒng)定價方法進行比較發(fā)現(xiàn)動態(tài)定價方法要優(yōu)于傳統(tǒng)定價方法,同時進行了市場、庫存、成本、消費者價格敏感程度等因素的敏感性分析。最后對文章進行總結(jié),從動態(tài)定價模型、動態(tài)定價方法和因素的靜態(tài)敏感性分析三個方面展開,得到關(guān)于企業(yè)應(yīng)利用價格調(diào)節(jié)作用、關(guān)注價格影響因素出現(xiàn)的奇異值等七條結(jié)論并提出了相應(yīng)的管理建議。
[Abstract]:With the change of economic environment, there are more and more loopholes in the practical application of traditional pricing methods, which can not accurately predict the change of consumption demand, can not use accurate mining potential consumption demand, can not achieve the maximum profit of enterprises. It has become a difficult problem in revenue management of service enterprises. In this paper, we study the pricing of service-oriented products in the context of revenue management, and study and summarize the relevant literature on dynamic pricing of service-oriented products. This paper clarifies the characteristics of service products and some loopholes in the pricing of service products at present. According to the current research results of dynamic pricing at home and abroad, the influencing factors of dynamic pricing are summarized and the dynamic pricing model of this paper is established. Then we select the current popular reinforcement learning method which accords with the multi-dimension characteristic to solve the model and get the optimal state action pair of the model to form the optimal path of the environment and behavior so as to obtain the maximum profit. In order to verify the whole model and the final result, this paper makes case simulation and analysis on whether there is space movement of service product, and obtains the optimal price and profit under different background. In the case of fixed service product, this paper selects the income management of hotel room service as the background, and establishes the dynamic pricing model of hotel room. Pre-sale and pricing are considered to make price decisions to maximize corporate profits. In the case of mobile service product, this paper selects the high-speed railway ticket price as the background, establishes the high-speed railway passenger ticket dynamic pricing model, the model combines the high-speed railway station interval, the remaining seat quantity, the consumer strategy behavior. Multiple factors, such as lead time and price, ensure high attendance and maximize profit. In the process of seeking the optimal solution in two cases, this paper uses reinforcement learning theory and Q learning method to transform the dynamic pricing problem of revenue management into the process of AGENT traversal state and action. The optimal state action pair is obtained by Q learning iterative profit function, which directly guides the enterprise pricing behavior. After comparing the results of the dynamic pricing model with the traditional pricing method, it is found that the dynamic pricing method is superior to the traditional pricing method. At the same time, the sensitivity of market, inventory, cost, consumer price sensitivity and other factors are analyzed. Finally, the paper summarizes the dynamic pricing model, the dynamic pricing method and the static sensitivity analysis of factors, and gets the conclusion that enterprises should make use of the role of price regulation. Seven conclusions such as singular value of price influencing factors are concerned and corresponding management suggestions are put forward.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F224;F274

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