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研究生:洪子晏
研究生(外文):Tzu-Yen Hong
論文名稱:競標模型於宅配物流配送時段管理之研究
論文名稱(外文):A Simulation-based Auction Model for Attended Home Delivery Time Slot Management
指導教授:陳正杰陳正杰引用關係
指導教授(外文):Cheng-Chieh Chen
學位類別:碩士
校院名稱:國立東華大學
系所名稱:運籌管理研究所
學門:商業及管理學門
學類:行銷與流通學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
論文頁數:114
中文關鍵詞:動態訂價競標宅配物流配送時段分派
外文關鍵詞:Dynamic PricingAuction MechanismAttended Home Delivery LogisticsTime Slots Allocation
相關次數:
  • 被引用被引用:4
  • 點閱點閱:758
  • 評分評分:
  • 下載下載:171
  • 收藏至我的研究室書目清單書目收藏:2
近年來,由於通訊科技的發達及網路科技的進步,國人的消費型態也趨於多元化。消費者的選擇從原本單一實體店面購物,到如今日漸習慣於網路購物、宅配到府,而宅配在各產業的廣泛應用亦使物流體系已然成為企業強大的後盾,根據創市際市場研究顧問於2011年的調查中顯示「消費者可指定配送到達時段」為宅配物流業中能夠最有效吸引顧客的策略。然而,宅配物流業目前遇到最大挑戰之一是尖離峰時段需求不均衡問題;尖峰時段配送經常出現供不應求的狀況,導致經常出現送貨時間延遲的問題,相反地,離峰時段配送卻使得業者有閒置成本問題的產生。

本研究擬結合定價與競標機制,進行宅配物流輸配送系統管理之相關探討。物流業者藉由提供消費者指定配送到達時段之服務,可轉移尖峰時段需求、提升運具使用率、減少再配送成本,同時減少消費者之平均等待時間。透過定價模式可決定每一開放競標時段之運費與最低得標價格;藉由競標模式將配送時段指派給提供業者最大收益之競標者。本研究也運用模擬方式來進行數種競標機制之系統績效評估。藉由改變標定價格、最低可接受價格以及各時段容量等不同變數,探討影響期望收益之可能原因。同時分析顧客行為中參考價格對顧客願意支付運費之影響性(錨點效應),運用敏感度分析探討受錨點效應影響之人數比率對系統總期望收益之影響。

With the high utilization of information technology nowadays, the diversity of consumed styles are wider than before. Instead of shopping in the physical store, people are getting used to shop in the online market and receive their purchased products at home now. The home delivery logistics has already become the powerful backing force of companies because of the wide range of applications in each industry. In the investigation made by the marketing research consultant company in 2011, the most attractive policy for the home delivery companies to attract the customers is to provide the service of “the customer can choose a specific arrival time slot ”. However, the biggest obstacle for the home delivery companies is the unbalance of the peak and off peak time slots. In the peak time slot, the high demand may result in the delivering delay of the products. Conversely, the low demand in the off peak time slot may result in the idol cost.

In our study, we try to investigate the management of home delivery service system integrated both the pricing and the auction mechanism. Through providing the customers to choose a specific time slot, the home delivery service providers can shift peak demand off peak time slot, enhance the utilization of the truck, decrease the redelivery cost, and decrease the average waiting time of the customers at the same time. Through the pricing model, the logistics providers can determine the posted price and the minimum acceptable price of each opening time slot. We can also assign the time slot to the bidders who will maximize the revenue through the auction model. In our study, we will also investigate the performance evaluation of different auction mechanisms. By adjusting the variables such as posted price, the minimum acceptable price, and the capacity of each time slot, we look forward to observing the reasons which will impact the value of expect revenue. On the other hand, we will also analyze the effect of reference price in customer’ behaviors on the shipping price that customers are willing to pay (anchoring effect). The relationship between the ratio of affected customer and the value of revenue is also investigated by sensitivity analysis.

摘要 I
Abstract II
誌謝 III
目次 V
圖目錄 VII
表目錄 IX
第一章 緒論 1
1.1 研究動機與背景 1
1.2 研究目的 6
1.3 研究範圍與對象 7
1.4 研究流程與架構 10
第二章 文獻回顧 13
2.1 宅配 13
   2.1.1 定義 13
   2.1.2 宅配市場現況與面臨課題 15
   2.1.3 宅配相關文獻演進 19
2.2 競標 22
   2.2.1 定義 22
   2.2.2 參與目的及動機 23
   2.2.3 競標模型中消費者之分類 25
   2.2.4 競標機制介紹 28
   2.2.5 競標相關文獻演進 30
2.3 顧客行為 32
   2.3.1 顧客行為之定義及發展 32
   2.3.2 顧客行為於網路競標之應用 33
   2.3.3 顧客行為相關文獻演進 36
2.4 文獻評析 40
第三章 研究方法 45
3.1 研究架構 45
3.2 研究課題 47
3.3 模型假設 49
3.4 數學模型建構 51
   3.4.1 模型符號說明 51
   3.4.2 競標模型建構 53
3.5 模擬模型建構 55
第四章 情境模擬 63
4.1 情境設計 65
4.2 情境模擬結果 81
4.3 管理意涵 103
第五章 結論與建議 105
5.1 結論 105
5.2 未來研究建議 108
參考文獻 111

1. Agatz, N. A., Fleischmann, M., & Van Nunen, J. A. (2008). E-fulfillment and multi-channel distribution–A review. European Journal of Operational Research,187(2), 339-356.

2. Ariely, D., & Simonson, I. (2003), "Buying, Bidding, Playing, or Competing? Value Assessment and Decision Dynamics in Online Auctions," Journal of Consumer Psychology, 13 (1), 113-23

3. Asdemir, K., Jacob, V. S., & Krishnan, R. (2009). Dynamic pricing of multiple home delivery options. European Journal of Operational Research, 196(1), 246-257.

4. Bapna, R., Goes, P. B., & Gupta, A. (2001). Insights and analyses of online auctions. Commun. ACM, 44(11), 42-50

5. Belk, R.W (1975). Situational variables and consumer behavior. Journal of Consumer Research, Vol. 2, No. 3, pp. 157-164.

6. Boyer, K. K., Hult, G. T., & Frohlich, M. (2003). An exploratory analysis of extended grocery supply chain operations and home delivery. Integrated Manufacturing Systems, 14(8), 652-663.

7. Boyer, K. K., Prud'homme, A. M., & Chung, W. (2009). The last mile challenge: evaluating the effects of customer density and delivery window patterns. Journal of Business Logistics, 30(1), 185-201.

8. Caldentey, R., & Vulcano, G. (2007). Online auction and list price revenue management. Management Science, 53(5), 795-813.

9. Campbell, A. M., & Savelsbergh, M. W. (2005). Decision support for consumer direct grocery initiatives. Transportation Science, 39(3), 313-327.

10. Campbell, A. M., & Savelsbergh, M. (2006). Incentive schemes for attended home delivery services. Transportation science, 40(3), 327-341.

11. Chandran, S., & Morwitz, V. G. (2005). Effects of participative pricing on consumers’ cognitions and actions: a goal theoretic perspective. Journal of Consumer Research, 32(2), 249-259.

12. Chen, S. & Liu, X (2010). Analyzing the consumers’ decision When simultaneous use of auction and posted price. Logistics Engineering and Intelligent Transportation Systems (LEITS), 2010 International Conference 1-4.

13. Ding, M., Eliashberg, J., Huber, J., & Saini, R. (2005). Emotional bidders—An analytical and experimental examination of consumers' behavior in a priceline-like reverse auction. Management Science, 51(3), 352-364.

14. Etzion, H., Pinker, E., & Seidmann, A. (2006). Analyzing the simultaneous use of auctions and posted prices for online selling. Manufacturing & Service Operations Management, 8(1), 68-91.

15. Gevaers, R., Van de Voorde, E., & Vanelslander, T. (2011). Charateristics and Typology of Last-mile Logistic from an Innovation Perspective in an Urban Context. City Distribution and Urban Freight Transport: Multiple Perspectives, Edward Elgar Publishing, 56-71.

16. Kim, J. Y., Natter, M., & Spann, M. (2009). Pay what you want: A new participative pricing mechanism. Journal of Marketing, 73(1), 44-58.

17. Klein, S (1997). Introduction to electronic auctions. Electronic Markets, - Taylor & Francis.

18. Klemperer, P. (1999). Auction theory: A guide to the literature. Journal of economic surveys, 13(3), 227-286.

19. Kwak, H., Fox, R. J., & Zinkhan, G. M. (2002). What products can be successfully promoted and sold via the Internet? Journal of Advertising Research, 42(1), 23-38.
20. Lee, J. S., & Szymanski, B. K. (2007). Auctions as a dynamic pricing mechanism for e-services. In Service Enterprise Integration (pp. 131-156). Springer US.

21. Park, M., & Regan, A. (2004). Issues in emerging home delivery operations.

22. Popescu, I., Y. Wu. 2007. Dynamic pricing strategies with reference effects. Operations Research 55(3) 413–429.

23. Punakivi, M., & Saranen, J. (2001). Identifying the success factors in e-grocery home delivery. International Journal of Retail & Distribution Management, 29(4), 156-163.

24. Stafford, MR., & Stern, B (2002) - Consumer bidding behavior on Internet auction sites. International Journal of Electronic Commerce ,(7)1, 135-150.

25. Shen, Z. J. M., & Su, X. (2007). Customer behavior modeling in revenue management and auctions: A review and new research opportunities. Production and operations management, 16(6), 713-728.

26. Su, X. 2008. Bounded rationality in newsvendor models. Manufacturing Service Oper. Management 10(4), 566–589.

27. Su, X. (2009). A model of consumer inertia with applications to dynamic pricing.Production and Operations Management, 18(4), 365-380.

28. Talluri, K., & Van Ryzin, G. (1998). An analysis of bid-price controls for network revenue management. Management Science, 44(11-Part-1), 1577-1593.

29. Talluri, K., & Van Ryzin, G. (2004). Revenue management under a general discrete choice model of consumer behavior. Management Science, 50(1), 15-33.

30. Weatherford, L. R., & Bodily, S. E. (1992). A taxonomy and research overview of perishable-asset revenue management: yield management, overbooking, and pricing. Operations Research, 40(5), 831-844.

31. Zhao, L., Tian, P., & Li, X. (2012). Dynamic pricing in the presence of consumer inertia. Omega, 40(2), 137-148.

32. 陳毓潔(2012). 動態訂價於宅配物流配送時段管理之研究

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