HAN Xun, ZHANG Jin. Optimization of differentiated service level for multi-level customers[J]. Journal of Traffic and Transportation Engineering, 2020, 20(1): 192-203. doi: 10.19818/j.cnki.1671-1637.2020.01.016
Citation: HAN Xun, ZHANG Jin. Optimization of differentiated service level for multi-level customers[J]. Journal of Traffic and Transportation Engineering, 2020, 20(1): 192-203. doi: 10.19818/j.cnki.1671-1637.2020.01.016

Optimization of differentiated service level for multi-level customers

doi: 10.19818/j.cnki.1671-1637.2020.01.016
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  • Author Bio:

    XIEHAN Xun(1991-), female, lecturer, PhD, hldwxhx@163.com

  • Corresponding author: ZHANG Jin(1963-), male, professor, PhD, zhjswjtu@swjtu.edu.cn
  • Received Date: 2019-06-05
  • Publish Date: 2020-02-25
  • Based on the premise that satisfaction degrees of customers were affected by the current supply factors and the past service experience, a customer utility function was set up considering the effects of multiple factors such as distance, service level and price under the reference effect. Comparing the utility of customers in different companies, a probability model was introduced to characterize customers' selection behaviors. The correlation function between service level and unit service cost was proposed, and the service level optimization model of maximizing profit was constructed and solved. Taking the pickup service level in a specific business circle as an example, the impacts of sensitivity coefficient for service level and the competing company strategy on the optimization results were compared and analyzed. Research result shows that when the sensitivity coefficient changes from 0.2 to 1.0, a high service level provided by the target company leads to a 7.2% increase in demand coverage and a 10.4% increase in total profit. When the competing company adopts a service level strategy of 0.2 and a price strategy of 1.2, the target company provides a service level of 0.39 higher than the competing company to attract customers, and increases unit revenue through high prices. When the competitor company adopts a service level strategy of 0.8 and a price strategy of 1.8, the target company rises the service level from 0.59 to 0.63 to cope with competition, and maintains low price to retain some price-sensitive customers. When the competitor company adopts a service level strategy of 0.2 and a price strategy of 1.8, the target company improves market coverage by providing a service level of 0.47 higher than that of the competitor, and guarantees profit margin by a quite similar price. When the competitor company adopts a service level strategy of 0.8 and a price strategy of 1.2, the target company provides relatively low service level and price to control cost and maintain market share.

     

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  • [1]
    GEFEN D, STRAUB D W. Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services[J]. Omega, 2004, 32(6): 407-424. doi: 10.1016/j.omega.2004.01.006
    [2]
    VAN DER VEEKEN D J M, RUTTEN W G M M. Logistics service management: opportunities for differentiation[J]. International Journal of Logistics Management, 1998, 9(2): 91-98. doi: 10.1108/09574099810805861
    [3]
    LIN Yong, LUO Jing, ZHOU Li, et al. The impacts of service quality and customer satisfaction in the e-commerce context[C]//IEEE. 11th International Conference on Service Systems and Service Management. New York: IEEE, 2014: 1-6.
    [4]
    KOSTAMI V, KOSTAMIS D, ZIYA S. Pricing and capacity allocation for shared services[J]. Manufacturing and Service Operations Management, 2017, 19(2): 230-245. doi: 10.1287/msom.2016.0606
    [5]
    NAN Guo-fang, LYU Kun. Tiered service model and optimal pricing strategies for duopoly competition[J]. Journal of Tianjin University (Social Science), 2016, 18(3): 193-199. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TDXS201603001.htm
    [6]
    LU Chao, ZHANG Kang-kang, TAO Jie. A delivery time based service pricing study for express logistics enterprises[J]. Industrial Engineering and Management, 2017, 22(5): 59-64, 73. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GYGC201705009.htm
    [7]
    LIU Chang, AN Shi, XIE Bing-lei. E-tailer's decision strategies considering customer's self-pickup behavior[J]. Soft Science, 2018, 32(3): 114-117. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XUXI201803025.htm
    [8]
    TVERSKY A, KAHNEMAN D. Loss aversion in riskless choice: a reference-dependent model[J]. The Quarterly Journal of Economics, 1991, 106(4): 1039-1061. doi: 10.2307/2937956
    [9]
    FRANK R H. The frame of reference as a public good[J]. The Economic Journal, 1997, 107: 1832-1847. doi: 10.1111/j.1468-0297.1997.tb00086.x
    [10]
    WINER R S. A reference price model of brand choice for frequently purchased products[J]. Journal of Consumer Research, 1986, 13(2): 250-256. doi: 10.1086/209064
    [11]
    KOPALLE P K, RAO A G, ASSUNÇÃO J L. Asymmetric reference price effects and dynamic pricing policies[J]. Marketing Science, 1996, 15(1): 60-85. doi: 10.1287/mksc.15.1.60
    [12]
    WU Lin-liang, WU De-sheng. Dynamic pricing and risk analytics under competition and stochastic reference price effects[J]. IEEE Transactions on Industrial Informatics, 2016, 12(3): 1282-1293. doi: 10.1109/TII.2015.2507141
    [13]
    CHEN Xin, HU Peng, HU Zhen-yu. Efficient algorithms for the dynamic pricing problem with reference price effect[J]. Management Science, 2017: 63(12): 4389-4408. doi: 10.1287/mnsc.2016.2554
    [14]
    WU Sheng, LUO Xing-gang, CHEN Zhen-song, et al. Pricing and ordering decisions with price and consumer time preference dependent demand[J]. Control and Decision, 2016, 31(9): 1594-1602. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201609009.htm
    [15]
    LIN Zhi-bing. Pricing strategy of supply chain based on manufacturer's suggested retail price[J]. Chinese Journal of Management Science, 2016, 24(11): 153-161. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGK201611018.htm
    [16]
    LIU Guang-qian. Dynamic product pricing strategy based on after-sales service level[J]. Enterprise Economy, 2014(10): 48-51. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-QUIT201410012.htm
    [17]
    LU Li-hao, GOU Qing-long, TANG Wan-sheng, et al. Joint pricing and advertising strategy with reference price effect[J]. International Journal of Production Research, 2016, 54(17): 5250-5270. doi: 10.1080/00207543.2016.1165878
    [18]
    CHENAVAZ R. Dynamic quality policies with reference quality effects[J]. Applied Economics, 2017, 49(32): 3156-3162. doi: 10.1080/00036846.2016.1254345
    [19]
    DUAN Yong-rui, XU Jian, HUO Jia-zhen. Dynamic pricing of private label with reference effect[J]. Industrial Engineering and Management, 2017, 22(1): 14-21. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GYGC201701002.htm
    [20]
    HAN Xun, ZHANG Jin, CHEN Yi-you. Multi-level pickup point location based on customer demand heterogeneity[J]. Industrial Engineering and Management, 2017, 22(4): 23-29, 39. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GYGC201704004.htm
    [21]
    BERMAN O, DREZNER Z, KRASS D. Generalized coverage: new developments in covering location models[J]. Computers and Operations Research, 2010, 37(10): 1675-1687. doi: 10.1016/j.cor.2009.11.003
    [22]
    LI Xue-ping, RAMSHANI M, HUANG Yu. Cooperative maximal covering models for humanitarian relief chain management[J]. Computers and Industrial Engineering, 2018, 119: 301-308. doi: 10.1016/j.cie.2018.04.004
    [23]
    LIU Hui, YANG Chao, ZHANG Zong-xiang. The research on cooperative coverage modeling based on the effectiveness of the location[J]. Operations Research and Management Science, 2017, 26(5): 95-101. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YCGL201705014.htm
    [24]
    HAN Xun, ZHANG Jin, CHEN Yi-you. Service composition of pickup point under cooperative coverage[J]. Chinese Journal of Management, 2018, 15(6): 927-935. (in Chinese). doi: 10.3969/j.issn.1672-884x.2018.06.016
    [25]
    CHEN Yi-you, HAN Xun, ZENG Qian. Multi-objective pickup point location problem considering the impact of home delivery[J]. Computer Integrated Manufacturing Systems, 2016, 22(11): 2679-2690. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ201611021.htm
    [26]
    KAHNEMAN D, TVERSKY A. Prospect theory: an analysis of decision under risk[J]. Econometrica: Journal of the Econometric Society, 1979, 47(2): 263-292. doi: 10.2307/1914185
    [27]
    SCHÜLLER D, PEKÁREK J, CHLEBOVSKÝ V, et al. Novel method of price determination based on reference price[J]. Engineering Economics, 2018, 29(1): 13-23
    [28]
    HAN Shuang, ZHANG Lin, TAN Zhi-hua, et al. Bi-level programming of logistics distribution center in dynamic competitive environment[J]. Control and Decision, 2014, 29(11): 2055-2060. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201411023.htm
    [29]
    WANG Jiu-he, ZHAO Gui-wen. Measurement of the optimal logistics service level of enterprises under OTO mode[J]. Statistics and Decision, 2014(22): 170-173. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TJJC201422051.htm
    [30]
    ZHU Li, DING Jia-lan, JI Meng-ting. Location-allocation optimization of emergency relief materials considering regional heterogeneity[J]. Journal of Systems and Management, 2018, 27(6): 1142-1149. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTGL201806016.htm

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