바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

Designing a Distribution Network for Faster Delivery of Online Retailing : A Case Study in Bangkok, Thailand

The Journal of Industrial Distribution & Business / The Journal of Industrial Distribution & Business, (E)2233-5382
2018, v.9 no.5, pp.25-35
https://doi.org/https://doi.org/10.13106/ijidb.2018.vol9.no5.25.
Amchang, Chompoonut
Song, Sang-Hwa
  • Downloaded
  • Viewed

Abstract

Purpose - The purpose of this paper is to partition a last-mile delivery network into zones and to determine locations of last mile delivery centers (LMDCs) in Bangkok, Thailand. Research design, data, and methodology - As online shopping has become popular, parcel companies need to improve their delivery services as fast as possible. A network partition has been applied to evaluate suitable service areas by using METIS algorithm to solve this scenario and a facility location problem is used to address LMDC in a partitioned area. Research design, data, and methodology - Clustering and mixed integer programming algorithms are applied to partition the network and to locate facilities in the network. Results - Network partition improves last mile delivery service. METIS algorithm divided the area into 25 partitions by minimizing the inter-network links. To serve short-haul deliveries, this paper located 96 LMDCs in compact partitioning to satisfy customer demands. Conclusions -The computational results from the case study showed that the proposed two-phase algorithm with network partitioning and facility location can efficiently design a last-mile delivery network. It improves parcel delivery services when sending parcels to customers and reduces the overall delivery time. It is expected that the proposed two-phase approach can help parcel delivery companies minimize investment while providing faster delivery services.

keywords
Network Partition, Last Mile Delivery Center, Facility Location

Reference

1.

Aggarwal, C. C., & Wang, H. (2010). A Survey of Clustering Algorithms for Graph Data. In C.C. Aggarwal, & H. Wang (eds.). Managing and Mining Graph Data in Advances, Database Systems, 40, 275–301, Boston, MA: Springer. https://doi.org/10.1007/978-1-4419-6045-0_9

2.

Amchang, C., & Song, S. (2018). Locational Preference of Last Mile Delivery Centres : A Case Study of Thailand Parcel Delivery Industry. International Journal of Industrial Distribution & Business, 9(3), 7–17. https://doi.org/doi:http://dx.doi.org/10.13106/ijidb. 2018.vol9.no3.7.

3.

Anand, N., Quak, H., van Duin, R., & Tavasszy, L. (2012). City Logistics Modeling Efforts: Trends and Gaps - A Review. In Procedia - Social and Behavioral Sciences, 39, 101–115. https://doi.org/10.1016/j.sbspro.2012.03.094

4.

Anwar, T., Liu, C., Vu, H., & Leckie, C. (2014). Spatial Partitioning of Large Urban Road Networks. EDBT2014, 343–354. Retrieved March 15, 2018 from http://openproceedings.net/EDBT/2014/paper_241.pdf

5.

Barreto, S., Ferreira, C., Paixão, J., & Santos, B. S. (2007). Using clustering analysis in a capacitated location-routing problem. European Journal of Operational Research, 179(3), 968–977. https://doi.org/10.1016/j.ejor.2005.06.074

6.

Choi, B. N., & Yang, H. C. (2018). A Study on Revitalization of Revenue through Difference of Consumer Perception of Characteristics of Mobile Social Commerce. The East Asian Journal of Business Management, 8(1), 31-38. https://doi.org/10.13106/eajbm.2018.vol8.no1.31

7.

Choi, I. S., & Lee, S. (2012). A study on the Regulatory Environment of the French Distribution Industry and the Intermarche s Management strategies. Journal of Industrial Distribution & Business, 3(1), 7–16.

8.

Cotilla-Sanchez, E., Hines, P. D. H., Barrows, C., Blumsack, S., & Patel, M. (2013). Multi-attribute partitioning of power networks based on electrical distance. IEEE Transactions on Power Systems, 28(4), 4979–4987. https://doi.org/10.1109/TPWRS.2013.2263886

9.

Ding, C. H. Q., He, X., Aha, H., Gu, M., & Simon, H. D. (2001). A min-max cut algorithm for graph partitioning and data clustering. Proceedings 2001 IEEE International Conference on Data Mining, 107–114. https://doi.org/10.1109/ICDM.2001.989507

10.

Ferreira, C. E., Martin, A., de Souza, C. C., Weismantel, R., & Wolsey, L. A. (1998). The Node Capacitated Graph Partitioning Problem: A Computational Study. Math Program, 81(2), 229–256. https://doi.org/10.1007/BF01581107

11.

Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7821–7826. https://doi.org/10.1073/pnas.122653799

12.

Guadalupe, G. R. R. (2009). District Design for a Parcel Delivery and Pick up Problem. Doctoral dissertation, Technologico de Monterrey. Retrieved March 15, 2018 from https://repositorio.itesm.mx/bitstream/handle/11285/572496/DocsTec_7238.pdf?sequence=1

13.

Gutiérrez-Gutiérrez, E. V., & Vidal, C. J. (2015). A Home Health Care Districting Problem in a Rapid-Growing City. Ingenieria Y Universidad, 19(1), 87–113. https://doi.org/10.11144/Javeriana.iyu19-1.ahhc

14.

Hwang, H. J., & Lee, S. M. (2011), A study on transferring the effects of brand reputation and level of service satisfaction of an offline channel company when it is expanding to an online distribution channel. The Journal of Distribution Science, 9(2), 31-36. https://doi.org/10.15722/jds.9.2.201106.31

15.

Iwan, S., Kijewska, K., & Lemke, J. (2016). Analysis of Parcel Lockers’ Efficiency as the Last Mile Delivery Solution - The Results of the Research in Poland. Transportation Research Procedia, 12, 644–655. https://doi.org/10.1016/j.trpro.2016.02.018

16.

Jiang, Y., Sun, B., Li, G., Lin, Z., Zheng, C., & Shen, X. (2017). Highway Passenger Transport Based Express Parcel Service Network Design: Model and Algorithm. Journal of Advanced Transportation, 2017.

17.

Kafle, N., Zou, B., & Lin, J. (2017). Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery. Transportation research part B:Methodological, 99, 62-82.

18.

Kalcsics, J., Nickel, S., & Schröder, M. (2005). Towards a unified territorial design approach - applications, algorithms and GIS integration. Top, 13(1), 1–56. https://doi.org/10.1007/bf02578982

19.

Karypis, G., & Kumar, V. (2013). A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal of Scientific Computing, 20(1), 359-392.

20.

Kim, J. J. (2017). The Effects of Elderly(Senior) Buying Factors and Satisfaction on Retailer's Online Shopping. The Journal of Distribution Science, 15(7). 43-52.

21.

Lee, E. K., Chen, C. H., Pietz, F., & Benecke, B. (2009). Modeling and optimizing the public-health infrastructure for emergency response. Interfaces, 39(5), 476–490. https://doi.org/10.1287/inte.1090.0463

22.

Lee, H. J. (2017). The Differential Factors Influencing Online & Mobile Shopping Behavior. The Journal of Distribution Science, 15(9), 27-36.

23.

Lee, U. K., & Feng, Z. (2015). The Effect of Essential Online Elements on Consumer Purchase Intention:Insights from a Taobao Perspective. The Journal of Distribution Science, 13(5), 15-22.

24.

Liao, K., & Guo, D. (2008). A Clustering-based approach to the capacitated facility location problem. Transactions in GIS, 12(3), 323–339. https://doi.org/10.1111/j.1467-9671.2008.01105.x

25.

Lim, H., & Koo, M. W. (2016). Promoting cost efficiency and uniformity in parcel delivery centre locations and service areas: A GIS-based analysis. International Journal of Logistics Research and Applications, 19(5), 369-379.

26.

Lin, C. C., & Lee, S. C. (2018). Hub network design problem with profit optimization for time-definite LTL freight transportation. Transportation Research Part E: Logistics and Transportation Review, 114, 104-120.

27.

Malliaros, F. D., & Vazirgiannis, M. (2013). Clustering and community detection in directed networks: A survey. Physics Reports, 533(4), 95–142. https://doi.org/10.1016/j.physrep.2013.08.002

28.

Morganti, E., Dablanc, L., & Fortin, F. (2014). Final deliveries for online shopping: The deployment of pickup point networks in urban and suburban areas. Research in Transportation Business and Management, 11, 23–31. https://doi.org/10.1016/j.rtbm.2014.03.002

29.

Newman, M. E. J. (2013). Spectral methods for network community detection and graph partitioning. Physical Review E, 88, 042822. https://doi.org/10.1103/PhysRevE.88.042822

30.

Newman, M. E. J., & Girvan, M. (2003). Finding and evaluating community structure in networks, Physical Review E, 69, 1–15. https://doi.org/10.1103/PhysRevE.69.026113

31.

Olsen, G., Gergele, O., Ghee Chua, S., & Bartolucci, F. (2015). Lifting the Barriers to E-Commerce in ASEAN. Retrieved March 15, 2018 from http://tinyurl.com/nf7uhw2

32.

Phuong, N. N. D., & Dat, N. T. (2017). The Effect of Country-of-Origin on Customer Purchase Intention:A Study of Functional Products in Vietnam. The Journal of Asian Finance, Economics and Business, 4(3), 75-83. http://dx.doi.org/10.13106/jafeb.2017.vol4.no3.75

33.

Polzin, S. (2017). First Mile-Last Mile, Intermodalism, and Making Public Transit More Attractive. Retrieved March 15, 2018 from https://www.planetizen.com/node/93909/first-mile-last-mile-intermodalism-and-ma king-public-transit-more-attractive

34.

Prashar, S., Verma, P., Parsad, C., & Sai Vijay, T. (2015). Factors Defining Store Atmospherics in Convenience Stores: An Analytical Study of Delhi Malls in India. The Journal of Asian Finance, Economics and Business, 2(3), 5-15. https://doi.org/10.13106/jafeb.2015.vol2.no3.5.

35.

Rahman, M., Ismail, Y., Albaity, M., & Isa, C. R. (2017). Brands and Competing Factors in Purchasing Hand Phones in the Malaysian Market. The Journal of Asian Finance, Economics and Business, 4(2), 75-80. http://dx.doi.org/10.13106/jafeb.2017.vol4.no2.75

36.

Serper, E. Z., & Alumur, S. A. (2016). The design of capacitated intermodal hub networks with different vehicle types. Transportation Research Part B:Methodological, 86, 51-65.

37.

Singh, D. P. (2014). Online Shopping Motivations, Information Search, and Shopping Intentions in an Emerging Economy. The East Asian Journal of Business Management, 4(3), 5-12.

38.

Su, S., & Youn, M. K. (2011). Using Huff Model for Predicting the Potential Chinese Retail Market. The East Asian Journal of Business Management, 1(1), 9-12.

39.

Schewel, L., & Schipper, L. (2012). A historical and political analysis of retail goods movement in the United States. Environmental Science and Technology, 46(18), 9813–9821.

40.

TomTom Tracffic Index. (2016). TomTom Traffic Index. Retrieved March 15, 2018 from https://www.tomtom.com/ es_mx/trafficindex/

41.

Wang, C., & Mu, D. (2015). Design of the Distribution Network for a "Collect-on-Delivery" Company in a Metropolitan Context using Simulated Annealing with Path Relinking. Applied Mathematics &Information Sciences, 9(3), 1529.

The Journal of Industrial Distribution & Business