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E-commerce information system data analytics by advanced ACO for asymmetric capacitated vehicle delivery routing

  • Yuan Zhang
  • Yu YuanEmail author
  • Kejing Lu
Original Article
  • 39 Downloads

Abstract

Logistic industry is experiencing its golden era for development due to its supportive role of electronic commerce operation. Big data retrieved from electronic business information system is becoming one of core competitive enterprise resources. Data analytics is playing a pivotal role to enhance effectiveness and efficiency of operation management. Generally, a well-designed delivery routing plan can reduce logistics cost and improve customer satisfaction for online business to a large extent. According to this, literatures on improvement of delivery efficiency are reviewed in this research. In existing literatures, for instance, ant colony algorithm, genetic algorithm and other combined algorithm are quite popular for such a kind of problem. Even though some algorithms are quite advanced, they are still difficult for implementation due to different constraints and larger-scale of raw electronic commerce data obtained from information system. In this paper, an advanced ant colony algorithm, as a heuristic algorithm, is implemented to optimize planning for an asymmetric capacitated vehicle routing problem. This paper not only emphasizes on ACO algorithm improvement and avoiding premature convergence, but also implementation in a real-world e-commerce delivery, which has more practical meaning for big data analytics and operation management.

Keywords

E-commerce Information system Ant colony optimization Asymmetric CVRP Optimized path Operation management Data analytics Heuristic algorithms 

Notes

Acknowledgements

This work is sponsored by “Chenguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (16CGB07) and Shanghai Young University Teachers Training Funding Programme (Z20001.18.804).

References

  1. Altinel IK, Oncan T (2005) A new enhancement of the Clarke and Wright savings heuristic for the capacitated vehicle routing problem. J Oper Res Soc 56:954–961Google Scholar
  2. Bae ST, Hwang HS, Cho GS, Goan MJ (2007) Integrated GA-VRP solver for multi-depot system. Comput Ind Eng 53(2):233–240Google Scholar
  3. Barbucha D (2014) A cooperative agent-based multiple neighborhood search for the capacitated vehicle routing problem. Recent Adv Knowl-Based Paradig Appl 234:129–143Google Scholar
  4. Battarra M, Golden B, Vigo D (2008) Tuning a parametric Clarke–Wright heuristic via a genetic algorithm. J Oper Res Soc 59:1568–1572Google Scholar
  5. Choi TM, Chan HK, Yue X (2016) Recent development in big data analytics for business operations and risk management. IEEE Trans Cybern 47(1):81–92Google Scholar
  6. Clarke G, Wright J (1964) Scheduling of vehicles from a central depot to a number of delivery points. Oper Res 12:568–581Google Scholar
  7. Corominas A, Garcia-Villoria A, Pastor R (2010) Fine-tuning a parametric Clarke and Wright heuristic by means of EAGH (empirically adjusted greedy heuristics). J Oper Res Soc 61:1309–1314Google Scholar
  8. Dantzig GB, Ramser JH (1959) The truck dispatching problem. Manag Sci 4(6):80–91Google Scholar
  9. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1:53–66Google Scholar
  10. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26:29–41Google Scholar
  11. Dotan T (2002) How can ebusiness improve customer satisfaction? Case studies in the financial service industry. J Inf Technol Case Appl Res 4(4):22–48Google Scholar
  12. Frasch W, Spetzler D, York J, Xiong F (2012) Methods for generating a distribution of optimal solutions to nondeterministic polynomial optimization problemsGoogle Scholar
  13. Frei FX (2006) Breaking the trade-off between efficiency and service. Harv Bus Rev 84(11):93–101Google Scholar
  14. Gligor DM, Holcomb MC (2012) Understanding the role of logistics capabilities in achieving supply chain agility: a systematic literature review. Supply Chain Manag Int J 17(4):438–453Google Scholar
  15. Jie YU, Subramanian N, Ning K, Edwards D (2015) Product delivery service provider selection and customer satisfaction in the era of internet of things: a Chinese e-retailers’ perspective. Int J Prod Econ 159:104–116Google Scholar
  16. Jin M, Wang H, Zhang Q, Zeng Y (2019) Supply chain optimization based on chain management and mass customization. Inf Syst E-Bus Manag.  https://doi.org/10.1007/s10257-018-0389-8 Google Scholar
  17. Juan A, Faulin J, Jorba J, Riera D, Masip D, Barrios B (2011) On the use of Monte Carlo simulation, cache and splitting techniques to improve the Clarke and Wright savings heuristics. J Oper Res Soc 62:1085–1097Google Scholar
  18. Ke L, Feng Z (2013) A two-phase metaheuristic for the cumulative capacitated vehicle routing problem. Comput Oper Res 40(2):633–638Google Scholar
  19. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of IEEE international conference on neural networks. IEEE Press, Perth Western, pp 1942–1948Google Scholar
  20. Kirk J (1973) Fixed endpoints open traveling salesman problem—genetic algorithm. Acta Univ Carol Med Monogr 56(2):7–20Google Scholar
  21. Laporte G, Nobert Y (1983) A branch and bound algorithm for the capacitated vehicle routing problem. Oper Res Spektrum 5(2):77–85Google Scholar
  22. Lim S, Jin X, Srai JS (2018) Consumer-driven e-commerce: a literature review, design framework and research agenda on last-mile logistics models. Int J Phys Distrib Logist Manag 48:308–332Google Scholar
  23. Nilsson BJ, Ottmann T, Schuierer S, Icking C (1992) Restricted orientation computational geometry. Data Structures and Efficient Algorithms, Final Report on the Dfg Special Joint Initiative. SpringerGoogle Scholar
  24. Nizar AH, Zhao JH, Dong ZY (2007) Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market. In: International conference on power system technologyGoogle Scholar
  25. Onoyama T, Maekawa T, Komoda N (2006) GA applied VRP solving method for a cooperative logistics network. In: IEEE conference on emerging technologies and factory automationGoogle Scholar
  26. Osaba E, Diaz F, Onieva E (2013) Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Appl Intell 41(1):145–166Google Scholar
  27. Paz A, Moran S (1977) Non-deterministic polynomial optimization problems and their approximation. Theor Comput Sci 15(3):251–277Google Scholar
  28. Ralphs TK, Kopman L, Pulleyblank WR, Trotter LE (2003) On the capacitated vehicle routing problem. Math Program 94(2–3):343–359Google Scholar
  29. Renaud J, Boctor FF (2002) A sweep based algorithm for the fleet size and mix vehicle routing problem. Eur J Oper Res 140:618–628Google Scholar
  30. Sharvani GS, Ananth AG, Rangaswamy TM (2012) Analysis of different pheromone decay techniques for ACO based routing in ad hoc wireless networks. Int J Comput Appl 56(2):31–38Google Scholar
  31. Stubbs E (2014) Big data, big innovation: enabling competitive differentiation through business analytics. Wiley, New YorkGoogle Scholar
  32. Tao N, Chen G, Tao N (2012) Solving VRP using ant colony optimization algorithm. Comput Model Eng Sci 83(1):23–55Google Scholar
  33. Toth P, Vigo D (1997a) An exact algorithm for the vehicle routing problem with backhauls. Transp Sci 31(4):372–385Google Scholar
  34. Toth P, Vigo D (1997b) Heuristic algorithms for the handicapped persons transportation problem. Transp Sci 31(1):60–71Google Scholar
  35. Tyworth JE, Joseph LC, John CL (1987) Traffic management: planning, operations, and control. Addison-Wesley, ReadingGoogle Scholar
  36. Wang CB, Guo J (2013) A new hybrid algorithm based on artificial fish swarm algorithm and genetic algorithm for VRP. Appl Mech Mater 325–326:1722–1725Google Scholar
  37. Yu B, Yang Z, Yao B (2009) An improved ant colony optimization for vehicle routing problem. Eur J Oper Res 196:171–176Google Scholar
  38. Zhang Y (2017) Data pre-processing for real-world e-commerce delivery address clustering. Adv Intell Syst Res (AISR) 2017(150):164–168Google Scholar
  39. Zhang X, Gong H, Guo Y, Zhang L (2015) Improved chaos particle swarm algorithm on VRP. Comput Digit Eng 43(12):2106–2109Google Scholar
  40. Zhou Y, He J, Nie Q (2009) A comparative runtime analysis of heuristic algorithms for satisfiability problems. Artif Intell 173(2):240–257Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Xianda College of Economics and HumanitiesShanghai International Studies UniversityShanghaiChina
  2. 2.Institute of Industrial EconomicsChinese Academy of Social SciencesShanghaiChina
  3. 3.Ningbo University of Finance and EconomicsZhejiangChina

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