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RETRACTED ARTICLE: E-commerce information system data analytics by advanced ACO for asymmetric capacitated vehicle delivery routing

Author

Listed:
  • Yuan Zhang

    (Shanghai International Studies University
    Chinese Academy of Social Sciences)

  • Yu Yuan

    (Shanghai International Studies University)

  • Kejing Lu

    (Chinese Academy of Social Sciences
    Ningbo University of Finance and Economics)

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.

Suggested Citation

  • Yuan Zhang & Yu Yuan & Kejing Lu, 2020. "RETRACTED ARTICLE: E-commerce information system data analytics by advanced ACO for asymmetric capacitated vehicle delivery routing," Information Systems and e-Business Management, Springer, vol. 18(4), pages 911-929, December.
    • Handle: RePEc:spr:infsem:v:18:y:2020:i:4:d:10.1007_s10257-019-00405-y
    DOI: 10.1007/s10257-019-00405-y
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    References listed on IDEAS

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    1. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
    2. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    3. A A Juan & J Faulin & J Jorba & D Riera & D Masip & B Barrios, 2011. "On the use of Monte Carlo simulation, cache and splitting techniques to improve the Clarke and Wright savings heuristics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1085-1097, June.
    4. İ K Altınel & T Öncan, 2005. "A new enhancement of the Clarke and Wright savings heuristic for the capacitated vehicle routing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 954-961, August.
    5. Yu, Bin & Yang, Zhong-Zhen & Yao, Baozhen, 2009. "An improved ant colony optimization for vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 196(1), pages 171-176, July.
    6. Renaud, Jacques & Boctor, Fayez F., 2002. "A sweep-based algorithm for the fleet size and mix vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 140(3), pages 618-628, August.
    7. A Corominas & A García-Villoria & R Pastor, 2010. "Fine-tuning a parametric Clarke and Wright heuristic by means of EAGH (empirically adjusted greedy heuristics)," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(8), pages 1309-1314, August.
    8. YU, Jie & Subramanian, Nachiappan & Ning, Kun & Edwards, David, 2015. "Product delivery service provider selection and customer satisfaction in the era of internet of things: A Chinese e-retailers’ perspective," International Journal of Production Economics, Elsevier, vol. 159(C), pages 104-116.
    9. Paolo Toth & Daniele Vigo, 1997. "Heuristic Algorithms for the Handicapped Persons Transportation Problem," Transportation Science, INFORMS, vol. 31(1), pages 60-71, February.
    10. Paolo Toth & Daniele Vigo, 1997. "An Exact Algorithm for the Vehicle Routing Problem with Backhauls," Transportation Science, INFORMS, vol. 31(4), pages 372-385, November.
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