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RETRACTED ARTICLE: E-commerce information system data analytics by advanced ACO for asymmetric capacitated vehicle delivery routing

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This article was retracted on 23 November 2022

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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.

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References

  • 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 56:954–961

    Article  Google Scholar 

  • Bae ST, Hwang HS, Cho GS, Goan MJ (2007) Integrated GA-VRP solver for multi-depot system. Comput Ind Eng 53(2):233–240

    Article  Google Scholar 

  • Barbucha D (2014) A cooperative agent-based multiple neighborhood search for the capacitated vehicle routing problem. Recent Adv Knowl-Based Paradig Appl 234:129–143

    Article  Google Scholar 

  • Battarra M, Golden B, Vigo D (2008) Tuning a parametric Clarke–Wright heuristic via a genetic algorithm. J Oper Res Soc 59:1568–1572

    Article  Google Scholar 

  • Choi TM, Chan HK, Yue X (2016) Recent development in big data analytics for business operations and risk management. IEEE Trans Cybern 47(1):81–92

    Article  Google Scholar 

  • Clarke G, Wright J (1964) Scheduling of vehicles from a central depot to a number of delivery points. Oper Res 12:568–581

    Article  Google Scholar 

  • 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 61:1309–1314

    Article  Google Scholar 

  • Dantzig GB, Ramser JH (1959) The truck dispatching problem. Manag Sci 4(6):80–91

    Article  Google Scholar 

  • Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1:53–66

    Article  Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26:29–41

    Article  Google Scholar 

  • Dotan T (2002) How can ebusiness improve customer satisfaction? Case studies in the financial service industry. J Inf Technol Case Appl Res 4(4):22–48

    Google Scholar 

  • Frasch W, Spetzler D, York J, Xiong F (2012) Methods for generating a distribution of optimal solutions to nondeterministic polynomial optimization problems

  • Frei FX (2006) Breaking the trade-off between efficiency and service. Harv Bus Rev 84(11):93–101

    Google Scholar 

  • 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):438–453

    Article  Google Scholar 

  • 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 159:104–116

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 62:1085–1097

    Article  Google Scholar 

  • Ke L, Feng Z (2013) A two-phase metaheuristic for the cumulative capacitated vehicle routing problem. Comput Oper Res 40(2):633–638

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of IEEE international conference on neural networks. IEEE Press, Perth Western, pp 1942–1948

  • Kirk J (1973) Fixed endpoints open traveling salesman problem—genetic algorithm. Acta Univ Carol Med Monogr 56(2):7–20

    Google Scholar 

  • Laporte G, Nobert Y (1983) A branch and bound algorithm for the capacitated vehicle routing problem. Oper Res Spektrum 5(2):77–85

    Article  Google Scholar 

  • 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 48:308–332

    Article  Google Scholar 

  • 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. Springer

  • 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 technology

  • 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 automation

  • 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):145–166

    Article  Google Scholar 

  • Paz A, Moran S (1977) Non-deterministic polynomial optimization problems and their approximation. Theor Comput Sci 15(3):251–277

    Article  Google Scholar 

  • Ralphs TK, Kopman L, Pulleyblank WR, Trotter LE (2003) On the capacitated vehicle routing problem. Math Program 94(2–3):343–359

    Article  Google Scholar 

  • Renaud J, Boctor FF (2002) A sweep based algorithm for the fleet size and mix vehicle routing problem. Eur J Oper Res 140:618–628

    Article  Google Scholar 

  • 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):31–38

    Google Scholar 

  • Stubbs E (2014) Big data, big innovation: enabling competitive differentiation through business analytics. Wiley, New York

    Book  Google Scholar 

  • Tao N, Chen G, Tao N (2012) Solving VRP using ant colony optimization algorithm. Comput Model Eng Sci 83(1):23–55

    Google Scholar 

  • Toth P, Vigo D (1997a) An exact algorithm for the vehicle routing problem with backhauls. Transp Sci 31(4):372–385

    Article  Google Scholar 

  • Toth P, Vigo D (1997b) Heuristic algorithms for the handicapped persons transportation problem. Transp Sci 31(1):60–71

    Article  Google Scholar 

  • Tyworth JE, Joseph LC, John CL (1987) Traffic management: planning, operations, and control. Addison-Wesley, Reading

    Google Scholar 

  • Wang CB, Guo J (2013) A new hybrid algorithm based on artificial fish swarm algorithm and genetic algorithm for VRP. Appl Mech Mater 325–326:1722–1725

    Article  Google Scholar 

  • Yu B, Yang Z, Yao B (2009) An improved ant colony optimization for vehicle routing problem. Eur J Oper Res 196:171–176

    Article  Google Scholar 

  • Zhang Y (2017) Data pre-processing for real-world e-commerce delivery address clustering. Adv Intell Syst Res (AISR) 2017(150):164–168

    Google Scholar 

  • Zhang X, Gong H, Guo Y, Zhang L (2015) Improved chaos particle swarm algorithm on VRP. Comput Digit Eng 43(12):2106–2109

    Google Scholar 

  • Zhou Y, He J, Nie Q (2009) A comparative runtime analysis of heuristic algorithms for satisfiability problems. Artif Intell 173(2):240–257

    Article  Google Scholar 

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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).

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Authors and Affiliations

  1. Xianda College of Economics and Humanities, Shanghai International Studies University, Shanghai, China

    Yuan Zhang & Yu Yuan

  2. Institute of Industrial Economics, Chinese Academy of Social Sciences, Shanghai, China

    Yuan Zhang & Kejing Lu

  3. Ningbo University of Finance and Economics, Zhejiang, China

    Kejing Lu

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  1. Yuan Zhang
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  2. Yu Yuan
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  3. Kejing Lu
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Correspondence to Yu Yuan.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10257-022-00598-9

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Zhang, Y., Yuan, Y. & Lu, K. RETRACTED ARTICLE: E-commerce information system data analytics by advanced ACO for asymmetric capacitated vehicle delivery routing. Inf Syst E-Bus Manage 18, 911–929 (2020). https://doi.org/10.1007/s10257-019-00405-y

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