Modified fruit fly optimization algorithm of logistics storage selection

  • Wen-Tsao Pan
  • Wen-Zhong ZhuEmail author
  • Fei-Xiong Ma
  • Zu-Chang Zhong
  • Xiao-Fang Yuan


From the perspective of the cost analysis of commodity marketing, the logistics cost accounts for about 30% of the final price of commodities, and the cost of storage selection accounts for about 75% of all logistics costs. This means that the management of the operation efficiency of storage is an essential part of logistics management. With four different modified fruit fly optimization algorithms, this study aimed to optimize the logistics storage selection. First, this study selected 10, 20, and 30 freight sections in a random manner; second, four fruit fly optimization algorithms were used to create some populations and fruit flies, which were taken as freight section spots; third, the algorithms were adopted to seek the logistics storage selection that costs the shortest time. According to analysis of the result, the chaos fruit fly algorithm, out of the four, was the one that took the shortest time and created the greatest effect when 10, 20, or 30 freight sections were considered. The 100 repetitious experiments also demonstrated that the chaos fruit fly algorithm cost the shortest time in the selection, and had the lowest time value variable.


Logistics cost Selection Fruit fly algorithm Chaos fruit fly optimization algorithm 


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  1. 1.
    Hu YH, Huang SY, Chen CY (2005) Travel time analysis of a new automated storage and retrieval system. Comput Oper Res 32(6):1515–1544CrossRefzbMATHGoogle Scholar
  2. 2.
    Potrc I, Lerher T, Kramberger J (2004) Simulation of multi-shuttle automated storage and retrieval systems. J Mater Technol 157:236–244CrossRefGoogle Scholar
  3. 3.
    Gagliardi JP, Renaud J, Ruiz A (2012) Models for automated storage and retrieval systems: a literature review. Int J Prod Res 50(24):7110–7125CrossRefGoogle Scholar
  4. 4.
    Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl-Based Syst 26:69–74CrossRefGoogle Scholar
  5. 5.
    Wang L, Shi Y, Liu S (2015) An improved fruit fly optimization algorithm and its application to joint replenishment problems. Expert Syst Appl 42(9):4310–4323CrossRefGoogle Scholar
  6. 6.
    Mousavi SM, Alikar N, Niaki STA, Bahreininejad A (2015) Optimizing a location allocation-inventory problem in a two-echelon supply chain network: a modified fruit fly optimization algorithm. Comput Ind Eng 87:543–560CrossRefGoogle Scholar
  7. 7.
    Li CQ, Xu SP, Li W, Hu LG (2012) A novel modified fly optimization algorithm for designing the self-tuning proportional integral derivative controller. J Convergence Inf Technol 7(16):69–77CrossRefGoogle Scholar
  8. 8.
    Han JY, Liu CZ (2013) Fruit fly optimization algorithm with adaptive mutation. Appl Res Comput\ 30(9):2641–2644Google Scholar
  9. 9.
    Yuan XF, Liua YM, Xianga YZ, Yan XG (2015) Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm. Appl Math Comput 268:1267–1281MathSciNetGoogle Scholar
  10. 10.
    Ning X, Hu H (2014) Multiple-population fruit fly optimization algorithm for scheduling problem of order picking operation in automatic warehouse. J Lanzhou Jiaotong Univ 33(3):108–113Google Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  • Wen-Tsao Pan
    • 1
  • Wen-Zhong Zhu
    • 1
    Email author
  • Fei-Xiong Ma
    • 1
  • Zu-Chang Zhong
    • 1
  • Xiao-Fang Yuan
    • 2
  1. 1.School of BusinessGuangdong University of Foreign StudiesGuangzhouChina
  2. 2.College of Electrical and Information EngineeringHunan UniversityChangshaChina

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