Total views : 213

Multi Objective Optimization of Heterogeneous Bin Packing using Adaptive Genetic Approach


  • Department of Mechanical Engineering, Vels University, Chennai - 600117, Tamil Nadu, India
  • Lough borough University, Loughborough, United Kingdom


Objectives: The packing of goods in any industry is a tedious work. The proposed system evaluates the optimal packing and prediction of 3D bin packing maximize the maximize profit. Methods/Statistical Analysis: The Adaptive Genetic Algorithm (AGA) is used to solve the 3D single bin packing problem by getting the user input data such as number of bins, its size, shape, weight, and constraints if any along with standard container dimension. These inputs were stored in the database and encoded to string (chromosomes) format which were normally acceptable by AGA. Findings: The performance of the hybrid GA the Tuning algorithm is satisfactory and gives the feasible solution when compared with the other standard search algorithms. The minimum number of boxes left unloaded by using this algorithm will helps to validating the developed bin packing system. The developed Adaptive Genetic Algorithm was validated using the mathematical function. This research work is the good background of further development and analysis in this transportation domain of the following cases- Case 1: Homogenous boxes of same dimensions: all the boxes packed without gap. Case 2: Homogenous boxes of arbitrary dimensions: all the boxes packed with small gaps. Case 3: Homogenous/Heterogeneous boxes of arbitrary dimensions: all the boxes packed with gaps. Application/Improvements: The proposed adaptive genetic approach is very helpful in the logistic industries, especially for cargo packaging for export this is very helpful and can be easily implement any logistic industry.


AGA, Bin Packing, Genetic Approach, Optimization, Tuning Algorithm.

Full Text:

 |  (PDF views: 166)


  • Goldberg DE. Genetic Algorithm in search. Optimization and Machine Learning. USA: Addison Wesley; 1989.
  • Mitchell M. An introduction to Genetic Algorithm.Cambridge: MIT Press; 1996. p. 1–162.
  • Hopper E, Turton B. Application of genetic algorithm to packing problems– A review. London: Springer Verlag; 1997.
  • Korf R. A new algorithm of optimal bin packing. Proceeding of AAAI; 2002. p. 731–6.
  • Gehring H, Bortfeldt A. A genetic algorithm for solving the container loading problem. International Transactions in Operation Research. 1997; 4(5-6):401–18.
  • Dowsland KA, Herbet EA. Using tree search bounds to enhance a genetic algorithm approach to two rectangle packing problems. European Journal of Operation Research.2004; 168(2):390–402.
  • Martello S, Pisinger D. The three dimensional bin packing problem. Operation Research. 2000; 48(2):256–67.
  • Pisinger D. Heuristic for container loading problem. European Journal of Operation Research. 2002; 141(1):292–382.
  • Bischoff EE. Three dimensional packing of items with limited load bearing strength. European Journal of Operation Research. 2004; 168(3):952–66.
  • Bortfeldt A, Gehring H. A hybrid genetic algorithm for container loading problem. European Journal of Operation Research. 2001; 131(1):143–61.
  • Container loading with multi-drop constraint. masters thesis. Informatics and mathematical modeling. Lyngby: Technical University of Denmark, DTU. Available from:
  • Davies AP, Bischoff EE. Weight distribution considerations in container loading. European Journal of Operation Research. 1999; 114(3):509–27.
  • George, George JM. Packing different sized circles into a rectangular container. European Journal of Operation Research. 1995; 84(3):693–712.
  • Kang K, Moon I, Wang H. A hybrid genetic algorithm with a new packing strategy for the three-dimensional bin packing problem. Applied Mathematics and Computation. 2012; 219(3):1287–99.
  • Bortfeldt A. A reduction approach for solving the rectangle packing area minimization problem. European Journal of Operational Research. 2013; 224(3):486–96.
  • Pugazhenthi. Optimization of permutation flow shop with multi-objective criteria. International Journal of Applied Engineering Research. 2013; 8(15):1807–13.
  • Swapna BS, Sivanandam N. A survey on cryptography using optimization algorithms in WSNs. Indian Journal of Science and Technology. 2015; 8(3):216–21.
  • Anita CJ, Ramesh R, Vaishali D. Bio-inspired computational algorithms for improved image steganalysis. Indian Journal of Science and Technology. 2016; 9(10):1–10.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.