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Multi Objective Optimization of Heterogeneous Bin Packing using Adaptive Genetic Approach
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.
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