Optimization ofWarehouse Operations withGenetic Algorithms

Applied Sciences

卷:10

Sławomir Golak¹  Marcin Blachnik¹  Jan Boryczko²  Miroslaw Kordos² 

[1] Department of Applied Informatics, Silesian University of Technology, 44-100 Katowice, Poland;

[2] Department of Computer Science and Automatics, University of Bielsko-Biała, 43-340 Bielsko-Biała, Poland;

关键词: warehouse optimization;    genetic algorithms;    crossover;   

DOI  :  10.3390/app10144817

来源: DOAJ

【 摘 要 】

We present a complete, fully automatic solution based on genetic algorithms for the
optimization of discrete product placement and of order picking routes in a warehouse. The solution
takes as input the warehouse structure and the list of orders and returns the optimized product
placement, which minimizes the sum of the order picking times. The order picking routes are
optimized mostly by genetic algorithms with multi-parent crossover operator, but for some cases
also permutations and local search methods can be used. The product placement is optimized by
another genetic algorithm, where the sum of the lengths of the optimized order picking routes is
used as the cost of the given product placement. We present several ideas, which improve and
accelerate the optimization, as the proper number of parents in crossover, the caching procedure,
multiple restart and order grouping. In the presented experiments, in comparison with the random
product placement and random product picking order, the optimization of order picking routes
allowed the decrease of the total order picking times to 54%, optimization of product placement with
the basic version of the method allowed to reduce that time to 26% and optimization of product
placement with the methods with the improvements, as multiple restart and multi-parent crossover
to 21%.

【 授权许可】

Unknown