# Optimization of Warehouse Operations with Genetic Algorithms
## 4 Related Works
### Genetic Algorithms in Warehouse Optimization  
Artificial intelligence methods, in particular genetic algorithms, are solutions that have numerous successful implementations and that have been rapidly gaining popularity in recent years and were applied also to warehouse operation optimizations [12; 13; 14]. Genetic algorithms have two important advantages: fast intelligent search used to find the product placement and a global cost function. Their first advantage is that due to intelligent searching, genetic algorithms do not need to analyze all solutions (all possible permutations of product locations), which is impossible due to their number. They usually analyze only a few thousands up to few hundred thousands of product placements (and not all \(N!\) possibilities) to find the solution. Although genetic algorithms do not guarantee finding the optimal solution every time, the solutions found are close enough to the optimal solution so that in practice this does not make a significant difference. Their second advantage is that using genetic algorithms, we do not have to define ourselves the rules that characterize good solutions. This is a very important, because usually we do not know how to define the rules optimally, and we only have some intuitive knowledge (e.g., goods often purchased together should be close to each other). However, the expression of this knowledge in the strict mathematical form is impossible because of the complexity of the system and the frequently opposite optimums of various order completions. With genetic algorithms it is enough to formulate the cost function, which is expressed here as the sum of all order picking route lengths or as the total time required to complete all orders from a certain period. In genetic algorithms, the problem is coded using arrays called chromosomes by analogy with encoding in the chromosomes of biological organisms [12; 13]. Each product placement encoded by a chromosome represents one solution (one individual). Initially, a pool of random solutions is generated (in each solution the products are randomly assigned to the locations in a warehouse). Then an intelligent search is applied with the help of three basic operations--selection, crossover and mutation. The selection mechanism selects individuals for the crossover operator. It is organized by analogy to the biological process, where the better individuals have a higher probability of becoming parents and exchanging information to create offspring. The crossover operator generates a new individual (child) from the existing ones (parents). In this way it allows to combine the information encoded in the chromosomes of different individuals into one new individual. The mutation mechanism exchanges the values between some positions in an individual chromosome. Then selected individuals are promoted to the next generation. The process is repeated iteratively as long as the satisfactory solution is found or as long as the improvement in the solutions is still occurring. To sum up, it should be stated that the use of genetic algorithms or other evolutionary optimization methods in applications to warehouse systems, including the optimization of the distribution of goods and order picking routes can bring measurable benefits to companies using these solutions, accelerating their work and reducing operating costs. Although the idea of applying genetic algorithms to warehouse optimization or order picking route optimization was presented in some literature positions, we have not found a complete automatic solution, which considers the order distribution, as we present in this paper. As the optimization of the order picking route (which is equivalent to the traveling salesman problem-- see Section 2) is a much simpler problem than the optimization of product placement in the warehouse, as it can be expected, much more papers are dedicated to the order picking route optimization and only few papers discuss the product placement optimization. Below we shortly present some of them.