Route Optimization of Automated Warehouse with the Aid of Modified Genetic Algorithms (MGA)

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An important issue faced by many of the organizations, nowadays, is “delivering the product to customers within a short interval of time”.  An automated warehouse optimization is adopted for providing a proof of concept, test warehoused performances with different throughput, optimized layout and storage strategies etc. It is important to conduct a research into the time consumption and efficiency of routes in the automated warehouse system. The great advantages of such systems are those which they can bring in terms of productivity, safety, quality and competitiveness. This paper presents the picking route optimization of automated warehouse optimization with modified Genetic algorithm (MGA), which is a combination of Traveling Salesman Problem (TSP) and Genetic Algorithm. The purpose of TSP is to find complete route among the various routes and the function of GA is to find the optimum route. The overall structure of the proposed method is as follows. Initially we generate nodes in a random manner for finding the complete route, and then apply the input parameters to modified GA by which we get the optimum route with low distance and time. The experimental results expose the optimum route and corresponding time interval of the system.  Moreover, the performance is discussed in detail with the existing system in the working platform form of MATLAB
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Automated Warehouse Optimization; Traveling Salesman Problem (TSP); Genetic Algorithm (GA); Adaptive Genetic Algorithm (AGA)

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