This paper explores the use of a genetic algorithm (GA) to optimise item allocation in a warehouse, with the ultimate purpose of reducing the travel time of pickers, thus streamlining order picking operations. The GA is described along with a numerical example, reflecting a fast moving consumer goods warehouse, where items are assumed to be allocated according to a class-based storage system. Starting from that configuration, and taking into account the set of orders to be fulfilled, the GA identifies a new item allocation, which significantly decreases the travel distance (by approximately 20%). This involves a corresponding decrease in the cost of picking operations, and allows the warehouse to respond quicker to the requests of customers. The GA and its numerical implementation are supported by a general purpose software, such as Microsoft Excel™, programmed under visual basic for applications; the resulting tool is thus easy to use in real scenarios.
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