Making Warehouse Logistics Smart by Effective Placement Strategy Based on Genetic Algorithms

Abstract

Abstract Supply chain executives are faced with the challenge of reducing labor costs. Travel time or picking efficiency can easily account for 50% or more of order picking time. If we omit human factor and the technical equipment of the warehouses, picking efficiency is mostly affected by two factors: correct combining orders into a single travel instance and picking orders in batch is the first factor; the second one is a goods placement – the more effective the goods are located, the shorter will be the picking distance for each order or batch of orders. It means that individual orders will be picked faster. Usually to determine the correct location for the goods 3PL’s are using ABC analysis that includes indicators like count of orders, goods turnover, picking rate, weight etc. There are also more complicated indicators like goods adjacency. Such indicators are harder to take into account using ABC analysis, as it requires sophisticated analysis of customer orders. In recent publication goods placing by results of ABC analysis was compared to the genetic algorithm approach. It was showed that genetic algorithm much more effective for goods placing. The goal of this paper is to improve developed genetic algorithm and include in calculations factors of the labor costs and warehouse topology. These factors will make algorithm usable in real warehouses and WMS (warehouse management system) information systems.