| Thanks to the blooming of contemporary information technology,the competition in business environment is getting tenser.Customers are demanding diversity.It is more and more difficult to gain the advantage in a competition via purely applying new technologies.However,more and more scholars and practitioners started to realize the benefit from reducing the logistics cost by improving the working efficiency.This paper takes a warehouse,in which the operations are mainly handled by manual,as an instance to study.First,we investigate the work flow of those major operations in the warehouse and find out the objective of this study is to shorten the picking route with a smart routing strategy and a considerate storage location assignment strategy.Then build up optimization models according to each of the objectives and solve them.The model for picking route optimization is solved with Genetic Algorithm.In order to improve the quality of the output,the Adaptive Neighborhood method is introduced to GA,which will generate individuals which carrying local or global optimal information and delivering to the population of each generation as immigrants.With these immigrants,the GA is able to converge quickly without losing diversity in the population.Like many warehouse still under development,the warehouse for study is in lack of those data on the key features of the goods stocked in,such as weight and dimension,which is critical to the practicability of the model.To overcome this problem,a 2-step solution comes out.First,all the locations are marked with an ID based on their volume and physical position in the warehouse.Second,define a rule for relocation,which only allows the relocation carried out within those locations with certain ID(s).Finally a simulation is made with the actual orders from business and proves the optimization effective.The result of this study is applicable to those distribution centers,which are mainly run on manual and seeking for performing operation efficiency. |
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