Generalized Objective Function to Ensure Robust Evaluation for Evolutionary Storage Location Assignment Algorithms

Abstract

The efficiency of warehouse operations can be measured by various indicators, but the main one is the lead time, which is heavily influenced by the order picking, as this is the most time- and labor-intensive process in the warehouse operation. In order to reduce lead times, many researchers are working on the topic of Storage Location Assignment Problem (SLAP) The optimized SLA is designed to improve picking efficiency, so that the picker does not have to travel long distances unnecessarily in a picker-to-parts system. During the optimization process, it is necessary to evaluate the SLA in an appropriate way, on the basis of which it is possible to measure whether the objectives are approximated by the results or not. It is also very important to evaluate regularly the SLA during the period after optimization to get an up-to-date information about the assignment of the storage items. The results of regular evaluations can be used to check whether the SLA is effective and lead times are good or whether optimization and reassignment is necessary. Based on studies and experience, SLAs are reassessed and optimized following significant inefficiencies, resulting in relocation tasks and additional work and costs for warehouses. The authors’ research concept includes avoiding large-scale relocation tasks by continuously review the SLA. While other studies evaluate the optimized SLA by running picking lists, but it usually would be necessary to get information about the assignment of the entire warehouse. Furthermore, since assigning thousands of items to thousands of positions is a huge combinational problem, evolutionary algorithm would be necessary to apply. It is also requiring time-effective and generalized individual evolution method to make us possible tactical SLA optimization.

The aim of this paper is to describe a novel generalized SLA evaluation method where each of the located items is evaluated to obtain a more accurate optimization result. Furthermore, unlike other research, the aim is to ensure that the optimization concept and the evaluation method are not only specified for one warehouse but can be used in other warehouses as well.