In the low-level picker-to-parts warehouses the order picking is the most time- and cost consuming process. The performance indicator for order picking is lead time. The system can be considered efficient if these lead times can be kept low, but this is heavily influenced by the storage location assignment in the warehouse, the routing, and the warehouse layout. The objective of this research is to investigate in what cases and to what extent reassignment and repositioning tasks following efficiency deterioration as well as seek answers to how to minimise these costly tasks and maintain a near-ideal storage location assignment. To solve this problem, an intelligent system concept is presented, which aims to support the warehouse operator in making replenishment decisions, which picking storage to replenish based on the current rotation of item, and which products to repositioning, while maintaining a near ideal storage location assignment. The aim of this paper is to highlight the potential decision points and circumstances, when adaptive storage location reassignment would be necessary and how this concept can help everyday warehouse logistics.

Recognized as an NP-hard combinatorial challenge, Storage Location Assignment Problem (SLAP) demands heuristic or algorithmic solutions for effective optimization. This paper specifically examines the enhancement of SLAP through the utilization of evolutionary algorithms, as they are particularly suitable for complex cases. Among others, the genetic algorithm (GA) is typically applied to solve this problem. This paper investigates the Bacterial Memetic Algorithm (BMA) as a possible solution for optimization. Though the comparative analysis of the BMA with the previously well-used GA algorithm under certain test parameters reveals that BMA is suitable for SLA optimization, BMA failed to achieve better results. We attribute the unsatisfactory results to the parameter settings, as illustrated by a few specific examples. However, the complexity of the problem and the parameterization does not allow for continuous manual parameter adjustment, which is why we have identified the need for a concept that automatically and adaptively adjusts the parameter settings based on the statistics and fitness values obtained during the execution. The novelty of this paper is to specify the concept of adaptive BMA parameterization and rules.

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.