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.

Achieving optimal operation is a fundamental objective in most production and service systems. The core of this pursuit involves seeking the minimum or maximum values of one or more target functions within specific constraints. For instance, it is common to strive for the minimization of operating costs, lead times, and resource utilization, or the maximization of revenue and coverage. These objectives must be balanced against a range of factors, including market constraints, resource capacities, budgets, and technological conditions. A significant challenge in the quest for optimal operation is the quality and documentation of data. Often, data are neither clean nor well-documented, which complicates the process of finding and implementing optimal operational strategies. This paper focuses on the human element in decision-making processes, acknowledging the limitations inherent in human capabilities when making decisions. The primary aim of this paper is to present a case study that highlights the constraints of human decision-making processes. Through this case study, we examine the impact of human factors on achieving optimal operation in production and service systems. The study provides insights into how human limitations can affect the decision-making process, particularly in the context of imperfect data and the complex interplay of various operational constraints.

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.

Order picking is the most labour and capital intensive warehousing operation whose primary development field is routing optimisation due to its time consuming nature. The Order Picking Routing Problem is a special case of the vehicle routing problem with loading constraints, when the operator visits picking positions and collects items to build transport unit. Where the stacking and stability challenges are relevant during the picking of ordered items and exact routing algorithms are not available, the order picking operators have huge challenges to sequence the order picking list. They should take into consideration several factors by themselves, such as product properties, order picking list characteristics, and order picking system properties. The goal of the proposed research is to support the order picking operators in order to make more objective decisions in decreasing the order picking lead time, building stable transport units, and avoiding product damages, when industrially relevant, but rarely discussed, order picking sequencing based on stacking property is necessary. The paper defines the Order Picking Routing Problem based on Pallet Loading Feature (OPRP-PLF) and presents Bacterial Memetic Algorithm (BMA) based solutions for it, which is compared to Simulated Annealing (SA) algorithms. BMA has already been applied for Travelling Salesman Problem (TSP) but never used for the defined OPRP-PLF. The paper describes several BMA operators, most of them have an alternative which can be completed with SA based decisions. Using the BMA operators with SA methodology is a novelty of the proposed algorithms, which might support a quicker approximation to the global optimum. The possible combination of BMA operators will be evaluated with shorter and longer order picking lists and compared to SA algorithms on the same basis. The simulation results highlight, that allowing unit load reconstruction could decrease the order picking lead time and the developed BMA algorithms are more effective for OPRP-PLF than the SA algorithms. The paper concludes that the SA combined BMA operators are more effective than the SA-less operators in the case of shorter (less than about 20 records) order picking lists. While the shorter lists are the most commonly occurring order picking lists of warehouses, the SA combined BMA operators can increase the effectiveness of the OPRP-PLF optimisation.

Order picking is the most labor-intensive and costly activities in many warehouses by consuming ca. 60 % of the total operating expenses. Order picking development strategies mostly concentrate on warehouse layout, storage assignment policy, routing, zoning and on batching methods, while the physical parameters of the products - which are hardly ever taken into account - do also have a significant impact on the processes. Researchers of the pallet-loading problem could provide a wider horizon on considerable parameters, but their results are rarely implemented into order picking processes. In order to design a successful and optimal order picking system, considering all influential parameters is inevitable, since all of them are strongly connected to each other. This paper introduces an interactive training and modeling tool, which allows us to model, test, analyze and to evaluate order picking algorithms by taking numerous influencing factors into consideration. We describe an application of the simulation environment designed for highlighting the importance of physical product parameters in order picking performance.