###### Abstract  
Order-picking process management is one of the most demanding tasks within the operations of a warehouse system. It is especially evident in companies that have a high intensity of product flows, so the question of increasing the productivity of order picking arises. In this paper, a novel integrated fuzzy MCDM (Multicriteria Decision-Making) model was developed for the evaluation and selection of information technologies for order picking in a warehouse system, which is one of the most important novelties and contributions of the paper. Barcode, pick-to-light, pick-to-voice, and pick-to-vision technologies were evaluated based on IMF SWARA (improved fuzzy stepwise weight assessment ratio analysis) and fuzzy EDAS (evaluation based on distance from average solution) based on Z numbers. IMF SWARA-Z was applied to determine the importance of four criteria while the information technologies for order picking were evaluated with the fuzzy EDAS-Z method. The averaging of the estimates of the critera and alternatives was performed using the fuzzy Dombi aggregator. The results show that in this particular case under these research conditions, pick-to-vision is the best order-picking technology. Subsequently, validation tests were carried out, and they included the simulation of criteria weights and the impact of the reverse rank matrix. aff1 Faculty of Transport and Traffic Engineering, University of East Sarajevo, Vojvode Misica 52, 74000 Doboj, Bosnia and Herzegowina  
2  
Medical Based on Z Numbers for Order Picking in Warehouses  
is reflected in an increased readiness for delivery, shortened delivery time, greater delivery reliability, etc. that the user expects from the supplier of goods, and, at the same time, reduces the total costs of logistics. Such requirements set for storage systems significantly complicate their structure and functioning above all the processes that are carried out in the warehouse [1]. Storage location and order-picking process represent two interrelated procedures that appear in storage systems that need to be solved independently as stated in [2]. Within warehouse systems, the order-picking process represents an extremely important and indispensable component that contributes to a large extent to the overall efficiency of the warehouse. This is confirmed by the fact that the order-picking process is the most resource-intensive process in a warehouse [3], which is largely influenced by the storage location policy applied. Order picking is an activity that significantly defines the quality of service the warehouse provides to users, which in modern business conditions, has special importance. This paper defines a model that deals with the issue of order picking with the aim of increasing the productivity of order pickers. Order picking, as the most common form of handling goods in warehouses, represents the formation of a set of different items that fulfill a certain need. Order-picking costs are often over 50% of the total operating costs of the warehouse while at the same time, 60% of the labor engagement in the warehouse belongs to these processes. Numerous studies [4; 5; 6] have proven that the implementation of order-picking technology significantly affects business efficiency, enables cost reduction, and improves competitive advantage in the market. Today industrial processes need automation for an efficient and well-designed control system while soft computing approaches are most often used in these terms, as shown in [7]. This is one of the motivations and justifications for developing soft tools in our paper. The aim of this paper from a professional aspect is to evaluate order-picking technologies in order to increase the productivity of the activities performed in warehouse systems. The criteria that will be applied to the selection of the order-picking technology are the possibility of implementing the technology, the costs of implementing the technology, and the possibility of improving its own performance and reliability.