## 1 Introduction
The literature has also proposed multiple new technologies to further improve productivity and accuracy during the order picking process. Although many of those technologies are still in the laboratory phase, they show promising results in terms of error reduction and productivity improvement. One of the proposed systems is the head-up display (HUD). In this system, the picker wears an HUD that contains the pick charts needed for each shelving unit. The next pick chart is shown when the picker drops items into the order bin. In all of the surveyed literature, the pick-by-HUD system surpassed pick-by-light and pick-by-paper for all the metrics analyzed. In terms of average task time, Ref. [17; 18; 19] reveal that the pick-by-HUD method was faster than the pick-by-light and the pick-by-paper methods. Likewise, the pick-by-HUD method was more accurate than all other methods considered. Overall, the pick-by-HUD method can significantly improve the performance of order picking processes. Some other studies have also explored the joint role of picker personality and the use of picking technologies. They found that picker's personality has a significant effect on picking errors and throughput and that those effects vary with picking technology, such as voice picking and RF-terminal picking. For example, Ref. [20] showed that extraversion and neuroticism relate to picking errors, but the effect is dependent on the picking technology employed. They also found that voice picking is significantly more productive than RF-terminal and produce on average 21.4% less errors. The authors of Ref. [21] reported on the acceptance of order picking support systems, such as pick-by-vision, pick-by-voice, or pick-by-light. All those systems outperform the paper-based system in terms of accuracy and productivity. However, it was revealed that adopting those technologies is not easy based on the perception of warehouse workers. In their consolidated review, Ref. [21] found that there are seven barriers to order picking support systems adoption: (1) an overwhelmingly high subjective task load, (2) loss of autonomy, (3) loss of social interaction, (4) negative influences from co-workers, (5) high complexity in handling the technology, (6) a lack of training, and (7) a lack of maturity of the technology. However, these may not be the only barriers to adoption. Implementation cost can also limit the adoption of paperless picking. Therefore, our study particularly considers the cost efficiency of paperless technologies with regard to picking accuracy or picking errors. The literature has identified two kinds of picking errors: detectable and propagating [15; 22]. The latter directly involved the distance or time travelled by the picker, since when a wrong item or quantity is picked, the picker has to travel back to the storage location to correct the error. The distance travelled is therefore associated with the routing method used, and with the picking technology adopted due to errors' occurrence. As the selected literature shows, routing methods and pickingtechnologies differ in terms of performance. However, the joint performance of picking technology and routing methods is still unknown. Especially, how the joint performance of routing heuristics and paperless picking affect the cost efficiency of the order picking system is lacking in the literature. Therefore, this paper aims to help fill this gap. Based on the selected literature, the S-shape or traversal is the most frequently used in practice [23]. This is primarily due to its intuitive nature to order pickers [5], and its route is less likely to be influenced by pickers' behavior, such as blocking. Therefore, in this study, our focus is on evaluating and comparing the performance of S-shape, return, and midpoint heuristics. Furthermore, the order picking system is a highly cost-driven process involving labor and equipment costs.