## 1 Introduction  
Order picking is one of the most studied topics in warehouse management due to the amount of resources involved in the process and its role in warehouses' performance. It is the most labor-intensive, expensive, and time-critical part of a warehouse or distribution center [1]. In fact, it can be viewed as a source of non-value-adding activities, which hinders the distribution and delivery to retailers [2]. It was estimated that 55% of warehouse cost can be attributed to order picking activity [3]. Due to this observation, researchers have sought to establish numerous methods and solutions to optimize the order picking process. The main target is reducing the travel time or travel distance, with or without consideration of human factors [4, 5]. It is important to note that travel time constitutes 50% of the average picking time [6]. However, the most effective way identified to optimize the order picking process is the reduction of inefficiencies linked to human factors and operators' travel time by adopting the right routing policies and technologies. Against this backdrop, this paper contributes to the literature a report of the joint performance of routing policies and picking technologies, and insights on the best ways to combine routing strategies and paperless solutions in order to optimize cost efficiency. Routing policies have a major impact on the efficiency of the picking processes, especially in terms of travel distance and/or time. They are directly affected by pickers' behavior, relatively easy to change, and therefore remain on of the primary sources of management [7]. The role of routing policies is central in achieving this objective as they sequence the items from one picking position to another to ensure an optimal route through the warehouse. In a route, the order picker starts at the depot, picks the sequence of items in the picking list along the established route, and returns to the depot. The position of the depot, however, plays a key role in the route traveled as warehouses can either adopt a centralized or a decentralized depositing. The centralized depositing is the most common. The order picker starts and return to the same depot, which is usually close to the first aisle, while in a decentralized depositing, the order picker may finish a picking route in any aisle and proceed with the new route in the same aisle [8,9]. Routes can be determined based on exact, heuristic, or metaheuristic algorithms. Exact algorithms find the shortest route possible to an order picker routing problem but are highly problem dependent [9]. They are considered to be very complex, illogical, and confusing to the order pickers [10]. Heuristic and metaheuristic algorithms, on the other hand, are applicable to any problem and do not heavily, if at all, rely on the specifics of any problem as they are developed based on a set of guidelines or strategies [11]. Contrary to exact algorithms, heuristic algorithms also provide solutions to a problem in a timely manner. They are the most used, as reported in the literature, because they are easy to implement and understand by the order pickers, and generate tours that are intuitive [5]. In sum, real-life combinatorial optimization problems are more easily tackled with heuristic methods. Routing methods alone do not guarantee optimal routing as human behaviors are involved in and influence the process. Many aspects of human behaviors influence the routing process. Pickers' deviations, for example, may occur when the given route is confusing or illogical. However, pickers who fail to accurately follow the guidelines may block other pickers and cause delay, and so on. The performance of routing methods is, however, influenced differently by human behaviors. For instance, studies have investigated the performance of sophisticated (largest-gap, combined, etc.) and simple routing (S-shape, return, etc.)