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.) heuristics and found that blocking has a stronger negative impact on simple heuristics, particularly the work of [
7,
10], who analyzed the effect of picker blocking on routing heuristics. The authors of Ref. [
7] combined routing strategies between three pickers. The most common combination in practice, the S-shape heuristic, yielded a 10.2% longer throughput time than the best combination while the throughput time for return routing was 31.83% longer as it led to a higher number of blockings. Likewise, Ref. [
10] found that the S-shape led to the longest mean throughput times for most of the combinations investigated because of its lack of flexibility. The midpoint heuristic, in combination with random storage assignment, outperforms the S-shape and return policies. The authors argued that the reason is likely because midpoint policy divides the storage area into two halves and in a random storage assignment, the stock keeping units (SKUs) are almost evenly distributed over the whole storage area.
Blocking can be mostly avoided in warehouses with large aisles, but route deviation always remains a challenge. In practice, route deviation by the order picker is very common. Deviation may occur in two ways, either by skipping an aisle containing items from the picklist or by skipping items in an aisle, or both [
12]. In either case, deviation is a behavioral issue that is detrimental to the efficiency of routing policies. In fact, the literature has shown that deviation leads to underperformed policies and that its negative effects are stronger on certain policies than on others. For instance, Ref. [
12] investigated the effect of deviations from given routes on the efficiency of routing heuristics. The design experiment revealed that the midpoint without deviations outperformed the S-shape and return (without deviations) for orders containing a small number of picks. For a large number of picks, the S-shape outperformed the return and midpoint, assuming no deviations.
To eliminate pickers’ deviations and thus decrease operating cost, Ref. [
12] proposed that training be held and handheld guiding devices be provided to the order pickers to help them adhere to the optimal routing or the predetermined route. However, integrating technologies into the picking system does not guarantee a decrease in operating cost due to picking errors. The authors of Ref. [
13] demonstrated that the introduction of modern technological solutions alone in the warehouse does not successfully eliminate errors. They devised a strategic framework that centers around three aspects of the warehouse: organizational, human, and technological. Unless the warehouse has a clear logistic strategy, well-trained order pickers, and adequate technologies, minimizing the number of errors will be limited. In order words, identical warehouses with the same technologies may differ in picking accuracy. Picking errors are therefore still existent in picker-to-parts order picking systems and may cause additional time to fulfill an order depending on the handheld or paperless technology.
In both practical and laboratory experiments, it has been shown that pickers’ probability to make piking errors also depends on the type of device used and the warehouse configuration. For instance, Ref. [
14] made a comparison of voice, handheld, and paper technologies and found that the use of handheld technologies was associated with lower errors than voice technologies. They concluded that handheld technologies could detect upstream errors, such as receiving, replenishment, or inventory control, better than the voice technology, since voice technologies require selectors to query the computer for additional information, which does not easily happen. The authors of Ref. [
15], on the other hand, found that a low-level picking warehouse favors voice technologies comparative to handheld technologies based on the cost efficiency when they control for error occurrence, while Ref. [
16] rank voice and handheld technologies equally in terms of errors’ interception.
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 picking technologies 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. Although the main focus of the warehouse management resides on picking time reduction or productivity growth, the efficiency at which productivity is achieved is also very crucial due to budget constraint. For example, will a warehouse be better off investing in a voice picking system to increase the picking time and decreasing error occurrence? Considering that decision makers face multiple choices regarding paperless solutions, choosing the right one is proven to be a challenge. The performance of paperless solutions is largely influenced by several factors, including the size of the warehouse, the business activity, and the routing policy. Measuring the effect of routing decisions and technology on order picking cost efficiency is therefore very important.
Thus, the study reported in this paper assessed the joint performance of routing heuristics and paperless picking and their effects on order picking cost efficiency. We assumed that blocking is negligible, and deviation is inexistent due paperless technologies, and therefore focused on the picking errors. Most importantly, this analysis provides a framework to evaluate multiple warehouse configurations given different characteristics and budget constraints. It also offers a way to evaluate the performance of picking technologies and routing heuristics in different warehouse configurations. Finally, we propose a modified cost function of Ref. [
15] to determine the input costs of the order picking systems. The rest of the paper is organized as followed:
Section 2 contains the problem formulation and methodology;
Section 3 deals with experiment’s results and discussions; and
Section 4 concludes the paper.