Manual Order Picking Research Articles

The Impact of Writing Direction on Order-Picking Performance: Evidence on Diversity and Efficiency in Operations Management

Language system diversity is a source of individual differences. Research on human cognition has established that writing direction influences non-linguistic mental schemata such as spatial orientation. However, there is little empirical evidence of its impact on task performance. We examine whether task performance in manual order-picking is higher when the in-aisle travel direction follows the writing direction of order pickers. We conducted this study in cooperation with a German brick-and-mortar grocery retailer, allowing us to employ a unique real-world data set comprising 3,200,534 storage-location visits by 113 order pickers, 61 of whom had a left-to-right and 52 a right-to-left writing direction. Our statistical analyses suggest that order-picking task performance improves when the in-aisle travel direction follows individual writing direction. This creates a path to diversity-inspired operations management that treats efficiency and the diversity and inclusion of human workers as equally important for optimization.

A hybrid approach integrating genetic algorithm and machine learning to solve the order picking batch assignment problem considering learning and fatigue of pickers

Modeling human behaviors has become increasingly relevant to improving the performance of manual order-picking systems. However, although a vast corpus of literature has recently started to consider the human factors in these systems, several gaps remain uncovered. Specifically, mental and physical human factors, like learning and fatigue, and quantitative and spatial features of picking orders have never been considered jointly to estimate the time a human order picker requires to execute a specific picking mission. Furthermore, little attention has been given to assigning and sequencing orders to pickers to minimize the picking time acting on their individual learning and fatigue characteristics. This study thus proposes a novel approach integrating machine learning and genetic algorithms to solve the problem. A non-linear machine learning-based predictive model has been adopted to predict the picking time of batches of orders based on quantitative and spatial features of batches and learning and fatigue indicators of pickers. These predictions have thus been adopted to guide a genetic algorithm to find the best assignment of future planned batches of orders to pickers. One year of picking data collected from the warehouse of a grocery retailer has been adopted to investigate the potential of the proposed approach. Furthermore, multiple comparisons have been performed. First, the advantages of predicting the batch-picking time with the proposed non-linear model have been compared with predictions executed based on linear models. In addition, an ablation analysis has been performed to investigate the advantages of predicting the batch picking time while simultaneously considering the quantitative and spatial features of batches and the learning and fatigue indicators of pickers. Moreover, the advantages of the proposed batch assignment strategy, which considers learning and fatigue indicators, have been compared with an assignment strategy that does not optimize these elements. Lastly, an explainability analysis of the predictive model has been performed to understand how and how much quantitative and spatial features of batches and learning and fatigue indicators of pickers affect the batch picking time.

Manual Order Picking Route Optimization in Distribution Warehouse of Chain Furniture Retail Enterprise

Manual Order Picking Route Optimization in Distribution Warehouse of Chain Furniture Retail Enterprise

Enhancing Manual Order Picking through a New Metaheuristic, Based on Particle Swarm Optimization

This paper proposes a new metaheuristic algorithm called Particle Swarm-based picking time minimization (Pkt_PSO), ideated for picking time minimization in manual warehouses. As the name suggests, Pkt_PSO is inspired by Particle Swarm Optimization (PSO), and it is specifically designed to minimize the picking time in order case picking contexts. To assess the quality and the robustness of Pkt_PSO, it is compared to five alternative algorithms used as benchmarks. The comparisons are made in nine different scenarios obtained by changing the layout of the warehouse and the length of the picking list. The results of the analysis show that Pkt_PSO has a slower convergence rate and suffers less of early stagnation in local minima; this ensures a more extensive and accurate exploration of the solution space. In fact, the solutions provided by Pkt_PSO are always better (or at least comparable) to the ones found by the benchmarks, both in terms of quality (closeness to the overall best) and reliability (frequency with which the best solution is found). Clearly, as more solutions are explored, the computational time of Pkt_PSO is longer, but it remains compatible with the operational needs of most practical applications.

An Innovative Layout Design and Storage Assignment Method for Manual Order Picking with Respect to Ergonomic Criteria

Background: This paper aims to improve the overall performance of manual warehouse Order Picking (OP) processes by proposing an innovative method for designing a picking area layout, and introducing a storage assignment strategy with respect to ergonomics and workers’ physical fatigue. Methods: The proposed method categorizes the available picking slots based on size and ABC analysis. Ιt takes into consideration a set of ergonomic constraints pertinent to the rack heights and travel distance restrictions for each slot type, leading to the assignment of a location to each slot type based on its individual characteristics. In doing so, the proposed method introduces an innovative ‘flame-shape’ aisle layout. Finally, the products are assigned to their optimal locations, targeting OP time minimization, balanced workload allocation, and ergonomics optimization through a ranking system measuring the ‘difficulty’ of retrieving the products based on their weight, popularity, and slot location. Results: The proposed method led to a productivity rise of 14.9% along with a significant decrease of the ‘difficulty’ index, by 31%. Conclusions: The results prove that a prominent performance improvement can be achieved when both travel distance and manual workload minimization are targeted for determining the picking area layout and storage design.

Intralogistics synchronization in robotic forward-reserve warehouses for e-commerce last-mile delivery

This paper treats intralogistics processing in a typical robotic forward-reserve e-commerce warehouse involving a reserve area with manual order-picking operations and a forward area with robotic parts-to-picker order sorting operations. It is critical to synchronize intralogistics operations between the two areas considering delivery requirements to optimize the performance in terms of makespan and costs. This challenge is formulated as a delivery-driven intralogistics synchronization (DDIS) problem. This paper develops a tailored variable neighborhood search solution method. A series of comprehensive numerical experiments under various scenarios show the superiority and stability of the DDIS as benchmarked with sequential approaches. A substantial reduction can be achieved in both makespan and forward area size, indicating significantly improved intralogistics operational efficiency and space utilization. Managerial insights are also discussed for specific action plans regarding the market size, the labor force, and the trolley configuration.

Potential of Smart Glasses in Manual Order Picking Systems

Full automation of order picking “man-to-goods” workplaces still will not be rational in the near future. Humans will stay central actors and as such, they will determine system’s effectiveness and efficiency. Order picking activities are labour intensive and time-consuming. Smart glasses are one of the possibilities to relieve order-picker’s hands, resulting in reduced time consumption. In the paper, we review scientific literature on use of smart glasses in manual order picking system and outline the opportunities for future research. Available scientific papers on smart glasses topic deal with unique environments characterized by layout, equipment, process, specific combination of technologies etc. Most authors agree that their findings cannot be generalized. There are no studies in the literature on the effects of multiple-hour use of smart eyeglasses on the eyes. Warehouse systems are mostly not identical, even employees have different characteristics and attentiveness to technology. It is proposed to test each system before implementation and try to combine technologies for best results. Different systems tends to different goals, for example fast service or zero errors. The production system’s outputs are also very likely influenced by software and user interface design, which indicates the need for additional research. The growing number of studies certainly have a positive impact on clarifying the unexplored. The area of smart glasses currently represents a great research potential.

Research on the Work-rest Scheduling in the Manual Order Picking Systems to Consider Human Factors

As the status of order picking in the warehousing and distribution system has been raised, the work-rest scheduling of picking becomes particularly important. Although science and technology have developed rapidly, manual picking is still essential and indispensable. However, previous researches focused on the study of the sequencing, ignoring human factors. The paper presents a work-rest schedule model in parts to picker picking system. Two objectives are proposed that include minimizing the picking time and minimizing picking error rate. And workers’ fatigue, workload is taken into account in the manual order picking systems because the fatigue can have a large influence on the picking time and the picking error rate. A genetic algorithm is used to solve a multi-objective optimization problem that the model concerns and looking for a Pareto front as the most effective methods for solving this problem. Once the original data is given, the work-rest scheduling model is built and the work sequence, and the number of breaks are determined to be chosen by decision makers. In addition, a case study of the model is used to confirm that the model is effective and it is necessary to consider the human factor in the picking system.

The effect of worker fatigue on the performance of a bucket brigade order picking system

Order picking (OP) remains a very costly process with a high amount of manual human work. Different management policies have been developed in the past to improve the performance of order picking systems. Among these is the Bucket Brigade (BB), which is a self-organizing concept for manual OP systems with promising impact on the throughput rate. One general problem of manual OP systems is that worker fatigue can become an issue leading to decreased worker performance and an increased risk of injuries. This is one of the reasons why some researchers highlighted the importance of considering human factors in the design and operations of manual OP systems. This paper develops a mathematical model for managing a Bucket Brigade order picking (BBOP) system subject to worker fatigue. Numerical experiments illustrate the behavior of the model and how worker fatigue affects the performance of a BBOP system.

A Distributed Adaptive Coordination Protocol to Enhance Manual Order Picking Operations

This paper proposes a distributed adaptive coordination protocol for efficient management of manual order picking operations in the presence of unexpected delays and process disruptions. In manual order picking operations, multiple pickers work concurrently to retrieve inventory from aisles in response to customer orders. The lack of pickers coordination often leads to congestion incidents and high non-value added time as pickers attempt to locally optimize their routing decisions. In this research, pickers are modeled as a collaborative network in which they share their work and traffic information, and accordingly adjust their routes to avoid congestion and manage aisle competition. An adaptive coordination protocol is developed to enable pickers efficiently reach consensus (i.e., agreement) state on route adjustments. The proposed protocol is compared using simulation against the baseline case, and the experimental results indicate that it minimizes total order picking time and pickers congestion by 10.4% and 62.6%, respectively.

Using Simulation to Analyze Picker Blocking in Manual Order Picking Systems

The rise of the e-commerce practice makes the warehouses be confronted with ever smaller orders that must be met ever faster, often within a 24-h period. This pressures the order picking process as the orders pickers’ workload becomes higher and higher, leading subsequently to congestion in the warehouse and impacting its productivity. It is therefore crucial to determine which order batching and picking policies enhance the performance of order picking activities. This paper carries out an intensive simulation study to examine the performance of different order picking policies with batching in a wide-aisle warehouse with a low-level picker-to-parts system. The performance of the system is measured in terms of total travelled distance, number of collisions between operators (congestion) and order lead times. A full factorial design is set up and the simulation output is statistically analyzed. The results are reported and thoroughly discussed.

Human factors in order picking: a content analysis of the literature

Order picking (OP) is one of the most labour- and time-intensive processes in internal logistics. Over the last decades, researchers have developed various mathematical planning models that help to increase the efficiency of OP systems, for example, by optimising storage assignments or by specifying routes for the order pickers that minimise travel distance in the warehouse. Human characteristics that are often a major determinant of OP system performance have, however, widely been ignored in this stream of research. This paper systematically evaluates the literature on manual OP systems and conducts a content analysis to gain insights into how human factors (HF) have been considered and discussed in the scientific literature. The results of the analysis indicate that management-oriented efficiency criteria dominated prior research on OP, and that there is a clear lack of attention to HF in the design and management of OP systems. This poses an opportunity for research and design of manual OP systems.

Study on the Improvement of the Additional Distance for Sorted-Storage S-Type Manual Order Picking

Study on the Improvement of the Additional Distance for Sorted-Storage S-Type Manual Order Picking

A continuous estimation of distribution algorithm for the online order-batching problem

In manual order-picking systems such as picker-to-parts, order pickers walk through a warehouse in order to pick up articles required by customers. Order batching consists of combining these customer orders into picking orders. In online batching, customer orders arrive throughout the scheduling. This paper considers an online order-batching problem in which the turnover time of all customer orders has to be minimized, i.e., the time period between the arrival time of the customer order and its completion time. A continuous estimation of distribution algorithm-based approach is proposed and developed to solve the problem and implement the solution. Using this approach, the warehouse performance can be noticeably improved with a substantial reduction in the average turnover time of a set of customer orders.

A U-shaped layout for manual order-picking systems

In manual order-picking systems, order pickers walk or ride through a warehouse in order to collect items required by customers. When designing such a warehouse, two conflicting goals have to be considered, namely the maximization of space utilization and the minimization of the average length of the tours on which the requested items are collected. The achievement of these goals is significantly dependent on the layout of the warehouse. Whereas in a traditional warehouse layout, the picking aisles run in parallel to each other, the picking aisles in the warehouse layout presented here are arranged around a U-shaped central aisle. This type of layout, so-called U-shaped layout, provides a kind of compromise solution with respect to the above-mentioned goals. It has recently been implemented for spare-parts warehouses in practice, but has not been studied in the scientific literature so far. In this paper, we present a new routing strategy for the order pickers who operate in such a warehouse, and we derive an analytical expression for the expected tour length per picking order. By comparing this estimation with those of routing schemes for traditional warehouse layouts, it is demonstrated under which conditions such U-shaped layouts allow for operating warehouses more efficiently.

Metaheuristics for the Order Batching Problem in Manual Order Picking Systems

AbstractIn manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations efficiently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem: the first one is based on Iterated Local Search; the second on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods but provide solutions which may allow distribution warehouses to be operated significantly more efficiently.