Order Picking Research Articles

Towards human-centric warehousing: the impact of rack configuration and cognitive demands on order picking performance

Within warehouse logistics processes, design concepts and management decisions regarding operations systems determine the demands placed on human operators. While the literature proposes that human-centric work system design positively affects the operator, the impact on operator performance remains under-explored. We analyse the case of a German retail warehouse where the shelf numbering follows the pickers' walking direction from right to left. However, all human order pickers working in this system are familiar with a left-to-right writing system due to their schooling and training. Because cognitive psychology proposes that writing system direction impacts spatial orientation in goal-oriented visual search, we are motivated to explore the interplay of rack configuration and human cognitive demands. We ground our mixed-effects model on archival data, including 470,968 storage location visits. Our results indicate that order pickers work 18.7% faster when picking items starting from the left side of the shelf compared to starting their visual search from the right side. Our findings suggest that human search strategies in warehousing operations are influenced by attention templates like writing direction on a similar level as by system design elements of the warehouse itself. This insight underscores the potential for diversified future human-centric design approaches in warehouse logistics.

Labor-Saving in Order Picking- A Real Time Study Advancing Lean Operations

Purpose: Efficient warehouse operations rely heavily on optimizing order picking processes, which are known to constitute a significant portion of overall operational costs. The aim is to enhance operational efficiency and cost-effectiveness by minimizing non-value-added tasks in warehouse operations. Materials and Methods: This study investigates the transition from an Assisted-Picker model to a Solo-Picker model at a prominent 3PL service provider in India. The analysis compares two approaches for picking orders—pairing laborers versus employing a Solo-Picker—using metrics such as travel time and picking time. Findings: Results reveal that while the Assisted-Picker model may initially show faster completion times for individual orders, the Solo-Picker model proves more efficient overall. This is attributed to the elimination of idle time associated with helpers and enhanced labor utilization efficiency. Real-time productivity data collected during the study period demonstrate a remarkable 44% increase in productivity with the Solo-Picker model, resulting in substantial Labor-Savings and significant reductions in operational costs. Implications to Theory, Practice and Policy: This study highlights the critical importance of reevaluating traditional labor allocation strategies in warehouse environments. By identifying and eliminating non-value-added work, warehouses can move closer to lean and efficient supply chain management practices, ultimately enhancing overall productivity and cost-efficiency.

Integrating storage allocation with manual order picking and replenishment operations in a distribution centre

ABSTRACT This paper introduces a mathematical programming formulation and a simulation-based heuristic for the allocation of storage positions to products picked by human operators on man-aboard vehicles traveling through the warehouse of a wholesale company. In this problem, the tactical level of the assignment decisions affects the operational level of the picking process. We propose a simulation-optimisation framework that integrates the two. Our formulation of the storage location assignment problem also handles the constraint according to which a picking position should be paired with a (vertical) replenishment position for a given item. To solve realistic instances, we design an iterated local search (ILS) metaheuristic with an embedded discrete-event simulator (DES) that evaluates the most promising moves at each iteration. The DES allows reproducing the handling operations performed by multiple order pickers under uncertainty, mutual interferences and congestion-related phenomena. Overall, the flexible simulation-optimisation (SO) framework evaluates the operational times and daily productivity of the order picking organisation. Numerical results are presented for real data, under an S-shape picking policy with a skip-and-go rule to deal with lacking items. Under a proper tuning of the ILS parameters, the SO framework allows to achieve a nearly 17% improvement in warehouse productivity.

The Optimization of Picking in Logistics Warehouses in the Event of Sudden Picking Order Changes and Picking Route Blockages

The Optimization of Picking in Logistics Warehouses in the Event of Sudden Picking Order Changes and Picking Route Blockages

Optimizing order picking processes in warehouses: strategies for efficient routing and clustering

The study employed a simulation based on a detailed dataset containing over 307,046 unique order identifiers and 1,050 unique product identifiers. This dataset included information such as order placement dates, product codes, quantities, and precise locations within the warehouse, including coordinates. The simulation modeled the order-picking route using the Single-Picker Routing Problem (SPRP) algorithms to minimize distance and travel time. The methods compared various wave-picking strategies and grouping methods (single-line and multi-line) for their effectiveness.The applied method significantly reduced the travel distance required by the order picker in the warehouse. The key to this optimization was consolidating orders into waves of specific sizes, achieving a fourfold distance reduction for the studied dataset. Additionally, the solution proposed grouping products by location within the warehouse, either in a single aisle or across multiple aisles based on proximity. Although this method often enhances efficiency, it did not in this particular case. However, it was included as it may yield better results with different datasets and further reduce travel distances in the warehouse. The research underscored the critical role of efficient routing and grouping strategies in warehouse operations. Although wave picking significantly reduced travel distances, the effectiveness of clustering strategies depended on the characteristics of the specific dataset, suggesting the need for tailored solutions based on the warehouse layout and the features of the orders. Future research could extend to integrating product volume and weight variations, which may further optimize order-picking strategies.

To walk or not to walk? Designing intelligent order picking warehouses with collaborative robots

Order picking is a physically demanding, time-consuming, and costly process in most warehouses. To make the process more efficient, recently, ride-on autonomous robotic order pick trucks (collaborative robots or cobots) have been introduced, that assist the order picker. The order picker can ride on the cobot to travel large distances, but when picking, the cobot behaves as a robot and moves autonomously to the next stop location. The question is where the order picker should get on the cobot (step-on location) to ride further. Using traditional low-level order pick trucks, the order picker rides to every stop location and steps on the truck every time immediately after depositing the picked item on the pick pallet or roll cage. Although riding the truck may be faster than walking, stepping off/on the truck is time-consuming and also demanding for order pickers as it puts much pressure on the knee joints. The cobots allow reducing both travel time (compared to walking only) and knee flexion (compared to riding only). We determine the benefits of choosing optimal step-on locations by formulating an optimization model to minimize total time, including a penalty on the number of knee flexes of the order picker. Since the problem is computationally intractable for large-sized problems, we propose a dynamic programming approach which finds the shortest path of subproblems in each aisle. We find that the optimal collaboration strategy will decrease total travel time, as well as knee flexion of the order picker. Based on Monte Carlo simulation, our results indicate time savings up to 27.9% for one-block and 26.5% for two-block warehouses compared to a heuristic from practice. Based on the data and working practice we obtained from a retail warehouse, the optimal collaboration strategy can improve current practice between 14.5% and 24.1%.

Optimizing Robotic Mobile Fulfillment Systems for Order Picking Based on Deep Reinforcement Learning.

Robotic Mobile Fulfillment Systems (RMFSs) face challenges in handling large-scale orders and navigating complex environments, frequently encountering a series of intricate decision-making problems, such as order allocation, shelf selection, and robot scheduling. To address these challenges, this paper integrates Deep Reinforcement Learning (DRL) technology into an RMFS, to meet the needs of efficient order processing and system stability. This study focuses on three key stages of RMFSs: order allocation and sorting, shelf selection, and coordinated robot scheduling. For each stage, mathematical models are established and the corresponding solutions are proposed. Unlike traditional methods, DRL technology is introduced to solve these problems, utilizing a Genetic Algorithm and Ant Colony Optimization to handle decision making related to large-scale orders. Through simulation experiments, performance indicators-such as shelf access frequency and the total processing time of the RMFS-are evaluated. The experimental results demonstrate that, compared to traditional methods, our algorithms excel in handling large-scale orders, showcasing exceptional superiority, capable of completing approximately 110 tasks within an hour. Future research should focus on integrated decision-making modeling for each stage of RMFSs and designing efficient heuristic algorithms for large-scale problems, to further enhance system performance and efficiency.

A real‐life study on the value of integrated optimization in order picking operations under dynamic order arrivals

AbstractOptimizing the order picking operations is indispensable for warehouses that promise a high customer service level. While many areas for improvement have been identified and studied in the literature, a large gap remains between academia and practice. To help with closing this gap, we perform a case‐study in collaboration with a spare‐parts warehouse in Belgium. In this study, we optimize the order picking operations of the company, using the actual warehouse layout and real order data. A state‐of‐the‐art online integrated order batching, picker routing and batch scheduling algorithm is adapted to consider multiple real‐life constraints. More specifically, the dynamic arrival of new orders is considered, and a capacity constraint on the sorting installation should be respected. Furthermore, a new waiting strategy is studied in which order pickers can temporarily postpone certain orders, as combining them with possible future order arrivals may allow for more efficient overall picking performance. Finally, the performance of the current operating policy is compared with that of both a seed batching heuristic and our metaheuristic algorithm by use of an ANOVA analysis. The results indicate that the number of order pickers can be reduced by 12.5% if the new optimization algorithm is used, accompanied by an improvement in the offered customer service level.

Optimising Warehouse Order Picking Through An Integrated Harmony Search and Simulated Annealing Algorithm: Application For A Food Packaging Manufacturer

Optimising Warehouse Order Picking Through An Integrated Harmony Search and Simulated Annealing Algorithm: Application For A Food Packaging Manufacturer

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.

Order Picking Optimization in Smart Warehouses With Human–Robot Collaboration

Order Picking Optimization in Smart Warehouses With Human–Robot Collaboration

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.

Comparison of MOEAs in an Optimization-Decision Methodology for a Joint Order Batching and Picking System

This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area.

Balanced Order Batching in Bucket Brigade Order Picking Systems with Non-identical Pickers

Balanced Order Batching in Bucket Brigade Order Picking Systems with Non-identical Pickers

The impact of resource sharing on the design of multi-client warehouses

Third-party logistics (3PL) services are essential in many supply chains and aim at providing flexible and cost-effective transport and warehouse solutions. This study focuses on Warehouse Service Providers (WSPs) with shared, multi-client warehouses. We investigate how the main internal resources should be shared among clients. We focus on labor (order pickers and sorters) and pick storage space (zones) as the main internal resources in the order picking process in such a warehouse. We evaluate different possible resource-sharing configurations and develop a model to find the configuration that can satisfy set service-level agreements at minimum cost. The cost consists of labor and space cost, whereas the service-level requirements are based on the mean and tail of the order-throughput-time distribution. The results show that whether to share the resources or not highly depends on the clients’ characteristics, such as the order demand rate. Generally, for low-demand clients, sharing all the resources is optimal, whereas for high-demand clients, dedicating all resources is preferred. In addition, the decision to share resources depends on the warehouse’s characteristics, similarities between clients’ product and order picking characteristics. The findings of this article provide valuable insights for WSPs to minimize cost, while meeting the service-level requirements of clients by optimally selecting the warehouse configuration. This may, in turn, benefit their clients by offering better pricing policies to them, which can also be a compelling selling point, ultimately contributing to business growth and attracting more clients.

Joint green dynamic order batching and picker routing problem using PSO with global worst experience

With the increasing importance of various countries’ initiatives on reducing carbon emissions (e.g., carbon border adjustment mechanism, CBAM), intelligent order picking systems have assisted electronic retailers in realizing a green supply chain. Previous studies on picker routing operations focused on adopting offline static customer order information to carry out operational decisions. However, in practice, the customer order information is updated dynamically; and high-efficiency warehouse layouts have been increasingly concerned. Therefore, this study creates a mixed-integer programming model for minimizing both the total carbon footprint and the total penalty cost for delayed orders in the joint order batching and picker routing problem with dynamic arriving orders and shipping time constraints in a high-efficiency fishbone warehouse layout. To solve this complex problem, a particle swarm optimization algorithm that integrates the swarm’s previous global and local worst experiences and a migration mechanism is further proposed to increase solution quality and computing efficiency. In addition, a three-dimensional space for the warehouse floorplan and shipping time constraints is designed to divide orders into batches to address the constraints. Theoretical analysis of this algorithm is conducted, and experimental analysis shows that this algorithm finds superior solutions than current practical strategies, to offer a valuable reference for carbon emission reduction in greening warehousing.

A Novel Fulfillment-Focused Simultaneous Assignment Method for Large-Scale Order Picking Optimization Problem in RMFS

A Novel Fulfillment-Focused Simultaneous Assignment Method for Large-Scale Order Picking Optimization Problem in RMFS

A Systematic Literature Review: Design and Control of Warehouse Order Picking in Development of E-commerce Transaction

Order picking is the process of retrieving items from storage to meet a specific customer order, which is known to be the most labor-intensive and costly function among all the warehouse functions. This function is also important in that it has a critical impact on downstream customer service especially in facing the current covid-19 pandemic. This research aims to provide information, description and more comprehensively identifies the design and control of warehouse order picking by using empirical literature review. In this research, the stages are Identification of potential study, Study Selection based on criteria, quality assessment, data extraction, and data analysis. A systematic literature review identified 37 relevant articles for the research purpose. These articles focus on the design and control of warehouse order picking, aiming to enhance e-commerce transactions.

Exploring the performance impact of unit load selection in order picking: evidence from a cold retail supply chain

PurposePrior literature has widely established that the design of storage locations impacts order picking task performance. The purpose of this study is to investigate the performance impact of unit loads, e.g. pallets or rolling cages, utilized by pickers to pack products after picking them from storage locations.Design/methodology/approachAn empirical analysis of archival data on a manual order picking system for deep-freeze products was performed in cooperation with a German brick-and-mortar retailer. The dataset comprises N = 343,259 storage location visits from 17 order pickers. The analysis was also supported by the development and the results of a batch assignment model that takes unit load selection into account.FindingsThe analysis reveals that unit load selection affects order picking task performance. Standardized rolling cages can decrease processing time by up to 8.42% compared to standardized isolated rolling boxes used in cold retail supply chains. Potential cost savings originating from optimal batch assignment range from 1.03% to 39.29%, depending on batch characteristics.Originality/valueThis study contributes to the literature on factors impacting order picking task performance, considering the characteristics of unit loads where products are packed on after they have been picked from the storage locations. In addition, it provides potential task performance improvements in cold retail supply chains.

A new policy for scattered storage assignment to minimize picking travel distances

Various retail and e-commerce companies face the challenge of picking a large number of time-critical customer orders that include both a small number of items and multiple order lines. To reduce the unproductive work time of order pickers, several storage assignment policies have been proposed in the literature and in practice. In case of the scattered storage assignment (SSA) policy individual items are intentionally distributed to multiple positions in the picking area to increase the probability that items belonging to the same order can be picked at nearby positions. In this paper, we examine our recently proposed SSA policy that seeks to minimize the sum of pairwise distances (SPD) between all item positions that belong to the same order, including a drop-off point. We develop an efficient variable neighborhood search (VNS) metaheuristic to solve large instances in a reasonable computation time. We tested our SSA-SPD strategy by implementing a picking algorithm that considers multiple drop-off points and tracks inventory in the meantime. Our results show that our SSA-SPD policy helps reduce picking distances by up to 36% compared to a random scatter policy and 56% compared to a volume-based policy, depending on the number of order lines and drop-off points in the problem instance.