Automated Order Picking Systems and the Links between Design and Performance: A Systematic Literature Review

With new market developments and e-commerce, there is an increased use of and interest in automation for order picking. This paper presents a systematic review and content analysis of the literature. It has the purpose of understanding the relevant performance aspects for automated, or partly automated, OPSs and identifying the studied links between design and performance, i.e. identifying which combinations of design aspects and performance aspects have been studied in previous research. For this purpose, 74 papers were selected and reviewed. From the review, it is clear that there has been an increased number of papers dealing with the performance of automated, or partly automated, OPSs in recent years. Moreover, there are differences between the different OPS types, but, overall, the performance categories of throughput, lead time, and operational efficiency have received the most attention in the literature. The paper identifies links between design and performance that have been studied, as well as links that appear to be under-researched. For academics, this paper synthesises the current knowledge on the performance of automation in OPSs and identifies opportunities for future research. For practitioners, the paper provides knowledge that can support the decision-making process of automation in OPSs

On the performance of robotic parts-to-picker order picking systems

Order picking is the activity in which a number of items are retrieved from a warehousing system to satisfy a number of customer orders. Automating order picking systems has become a common response to the wide variety of products and components stored in today’s warehouses and the short delivery lead times requested by today’s customers. As a result, new technical solutions have reached the market, including robotic parts-to-picker order picking systems such as robot-based compact storage and retrieval systems (RCSRSs) and robotic mobile fulfilment systems (RMFSs).\ua0Despite the increased use of robotic parts-to-picker order picking systems, knowledge about how they perform in terms of throughput, order lead time, human factors, quality, flexibility, operational efficiency, and investment and operational costs needs to be further developed, as does knowledge about how their performance is affected by the order picking system’s design and context. Accordingly, the purpose of this thesis is to expand knowledge about the performance of robotic parts-to-picker order picking systems by investigating how their design and context influence their performance. \ua0The thesis is built upon three studies: a systematic literature review study focusing on automated order picking systems, a multiple-case study on RCSRSs, and a single-case study on RMFSs. First, the systematic literature review study on the performance of automated order picking systems provides an overview of literature on order picking systems to date, aspects of their performance, and how their performance relates to their design. Second, the multiple-case study sheds light on characteristics of the performance of RCSRSs and the relationships between their performance and design. Third and last, the single-case study affords insights on how the context of RMFSs affects their performance.\ua0The thesis contributes to practice by providing guidance to decision makers within industry in terms of the performance to expect of robotic parts-to-picker OPSs depending on their design and context. In turn, such knowledge can facilitate the selection and design of an OPS or else the redesign of a current system. At the same time, the thesis contributes to theory by providing a synthesis of literature addressing the performance of automated OPSs and by outlining the relationships between their design and performance

Optimization of storage and picking systems in warehouses

La croissance du commerce électronique exige une hausse des performances des systèmes d'entreposage, qui sont maintenant repensés pour faire face à un volume massif de demandes à être satisfait le plus rapidement possible. Le système manuel et le système à robots mobile (SRM) sont parmi les plus utilisés pour ces activités. Le premier est un système centré sur l'humain pour réaliser des opérations complexes que les robots actuels ne peuvent pas effectuer. Cependant, les nouvelles générations de robots autonomes mènent à un remplacement progressif par le dernier pour augmenter la productivité. Quel que soit le système utilisé, plusieurs problèmes interdépendants doivent être résolus pour avoir des processus de stockage et de prélèvement efficaces. Les problèmes de stockage concernent les décisions d'où stocker les produits dans l'entrepôt. Les problèmes de prélèvement incluent le regroupement des commandes à exécuter ensemble et les itinéraires que les cueilleurs et les robots doivent suivre pour récupérer les produits demandés. Dans le système manuel, ces problèmes sont traditionnellement résolus à l'aide de politiques simples que les préparateurs peuvent facilement suivre. Malgré l'utilisation de robots, la même stratégie de solution est répliquée aux problèmes équivalents trouvés dans le SRM. Dans cette recherche, nous étudions les problèmes de stockage et de prélèvement rencontrés lors de la conception du système manuel et du SRM. Nous développons des outils d'optimisation pour aider à la prise de décision pour mettre en place leurs processus, en améliorant les mesures de performance typiques de ces systèmes. Certains problèmes traditionnels sont résolus avec des techniques améliorées, tandis que d'autres sont intégrés pour être résolus ensemble au lieu d'optimiser chaque sous-système de manière indépendante. Nous considérons d'abord un système manuel avec un ensemble connu de commandes et intégrons les décisions de stockage et de routage. Le problème intégré et certaines variantes tenant compte des politiques de routage communes sont modélisés mathématiquement. Une métaheuristique générale de recherche de voisinage variable est présentée pour traiter des instances de taille réelle. Des expériences attestent de l'efficience de la métaheuristique proposée par rapport aux modèles exacts et aux politiques de stockage communes. Lorsque les demandes futures sont incertaines, il est courant d'utiliser une stratégie de zonage qui divise la zone de stockage en zones et attribue les produits les plus demandés aux meilleures zones. Les tailles des zones sont à déterminer. Généralement, des dimensions arbitraires sont choisies, mais elles ignorent les caractéristiques de l'entrepôt et des demandes. Nous abordons le problème de dimensionnement des zones pour déterminer quels facteurs sont pertinents pour choisir de meilleures tailles de zone. Les données générées à partir de simulations exhaustives sont utilisées pour trainer quatre modèles de régression d'apprentissage automatique - moindres carrés ordinaire, arbre de régression, forêt aléatoire et perceptron multicouche - afin de prédire les dimensions optimales des zones en fonction de l'ensemble de facteurs pertinents identifiés. Nous montrons que tous les modèles entraînés suggèrent des dimensions sur mesure des zones qui performent meilleur que les dimensions arbitraires couramment utilisées. Une autre approche pour résoudre les problèmes de stockage pour le système manuel et pour le SRM considère les corrélations entre les produits. L'idée est que les produits régulièrement demandés ensemble doivent être stockés près pour réduire les coûts de routage. Cette politique de stockage peut être modélisée comme une variante du problème d'affectation quadratique (PAQ). Le PAQ est un problème combinatoire traditionnel et l'un des plus difficiles à résoudre. Nous examinons les variantes les plus connues du PAQ et développons une puissante métaheuristique itérative de recherche tabou mémétique en parallèle capable de les résoudre. La métaheuristique proposée s'avère être parmi les plus performantes pour le PAQ et surpasse considérablement l'état de l'art pour ses variantes. Les SRM permettent de repositionner facilement les pods d'inventaire pendant les opérations, ce qui peut conduire à un processus de prélèvement plus économe en énergie. Nous intégrons les décisions de repositionnement des pods à l'attribution des commandes et à la sélection des pods à l'aide d'une stratégie de prélèvement par vague. Les pods sont réorganisés en tenant compte du moment et de l'endroit où ils devraient être demandés au futur. Nous résolvons ce problème en utilisant la programmation stochastique en tenant compte de l'incertitude sur les demandes futures et suggérons une matheuristique de recherche locale pour résoudre des instances de taille réelle. Nous montrons que notre schéma d'approximation moyenne de l'échantillon est efficace pour simuler les demandes futures puisque nos méthodes améliorent les solutions trouvées lorsque les vagues sont planifiées sans tenir compte de l'avenir. Cette thèse est structurée comme suit. Après un chapitre d'introduction, nous présentons une revue de la littérature sur le système manuel et le SRM, et les décisions communes prises pour mettre en place leurs processus de stockage et de prélèvement. Les quatre chapitres suivants détaillent les études pour le problème de stockage et de routage intégré, le problème de dimensionnement des zones, le PAQ et le problème de repositionnement de pod. Nos conclusions sont résumées dans le dernier chapitre.The rising of e-commerce is demanding an increase in the performance of warehousing systems, which are being redesigned to deal with a mass volume of demands to be fulfilled as fast as possible. The manual system and the robotic mobile fulfillment system (RMFS) are among the most commonly used for these activities. The former is a human-centered system that handles complex operations that current robots cannot perform. However, newer generations of autonomous robots are leading to a gradual replacement by the latter to increase productivity. Regardless of the system used, several interdependent problems have to be solved to have efficient storage and picking processes. Storage problems concern decisions on where to store products within the warehouse. Picking problems include the batching of orders to be fulfilled together and the routes the pickers and robots should follow to retrieve the products demanded. In the manual system, these problems are traditionally solved using simple policies that pickers can easily follow. Despite using robots, the same solution strategy is being replicated to the equivalent problems found in the RMFS. In this research, we investigate storage and picking problems faced when designing manual and RMFS warehouses. We develop optimization tools to help in the decision-making process to set up their processes and improve typical performance measures considered in these systems. Some classic problems are solved with improved techniques, while others are integrated to be solved together instead of optimizing each subsystem sequentially. We first consider a manual system with a known set of orders and integrate storage and routing decisions. The integrated problem and some variants considering common routing policies are modeled mathematically. A general variable neighborhood search metaheuristic is presented to deal with real-size instances. Computational experiments attest to the effectiveness of the metaheuristic proposed compared to the exact models and common storage policies. When future demands are uncertain, it is common to use a zoning strategy to divide the storage area into zones and assign the most-demanded products to the best zones. Zone sizes are to be determined. Commonly, arbitrary sizes are chosen, which ignore the characteristics of the warehouse and the demands. We approach the zone sizing problem to determine which factors are relevant to choosing better zone sizes. Data generated from exhaustive simulations are used to train four machine learning regression models - ordinary least squares, regression tree, random forest, and multilayer perceptron - to predict the optimal zone sizes given the set of relevant factors identified. We show that all trained models suggest tailor-made zone sizes with better picking performance than the arbitrary ones commonly used. Another approach to solving storage problems, both in the manual and RMFS, considers the correlations between products. The idea is that products constantly demanded together should be stored closer to reduce routing costs. This storage policy can be modeled as a quadratic assignment problem (QAP) variant. The QAP is a traditional combinatorial problem and one of the hardest to solve. We survey the most traditional QAP variants and develop a powerful parallel memetic iterated tabu search metaheuristic capable of solving them. The proposed metaheuristic is shown to be among the best performing ones for the QAP and significantly outperforms the state-of-the-art for its variants. The RMFS allows easy repositioning of inventory pods during operations that can lead to a more energy-efficient picking process. We integrate pod repositioning decisions with order assignment and pod selection using a wave picking strategy such that pods are parked after being requested considering when and where they are expected to be requested next. We solve this integrated problem using stochastic programming considering the uncertainty about future demands and suggest a local search matheuristic to solve real-size instances. We show that our sample average approximation scheme is effective to simulate future demands since our methods improve solutions found when waves are planned without considering the future demands. This thesis is structured as follows. After an introductory chapter, we present a literature review on the manual and RMFS, and common decisions made to set up their storage and picking processes. The next four chapters detail the studies for the integrated storage and routing problem, the zone sizing problem, the QAP, and the pod repositioning problem. Our findings are summarized in the last chapter

Design and optimization of an explosive storage policy in internet fulfillment warehouses

This research investigates the warehousing operations of internet retailers. The primary physical process in internet retail is fulfillment, which typically involves a large internet fulfillment warehouse (IFW) that has been built and designed exclusively for online sales and an accompanying parcel delivery network. Based on observational studies of IFW operations at a leading internet retailer, the investigations find that traditional warehousing methods are being replaced by new methods which better leverage information technology and efficiently serve the new internet retail driven supply chain economy. Traditional methods assume a warehouse moves bulk volumes to retail points where the bulks get broken down into individual items and sold. But in internet retail all the middle elements of a supply chain are combined into the IFW. Specifically, six key structural differentiations between traditional and IFW operations are identified: (i) explosive storage policy (ii) very large number of beehive storage locations (iii) bins with commingled SKUs (iv) immediate order fulfillment (v) short picking routes with single unit picks and (vi) high transaction volumes with total digital control. In combination, these have the effect of organizing the entire IFW warehouse like a forward picking area. Several models to describe and control IFW operations are developed and optimized. For IFWs the primary performance metric is order fulfillment time, the interval between order receipt and shipment, with a target of less than four hours to allow for same day shipment. Central to achieving this objective is an explosive storage policy which is defined as: An incoming bulk SKU is exploded into E storage lots such that no lot contains more than 10% of the received quantity, the lots are then stored in E locations anywhere in the warehouse without preset restrictions. The explosion ratio Ψo is introduced that measures the dispersion density, and show that in a randomized storage warehouse Ψoo\u3e0.40. Specific research objectives that are accomplished: (i) Develope a descriptive and prescriptive model for the control of IFW product flows identifying control variables and parameters and their relationship to the fulfillment time performance objective, (ii) Use a simulation analysis and baseline or greedy storage and picking algorithms to confirm that fulfillment time is a convex function of E and sensitive to Ǩ, the pick list size. For an experimental problem the fulfillment time decrease by 7% and 16% for explosion ratios ranging between Ψo=0.1 and 0.8, confirming the benefits of an explosive strategy, (iii) Develope the Bin Weighted Order Fillability (BWOF) heuristic, a fast order picking algorithm which estimates the number of pending orders than can be filled from a specific bin location. For small problems (120 orders) the BWOF performes well against an optimal assignment. For 45 test problems the BWOF matches the optimal in 28 cases and within 10% in five cases. For the large simulation experimental problems the BWOF heuristic further reduces fulfillment time by 18% for Ǩ =13, 27% for Ǩ =15 and 39% for Ǩ =17. The best fulfillment times are achieved at Ψo=0.5, allowing for additional benefits from faster storage times and reduced storage costs

Correlated storage assignment approach in warehouses: A systematic literature review

Purpose: Correlation-based storage assignment approach has been intensively explored during the last three decades to improve the order picking efficiency. The purpose of this study is to present a comprehensive assessment of the literature about the state-of-the-art techniques used to solve correlated storage location assignment problems (CSLAP). Design/methodology/approach: A systematic literature review has been carried out based on content analysis to identify, select, analyze, and critically summarize all the studies available on CSLAP. This study begins with the selection of relevant keywords, and narrowing down the selected papers based on various criteria. Findings: Most correlated storage assignment problems are expressed as NP-hard integer programming models. The studies have revealed that CSLAP is evaluated with many approaches. The solution methods can be mainly categorized into heuristic approach, meta-heuristic approach, and data mining approach. With the advancement of computing power, researchers have taken up the challenge of solving more complex storage assignment problems. Furthermore, applications of the models developed are being tested on actual industry data to comprehend the efficiency of the models. Practical implications: The content of this article can be used as a guide to help practitioners and researchers to become adequately knowledgeable on CSLAP for their future work. Originality/value: Since there has been no recent state-of-the-art evaluation of CSLAP, this paper fills that need by systematizing and unifying recent work and identifying future research scopes

Material handling optimization in warehousing operations

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2018-2019.Les activités de distribution et d’entreposage sont des piliers importants de la chaîne d’approvisionnement. Ils assurent la stabilité du flux de matières et la synchronisation de toutes les parties prenantes du réseau. Un centre de distribution (CD) agit comme un point de découplage entre l’approvisionnement, la production et les ventes. La distribution comprend un large éventail d’activités visant à assurer la satisfaction de la demande. Ces activités passent de la réception au stockage des produits finis ou semi-finis, à la préparation des commandes et à la livraison. Les opérations d’un CD sont maintenant perçues comme des facteurs critiques d’amélioration. Elles sont responsables de la satisfaction d’un marché en évolution, exigeant des délais de livraison toujours plus rapides et plus fiables, des commandes exactes et des produits hautement personnalisés. C’est pourquoi la recherche en gestion des opérations met beaucoup d’efforts sur le problème de gestion des CDs. Depuis plusieurs années, nous avons connu de fortes avancées en matière d’entreposage et de préparation de commandes. L’activité de préparation de commandes est le processus consistant à récupérer les articles à leur emplacement de stockage afin d’assembler des commandes. Ce problème a souvent été résolu comme une variante du problème du voyageur de commerce, où l’opérateur se déplace à travers les allées de l’entrepôt. Cependant, les entrepôts modernes comportent de plus en plus de familles de produits ayant des caractéristiques très particulières rendant les méthodes conventionnelles moins adéquates. Le premier volet de cette thèse par articles présente deux importants et complexes problèmes de manutention des produits lors de la préparation des commandes. Le problème de préparation des commandes a été largement étudié dans la littérature au cours des dernières décennies. Notre recherche élargit le spectre de ce problème en incluant un ensemble de caractéristiques associées aux installations physiques de la zone de prélèvement, comme les allées étroites, et aux caractéristiques des produits (poids, volume, catégorie, fragilité, etc.). Une perspective plus appliquée à la réalité des opérations est utilisée dans notre développement d’algorithmes. Les déplacements liés à la préparation des commandes sont fortement influencés par le positionnement des produits. La position des produits dans la zone de prélèvement est déterminée par une stratégie d’affectation de stockage (storage assignment strategy). Beaucoup de ces stratégies utilisent de l’information sur les ventes des produits afin de faciliter l’accès aux plus populaires. Dans l’environnement concurrentiel d’aujourd’hui, la durée de vie rentable d’un produit peut être relativement courte. Des promotions peuvent également être faites pour pousser différents produits sur le marché. Le positionnement fourni par la stratégie d’hier ne sera probablement plus optimal aujourd’hui. Il existe plusieurs études mesurant l’impact d’une bonne réaffectation de produits sur les opérations de prélèvement. Cependant, ils étudient la différence des performances avec les positionnements passés et actuels. La littérature démontre clairement que cela apporte des avantages en termes d’efficacité. Toutefois, les déplacements nécessaires pour passer d’une position à une autre peuvent constituer une activité très exigeante. Ceci constitue le second volet de cette thèse qui présente des avancées intéressantes sur le problème de repositionnement des produits dans la zone de prélèvement. Nous présentons le problème de repositionnement des produits sous une forme encore peu étudiée aux meilleurs de nos connaissances : le problème de repositionnement. Plus précisément, nous étudions la charge de travail requise pour passer d’une configuration à l’autre. Cette thèse est structuré comme suit. L’introduction présente les caractéristiques et les missions d’un système de distribution. Le chapitre 1 fournit un survol de la littérature sur les principales fonctions d’un centre de distribution et met l’accent sur la préparation des commandes et les décisions qui affectent cette opération. Le chapitre 2 est consacré à l’étude d’un problème de préparation de commandes en allées étroites avec des équipements de manutention contraignants. Dans le chapitre 3, nous étudions un problème de préparation des commandes où les caractéristiques des produits limitent fortement les routes de prélèvement. Le chapitre 4 présente une variante du problème de repositionnement (reassignment) avec une formulation originale pour le résoudre. La conclusion suit et résume les principales contributions de cette thèse. Mots clés : Préparation des commandes, entreposage, problèmes de routage, algorithmes exacts et heuristiques, réaffectation des produits, manutention.Distribution and warehousing activities are important pillars to an effective supply chain. They ensure the regulation of the operational flow and the synchronization of all actors in the network. Hence, distribution centers (DCs) act as crossover points between the supply, the production and the demand. The distribution includes a wide range of activities to ensure the integrity of the demand satisfaction. These activities range from the reception and storage of finished or semi-finished products to the preparation of orders and delivery. Distribution has been long seen as an operation with no or low added value; this has changed, and nowadays it is perceived as one of the critical areas for improvement. These activities are responsible for the satisfaction of an evolving market, requiring ever faster and more reliable delivery times, exact orders and highly customized products. This leads to an increased research interest on operations management focused on warehousing. For several years, we have witnessed strong advances in warehousing and order picking operations. The order picking activity is the process of retrieving items within the storage locations for the purpose of fulfilling orders. This problem has long been solved as a variant of the travelling salesman problem, where the order picker moves through aisles. However, modern warehouses with more and more product families may have special characteristics that make conventional methods irrelevant or inefficient. The first part of this thesis presents two practical and challenging material handling problems for the order picking within DCs. Since there are many research axes in the field of warehousing operations, we concentrated our efforts on the order picking problem and the repositioning of the products within the picking area. The order picking problem has been intensively studied in the literature. Our research widens the spectrum of this problem by including a set of characteristics associated with the physical facilities of the picking area and characteristics of the product, such as its weight, volume, category, fragility, etc. This means that a more applied perspective on the reality of operations is used in our algorithms development. The order picking workload is strongly influenced by the positioning of the products. The position of products within the picking area is determined by a storage assignment strategy. Many of these strategies use product sales information in order to facilitate access to the most popular items. In today’s competitive environment, the profitable lifetime of a product can be relatively short. The positioning provided by yesterday’s assignment is likely not the optimal one in the near future. There are several studies measuring the impact of a good reassignment of products on the picking operations. However, they study the difference between the two states of systems on the picking time. It is clear that this brings benefits. However, moving from one position to another is a very workload demanding activity. This constitutes the second part of this thesis which presents interesting advances on the repositioning of products within the picking area. We introduce the repositioning problem as an innovative way of improving performance, in what we call the reassignment problem. More specifically, we study the workload required to move from one setup to the next. This thesis is structured as follows. The introduction presents the characteristics and missions of a distribution system. Chapter 1 presents an overview of the literature on the main functions of a DC and emphasizes on order picking and decisions affecting this operation. Chapter 2 is devoted to the study of a picking problem with narrow aisles facilities and binding material handling equipment. In Chapter 3, we study the picking problem with a set of product features that strongly constrain the picking sequence. Chapter 4 presents a variant of the reassignment problem with a strong and new formulation to solve it. The conclusion follows and summarizes the main contributions of this thesis. Key words: Order-picking, warehousing, routing problems, exact and heuristic algorithms, products reassignment, material handling

Biased-Randomized Discrete-Event heuristics for dynamic optimization with time dependencies and synchronization

Many real-life combinatorial optimization problems are subject to a high degree of dynamism, while, simultaneously, a certain level of synchronization among agents and events is required. Thus, for instance, in ride-sharing operations, the arrival of vehicles at pick-up points needs to be synchronized with the times at which users reach these locations so that waiting times do not represent an issue. Likewise, in warehouse logistics, the availability of automated guided vehicles at an entry point needs to be synchronized with the arrival of new items to be stored. In many cases, as operational decisions are made, a series of interdependent events are scheduled for the future, thus making the synchronization task one that traditional optimization methods cannot handle easily. On the contrary, discrete-event simulation allows for processing a complex list of scheduled events in a natural way, although the optimization component is missing here. This paper discusses a hybrid approach in which a heuristic is driven by a list of discrete events and then extended into a biased-randomized algorithm. As the paper discusses in detail, the proposed hybrid approach allows us to efficiently tackle optimization problems with complex synchronization issuesPeer ReviewedPostprint (published version

Decision models for fast-fashion supply and stocking problems in internet fulfillment warehouses

Internet technology is being widely used to transform all aspects of the modern supply chain. Specifically, accelerated product flows and wide spread information sharing across the supply chain have generated new sets of decision problems. This research addresses two such problems. The first focuses on fast fashion supply chains in which inventory and price are managed in real time to maximize retail cycle revenue. The second is concerned with explosive storage policies in Internet Fulfillment Warehouses (IFW). Fashion products are characterized by short product life cycles and market success uncertainty. An unsuccessful product will often require multiple price discounts to clear the inventory. The first topic proposes a switching solution for fast-fashion retailers who have preordered an initial or block inventory, and plan to use channel switching as opposed to multiple discounting steps. The FFS Multi-Channel Switching (MCS) problem then is to monitor real-time demand and store inventory, such that at the optimal period the remaining store inventory is sold at clearance, and the warehouse inventory is switched to the outlet channel. The objective is to maximize the total revenue. With a linear projection of the moving average demand trend, an estimation of the remaining cycle revenue at any time in the cycle is shown to be a concave function of the switching time. Using a set of conditions the objective is further simplified into cases. The Linear Moving Average Trend (LMAT) heuristic then prescribes whether a channel switch should be made in the next period. The LMAT is compared with the optimal policy and the No-Switch and Beta-Switch rules. The LMAT performs very well and the majority of test problems provide a solution within 0.4% of the optimal. This confirms that LMAT can readily and effectively be applied to real time decision making in a FFS. An IFW is a facility built and operated exclusively for online retail, and a key differentiator is the explosive storage policy. Breaking the single stocking location tradition, in an IFW small batches of the same stock keeping unit (SKU) are dispersed across the warehouse. Order fulfillment time performance is then closely related to the storage location decision, that is, for every incoming bulk, what is the specific storage location for each batch. Faster fulfillment is possible when SKUs are clustered such that narrow band picklists can be efficiently generated. Stock location decisions are therefore a function of the demand arrival behavior and correlations with other SKUs. Faster fulfillment is possible when SKUs are clustered such that narrow band picklists can be efficiently generated. Stock location decisions are therefore a function of the demand behavior and correlations with other SKUs. A Joint Item Correlation and Density Oriented (JICDO) Stocking Algorithm is developed and tested. JICDO is formulated to increase the probability that M pick able order items are stocked in a δ band of storage locations. It scans the current inventory dispersion to identify location bands with low SKU density and combines the storage affinity with correlated items. In small problem testing against a MIP formulation and large scale testing in a simulator the JICDO performance is confirmed