Tabu Search Heuristics for the Order Batching Problem in Manual Order Picking Systems

In manual order picking systems, order pickers walk or ride through a distribution warehouse in order to collect items 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 necessary to collect all items is minimized. For the solution of this problem the authors suggest two approaches based on the tabu search principle. The first one is a straightforward classic Tabu Search algorithm (TS), the second one is the Attribute-Based Hill Climber (ABHC). In a series of extensive numerical experiments, the newly developed approaches are benchmarked against different solution methods from literature. It is demonstrated that the proposed methods are superior to existing methods and provide solutions which may allow for operating distribution warehouses significantly more efficiently

Order Batching in Order Picking Warehouses: A Survey of Solution Approaches

Order picking is a warehouse function dealing with the retrieval of articles from their storage location in order to satisfy a given demand specified by customer orders. Of all warehouse operations, order picking is considered to include the most cost-intensive ones. Even though there have been different attempts to automate the picking process, manual order picking systems are still prevalent in practice. This article will focus on order batching, one of the main planning issues in order picking systems. Order Batching has been proven to be pivotal for the efficiency of order picking operations. With respect to the availability of information about the customer orders, order batching can be distinguished into static batching and dynamic batching. Improved order batching reduces the total picking time required to collect the requested articles. According to experience from practice, this can result in significant savings of labor cost and into a reduction of the customer order\u27s delivery lead time.The aim of this contribution is to provide comprehensive insights into order batching by giving a detailed state-of-the-art overview of the different solution approaches which have been suggested in the literature. Corresponding to the available publications, the emphasis will be on static order batching.In addition to this, the paper will also review the existing literature for variants and extensions of static order batching (e.g. due dates, alternative objective functions). Furthermore, solution approaches for dynamic order batching problems (like time window batching) will be presented

Metaheuristics for Order Batching and Sequencing in Manual Order Picking Systems

Order picking deals with the retrieval of articles from their storage locations in order to satisfy customer requests. A major issue in manual order picking systems concerns of the transformation and consolidation of customer orders into picking orders (order batching). In practice, customer orders have to be completed by certain due dates in order to avoid delay in the shipment to customers or in production. The composition of the picking orders, their processing times and the sequence according to which they are released have a significant impact on whether and to which extent given due dates are violated. This paper presents how metaheuristics can be used in order to minimize the total tardiness for a given set of customer orders. The first heuristic is based on Iterated Local Search, the second one is inspired by the Attribute-Based Hill Climber, a heuristic based on a simple tabu search principle. In a series of extensive numerical experiments, the performance of these metaheuristics is analyzed for different classes of instances. We will show that the proposed methods provide solutions which may allow for operating order picking systems more efficiently. Solutions can be improved by 46% on average, compared to the ones obtained by standard constructive heuristics such as an application of the Earliest Due Date rule

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

Metaheuristics for Order Batching and Sequencing in Manual Order Picking Systems

Order picking deals with the retrieval of articles from their storage locations in order to satisfy customer requests. A major issue in manual order picking systems concerns of the transformation and consolidation of customer orders into picking orders (order batching). In practice, customer orders have to be completed by certain due dates in order to avoid delay in the shipment to customers or in production. The composition of the picking orders, their processing times and the sequence according to which they are released have a significant impact on whether and to which extent given due dates are violated. This paper presents how metaheuristics can be used in order to minimize the total tardiness for a given set of customer orders. The first heuristic is based on Iterated Local Search, the second one is inspired by the Attribute-Based Hill Climber, a heuristic based on a simple tabu search principle. In a series of extensive numerical experiments, the performance of these metaheuristics is analyzed for different classes of instances. We will show that the proposed methods provide solutions which may allow for operating order picking systems more efficiently. Solutions can be improved by 46% on average, compared to the ones obtained by standard constructive heuristics such as an application of the Earliest Due Date rule.Warehouse Management, Order Batching, Batch Sequencing, Due Dates, Iterated Local Search, Attribute-Based Hill Climber

A memetic algorithm for the integral OBP/OPP problem in a logistics distribution center

In this paper, we present a new decision-making tool aimed at improving the efficiency of the operational planning of pick-up processes in logistic distribution centers. It is based on a memetic algorithm (MA) solving both the Order Batching Problem (OBP) and the Order Picking Problem (OPP). The result yields a sequence of simultaneous pick up operations of lots for different clients in a storing facility, satisfying a previously defined distribution plan. The objective is the minimization of the operational cost of the entire process, which is directly proportional to the time spent on different activities involved. The failure to satisfy the conditions, either leads to overstocking, delays in delivery or creates inefficiency costs. The analysis of the results obtained with our algorithmic tool indicates that it has a good performance in comparison with other known algorithms used to solve this kind of problem.Fil: Miguel, Fabio. Universidad Nacional de Río Negro; ArgentinaFil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentin

Designing new models and algorithms to improve order picking operations

Order picking has been identified as a crucial factor for the competitiveness of a supply chain because inadequate order picking performance causes customer dissatisfaction and high costs. This dissertation aims at designing new models and algorithms to improve order picking operations and to support managerial decisions on facing current challenges in order picking. First, we study the standard order batching problem (OBP) to optimize the batching of customer orders with the objective of minimizing the total length of order picking tours. We present a mathematical model formulation of the problem and develop a hybrid solution approach of an adaptive large neighborhood search and a tabu search method. In numerical studies, we conduct an extensive comparison of our method to all previously published OBP methods that used standard benchmark sets to investigate their performance. Our hybrid outperforms all comparison methods with respect to average solution quality and runtime. Compared to the state-of-the-art, the hybrid shows the clearest advantages on the larger instances of the existing benchmark sets, which assume a larger number of customer orders and larger capacities of the picking device. Finally, our method is able to solve newly generated large-scale instances with up to 600 customer orders and six items per customer order with reasonable runtimes and convincing scaling behavior and robustness. Next, we address a problem based on a practical case, which is inspired by a warehouse of a German manufacturer of household products. In this warehouse, heavy items are not allowed to be placed on top of light items during picking to prevent damage to the light items. Currently, the case company determines the sequence for retrieving the items from their storage locations by applying a simple S-shape strategy that neglects this precedence constraint. As a result, order pickers place the collected items next to each other in plastic boxes and sort the items respecting the precedence constraint at the end of the order picking process. To avoid this sorting, we propose a picker routing strategy that incorporates the precedence constraint by picking heavy items before light items, and we develop an exact solution method to evaluate the strategy. We assess the performance of our strategy on a dataset provided to us by the manufacturer. We compare our strategy to the strategy used in the warehouse of the case company, and to an exact picker routing approach that does not consider the given precedence constraint. The results clearly demonstrate the convincing performance of our strategy even if we compare our strategy to the exact solution method that neglects the precedence constraint. Last, we investigate a new order picking problem, in which human order pickers of the traditional picker-to-parts setup are supported by automated guided vehicles (AGVs). We introduce two mathematical model formulations of the problem, and we develop a heuristic to solve the NP-hard problem. In numerical studies, we assess the solution quality of the heuristic in comparison to optimal solutions. The results demonstrate the ability of the heuristic in finding high-quality solutions within a negligible computation time. We conduct several computational experiments to investigate the effect of different numbers of AGVs and different traveling and walking speed ratios between AGVs and order pickers on the average total tardiness. The results of our experiments indicate that by adding (or removing) AGVs or by increasing (or decreasing) the AGV speed to adapt to different workloads, a large number of customer orders can be completed until the respective due date

Comparative analysis of order batching and routing problem in the picking regarding classical HVRP (heterogeneous vehicle routing problem)

Este artículo tiene como objetivo comparar la conformación de lotes con ruteo, en la preparación de pedidos respecto al problema HVRP (Heterogeneous Vehicle Routing Problem) basándose en la utilización de una metodología de la revisión sistemática de la literatura. Del análisis comparativo se identifica la necesidad de realizar modificaciones radicales e incluir nuevos componentes al problema HVRP, para modelar la conformación de lotes con ruteo de mínimo tiempo, en la preparación de pedidos, considerando K equipos de manejo de materiales (EMM) heterogéneos, n productos, m posiciones de almacenamiento, la disponibilidad del inventario y demás restricciones asociadas a la operación.This paper aims to compare the order batching and routing problem(OBRP) regarding heterogeneous vehicle routing problem (HVRP), in order to identify whether there are any differences and similarities between these ones. The OBRP consist in generating product groups, which are collected from storage locations using material specific handling equipment. Each product group(or batch) is matched to a route, which states the sequences to pick the products in the shortest time possible. On the other hand, HVRP is a variant of the Vehicle Routing Problem(VRP), in which customers are served by a heterogeneous fleet of vehicles with various capacities, in order to delivery products in a distribution network at the lowest possible cost. Additionally, in the related literature were not identified HVRP papers that tackled order batching and routing problem (OBRP), but they were focused primarily in transportation and distribution process. Therefore, it was detected a gap in the state of the art. The comparation analysis was developed using a variation of the methodology called Systematic Literature Review (SLR) , which was based on analysis of papers. This methodology was implemented eight stages, the most important of which are as follows: i) formulating the research questions and evaluation criteria (stage 2), ii) inclusion and exclusion criteria (stage 3), iii) results of systematic review (stage 6), iv) comparative analysis between OBRP and HVRP based on set evaluation criteria (stage 7) and v) conclusions and research opportunities (stage 8). The main findings of this paper were as follow: First, order batching was not modeled in HVRP, hence relevance of this gap. Second, in order batching and routing problem is necessary to represent K heterogeneous MHE with different speed travels, load capacities and lift heights. In HVRP papers the heterogeneity is only caused by vehicles in different load capacities. Third, a constraint among n products, m storage locations and K heterogeneous EMH should be implemented to ensure the feasibility of solutions of OBRP. This constraint is raised, since any MHE are not able to pick some products from storage locations, due to theirs technical characteristics. In addition, none of HVRP papers represented this constraint. Fourth, setup time and handling time were not modeled in reviewed HVRP papers, since these times were not as significant in transportation and distribution routes. Therefore, these times should be included in HVRP to represent OBRP. Fifth, available of inventory were not considered in HVRP papers, since this condition was not important in the modeled process. It should be noted that this condition is critical in OBRP, since only can be picked products with available inventory in Distribution Centre (DC). Based on findings, it was detected a significant gap in the state of the art related to the formulation and solution a minimum time OBRP considering n products, m storage locations and K heterogeneous MHE and described constraint. Therefore, this approach, not only it will fill this gap, but also contribute to knowledge in OBRP. In addition, this paper it will be one of the first to analyze HVRP in warehouse and DC