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

Flexible jobshop scheduling problem with resource recovery constraints.

Objectives and methods of study: The general objective of this research is to study a scheduling problem found in a local brewery. The main problem can be seen as a parallel machine batch scheduling problem with sequence-dependent setup times, resource constraints, precedence relationships, and capacity constraints. In the first part of this research, the problem is characterized as a Flexible Job-shop Scheduling Problem with Resource Recovery Constraints. A mixed integer linear formulation is proposed and a large set of instances adapted from the literatura of the Flexible Job-shop Scheduling Problem is used to validate the model. A solution procedure based on a General Variable Neighborhood Search metaheuristic is proposed, the performance of the procedure is evaluated by using a set of instances adapted from the literature. In the second part, the real problem is addressed. All the assumptions and constraints faced by the decision maker in the brewery are taken into account. Due to the complexity of the problem, no mathematical formulation is presented, instead, a solution method based on a Greedy Randomize Adaptive Search Procedure is proposed. Several real instances are solved by this algorithm and a comparison is carried out between the solutions reported by our GRASP and the ones found through the procedure followed by the decision maker. The computational results reveal the efficiency of our method, considering both the processing time and the completion time of the scheduling. Our algorithm requires less time to generate the production scheduling (few seconds) while the decision maker takes a full day to do it. Moreover, the completion time of the production scheduling generated by our algorithm is shorter than the one generated through the process followed by the decision maker. This time saving leads to an increase of the production capacity of the company. Contributions: The main contributions of this thesis can be summarized as follows: i) the introduction of a variant of the Flexible Job-shop Scheduling Problem, named as the Flexible Job-shop Scheduling Problem with Resource Recovery Constraints (FRRC); ii) a mixed integer linear formulation and a General Variable Neighborhood Search for the FRRC; and iii) a case study for which a Greedy Randomize Adaptive Search Procedure has been proposed and tested on real and artificial instances. The main scientific products generated by this research are: i) an article already published: Sáenz-Alanís, César A., V. D. Jobish, M. Angélica Salazar-Aguilar, and Vincent Boyer. “A parallel machine batch scheduling problem in a brewing company”. The International Journal of Advanced Manufacturing Technology 87, no. 1-4 (2016): 65-75. ii) another article submitted to the International Journal of Production Research for its possible publication; and iii) Scientific presentations and seminars

Order picking optimisation on a unidirectional cyclical picking line

Thesis (PhD)--Stellenbosch University, 2020.ENGLISH SUMMARY : The order picking system in a company's distribution centre is the biggest contributor to the operational cost within the DC. Optimisation should thus aim at running this activity as effciently as possible. The order picking process consists of three main activities, namely walking to the stock, picking stock in fullment of a customer order and handling the picked stock for further processing. While the total amount of work for the picking and handling activities remain constant, the minimisation of walking distance becomes the main objective when minimising the total picking effort. The minimisation of walking distance can be translated into a reduced overall picking time which can lead to a decrease in the total cost of operating the picking system. The main objective of this dissertation is to optimise the order picking system on a unidirectional cyclical picking line. Order batching is introduced to the picking system, since it is an effective methodology that minimises walking distance in operations research literature. Order batching has been introduced to the standard single block parallel-aisle warehouse layout, but not to the specic layout of a unidirectional cyclical picking line. Additionally, the unidirectional cyclical picking line can offer two conguration options that change the physical set up and thereby inffuence the way in which pickers walk during the order picking process. Order batching is introduced to the unidirectional cyclical picking line through picking location based order-to-route closeness metrics. These metrics are further extended by taking the characteristics of the layout into account. The distribution centre of a prominent South African retailer provides real life test instances. Introducing the layout specic stops non-identical spans metric in combination with the greedy smallest entry heuristic results in a reduction of 48:3% in walking distance. Order batching increases the pick density which may lead to higher levels in picker congestion. In a discrete event simulation, the reduction of the overall picking time through a decrease in walking distance is thus conrmed. On tested sample picking waves, the overall picking time can be reduced by up to 21% per wave. A good number of pickers in the picking system is dependent on the pick density. The pick density, amongst other explanatory variables, can also be used to predict the reduction in picking time. The effects of different structural options of the unidirectional cyclical picking line, namely the U- and Z-conguration, are investigated. This results in four decision tiers that have to be addressed while optimising the order picking system. The rst decision tier assigns stock to picking lines, the second arranges stock around a picking line, the third chooses the conguration and the last sequences the orders to be picked. Order batching is added as an additional layer. An increase in pick density benets the reduction of walking distance throughout the decision tiers and supports the choice of the U-conguration after evaluating different test instances. The total completion time of a picking wave can thus be reduced by up to 28% when compared to benchmark instances. The dissertation is concluded by suggesting further research directions.AFRIKAANSE OPSOMMING : Die opmaak van bestellings op 'n uitsoeklyn in 'n onderneming se distribusiesentrum is die grootste bydraer tot die bedryfskoste van 'n distribusiesentrum. Dit is dus belangrik om hierdie aktiwiteit so doeltreffend moontlik te maak. Die proses om bestellings op te maak bestaan uit drie hoofaktiwiteite, naamlik stap na die voorraad, uitsoek (kies en bymekaarsit) van die voorraad vir 'n bestelling en die pak van die gekose voorraad in kartonne vir verdere verwerking en verspreiding. Omdat die totale hoeveelheid werk vir die uitsoek- en hanteringsaktiwiteite konstant bly, word die vermindering van loopafstand die hoofdoelwit om die totale koste van hierdie proses te minimeer. Die minimering van loopafstand lei tot 'n vermindering in totale tyd om bestellings op te maak, wat op sy beurt weer lei tot 'n afname in die totale koste van die stelsel om bestellings op te maak. Die hoofdoel van hierdie proefskrif is om die stelsel vir die uitsoek van bestellings op 'n eenrigting sikliese uitsoeklyn te optimeer. Metodes vir die samevoeging of groepering (Eng.: batching) van bestellings (om gelyktydig opgemaak te word) word ontwikkel vir hierdie uitsoekstelsel aangesien operasionelenavorsingsliteratuur aantoon dat groepering van bestellings 'n effektiewe metode is om loopafstand te verminder. Groepering van bestellings is reeds gedoen vir die standaard blokuitleg van distribusiesentra, maar nie vir hierdie spesieke uitleg van 'n eenrigting sikliese uitsoeklyn nie. Daarbenewens het die eenrigting sikliese uitsoeklyn twee kongurasie-opsies wat die siese opstelling verander en sodoende die manier beinvloed waarop werkers tydens die uitsoekproses loop. Die groepering van bestellings word ontwikkel vir 'n eenrigting sikliese uitsoeklyn deur middel van 'n plek-gebaseerde maatstaf wat die nabyheid van bestellings se roetes meet. Hierdie maatstaf word verder uitgebrei deur die eienskappe van die uitleg in ag te neem. Regte voorbeelde van die probleem uit 'n distribusiesentrum van 'n prominente Suid-Afrikaanse kleinhandelaar word gebruik vir toetsing. Die ontwikkeling en implementering van 'n uitlegspesieke stop-nie identiese-strek-maatstaf in kombinasie met die gulsige kleinste-invoegingsheuristiek lei tot 'n vermindering van 48:3% in stapafstand. Die groepering van bestellings verhoog die digtheid van plekke waar werkers stop vir voorraad, wat kan lei tot ho er vlakke van kongestie vir werkers. 'n Diskrete-gebeurtenis-simulasie bevestig dat 'n afname in loopafstand ook 'n vermindering van die totale voltooiingstyd tot gevolg het. Met behulp van werklike historiese data kon die totale tyd vir die uitsoek van bestellings met tot 21% per golf verminder word. 'n Goeie aantal werkers in die uitsoekstelsel is afhanklik van die uitsoekdigtheid. Die uitsoekdigtheid en andere verklarende veranderlikes, kan ook gebruik word om die vermindering in totale tyd om bestellings op te maak, te voorspel. Die invloed van verskillende strukturele opsies van die eenrigting sikliese uitsoeklyn, naamlik die U- en Z-kongurasie, word ook ondersoek. Dit het tot gevolg dat vier besluitnemingsvlakke aangespreek moet word om die uitsoekstelsel te optimeer. Die eerste besluitnemingsvlak ken voorraad aan die uitsoeklyne toe, die tweede rangskik voorraad binne die uitsoeklyn, die derde kies die kongurasie van die lyn en die laaste kies die volgorde waarin die bestellings uitgesoek word. Groepering van bestellings word bygevoeg as 'n addisionele vlak. 'n Toename in werksdigtheid bevoordeel die vermindering van loopafstand deur die besluitvlakke en bevoordeel die U-kongurasie na evaluering van verskillende toetsdata. Die totale voltooiingstyd van 'n uitsoekgolf kan dus verminder word met tot 28% in vergelyking met eweknie voorbeelde. Die studie word afgesluit deur verdere navorsingsmoontlikhede voor te stel.Doctora

Improvement to an existing multi-level capacitated lot sizing problem considering setup carryover, backlogging, and emission control

This paper presents a multi-level, multi-item, multi-period capacitated lot-sizing problem. The lot-sizing problem studies can obtain production quantities, setup decisions and inventory levels in each period fulfilling the demand requirements with limited capacity resources, considering the Bill of Material (BOM) structure while simultaneously minimizing the production, inventory, and machine setup costs. The paper proposes an exact solution to Chowdhury et al. (2018)\u27s[1] developed model, which considers the backlogging cost, setup carryover & greenhouse gas emission control to its model complexity. The problem contemplates the Dantzig-Wolfe (D.W.) decomposition to decompose the multi-level capacitated problem into a single-item uncapacitated lot-sizing sub-problem. To avoid the infeasibilities of the weighted problem (WP), an artificial variable is introduced, and the Big-M method is employed in the D.W. decomposition to produce an always feasible master problem. In addition, Wagner & Whitin\u27s[2] forward recursion algorithm is also incorporated in the solution approach for both end and component items to provide the minimum cost production plan. Introducing artificial variables in the D.W. decomposition method is a novel approach to solving the MLCLSP model. A better performance was achieved regarding reduced computational time (reduced by 50%) and optimality gap (reduced by 97.3%) in comparison to Chowdhury et al. (2018)\u27s[1] developed model

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

Four decades of research on the open-shop scheduling problem to minimize the makespan

One of the basic scheduling problems, the open-shop scheduling problem has a broad range of applications across different sectors. The problem concerns scheduling a set of jobs, each of which has a set of operations, on a set of different machines. Each machine can process at most one operation at a time and the job processing order on the machines is immaterial, i.e., it has no implication for the scheduling outcome. The aim is to determine a schedule, i.e., the completion times of the operations processed on the machines, such that a performance criterion is optimized. While research on the problem dates back to the 1970s, there have been reviving interests in the computational complexity of variants of the problem and solution methodologies in the past few years. Aiming to provide a complete road map for future research on the open-shop scheduling problem, we present an up-to-date and comprehensive review of studies on the problem that focuses on minimizing the makespan, and discuss potential research opportunities

Management And Security Of Multi-Cloud Applications

Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers\u27 virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the use of multi-cloud management and innovative techniques for placement and performance management. We consider two classes of distributed applications – the virtual network services and the next generation of healthcare – that would benefit immensely from deployment over multiple clouds. This thesis deals with the design and development of new processes and algorithms to enable these classes of applications. We have evolved a method for optimization of multi-cloud platforms that will pave the way for obtaining optimized placement for both classes of services. The approach that we have followed for placement itself is predictive cost optimized latency controlled virtual resource placement for both types of applications. To improve the availability of virtual network services, we have made innovative use of the machine and deep learning for developing a framework for fault detection and localization. Finally, to secure patient data flowing through the wide expanse of sensors, cloud hierarchy, virtualized network, and visualization domain, we have evolved hierarchical autoencoder models for data in motion between the IoT domain and the multi-cloud domain and within the multi-cloud hierarchy

Holistic, data-driven, service and supply chain optimisation: linked optimisation.

The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis