Optimal Storage Rack Design for a 3-dimensional Compact AS/RS

In this paper, we consider a newly-designed compact three-dimensional automated storage and retrieval system (AS/RS). The system consists of an automated crane taking care of movements in the horizontal and vertical direction. A gravity conveying mechanism takes care of the depth movement. Our research objective is to analyze the system performance and optimally dimension of the system. We estimate the crane’s expected travel time for single-command cycles. From the expected travel time, we calculate the optimal ratio between three dimensions that minimizes the travel time for a random storage strategy. In addition, we derive an approximate closed-form travel time expression for dual command cycles. Finally, we illustrate the findings of the study by a practical example.AS/RS;Warehousing;Order Picking;Travel Time Model;Compact Storage Rack Design

Optimal Storage Rack Design for a 3-dimensional Compact AS/RS

In this paper, we consider a newly-designed automated storage and retrieval system (AS/RS). The system consists of an automated crane taking care of movements in the horizontal and vertical direction. A gravity conveying mechanism takes care of the depth movement. The aim of the research was to facilitate the problem of optimal design and performance evaluation of the system. We estimate the crane’s expected travel time for single command cycles. From the expected travel time, we calculate the optimal ratio between three dimensions that minimize the travel time for a random storage strategy. Finally, we illustrate the findings of the study by a practical example

Optimal Storage Rack Design for a 3-dimensional Compact AS/RS

In this paper, we consider a newly-designed compact three-dimensional automated storage and retrieval system (AS/RS). The system consists of an automated crane taking care of movements in the horizontal and vertical direction. A gravity conveying mechanism takes care of the depth movement. Our research objective is to analyze the system performance and optimally dimension of the system. We estimate the crane’s expected travel time for single-command cycles. From the expected travel time, we calculate the optimal ratio between three dimensions that minimizes the travel time for a random storage strategy. In addition, we derive an approximate closed-form travel time expression for dual command cycles. Finally, we illustrate the findings of the study by a practical example

Estimating travel times in dual shuttle AS/RSs.: A revised approach

Automated Storage and Retrieval Systems (AS/RSs) effectively support warehouse operations in order to increase production and logistics efficiency. Literature about travel time computation in multi-shuttle AS/RSs still needs to be enhanced since most of the existing contributions rely on the same formulation, namely the Meller and Mungwattana’s equation. Based on well-established theoretical assumptions and on a simulation model, the present work puts forward a revised version of the Meller and Mungwattana’s formula for dual shuttle systems. In particular, the constant factor multiplying the travel between time is replaced by a coefficient depending on the rack configuration and on the input and output points of the storage system. The new equation is tested against widely applied models for AS/RS travel time calculation and proves to result in shorter times than the original Meller and Mungwattana’s equation. A linear regression analysis is completed in order to find a numerical formulation of the proposed coefficient. Taking into account some key physical characteristics of a warehouse while estimating travel times allows improving the design and management of storage areas. Future research will focus on deepening multi-shuttle travel time calculation by addressing crane acceleration and deceleration, different rack and crane configurations, as well as class-based storage

Probabilistic Models for Order-Picking Operations with Multiple in-the-Aisle Pick Positions

The development of probability density functions (pdfs) for travel time of a narrow aisle lift truck (NALT) and an automated storage and retrieval (AS/R) machine is the focus of the dissertation. The multiple in-the-aisle pick positions (MIAPP) order picking system can be modeled as an M/G/1 queueing problem in which storage and retrieval requests are the customers and the vehicle (NALT or AS/R machine) is the server. Service time is the sum of travel time and the deterministic time to pick up and deposit a pallet (TPD). Our first contribution is the development of travel time pdfs for retrieval operations in an MIAPP order picking system supported by a narrow aisle lift truck (MIAPP-NALT); storage operations are assumed to occur when order picking is not being performed. A rectilinear travel metric is used for the NALT; pdfs are derived and finite population queueing and infinite population queueing models are used to analyze the retrieval operations under stochastic conditions. Our second contribution is the development of travel time pdfs for retrieval operations in an MIAPP order picking system supported by an AS/R machine (MIAPP-AS/RS); storage operations are assumed to occur when order picking is not being performed. A Chebyshev travel metric is used for the AS/R machine. For the MIAPP-AS/RS operation, pick positions are located at floor level and on a mezzanine. The pdfs for four scenarios are derived and finite population and infinite population queueing models are used to analyze the retrieval operation under stochastic conditions. Our final contribution is the development of travel time pdfs for storage and retrieval operations in an MIAPP-NALT system with two classes of stock keeping units (skus): fast movers and slow movers. A rectilinear travel metric is used and two levels of pick positions are considered. Non-preemptive priority queueing and non-priority queueing models are used to analyze storage and retrieval requests in the MIAPP-NALT system. Retrieval requests are given a higher priority than storage requests; alternately, storage and retrieval requests are served using a first come, first serve (FCFS) discipline

Otomatik depolama ve geri-alma sistemlerinin simülasyon ile performans analizi

Otomatik depolama ve geri-alma sistemleri, hem üretim hem dağıtım ortamlarında, ürünlerin depolanması ve geri-alınması için yaygın bir şekilde kullanılan sistemlerdir. Bu sistemlerin birçok avantajının yanında yüksek başlangıç yatırımları ve depo tasarımının değiştirilmesinin zorluğu gibi ekonomik faktörleri içeren dezavantajlarının da bulunması, sistemin fiziksel tasarım ve kontrol parametrelerinin doğru bir şekilde belirlenmesi gerekliliğini ön plana çıkarmaktadır. Dolayısıyla, sistem kurulmadan önce ya da kurulduktan sonra sistemin performans analizlerinin yapılması optimal bir tasarımın gerçekleştirilmesi açısından önem kazanmaktadır. Bununla birlikte, otomatik depolama ve geri-alma sistemlerinin tasarımında fiziksel tasarım ve kontrol parametrelerinin eş zamanlı olarak ele alınması karmaşık bir optimizasyon probleminin ortaya çıkmasına neden olduğundan, problemin çözümünde farklı parametre kombinasyonlarını içerecek şekilde bir tasarım esnekliği sağlanmak istenmektedir. Bu da, problemin çözümü için en uygun yaklaşımlardan birinin simülasyon olduğunu göstermektedir. Bu çalışmada, otomatik depolama ve geri-alma sistemlerinin simülasyonu ele alınmış olup, simülasyon modelinin oluşturulması için bir yaklaşım önerilmektedir. Önerilen yaklaşımın örneklendirilmesi amacıyla örnek bir depo oluşturulmuştur. Çalışma kapsamında, farklı depo içi atama politikaları altında farklı depolama ve geri-alma makinesi bekleme noktası parametreleri dikkate alınmıştır. Çeşitli senaryolar ile ilgili parametrelerin sistem üzerindeki etkisinin incelenmesi amaçlanarak, elde edilen sonuçlar sistem performansı açısından değerlendirilmiştir.Automated storage and retrieval systems are widely used in both production and distribution environments for the storage and retrieval of products. Besides the many advantages of these systems, the disadvantages (which include economic factors such as high initial investments and the difficulty of changing the warehouse design) highlights the need to correctly determine the system’s physical design and control parameters. In order to achieve an optimal design, it is therefore important to conduct a performance analysis of the system before or after installation. Dealing simultaneously with physical design and control parameters when designing automated storage and retrieval systems is, however, quite a complex undertaking whose solution requires the development of a flexible design incorporating several combinations of parameters. These considerations make the simulation approach the most appropriate means of solving the problem. The current study focuses on automated storage and retrieval systems, and proposes an approach for developing a simulation model. A sample warehouse is designed in order to illustrate the proposed approach. The study examines a variety of dwell point parameters of the storage and retrieval machine under different storage assignment policies. The findings obtained are finally evaluated with respect to the system’s performance in order to measure the impact of those parameters that are related to a variety of scenarios in the system

Desenvolvimento de algoritmos para recolha de produtos num armazém automático de alta densidade

Dissertação de mestrado em Engenharia de SistemasA presente dissertação realizou-se no âmbito do Mestrado em Engenharia de Sistemas da Universidade do Minho, inserida em contexto industrial na Pinto Brasil S.A., empresa dedicada à metalomecânica. Este projeto surgiu da necessidade de estudar o desempenho de um armazém automático de elevada densidade denominado Shopstocker. Os sistemas de armazenamento desempenham um papel fundamental na performance de toda a cadeia de abastecimento. A necessidade de adoção de políticas consentâneas com a Indústria 4.0, tais como Just-inTime/Just-in-Sequence, levou a que diversos tipos de armazéns fossem desenvolvidos. Um dos mais estudados e implementados é o Automated Storage and Retrieval System de elevada densidade. A Pinto Brasil S.A desenvolveu e implementou numa empresa fornecedora de para-choques, um sistema automatizado de elevada densidade para armazenamento de produto acabado. O Shopstocker implementado tem capacidade para armazenar um elevado número de para-choques e entrega os produtos de acordo com uma ordem pré-definida no tempo adequado. Devido à elevada variedade de referências de para-choques que atualmente são produzidos, o armazenamento do Shopstocker gera situações caóticas prejudicando a taxa de extração de produtos. No processo de extração a seleção de referências para uma ordem de carregamento é atualmente um procedimento muito complexo. Com o estudo da situação real do armazém implementado, modelou-se matematicamente um problema de otimização combinatória e implementou-se o modelo em linguagem de programação Python. Esta metodologia permitiu desenvolver três modelos de otimização, MPA, MF e MTFM, e dois algoritmos adicionais que permitem avaliar o desempenho do Shopstocker. Com os modelos e algoritmos desenvolvidos realizaram-se experiências computacionais de modo a avaliar o impacto que a configuração do armazém e as sequências de extração (encomendas) têm na eficiência do Shopstocker, nomeadamente nas medidas de desempenho, tempo total de extração, percentagem da regra First-In-First-Out cumprida e número de produtos movimentados. Os resultados demonstraram que o Shopstocker implementado apresenta uma perda de eficiência devido à elevada variedade de referências disponibilizadas. Assim, é recomendado que a empresa reduza o número de modelos disponibilizados, ou aumente o número de linhas de armazenamento, o que permitiria melhorar de forma significativa os valores das medidas de desempenho estudadas e, consequentemente, a eficiência do Shopstocker. O modelo desenvolvido neste projeto pode ainda ser implementado num Sistema de Apoio à Decisão e fornecerá uma solução otimizada para cada encomenda que chegue ao Shopstocker.This dissertation was carried out as part of the master’s degree in Systems Engineering at the University of Minho, and in industrial environment at Pinto Brasil S.A., a company dedicated to metalworking. This project emerged from the need to study the performance of a high-density automated warehouse called Shopstocker. The storage systems play a decisive role in the supply chain performance. The implementation of the Industry 4.0 policies, such as Just-in-Time/Just-in-Sequence, led to the development of several types of warehouses. One of the most studied and implemented is the high-density Automated Storage and Retrieval System. Pinto Brasil S.A. developed and implemented an automated warehouse of finished product in a bumper supplier company. The implemented Shopstocker can store many bumpers and delivers the products by a specific sequence at the right time. Due to the wide variety of bumper references currently provided, the Shopstocker storage process often results in a chaotic situation harming the product extraction rate. In the extraction process, selecting references for a loading order is a very complex procedure. The actual situation of the implemented warehouse was studied, and a combinatorial optimization problem was modelled and implemented with the Python programming language. This methodology allowed the development of three optimization models, MPA, MF and MTFM, and two additional algorithms that enable to evaluate the Shopstocker’s performance. Computational experiments were performed with the developed models to evaluate the impact that warehouse configuration and extraction sequences (orders) have in the Shopstocker’s efficiency, namely in the key performance indicators, total extraction time, percentage of the First-In-First-Out rule fulfilled and the number of moved bumpers. The results showed that the implemented Shopstocker has a loss of efficiency due to the wide variety of references currently provided. According to this study, it is recommended that the company reduce the number of references provided, or increase the number of storage lines, which will allow to improve the key performance indicators values and, consequently, the Shopstocker’s efficiency. The developed model can also be implemented in a Decision Support System and will provide an optimized solution to each order that arrive at Shopstocker

Design and Control of Efficient Order Picking Processes

Binnen een logistieke keten dienen producten fysiek te worden verplaatst van de ene locatie naar de andere, van producenten naar eindgebruikers. Tijdens dit proces worden producten gewoonlijk opgeslagen op bepaalde plaatsen (magazijnen) voor een bepaalde periode. Orderverzameling – het ophalen van producten uit de opslaglocatie in het magazijn naar aanleiding van een specifieke klantorder – is het meest kritieke magazijnproces. Het is een arbeidsintensieve operatie in handmatig bestuurde systemen, en een kapitaalintensieve operatie in geautomatiseerde systemen. Een niet optimaal functionerend orderverzamelingsproces kan leiden tot onbevredigende service en hoge operationele kosten voor het magazijn, en dientengevolge voor de hele keten. Om efficiënt te kunnen functioneren dient het orderverzamelingsproces robuust te zijn ontworpen en optimaal te worden bestuurd. Dit proefschrift heeft als doel analytische modellen te ontwerpen die het ontwerp en de besturing van efficiënte orderverzamelingsprocessen ondersteunen. Verschillende methoden worden voorgesteld voor het schatten van de route langs de locaties van de te verzamelen producten, het bepalen van de optimale grenzen van zones in het magazijn die bestemd zijn voor opslag, de indeling van het magazijn, het aantal producten die tegelijk (in één ronde) worden verzameld (de batch size) en het aantal zones in het magazijn die worden ingericht voor het verzamelen en gereedmaken van orders. De methoden worden getest middels simulatie experimenten en worden inzichtelijk gemaakt met behulp van rekenexperimenten.Tho Le-Duc was born in 1974 in Quang Ninh, Vietnam. He received a Bachelor degree in Navigation Science from the Vietnam Maritime University in 1996 and a Postgraduate Diploma in Industrial Engineering from the Asian Institute of Technology Bangkok Thailand (AIT) in 1998. Thanks to the financial support from the Belgian Development and Co-operations, he obtained his master degree in Industrial Management from the Catholic University of Leuven in 2000. Since May 2001, he started as a Ph.D. candidate (AIO) at the RSM Erasmus University (formerly Rotterdam School of Management/Faculteit Bedrijfskunde), the Erasmus University Rotterdam. For about more than four years, he performed research on order picking in warehouses. As the results, Tho Le-Duc has been presented his research at several conferences in the fields of operations research, material handling, logistics and supply chain management in both Europe and North America. His research papers have been published or accepted for publication in several refereed conference proceedings, scientific books and international journals.Within a logistics chain, products need to be physically moved from one location to another, from manufacturers to end users. During this process, commonly products are buffered or stored at certain places (warehouses) for a certain period of time. Order picking - the process of retrieving products from storage (or buffer area) in response to a specific customer request - is the most critical warehouse process. It is a labour intensive operation in manual systems and a capital intensive operation in automated systems. Order picking underperformance may lead to unsatisfactory service and high operational cost for the warehouse, and consequently for the whole chain. In order to operate efficiently, the order picking process needs to be designed and optimally controlled. Thesis Design and Control of Efficient Order Picking Processes aims at providing analytical models to support the design and control of efficient order picking processes. Various methods for estimating picking tour length, determining the optimal storage zone boundaries, layout, picking batch size and number of pick zones are presented. The methods are tested by simulation experiments and illustrated by numerical examples