Optimizing Retrieving Performance of an Automated Warehouse for Unconventional Stock Keeping Units

In recent years, the diffusion of automated warehouses in different industrial sectors has fostered the design of more complex automated storages and handling solutions. These circumstances, from a technological point of view, have led to the development of automated warehouses that are very different from the classic pallet Automated Storage and Retrieval Systems (AS/RS), both in terms of design and operating logic. A context in which these solutions have spread is the steel sector. Warehouses with innovative layouts and operating logics have been designed to move metal bundles of different sizes, weights and quality levels, instead of standard, interchangeable stock keeping units. Moreover, picking is often not allowed in these warehouses, due to the configuration of the loading units. In this work we propose a meta-heuristic algorithm based on the Simulated Annealing (SA) procedure, which aims to optimize performance during the retrieving phase of an automated warehouse for metal bundles. The algorithm translates the customers’ requests, expressed in terms of item code, quality and weight into a list of jobs. The goal is to optimize the retrieving performance, measured in missions per hour, minimizing the deviations in quality and weight between customer request and the material retrieved. For the validation, a simulation model of an existing warehouse has been created and the performance of the algorithm tested on the simulation model has been compared with the current performance of the warehouse

Evaluating dedicated and shared storage policies in robot-based compact storage and retrieval systems

Robot-based compact storage and retrieval systems (RCSRS) have seen many implementations over the last few years. In such a system, the inventory items are stored in bins, organized in a grid. In each cell of the grid, a certain number of bins are stored on top of each other. Robots with transport and lifting capabilities move on the grid roof to transport bins between manual workstations and storage stacks. We estimate performance and evaluate storage policies of RCSRS, considering both dedicated and shared storage policies coupled with random and zoned storage stacks. Semi-open queueing networks (SOQNs) are built to estimate the system performance, which can handle both immediate and delayed reshuffling processes. We approximate the models by reduced SOQNs with two load-dependent service nodes and use the Matrix-Geometric Method (MGM) to solve them. Both simulations and a real case are used to validate the analytical models. Assuming a given number of stored products, our models can be used to optimize not only the length to width ratio of the system, but also the stack height, depending on the storage strategy used. For a given inventory and optimal system configuration, we demonstrate that the dedicated storage policy outperforms the shared storage policy in terms of dual command throughput time. However, from a cost perspective, wit

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