Designing Automated Warehouses by Minimising Investment Cost Using Genetic Algorithms

The successful performance of the automated storage and retrieval systems is dependent upon the appropriate design and optimization process. In the present work a comprehensive model of designing automated storage and retrieval system for the single- and multi-aisle systems is presented. Because of the required conditions that the automated storage and retrieval systems should be technically highly efficient and that it should be designed on reasonable expenses, the objective function represents minimum total cost. The objective function combines elements of layout, time-dependent part, the initial investment and the operational costs. Due to the nonlinear, multi-variable and discrete shape of the objective function, the method of genetic algorithms has been used for the optimization process of decision variables. The presented model prove to be very useful and flexible tool for choosing a particular type of the single- or multi aisle system in designing automated storage and retrieval systems. Computational analysis of the design model indicates the model suitability for addressing industry size problems

Sequencing approaches for multiple-aisle automated storage and retrieval systems

Automated storage and retrieval systems (AS/RS) are used in high velocity distribution centres to provide accurate and fast order processing. While almost every industrial system is comprised of many aisles, most of the academic research on the operational aspects of AS/RS is devoted to single-aisle systems, probably due to the broadly accepted hypothesis proposing that an m aisles system can be modelled as m 1-aisle independent systems. In this article, we present two multi-aisles sequencing approaches and evaluate their performance when all the aisles are managed independently first, and then in a global manner. Computational experiments conducted on a multi-aisle AS/RS simulation model clearly demonstrate that a multi-aisle system cannot be accurately represented by multiple single-aisle systems. The numerical results demonstrate that, when dealing with random storage, globally sequencing multi-aisle AS/RS leads to makespan reductions ranging from 14 to 29% for 2- and 3-aisle systems, respectivelyKeywords: automated storage and retrieval systems; multi-aisle; sequencing; simulatio

An approach for computing AS/R systems travel times in a class-based storage configuration

This study provides an approach to compute the travel time for AS/R systems in a class-based storage environment. A regression analysis is completed in order to define the importance of the key predictors taken into account and to propose a formulation of travel times. The results show the reliability of the model and allow to evaluate the travel time through the identification of a complete list of predictors. The proposed approach supports managers in theex-ante definition of travel times for a warehouse. A correct evaluation of travel times enables a better monitoring of the performance of warehouse operations and can support practitioners in the choice of the configuration not only in terms of kind of cycle, but also from a policy assignment perspective. From a theoretical point of view, this work can be considered as an attempt to refine the existing methods to compute travel times

An approach for computing AS/R systems travel times in a class-based storage configuration

This study provides an approach to compute the travel time for AS/R systems in a class-based storage environment. A regression analysis is completed in order to define the importance of the key predictors taken into account and to propose a formulation of travel times. The results show the reliability of the model and allow to evaluate the travel time through the identification of a complete list of predictors. The proposed approach supports managers in theex-ante definition of travel times for a warehouse. A correct evaluation of travel times enables a better monitoring of the performance of warehouse operations and can support practitioners in the choice of the configuration not only in terms of kind of cycle, but also from a policy assignment perspective. From a theoretical point of view, this work can be considered as an attempt to refine the existing methods to compute travel times

Intralogistics and industry 4.0: designing a novel shuttle with picking system

Intralogistics is increasingly a matter of research and development as a form of optimization, automation, integration and management of the flow of materials and information that circulate within a business unit. With a strong connection to material handling equipment and automation solutions, intralogistics has proved to be one of the main factors responsible for something that is already happening: a fourth industrial revolution where it is possible to convert warehouses and manufacturing units into smart environments where the entire process can be controlled and supervised through a single system. It became necessary to develop more innovative and efficient solutions to the constant diversity of challenges proposed by the market. In this sense, it was proposed to develop something innovative within the area of Automated Storage and Retrieval Systems (AS/RS), a technology increasingly sought after by today's manufacturing plants. As such, the goal was to improve the most emergent AS/RS in recent years: the Pallet/Box Shuttle AS/RS. In order to achieve the proposed objective, it was necessary to analyze all the existing solutions in the market and, mainly, to find the main systems to be improved and the direction to follow in order to create a novel solution based on the existing advanced solutions. Moreover, regarding the recent needs required by the smart factories and Industry 4.0, it was intended that the new system would be able to make an optimized selection of products, forming sets or sub-sets of different products picking them from different places of the rack, a situation that is quite frequent in companies that produce and assemble equipment. The solution obtained shows that it is possible to increase the automation of the operations in the storage systems and improve the responsiveness of the system, taking this solution to a new level. Different products can be picked-up and put in a same box, providing a set of products/components able to be used in a production line or to be provided to a customer.info:eu-repo/semantics/publishedVersio

Ensemble Multi-Objective Biogeography-Based Optimization with Application to Automated Warehouse Scheduling

This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling

Ensemble Multi-Objective Biogeography-Based Optimization with Application to Automated Warehouse Scheduling

This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling

Evaluation of order picking systems using simulation

Sipari? toplama faaliyetleri, tedarik zinciri yönetiminde, hem üretim sistemleri açısından (montaj istasyonlarına alt parçaların tedarik edilmesi), hem de dağıtım i?lemleri açısından (mü?teri taleplerinin kar?ılanması) kritik rol oynamaktadır. Mü?teri sipari?lerindeki eğilimler, az sayıda ve yüksek miktarlarda sipari?lerin çok sayıda ve dü?ük miktarlarda sipari?lere dönü?tüğünü göstermektedir. Diğer yandan, talep edilen sipari? teslim süreleri ise her geçen gün kısalmaktadır. Bu deği?imler, i?letmelerin piyasada rekabet edebilmeleri için etkin ve esnek bir sipari? toplama sistemi benimsemelerini gerektirmektedir. Sipari? toplama süreci, tüm lojistik operasyonlarını ve mü?teriye sağlanan hizmet seviyesini büyük ölçüde etkilemektedir. Ayrıca, sipari? toplama süreci toplam depolama maliyetlerinin yarıdan fazlasını olu?turmaktadır. Bu nedenle, sipari? toplama faaliyetlerinin en etkin ?ekilde gerçekle?tirilmesi i?letmeler için büyük önem ta?ımaktadır. Bu çalı?manın amacı, sipari? toplama süresini kısaltarak, sipari? toplama etkinliğini arttırmaya yönelik deği?iklikler için sipari? toplama sistemini değerlendirmek ve geli?tirmektir. Sipari? toplama süresi, ürünlerin depolama alanlarından belirli bir mü?teri talebini kar?ılamak amacıyla toplanması süreci için geçen zamandır. Bu çalı?mada, ürünlerin depolama alanlarına atanma kararları ve rotalama metotları gibi kritik faktörlerin yanı sıra, daha önce gerçekle?tirilmi? çalı?malarda sıkça rastlanmayan depolama alanlarının ikmali problemi dikkate alınmı?tır. Bo?alan rafların yeniden doldurulması kararında, (S, s) envanter politikası uygulanmı?tır. Böylece, sipari? toplama sistemi dinamik olarak modellenmi?tir. Sipari? toplama performansını geli?tirmek için, bağlantı elemanları üreten bir firmanın ambarı temel alınarak olu?turulmu? hipotetik bir ambar üzerinde vii çalı?ılmı?tır. Ambara ait farklı benzetim modelleri olu?turulmu?, depolama ve rotalama politikalarının alternatif kombinasyonları geli?tirilerek bu benzetim modellerinde kullanılmı?tır. Elde edilen benzetim sonuçlarına göre, en küçük sipari? toplama süresini veren depolama ve rotalama politikası kombinasyonu belirlenmi?tir. Son olarak, benzetim sonuçları üzerinde bazı istatistiksel analiz metotları uygulanmı?tır Order picking activities play a critical role in supply chain management in terms of both production systems (supplying components to assembly operations) and distribution operations (meeting customer demands). Trends in customer orders reveal that orders are transformed from few-and-large orders to many-and-small ones. On the other hand, lead times of customer orders get consistently shorter. Because of these changes, companies need to adopt an effective and flexible order picking system in order to remain competitive in the market. Order picking affects both overall logistic operations and service level provided to customers. Additionally, order picking process constitutes more than half of the total warehousing cost. For these reasons, it is crucial for companies to design and perform an effective order picking process. The aim of this study is evaluating and improving of the order picking system so as to minimize the order retrieval time while increasing the picking efficiency. Order retrieval time can be defined as the time elapsed for the process of retrieving products from storage area to meet a specific customer demand. Besides the critical factors such as storage assignment decisions and routing methods, replenishment problem of the storage areas, which is rarely addressed in the previous studies, has been taken into consideration in this study. Replenishment of the empty storage locations has been conducted by using the (S, s) inventory policy. Thus, the order picking system was modeled as a dynamic system. A hypothetical distribution warehouse, based on the real life warehouse of a company specialized in production of fasteners, has been studied in order to improve the order picking performance. Alternative combinations of routing and storage policies have been developed. Moreover, different simulation models of the order picking process were constructed. In these models, proposed alternative storage and v routing policies were operated. According to the simulation results, the storage policy and routing policy combination which provides the shortest order retrieval time is determined. Finally, using simulation results, some statistical analysis methods have been implemented

Data mining framework for batching orders in real-time warehouse operations

Warehouse activities play a key role in the final customer service level. From the warehouse processes, order picking is the major contributor to this category overall expenses. Order batching is commonly employed to improve the resources efficiency. Several heuristics have been proposed for the order batching problem, most of them developed for static batching, although scarce research has been focused on dynamic batching via stochastic modeling. We present an a novel approach to the problem developing a framework based on machine learning application directly to historical order batches data; gaining valuable knowledge regarding how are the batches formed and what attributes are the most meaningful in this process. This knowledge is then translated into simple batching decision rules capable of batch orders in a real-time scenario (dynamically). The framework was compared to FCFS heuristics and single picking; the results indicate higher performance

A Roadmap towards an Automated Warehouse Digital Twin: current implementations and future developments

Automation and digitization increase the effectiveness and efficiency of logistics processes. In warehousing, Automated Storage and Retrieval Systems (AS/RS) are largely adopted due to their considerable advantages over traditional warehousing, namely high space utilization, shorter cycle times and improved inventory control. To further enhance such advantages, warehouse operations can be digitized via a Digital Twin (DT) which retrieves data from the real-world industrial process, mimics its behaviour and feeds specific inputs back to the real-world process, after elaboration from a simulation-based digital model. This work presents a DT proposal for a real-world AS/RS system, highlighting its current implementations together with its future developments