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

Robotized Warehouse Systems: Developments and Research Opportunities

Robotized handling systems are increasingly applied in distribution centers. They require little space, provide flexibility in managing varying demand requirements, and are able to work 24/7. This makes them particularly fit for e-commerce operations. This paper reviews new categories of robotized handling systems, such as the shuttle-based storage and retrieval systems, shuttle-based compact storage systems, and robotic mobile fulfillment systems. For each system, we categorize the literature in three groups: system analysis, design optimization, and operations planning and control. Our focus is to identify the research issue and OR modeling methodology adopted to analyze the problem. We find that many new robotic systems and applications have hardly been studied in academic literature, despite their increasing use in practice. Due to unique system features (such as autonomous control, networked and dynamic operation), new models and methods are needed to address the design and operational control challenges for such systems, in particular, for the integration of subsystems. Integrated robotized warehouse systems will form the next category of warehouses. All vital warehouse design, planning and control logic such as methods to design layout, storage and order picking system selection, storage slotting, order batching, picker routing, and picker to order assignment will have to be revisited for new robotized warehouses

A Framework for the Robust Design of Unit Load Storage Systems

The unit load storage assignment problem determines the assignment of a set of unit loads with known arrival and departure times to a set of unit storage locations in a warehouse. The material handling device(s) can carry at most one unit load at the time. In this research it is assumed that each of the storage locations can be accessed directly without load relocations or rearrangements and that the travel times between the storage locations and from and to the warehousing docks can be computed in advance. The objective is to minimize the total travel time of the material handling device for performing a number of storage and retrieval operations. This type of storage system is in widespread use and implemented in both mechanized and automated systems. It is by far one of the most common storage system architectures for unit loads. The formulation of this problem belongs to the class of Assignment Problems (AP) but finding the optimal solution for the most general variant is provably hard for large problem instances. A classification of the different variants of the APs for unit loads will be presented. The size of the instance problem is proportional to the product of the number of loads and the number of locations and the number of periods in the planning horizon and is typically very large for real world problem instances. Efficient solutions algorithms only exist for product-based storage policies or for the very special case of a perfectly balanced warehouse for load-based storage policies. However, for load-based storage policies the integrality property is not satisfied in general. This results in very large binary programming problems that to date cannot be solved to optimality. However, the formulations have special structure that can be exploited to design efficient solution algorithms. Properties and the special structure of the formulation will be presented. A specialized compound solution algorithm combines primal and dual approaches and heuristics to reduce the optimality gap. Initial computational experience will be shared. It is anticipated that the solution algorithm can either be directly implemented in commercial warehouse management systems or that it becomes a tool to evaluate the performance of commercially implemented storage policies. The above formulation is the sub problem in a decomposition algorithm for the design of unit load storage systems that identifies the tradeoffs between efficiency and risk of the performance of the storage system. Different risk measures such as the standard deviation and the downside risk can be used. An example based on realistic data values shows that in this case operator-controlled systems are less expensive and more risky than automated systems. However, if the same level of risk is mandated then the automated system is less expensive

A multi-objective optimization model for minimizing investment expenses, cycle times and co2 footprint of an automated storage and retrieval systems

A new optimization model of Automated Storage and Retrieval Systems (AS/RS) containing three objective and four constraint functions is presented in this paper. Majority of the researchers and publications in material handling field had performed optimization of different decision variables, but with single objective function only. Most common functions are: minimum travel time, maximum throughput capacity, minimum cost, maximum energy efficiency, etc. To perform the simultaneous optimization of objective functions (minimum: "investment expenses", "cycle times", "CO2 footprint") the Non-dominated Sorting Genetic Algorithm II (NSGA II) was used. The NSGA II is a tool for finding the Pareto optimal solutions on the Pareto line. Determining the performance of the system is the main goal of our model. Since AS/RS are not flexible in terms of layout and organizational changes once the system is up and running, the proposed model could be a very helpful tool for the warehouse planners in the early stages of warehouse design

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

WebASRS – a Web-based Tool for Modeling and Design of Abstract Unit-load Picking Systems

This paper describes a web-based tool that supports the modeling and design of abstract unit-load picking systems. The term “abstract” implies that the model is not specific to any equipment or vendors’ products, but, instead, focuses on the generic system components such as pallets, racks, slots, forklifts, cranes, etc. that comprise typical unitload picking systems. The objectives of the tool are to support the design of an AS/RSbased or a manual forklift-based picking system based on a set of design parameters and to be able to convert from an AS/RS design to a flat warehouse design and vice versa. The research objective is to design the formal model (the data structure and operational description) that supports the conversion from one type to the other and supports the generation of static and dynamic analysis models and the recording of the analysis results. The web implementation uses a mix of XML, HTML, JavaScript and PHP and implements two existing analysis methodologies from the literatur

A multi-objective optimization model for minimizing cost, travel time and Co2 emission in an AS/RS

U predstavljenom radu, diskutovano je i vršena je evaluacija višeciljne optimizacije automatskih skladišnih sistema. S obzirom da je većina istraživača u oblasti manipulacije materijalom i logistici, vršila optimizaciju promenljivih samo sa jednom funkcijom cilja (najčešće je to funkcija minimalno vreme ciklusa, maksimalni intenzitet opsluživanja, minimalna cena, tj. troškovi, maksimalna energetska efikasnost, itd.), predložen je model višeciljne optimizacije (sa trima funkcijama cilja: minimalni troškovi - minimalno vreme ciklusa - minimalna emisija CO2, odnosno maksimalna energetska efikasnost). Za optimizaciju promenljivih u funkcijama cilja, korišćeni su genetski algoritmi. Da bismo pronašli optimalna Pareto rešenja, upotrebili smo NSGA II genetske algoritme. Glavni zadatak našeg doprinosa jeste da se utvrde performanse automatskog skladišnog sistema, u skladu sa procedurom višeciljne optimizacije. Rezultati predloženog modela, mogu biti od koristi projektantima u ranim fazama projektovanja automatskog skladišnog sistema.A multi-objective optimization of automated warehouses is discussed and evaluated in the present paper. Since most of the researchers in material handling community had performed optimization of decision variables with single objective function only (usually named with minimum travel time, maximum throughput capacity, minimum cost, maximum energy efficiency, etc.), the multi-objective optimization (cost - travel time - CO2 emission/ energy efficiency) will be presented. For the optimization of decision variables in objective functions, the method with genetic algorithms was used. To find the Pareto optimal solutions, the NSGA II genetic algorithm was used. The main objective of our contribution is to determine the performance of the system according to the multi-objective optimization technique. The results of the proposed model could be useful tool for the warehouse designer in the early stage of warehouse design

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