Automated Storage/retrieval Systems Research Articles

Dual cycle time modelling and design optimisation for the bidirectional flow-rack AS/RS

This article discusses the problem of evaluating performance and optimising the design of Automated Storage/Retrieval Systems (AS/RS) in a steady state, with a focus on the dual cycle in the bidirectional flow-rack (BFR) AS/RS. To accomplish this, two well-known approaches (discrete and continuous) are utilised to model the dual cycle time. The developed models reveal that the system is near optimal when designed to be square in time. Furthermore, the continuous models are shown to be relatively accurate compared to the discrete counterparts, with discrepancies under 6%. Subsequently, the optimal dimensions of the system are identified through design optimisation aimed at minimising the cycle time. The findings indicate the optimal depth decreases as load rate increases, while optimal height and length follow a cubic root function of system volume. Approximate formulas are derived, allowing for direct calculation of optimal dimensions based on the system size and its load rate. Finally, the article evaluates the system's overall performance by calculating its throughput, providing a tool to estimate capacity. These findings provide an essential tool for optimising the system's performance in a steady state during the upstream design and installation phase, prior to making significant investments.

A Simulation-Based Experimental Design for Analyzing Energy Consumption and Order Tardiness in Warehousing Systems

For warehouses to be more sustainable and cost-effective, it is essential to consider energy consumption (EC) and order tardiness (OT) together in evaluating warehouse activities since improving both EC and OT at the same time is very demanding. While existing studies try to improve EC and OT, the current studies consider only either a reserve area or a forward area between the two major warehouse areas. Thus, this study proposes a simulation-based approach to assessing EC and OT when reserve and forward areas are considered together in one framework for different configurations of five important warehousing parameters: (i) number of forklifts, (ii) number of storage/retrieval (S/R) machines, (iii) number of automated storage/retrieval systems (AS/RS) input/output (I/O) points, (iv) order size, and (v) proportions of order flows through a reserve or forward area. In particular, we use real forklift movement and energy data for our simulation models to provide a more realistic analysis. By building the simulation model with the 25 full factorial experimental design, we analyze the results with analysis of variance (ANOVA). The resulting Pareto-optimal solutions show that less traffic flows through a reserve area can help improve both EC and OT while other factors have smaller or limited effects on the two responses. Also, the order flow factor has the largest effect on EC while order size has the largest effect on OT. The results from this study can help warehouse operators make informed decisions in considering and finding a trade-off between sustainability and customer satisfaction.

Development of an open-source data-driven simulator for the unit-load multi-aisle automated storage and retrieval systems

ABSTRACT Discrete-event simulations are widely used to research automated storage/retrieval systems (AS/RS). However, using commercial general-purpose simulators for this purpose has limitations such as lack of specific functionalities to capture the peculiarities of AS/RS, low model reusability, and lack of access to source code. Consequently, researchers have developed their own bespoke programs to meet their specific needs. These programs are specific to their research objective and are not meant for easy adoption, modification, or extension. As a result, there has been a lot of duplication of efforts across different studies. Motivated by this requirement for a customisable special-purpose simulator for AS/RS, this paper develops an open-source data-driven discrete-event simulator that allows its user to create and run simulation models of multi-aisle AS/RS without needing to write any code. The data-driven approach allows the quick creation of models of different multi-aisle AS/RS configurations and control policies. The simulator is developed in Python programming language, leveraging the functionalities of various libraries in its ecosystem. The simulator’s architecture is kept modular to facilitate its management, modification, and extension. The simulator’s features and ability to adapt to changes in input data are demonstrated through three example scenarios.

Modelling and optimisation of single-cycle time for mobile rack AS/RS

ABSTRACT This paper deals with mobile-rack automated storage/retrieval systems (AS/RS) that are a particular physical configuration of unit-deep AS/RS, i.e. the class of systems where each cell is designed to contain a single load. There are two main contributions in this paper: the first one is the analytical modelling of the average single-cycle time for mobile-rack AS/RS. The approach adopted for modelling is based on a continuous approximation, which is not recent, however, the approach proposed here for the transition from the discrete to the continuous models is different from those used previously in the literature. In a certain way, the multi-aisle and unit-load configurations can be seen as special cases of the mobile-rack configuration. This allows to deduce analytical models for the average single-cycle time for these two configurations from the one developed for mobile rack. The second main contribution of this paper is to find the dimensions of the AS/RS that minimise the average single-cycle time. The optimisation has been studied formally for the multi-aisle and unit-load configurations, and numerically for the mobile-rack configuration.

Simulation-Based Optimization Approaches for Dealing with Dual-Command Crane Scheduling Problem in Unit-Load Double-Deep AS/RS Considering Energy Consumption

Automated storage/retrieval systems (AS/RS) have been increasingly used to support operations in manufacturing firms, warehouses, and distribution centers. Usually, AS/RSs are expensive. To achieve a good return on investment (ROI), an AS/RS must operate optimally. This research focuses on solving the crane scheduling problem, which has a great and immediate impact on the performance of an AS/RS. To optimize the design and operations of an AS/RS, many past studies have applied the simulation approach. However, the simulation and optimization have been often loosely coupled, resulting in a rigorous and labor-intensive optimization procedure. Using population- and evolution-based metaheuristics to deal with the crane scheduling problem of an AS/RS is one of the research trends. However, the whale optimization algorithm (WOA) and its variants have not been used for this purpose. To address the said gaps, this research first proposes a framework for coupling the simulation and optimization closely, in which various heuristics/metaheuristics, including first-come first-serve (FCFS), RANDOM, WOA, genetic algorithms (GAs), particle swarm optimization (PSO), and especially an improved WOA (IWOA), together with dynamic programming (DP), have been used as alternative sequencing methods. Based on this framework, different simulation-based optimization approaches have been developed for solving the dual-command crane scheduling problem in a unit-load double-deep AS/RS. The experimental results show that IWOA+DP outperforms the others in terms of energy consumption.

Increasing Throughput in Warehouses: The Effect of Storage Reallocation and the Location of Input/Output Station

Automatic warehousing systems are a sort of green technology that is becoming increasingly popular in the logistics business. Automated Storage/Retrieval Systems (AS/RS) are one of the most significant components of advanced automated logistics and manufacturing systems. The majority of AS/RS systems use input/output (I/O) points located in the lower left corner of the rack. These systems are reaching their maximum capacity because of their layout design limitations. Breakthrough solutions are needed to enhance the performance of existing systems. In this study, we examined how the location of I/O station can affect the total travel time. Another strategy for enhancement is a two-step preparation method. In this strategy, the allocation of the storage is changed, in the idle time, to be closer to the I/O point to reduce the service time for a class-based storage assignment. An analytical model was used to introduce for the first time optimal configurations of this strategy. We tested the suggested strategy using a simulation model created using R software, specifically designed for this purpose. Results showed that the two-step preparation strategy took between 1.2 and 1.9 h before the shift starts. The enhancement on throughput is almost the same for both possible locations of the I/O point. The results also showed that the two strategies (location of the I/O point and reallocation of storage) could increase throughput by about 21% to 28%, depending on parameters such as the number of orders and the height of the storage rack.

Large-Scale Storage/Retrieval Requests Sorting Algorithm for Multi-I/O Depots Automated Storage/Retrieval Systems

This paper addresses the sequence sorting problem of large-scale storage/retrieval (S/R) requests in multiple Input/Output (multi-I/O) depots automated storage/retrieval systems (AS/RS), in which the cargoes can enter/leave the system through multi-I/O depots, the stacker can load only one cargo, and the load travel time of stacker is fixed. The problem is to find an optimal sequence for a certain S/R requests sequence, and it is a special kind of traveling salesman problem. In this paper, a heuristic algorithm based on assignment is proposed. In order to eliminate the subloops emerged in the sorting process, the equivalent merging and minimum cost merging methods of subloops are considered, and the proposed algorithm is modified. Experimental results indicate the effectiveness of the proposed algorithm.

A flexible approach to designing a single crane, multi-aisle automated storage/retrieval system considering storage policies, transport equipment and demand skew

ABSTRACTMulti-aisle automated storage/retrieval system (AS/RS) can be applied to situations where the throughput requirement is low relative to storage requirement for gaining cost advantage. This study considers such a system under class-based and full-turnover-based storage policies, which are generally neglected in the multi-aisle AS/RS context. The study develops expressions to determine the optimal number and dimensions of storage racks for different storage policies, demand skewness, and transport and storage equipment characteristics. Propositions are made related to the optimal dimensions, optimal travel time and equipment characteristics. Further, this study develops a comprehensive and flexible three-stage design methodology that systematically generates design alternatives, and filters and evaluates them using user defined criteria. Finally, this proposed design methodology is illustrated with an example.

Optimizing automated sorting in warehouses: The minimum order spread sequencing problem

In warehouses, order consolidation processes are inevitable whenever picking orders are assembled under a zoning and/or batching policy. In our specific warehouse setting, bins containing partial orders picked under a zoning and batching policy are intermediately stored in an automated storage/retrieval system (ASRS) and, afterwards, released on a conveyor system supplying the consolidation area. Here, a fully-automated sorter merges partial orders dedicated to the same customer in packing stations where, finally, the customers’ cardboard boxes are packed. For sequencing the release of bins from the ASRS we derive an elementary optimization problem, which aims to minimize the spread of orders in the release sequence so that picking orders are quickly assembled at their packing stations. The resulting problem is formalized, computational complexity is proven, and efficient solution procedures are provided. Additionally, managerial aspects are addressed with the help of a comprehensive simulation study.

Closed form models for dwell point locations with turnover-based storage assignment policy

This study investigates where an idle storage-retrieval device should dwell (locate) to minimise the expected response time of its next transaction for both an automated storage/retrieval system (AS/RS) and an automated storage carousel (ASC). A review of the relevant literature showed that previous attempts to obtain the dwell point location only considered a randomised storage assignment policy. This research proposes that under a myriad of operating conditions, the problem can be formulated as a p-median location problem on a tree network to incorporate the turnover-based storage assignment policy.

Travel-time model of dual-command cycles in a 3D compact AS/RS with lower mid-point I/O dwell point policy

Three-dimensional compact automated storage/retrieval systems (AS/RS) have been extensively applied in warehouses, with advantages of full automation, time efficiency and high space utilisation. While previous studies that use lower mid-point input/output (I/O) dwell point policy consider single-command cycles (SC), this paper builds travel-time models of dual-command cycles (DC). The S/R crane also dwells in the lower mid-point of the rack when it is idle. We validate analytical models using simulation and use analytical models to optimise system dimensions. Numerical experiments are used to compare DC with SC. The results show that DC outperform SC in terms of cycle time of one command.

Research on Modeling of Stereo Warehouse Based on Colored Timed Petri Nets

This paper expounds theory and method of constructing the model of Automated Storage /Retrieval Systems(AS/RS) via Colored Timed Petri Net (CTPN), in which token denotes the task number, stacker and RGV. The color domain is described as the sequence during the process of the task execution. Moreover, a process of the AS/RS modularization framework is illustrated in this thesis.

Time and energy optimal unit-load assignment for automatic S/R warehouses

Effectiveness in warehouse design and operations is crucial for the industrial companies to be competitive in the market arena by reducing the response time and inbound costs, increasing their global service level. Storage assignment deals with the definition of effective strategies to organise items into industrial warehouses to achieve high performances. This paper enhances the conventional approaches on storage assignment proposing a time and energy bi-objective model for single-deep rack automatic warehouses served by aisle captive automated storage/retrieval systems (AS/RSs). The model is based on the joint minimisation of the travel time and the energy required by the cranes to S/R the unit-loads. The analytic models to compute the expected single-command cycle time and energy are integrated into a bi-objective model, optimising the load assignment. An application, taken from the beverage industry, is, finally, discussed. The different perspectives of adopting time and energy to drive the load assignment are stressed proposing a practical trade-off rule. Results highlight the possibility to balance the time cycle and the energy intensity. Within the case application, an energy saving of 12.66% respect to the time model occurs with a time efficiency decrease of about 2.52%.

Model Predictive Control of an Automated Storage/Retrieval System

Discrete-event systems occur often in the manufacturing field, but due to the characteristics of these systems Model Predictive Control (MPC) is not frequently used for them. This paper approaches MPC of an Automated Storage/Retrieval System (AS/RS) by using Mixed Logical Dynamical (MLD) modelling. Propositional calculus is used to transform the non-linear dynamic model equations and constraints of the AS/RS into the MLD form. We consider a performance index that makes sure that customer demand is satisfied as soon and as efficiently as possible. The MLD model and performance index are reformulated as an integer linear programming problem. A case study is performed on a laboratory stacker crane, and the simulations results illustrate the good performance of the control algorithm.

Travel-time models and fill-grade factor analysis for double-deep multi-aisle AS/RSs

Double-deep multi-aisle automated storage/retrieval systems are increasingly applied for storing and retrieving unit loads, with advantages of increased space utilisation, reduced number of aisles and improved efficiency of storage rack (S/R) machines. In such systems, the retrieval process may consist of the rearrangement of blocking loads, based on the assumptions of uniformly distributed storage locations and random storage policy. We formulate analytical travel-time models of both single- and dual-command cycles under three rearrangement rules. We validate the analytical travel-time models by simulation and conduct numerical experiments to analyse the effect of the number of aisles an S/R machine serves, the fill-grade factor and the command cycles on the expected travel time of the S/R machine. The results show that the expected travel time of the S/R machine is increasing with the increase in the number of aisles an S/R machine serves and the increase in the fill-grade factor, and dual command cycle outperforms single-command cycle in terms of cycle time. To deal with the trade-off between the storage space cost and the operational cost of the S/R machine, we develop a decision model for finding an optimal fill-grade factor to minimise the total cost. We find the condition when an optimal fill-grade factor exists and show how to calculate it. Based on the decision model, we compare the performance of double-deep multi-aisle automated storage/retrieval system (AS/RSs) and single-deep single-aisle AS/RSs. The results show that double-deep multi-aisle AS/RSs outperform single-deep single-aisle AS/RSs in terms of total cost, although double-deep multi-aisle AS/RSs need more storage locations.

Variable neighborhood search heuristic for storage location assignment and storage/retrieval scheduling under shared storage in multi-shuttle automated storage/retrieval systems

This paper examines the joint optimization of storage location assignment and storage/retrieval scheduling in multi-shuttle automated storage/retrieval systems (AS/RSs) under shared storage, in which the reuse of empty location yielded by retrieval operation is allowed. From the view of analytical model, the advantage of operational mode under shared storage is verified. A variable neighborhood search (VNS) algorithm is developed to solve the large-sized problems. Various numerical experiments are conducted to evaluate the performance of the proposed algorithm and investigate the impact of different parameters on computational efficiency.

Mathematical modeling of retrieval travel time for flow-rack automated storage and retrieval systems

Automated storage/retrieval systems (AS/RS) are computer-controlled automated material handling systems, widely used in industry and warehousing systems. They have interesting advantages, the majors of which are a high throughput, efficient use of space and improvement of safety. All the disadvantages of AS/RS are related to economic factors such as high initial investments and difficulty to change layout. This implies the importance of careful design of the AS/RS by taking into account all constraints. The latter may be linked either to the AS/RS dimensions or to products. The evaluation of average travel time that the S/R machine takes to store and retrieve a product is necessary for an optimal design. The present paper is devoted to a particular configuration of AS/RS said flow-rack AS/RS. The flow-rack AS/RS consists of only one deep rack and two machines; the first is used for storing operations and the second for retrieval operations. Our aim is to model mathematically the average travel time of the retrieval machine under random storage. The mathematical model proposed hereunder is certainly function of the physical parameters of the flow-rack AS/RS such as length, width, depth, etc.; but it is also function of the variety of products stored in the system and of their proportions. The mathematical model could be useful for performance evaluation and for design considerations of the flow-rack AS/RS in real world applications. The validation of the model is done by simulation.

Improving the Performance of a New Storage and Retrieval Machine Based on a Parallel Manipulator Using FMEA Analysis

Automated Storage/Retrieval Systems (AS/RS) are computer-controlled material handling systems that can automatically store and retrieve loads with high throughput. In this work, we estimate important design parameters for a newly designed storage/retrieval (SGP-AS/RS) machine based on the specific input details to come up with an optimized design solution. The new system is based on cable- driven parallel manipulator (or wire robot) technology that is similar to a parallel Stewart-Gough platform architecture. For the reliability of such a system, a Failure Mode Effect Analysis (FMEA) is carried out to find out how much reliable is this machine as a whole based on the of DIN's EN 60812 standards. This International Standard describes methods for FMEA and helps not only to optimize the machine, but also to achieve a disturbance free operation.

Inventory holding costs measurement: a multi-case study

Purpose – Logisticians in the worldwide industry are frequently faced with the problem of measuring the total cost of holding inventories with simple and easy-to-use methodologies. The purpose of this paper is to look at the problem, and in particular illustrate the inventory holding cost rate computation, when different kind of warehousing systems are applied. Design/methodology/approach – A multiple case study analysis is here developed and supported by a methodological framework directly derived from the working group discussions and brainstorming activities. Two different field of application are considered: one related to five companies with manual warehousing systems operating with traditional fork lift trucks; the other is among five companies operating with automated storage/retrieval systems (AS/RS) to store inventories. Findings – The multi-case study helps to understand how the holding cost parameter is currently computed by industrial managers and how much the difference between manual and automated/automatic warehousing systems impacts on the inventory cost structure definition. The insights from the ten case studies provide evidence that the kind of storage system adopted inside the factory can impact on the holding cost rate computation and permit to derive important considerations. Practical implications – The final aim of this work is to help industrial engineers and logisticians in correctly understanding the inventory costs involved in their systems and their cost structure. In addition, the multi-case analysis leads to considerations, to be applied in different industrial contexts. As other industrial applications are identified, they may be analyzed by using the presented methodology, and with aid from the data from this paper. Originality/value – The relevance of this work is to help industrial engineers and logisticians in understanding correctly the inventory costs involved in their logistics systems and their cost structure. In addition, the multi-case analysis lead to interesting final considerations, easily to be applied in different industrial contexts. As other industrial applications are identified, they may be analyzed by using the methodology and extrapolating the data from this paper.

Order scheduling of multiple stacker cranes on common rails in an automated storage/retrieval system

An order scheduling method for improving the work efficiency of multiple cranes on a common rail in an automated storage/retrieval system (AS/RS) is proposed in this paper. Most AS/RSs are equipped with one stacker crane. However, this is logistically challenging, and higher work efficiency in warehouses, such as those using more than one stacker crane, is required. In this paper, a warehouse with multiple stacker cranes working simultaneously is proposed. Unlike warehouses with only one crane, order scheduling is very difficult because there are multiple cranes working simultaneously and collisions are possible. Warehouse order must be maintained while avoiding collisions. Sometimes, detours are necessary to avoid collisions. As a result, work efficiency will decrease if a sequence of orders is not properly planned. Nevertheless, as transport works in AS/RSs are occurring randomly, scheduling cannot be conducted in advance, which makes the order scheduling of multiple cranes difficult. A fast order scheduling method is proposed here. Several orders are combined into an order cluster, and dynamic programming is used to obtain the best combination of order clustering. By utilising this method, cranes can work cooperatively, improving work efficiency to 0.95 with a two-crane AS/RS and to 0.78 with a three-crane AS/RS.