Energy and Cycle Time Efficient Warehouse Design for Autonomous Vehicle-based Storage and Retrieval System

This study explores the best warehouse design for an autonomous vehicle based storage and retrieval system (AVS/RS) minimizing average energy consumption per transaction and average cycle time per transaction, simultaneously. In the design concept, we consider, rack design in terms of number of bays, number of tiers, number of aisles; number of resources, namely number of autonomous vehicles and lifts and; velocity profiles of lifts and autonomous vehicles in the AVS/RS. We completed 1,296 number of experiments in simulation to obtain Pareto solutions representing the “average energy consumption per transaction” and “average cycle time per transaction” trade-offs based on designs which is a very useful visual tool in decision making. Different from the existing studies, we approach to the warehouse design problem of AVS/RSs from a multi-objective view as well as energy efficient view minimizing both electricity consumption and cycle time per transaction in the system

A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system

With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches

Transaction processing policies in a flexible shuttle-based storage and re-trieval system by real-time data tracking under agent-based modelling

This study investigates priority assignment rules (PARs) for transaction processing in automated warehouses featuring a shuttle-based storage and retrieval system (SBSRS). By incorporating real-time data tracking through agent-based modeling, the research explores the unique aspect of the SBSRS design, which involves flexible travel of robotic order picker shuttles be-tween tiers. The paper proposes PARs under agent-based modeling to enhance multi-objective performance metrics, including average flow time (AFT), maximum flow time (MFT), outlier transaction AFT, and standard deviations of flow times (SD) within the system. Experimental evaluations are conducted with various warehouse designs, comparing the results against commonly used static scheduling rules. The findings demonstrate that real-time tracking policies significantly improve system performance. Specifically, prioritizing the processing of outliers based on transaction waiting time enhances MFT, SD, and other performance metrics, while minimizing adverse effects on AFT. Certain rules exhibit notable improvements in MFT and SD, while others achieve the lowest AFT values among all experiments. This paper contributes to the existing literature by presenting a multi-objective performance improvement procedure and highlighting the advantages of real-time data track-ing-based scheduling policies in automated warehousing systems

Simulation-based Energy and Cycle Time Analysis of Shuttle-based Storage and Retrieval System

This study explores the best warehouse design for shuttle-based storage and retrieval system (SBS/RS) minimizing average energy consumption per transaction and average cycle time per transaction, simultaneously. For that we provided average energy consumption per transaction versus average cycle time per transaction graphs, for different design scenarios of the studied SBS/RS warehouse. In the design concept, we considered, rack design in terms of number of bays, number of tiers, number of aisles, as well as velocity profiles of lifts in the system. We completed 144 number of experiments by simulation to see the trade-offs based on the design scenarios and provided them by two separate graphs. The results show that while the SBS/RS warehouse has low number of tiers, it has low energy consumption per transaction as well as low average cycle time per transaction in the two lift velocity scenarios

A Performance Calculator for Shuttle-based Storage and Retrieval System Design

In this study, we present an analytical model based tool that can estimate critical performance measures from a pre-defined shuttle-based storage and retrieval system (SBS/RS) design. SBS/RS is relatively a new automated storage and retrieval technology and mostly used for mini-load material handling. In this study, we develop an open queuing network model based tool estimating critical performance measures: the mean travel time of lifts/shuttles, utilization of lifts/shuttles, amount of energy consumption and energy regeneration per transaction, waiting times and number of jobs waiting in queues, etc., from a pre-defined SBS/RS design. By the developed tool, one can evaluate an SBS/RS design’s performance promptly by changing the input design parameters (e.g., distance between two adjacent bays/tiers, velocity of vehicles, acceleration/deceleration of vehicles, number of tiers, number of bays, number of aisles, arrival rates, weight of totes, etc.) in these systems

A Novel Autonomous Vehicle-based Storage and Retrieval System with Movable Lifts

This paper presents a novel autonomous vehicle-based storage and retrieval system with movable lifts (AVS/RS/ML) as an alternative automated warehousing technology to tier-captive shuttle-based storage and retrieval systems (SBS/RS). The proposed system aims to provide a cost-effective, highly efficient, and adaptable solution for warehouse operations utilizing automated guided vehicles (AGVs) capable of travelling both inside and outside the warehouse. We simulate and analyse the system\u27s performance based on its initial investment cost, throughput capacity, and average utilization of AGVs and movable lifts (MLs) under different warehouse designs

Transaction selection policy in tier-to-tier SBSRS by using deep q-learning

This paper studies a Deep Q-Learning (DQL) method for transaction sequencing problems in an automated warehousing system, Shuttle-based Storage and Retrieval System (SBSRS), in which shuttles can move between tiers flexibly. Here, the system is referred to as tier-to-tier SBSRS (t-SBSRS), developed as an alternative design to tier-captive SBSRS (c-SBSRS). By the flexible travel of shuttles between tiers in t-SBSRS, the number of shuttles in the system may be reduced compared to its simulant c-SBSRS design. The flexible travel of shuttles makes the operation decisions more complex in that system, motivating us to explore whether integration of a machine learning approach would help to improve the system performance. We apply the DQL method for the transaction selection of shuttles in the system to attain process time advantage. The outcomes of the DQN are confronted with the well-applied heuristic approaches: first-come-first-serve (FIFO) and shortest process time (SPT) rules under different racking and numbers of shuttles scenarios. The results show that DQL outperforms the FIFO and SPT rules promising for the future of smart industry applications. Especially, compared to the well-applied SPT rule in industries, DQL improves the average cycle time per transaction by roughly 43% on average

Multi-objective inventory optimization problem for a sustainable food supply network under lateral inventory share policy

This study dives deep into a lateral supply chain network for perishable food products and aims to determine optimal re-order and order up to levels for multiple e-groceries within a common network using a simulation-based optimization technique. The algorithm aims to minimize the average inventory carried within the network while accounting for parameters like reduced wastage, improved customer satisfaction level, and a limited number of replenishments

Autonomous mobile robot travel under deadlock and collision prevention algorithms by agent-based modelling in warehouses

Recent dramatic increase in e-commerce has also increased the adoption of automation technologies in warehouses. Autonomous mobile robots (AMRs) are from those technologies widely utilized in warehouse operations. It is important to design the operation of those robotic systems in such a way that, they meet the current and future system requirements correctly. In this paper, we study flexible travel of AMRs in warehouses by developing smart deadlock and collision prevention algorithms on agent-based modelling. By that, AMR agents can interact with each other and environment, so that they can make smart decisions maximizing their goals. We compare the performance of the developed flexible travel system with non-flexible designs where there is a single AMR dedicated to a specific zone so that no deadlock or collision possibility takes place. The results show that AMRs may provide up to 39% improvement in the flexible system compared to its non-flexible design