Requirements for Generating Learning Environments for Autonomous Systems Behavior in a Digital Continuum

Autonomous systems in material handling are increasingly prevalent in logistics, offering benefits such as flexibility, adaptability, robustness, and sustainability. To fully harness these advantages, a novel paradigm, the Digital Continuum, is proposed for the development and operation of such systems. A critical component of the Digital Continuum is a deeply integrated digital system model, which serves as a simulation, training, and test environment for virtual agents corresponding to physical robots. To ensure robust performance in learned behavior, a large number of learning environments is needed, thus highlighting the importance of an automated generation process. This process can significantly reduce modeling effort and is yet to be developed. This paper presents the derivation of requirements for an automated learning environment generation approach, unifying elements from Digital Continua, intralogistics, and robotics domains. Furthermore, the paper briefly discusses the research gap in the context of existing procedural content generation and domain randomization approaches. By addressing these requirements and bridging the research gap, a generation approach has the potential to profoundly facilitate the development and operation of autonomous systems in logistics

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

Regional Data Archiving and Management for Northeast Illinois

This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the regional transportation agencies, from both technical and business perspectives, about building such a comprehensive transportation information system. Several implementation alternatives are identified and analyzed. This research is carried out in three phases. In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect information on all data elements that they store, including the format, system, and granularity. Their perception of a data archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the hardware, software, labor, and resource requirements. We also identify possible revenue opportunities. A few implementation options for the archive system are summarized in this report; namely: 1. System hosted by a partnering agency 2. System contracted to a university 3. System contracted to a national laboratory 4. System outsourced to a service provider The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe

Order-picking workstations for automated warehouses

The FALCON (Flexible Automated Logistic CONcept) project aims at the development of a new generation of warehouses and distribution centers with a maximum degree of automation. As part of the FALCON project, this dissertation addresses the design and analysis of (automated) workstations in warehouses with an end-of-aisle order-picking system (OPS). Methods are proposed for architecting, quantifying performance, and controlling such a system. Four main topics are discussed in this dissertation. First, a modular architecture for an end-of-aisle OPS with remotely located workstations is presented. This architecture is structured into areas and operational layers. A hierarchical decentralized control structure is applied. A case of an industrial-scale distribution center is presented to demonstrate the applicability of the proposed architecture for performance analysis using the process algebra-based simulation language $\chi$ (Chi). Additionally, it is demonstrated how the architecture allows straightforward modification of the systems configurations, design parameters, and control heuristics. Second, a method to quantify the operational performance of order-picking workstations has been developed. The method is based on an aggregate modeling representation of the workstation using the EPT (Effective Process Time) concept. A workstation is considered in which a human picker is present to process one customer order at a time while products for multiple orders arrive simultaneously at the workstation. The EPT parameters are calculated from arrival and departure times of products using a sample path equation. Two model variants have been developed, namely for workstations with FCFS (First-Come-First-Serve) and for workstations with non-FCFS processing of products and orders. Both models have been validated using data from a real, operating workstation. The results show that the proposed aggregate modeling methodology gives good accuracy in predicting product and order flow time distributions. Third, the dissertation studies the design and control of an automated, remotely located order-picking workstation that is capable of processing multiple orders simultaneously. Products for multiple orders typically arrive out-of-sequence at the workstation as they are retrieved from dispersed locations in the storage area. The design problem concerns the structuring of product/order buffer lanes and the development of a mechanism that overcomes out-of-sequence arrivals of products. The control problem concerns the picking sequence at the workstation, as throughput deteriorates when a poor picking sequence is applied. An efficient control policy has been developed. Its performance is compared to a number of other picking policies including nearest-to-the-head, nearest neighbor, and dynamic programming. Subsequently, the resulting throughput and queue length distribution are evaluated under different settings. Insights for design considerations of such a system are summarized. Finally, the dissertation reflects on the findings from the proposed methods and uses them to come up with comprehensive design principles of end-of-aisle OPS with remotely located workstations. The various issues influencing the performance of such a system are highlighted. Moreover, the contribution of each proposed method with regards to these issues is delineated

Optimization of Automated Guided Vehicles (AGV) Fleet Size With Incorporation of Battery Management

An important aspect in manufacturing automation is material handling. To facilitate material handling, automated transport systems are implemented and employed. The AGV (automated guided vehicle) has become widely used for internal and external transport of materials. A critical aspect in the use of AGVs is determining the number of vehicles required for the system to meet the material handling requirements. Several models and simulations have been applied to determine the fleet size. Most of these models and simulations do not incorporate the battery usage of the vehicles and the effect it can have on the throughput and the number of AGVs required for the system. The goal of this research is to develop a simulation model to determine the optimized number of AGVs that is capable of increasing throughput while meeting the material handling requirements of the system. This model incorporates the battery management aspect and issues, which are usually omitted in AGV research. This includes the charging options and strategies, the number and location of charging stations, maintenance, and extended charging. The analysis entails studying various scenarios by applying different charging options and strategies and changing different parameters to achieve improved throughput and an optimized AGV fleet size. The results clearly show that battery management can have a significant effect on the average throughput and the AGV usage. It is important that the battery management of the AGVs is addressed adequately to run an AGV system efficiently

Hybrid order picking : A simulation model of a joint manual and autonomous order picking system

Order picking is a key process in supply chains and a determinant of business success in many industries. Order picking is still performed manually by human operators in most companies; however, there are also increasingly more technologies available to automate order picking processes or to support human order pickers. One concept that has not attracted much research attention so far is hybrid order picking where autonomous robots and human order pickers work together in warehouses within a shared workspace for a joint target. This study presents a simulation model that considers various system characteristics and parameters of hybrid order picking systems, such as picker blocking, to evaluate the performance of such systems. Our results show that hybrid order picking is generally capable of improving pure manual or automated order picking operations in terms of throughput and total costs. Based on the simulation results, promising future research potentials are discussed

Progress in Material Handling Research: 2016

Table of contents

Design choices for agent-based control of AGVs in the dough making process

In this paper we consider a multi-agent system (MAS) for the logistics control of Automatic Guided Vehicles (AGVs) that are used in the dough making process at an industrial bakery. Here, logistics control refers to constructing robust schedules for all transportation jobs. The paper discusses how alternative MAS designs can be developed and compared using cost, frequency of messages between agents, and computation time for evaluating control rules as performance indicators. Qualitative design guidelines turn out to be insufficient to select the best agent architecture. Therefore, we also use simulation to support decision making, where we use real-life data from the bakery to evaluate several alternative designs. We find that architectures in which line agents initiate allocation of transportation jobs, and AGV agents schedule multiple jobs in advance, perform best. We conclude by discussing the benefits of our MAS systems design approach for real-life applications

Using Semantic Web Services for AI-Based Research in Industry 4.0

The transition to Industry 4.0 requires smart manufacturing systems that are easily configurable and provide a high level of flexibility during manufacturing in order to achieve mass customization or to support cloud manufacturing. To realize this, Cyber-Physical Systems (CPSs) combined with Artificial Intelligence (AI) methods find their way into manufacturing shop floors. For using AI methods in the context of Industry 4.0, semantic web services are indispensable to provide a reasonable abstraction of the underlying manufacturing capabilities. In this paper, we present semantic web services for AI-based research in Industry 4.0. Therefore, we developed more than 300 semantic web services for a physical simulation factory based on Web Ontology Language for Web Services (OWL-S) and Web Service Modeling Ontology (WSMO) and linked them to an already existing domain ontology for intelligent manufacturing control. Suitable for the requirements of CPS environments, our pre- and postconditions are verified in near real-time by invoking other semantic web services in contrast to complex reasoning within the knowledge base. Finally, we evaluate our implementation by executing a cyber-physical workflow composed of semantic web services using a workflow management system.Comment: Submitted to ISWC 202