Challenges of developing an electro-optical system for measuring man's operational envelope

In designing work stations and restraint systems, and in planning tasks to be performed in space, a knowledge of the capabilities of the operator is essential. Answers to such questions as whether a specific control or work surface can be reached from a given restraint and how much force can be applied are of particular interest. A computer-aided design system has been developed for designing and evaluating work stations, etc., and the Anthropometric Measurement Laboratory (AML) has been charged with obtaining the data to be used in design and modeling. Traditional methods of measuring reach and force are very labor intensive and require bulky equipment. The AML has developed a series of electro-optical devices for collecting reach data easily, in computer readable form, with portable systems. The systems developed, their use, and data collected with them are described

A Supporting Decisions Platform for the Design and Optimization of a Storage Industrial System

Warehouses are one of the most critical resources in production systems, whose performance significantly depend on the availability of materials in the right location, in the right quantity and at the right time. Literature presents many contributions for the design and control of a storage system, but a few of them discuss on the importance of an integrated approach based on the adoption of different supporting decisions models and tools, from mixed integer linear programming (MILP) to visual interactive simulation (VIS), passing through heuristic procedures and cluster analysis (CA). This chapter presents a conceptual and integrated framework for the design, management, control and optimization of both manual, i.e. man-on-board, picker to part and automated, i.e. part to picker, storage systems, both unit-load and less than unit-load order picking systems (OPS), by the development and application of different models and tools. The proposed framework integrates the management decisions in order to find not a system configuration as a result of local optima, but the minimal cost warehousing system as a result of the following integrated decisions: the space allocation to the forward area and the bulk area in a OPS, the system layout, the storage allocation within each area, i.e. the determination of the storage level devoted to a stock keeping unit (sku) both in fast pick area and in reserve area, the storage locations assignment, i.e. the determination of the warehousing system location to be assigned to a sku, the routing policies, the operating procedures, etc. A discussion on supporting decisions models and tools useful for practitioners of industry to face these critical problems is presented and finally a case study illustrated

A machine learning approach for predictive warehouse design

Warehouse management systems (WMS) track warehousing and picking operations, generating a huge volumes of data quantified in millions to billions of records. Logistic operators incur significant costs to maintain these IT systems, without actively mining the collected data to monitor their business processes, smooth the warehousing flows, and support the strategic decisions. This study explores the impact of tracing data beyond the simple traceability purpose. We aim at supporting the strategic design of a warehousing system by training classifiers that can predict the storage technology (ST), the material handling system (MHS), the storage allocation strategy (SAS), and the picking policy (PP) of a storage system. We introduce the definition of a learning table, whose attributes are benchmarking metrics applicable to any storage system. Then, we investigate how the availability of data in the warehouse management system (i.e. varying the number of attributes of the learning table) affects the accuracy of the predictions. To validate the approach, we illustrate a generalisable case study which collects data from sixteen different real companies belonging to different industrial sectors (automotive, manufacturing, food and beverage, cosmetics and publishing) and different players (distribution centres and third-party logistic providers). The benchmarking metrics are applied and used to generate learning tables with varying number of attributes. A bunch of classifiers is used to identify the crucial input data attributes in the prediction of ST, MHS, SAS, and PP. The managerial relevance of the data-driven methodology for warehouse design is showcased for 3PL providers experiencing a fast rotation of the SKUs stored in their storage systems

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Augmented reality at the workplace : a context-aware assistive system using in-situ projection

Augmented Reality has been used for providing assistance during manual assembly tasks for more than 20 years. Due to recent improvements in sensor technology, creating context-aware Augmented Reality systems, which can detect interaction accurately, becomes possible. Additionally, the increasing amount of variants of assembled products and being able to manufacture ordered products on demand, leads to an increasing complexity for assembly tasks at industrial assembly workplaces. The resulting need for cognitive support at workplaces and the availability of robust technology enables us to address real problems by using context-aware Augmented Reality to support workers during assembly tasks. In this thesis, we explore how assistive technology can be used for cognitively supporting workers in manufacturing scenarios. By following a user-centered design process, we identify key requirements for assistive systems for both continuously supporting workers and teaching assembly steps to workers. Thereby, we analyzed three different user groups: inexperienced workers, experienced workers, and workers with cognitive impairments. Based on the identified requirements, we design a general concept for providing cognitive assistance at workplaces which can be applied to multiple scenarios. For applying the proposed concept, we present four prototypes using a combination of in-situ projection and cameras for providing feedback to workers and to sense the workers' interaction with the workplace. Two of the prototypes address a manual assembly scenario and two prototypes address an order picking scenario. For the manual assembly scenario, we apply the concept to a single workplace and an assembly cell, which connects three single assembly workplaces to each other. For the order picking scenario, we present a cart-mounted prototype using in-situ projection to display picking information directly onto the warehouse. Further, we present a user-mounted prototype, exploring the design-dimension of equipping the worker with technology rather than equipping the environment. Besides the system contribution of this thesis, we explore the benefits of the created prototypes through studies with inexperienced workers, experienced workers, and cognitively impaired workers. We show that a contour visualization of in-situ feedback is the most suitable for cognitively impaired workers. Further, these contour instructions enable the cognitively impaired workers to perform assembly tasks with a complexity of up to 96 work steps. For inexperienced workers, we show that a combination of haptic and visual error feedback is appropriate to communicate errors that were made during assembly tasks. For creating interactive instructions, we introduce and evaluate a Programming by Demonstration approach. Investigating the long-term use of in-situ instructions at manual assembly workplaces, we show that instructions adapting to the workers' cognitive needs is beneficial, as continuously presenting instructions has a negative impact on the performance of both experienced and inexperienced workers. In the order picking scenario, we show that the cart-mounted in-situ instructions have a great potential as they outperform the paper-baseline. Finally, the user-mounted prototype results in a lower perceived cognitive load. Over the course of the studies, we recognized the need for a standardized way of evaluating Augmented Reality instructions. To address this issue, we propose the General Assembly Task Model, which provides two standardized baseline tasks and a noise-free way of evaluating Augmented Reality instructions for assembly tasks. Further, based on the experience, we gained from applying our assistive system in real-world assembly scenarios, we identify eight guidelines for designing assistive systems for the workplace. In conclusion, this thesis provides a basis for understanding how in-situ projection can be used for providing cognitive support at workplaces. It identifies the strengths and weaknesses of in-situ projection for cognitive assistance regarding different user groups. Therefore, the findings of this thesis contribute to the field of using Augmented Reality at the workplace. Overall, this thesis shows that using Augmented Reality for cognitively supporting workers during manual assembly tasks and order picking tasks creates a benefit for the workers when working on cognitively demanding tasks.Seit mehr als 20 Jahren wird Augmented Reality eingesetzt, um manuelle Montagetätigkeiten zu unterstützen. Durch neue Entwicklungen in der Sensortechnologie ist es möglich, kontextsensitive Augmented-Reality-Systeme zu bauen, die Interaktionen akkurat erkennen können. Zudem führen eine zunehmende Variantenvielfalt und die Möglichkeit, bestellte Produkte erst auf Nachfrage zu produzieren, zu einer zunehmenden Komplexität an Montagearbeitsplätzen. Der daraus entstehende Bedarf für kognitive Unterstützung an Arbeitsplätzen und die Verfügbarkeit von robuster Technologie lässt uns bestehende Probleme lösen, indem wir Arbeitende während Montagearbeiten mithilfe von kontextsensitiver Augmented Reality unterstützen. In dieser Arbeit erforschen wir, wie Assistenztechnologie eingesetzt werden kann, um Arbeitende in Produktionsszenarien kognitiv zu unterstützen. Mithilfe des User-Centered-Design-Prozess identifizieren wir Schlüsselanforderungen für Assistenzsysteme, die sowohl Arbeitende kontinuierlich unterstützen als auch Arbeitenden Arbeitsschritte beibringen können. Dabei betrachten wir drei verschiedene Benutzergruppen: unerfahrene Arbeitende, erfahrene Arbeitende, und Arbeitende mit kognitiven Behinderungen. Auf Basis der erarbeiteten Schlüsselanforderungen entwerfen wir ein allgemeines Konzept für die Bereitstellung von kognitiver Assistenz an Arbeitsplätzen, welches in verschiedenen Szenarien angewandt werden kann. Wir präsentieren vier verschiedene Prototypen, in denen das vorgeschlagene Konzept implementiert wurde. Für die Prototypen verwenden wir eine Kombination von In-Situ-Projektion und Kameras, um Arbeitenden Feedback anzuzeigen und die Interaktionen der Arbeitenden am Arbeitsplatz zu erkennen. Zwei der Prototypen zielen auf ein manuelles Montageszenario ab, und zwei weitere Prototypen zielen auf ein Kommissionierszenario ab. Im manuellen Montageszenario wenden wir das Konzept an einem Einzelarbeitsplatz und einer Montagezelle, welche drei Einzelarbeitsplätze miteinander verbindet, an. Im Kommissionierszenario präsentieren wir einen Kommissionierwagen, der mithilfe von In-Situ-Projektion Informationen direkt ins Lager projiziert. Des Weiteren präsentieren wir einen tragbaren Prototypen, der anstatt der Umgebung den Arbeitenden mit Technologie ausstattet. Ein weiterer Beitrag dieser Arbeit ist die Erforschung der Vorteile der erstellten Prototypen durch Benutzerstudien mit erfahrenen Arbeitenden, unerfahrenen Arbeitenden und Arbeitende mit kognitiver Behinderung. Wir zeigen, dass eine Kontur-Visualisierung von In-Situ-Anleitungen die geeignetste Anleitungsform für Arbeitende mit kognitiven Behinderungen ist. Des Weiteren befähigen Kontur-basierte Anleitungen Arbeitende mit kognitiver Behinderung, an komplexeren Aufgaben zu arbeiten, welche bis zu 96 Arbeitsschritte beinhalten können. Für unerfahrene Arbeitende zeigen wir, dass sich eine Kombination von haptischem und visuellem Fehlerfeedback bewährt hat. Wir stellen einen Ansatz vor, der eine Programmierung von interaktiven Anleitungen durch Demonstration zulässt, und evaluieren ihn. Bezüglich der Langzeitwirkung von In-Situ-Anleitungen an manuellen Montagearbeitsplätzen zeigen wir, dass Anleitungen, die sich den kognitiven Bedürfnissen der Arbeitenden anpassen, geeignet sind, da ein kontinuierliches Präsentieren von Anleitungen einen negativen Einfluss auf die Arbeitsgeschwindigkeit von erfahrenen Arbeitenden sowohl als auch unerfahrenen Arbeitenden hat. Für das Szenario der Kommissionierung zeigen wir, dass die In-Situ-Anleitungen des Kommissionierwagens ein großes Potenzial haben, da sie zu einer schnelleren Arbeitsgeschwindigkeit führen als traditionelle Papieranleitungen. Schlussendlich führt der tragbare Prototyp zu einer subjektiv niedrigeren kognitiven Last. Während der Durchführung der Studien haben wir den Bedarf einer standardisierten Evaluierungsmethode von Augmented-Reality-Anleitungen erkannt. Deshalb schlagen wir das General Assembly Task Modell vor, welches zwei standardisierte Grundaufgaben und eine Methode zur störungsfreien Analyse von Augmented-Reality-Anleitungen für Montagearbeiten bereitstellt. Des Weiteren stellen wir auf Basis unserer Erfahrungen, die wir durch die Anwendung unseres Assistenzsystems in Montageszenarien gemacht haben, acht Richtlinien für das Gestalten von Montageassistenzsystemen vor. Zusammenfassend bietet diese Arbeit eine Basis für das Verständnis der Benutzung von In-Situ-Projektion zur Bereitstellung von kognitiver Montageassistenz. Diese Arbeit identifiziert die Stärken und Schwächen von In-Situ-Projektion für die kognitive Unterstützung verschiedener Benutzergruppen. Folglich tragen die Resultate dieser Arbeit zum Feld der Benutzung von Augmented Reality an Arbeitsplätzen bei. Insgesamt zeigt diese Arbeit, dass die Benutzung von Augmented Reality für die kognitive Unterstützung von Arbeitenden während kognitiv anspruchsvoller manueller Montagetätigkeiten und Kommissioniertätigkeiten zu einer schnelleren Arbeitsgeschwindigkeit führt

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments---active user modelling with preferences, and hierarchical reinforcement learning---and a discussion of the pros and cons of Bayesian optimization based on our experiences

DesignSense: A Visual Analytics Interface for Navigating Generated Design Spaces

Generative Design (GD) produces many design alternatives and promises novel and performant solutions to architectural design problems. The success of GD rests on the ability to navigate the generated alternatives in a way that is unhindered by their number and in a manner that reflects design judgment, with its quantitative and qualitative dimensions. I address this challenge by critically analyzing the literature on design space navigation (DSN) tools through a set of iteratively developed lenses. The lenses are informed by domain experts\u27 feedback and behavioural studies on design navigation under choice-overload conditions. The lessons from the analysis shaped DesignSense, which is a DSN tool that relies on visual analytics techniques for selecting, inspecting, clustering and grouping alternatives. Furthermore, I present case studies of navigating realistic GD datasets from architecture and game design. Finally, I conduct a formative focus group evaluation with design professionals that shows the tool\u27s potential and highlights future directions

Flexible manufacturing system utilizing computer integrated control and modeling

In today\u27s fast-automated production, Flexible Manufacturing Systems (FMS) play a very important role by processing a variety of different types of workpieces simultaneously. This study provides valuable information about existing FMS workcells and brings to light a unique concept called Programmable Automation. Another integrated concept of programmable automation that is discussed is the use of two feasibility approaches towards modeling and controlling FMS operations; the most commonly used is programmable logic controllers (PLC), and the other one, which has not yet implemented in many industrial applications is Petri Net controllers (PN). This latter method is a unique powerful technique to study and analyze any production line or any facility, and it can be used in many other applications of automatic control. Programmable Automation uses a processor in conventional metal working machines to perform certain tasks through program instructions. Drilling, milling and chamfering machines are good examples for such automation. Keeping the above issues in concem; this research focuses on other core components that are used in the FMS workcell at New Jersey Institute of Technology, such as; industrial robots, material handling system and finally computer vision