Complexity theory in axiomatic design

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2003.Includes bibliographical references (p. 177-182).During the last couple of decades, the term complexity has been commonly found in use in many fields of science, sometimes as a measurable quantity with a rigorous but narrow definition and other times as merely an ad hoc label. With an emphasis on pragmatic engineering applications, this thesis investigates the complexity concept defined in axiomatic design theory to avoid vague use of the term 'complexity' in engineering system design, to provide deeper insight into possible causes of complexity, and to develop a systematic approach to complexity reduction. The complexity concept in axiomatic design theory is defined as a measure of uncertainty in achieving a desired set of functional requirements. In this thesis, it is revisited to refine its definition. Four different types of complexity are identified in axiomatic design complexity theory: time-independent real complexity, time-independent imaginary complexity, time-dependent combinatorial complexity and time-dependent periodic complexity. Time-independent real complexity is equivalent to the information content, which is a measure of a probability of achieving functional requirements. Time-independent imaginary complexity is defined as the uncertainty due to ignorance of the interactions between functional requirements and design parameters. Time-dependent complexity consists of combinatorial complexity and periodic complexity, depending on whether the uncertainty increases indefinitely or occasionally stops increasing at certain point and returns to the initial level of uncertainty. In this thesis, existing definitions for each of the types of complexity are further elaborated with a focus on time-dependent complexity. In particular, time-dependent complexity is clearly defined using the concepts of time-varying system ranges and time-dependent sets of functional requirements.(cont.) Clear definition of the complexity concept that properly addresses the causes of complexity leads to a systematic approach for complexity reduction. As techniques for reducing time-independent complexity are known within and beyond axiomatic design theory, this thesis focuses on dealing with time-dependent complexity. From the definition of time-dependent complexity, combinatorial complexity must be transformed into periodic complexity to prevent the uncertainty from growing unboundedly. Time-dependence of complexity is attributed to two factors. One is a time-varying system range and the other is a time-dependent set of functional requirements. This thesis shows that achieving periodicity in time-varying system ranges and maintaining functional periodicity of time-dependent sets of functional requirements prevent a system from developing time-dependent combinatorial complexity. Following this argument, a re-initialization concept as a means to achieve and maintain periodicity is presented. Three examples are drawn from different fields, tribology, manufacturing system, and the cell biology, to support the periodicity argument and illustrate the re-initialization concept.by Taesik Lee.Ph.D

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

Efficient Passive Clustering and Gateways selection MANETs

Passive clustering does not employ control packets to collect topological information in ad hoc networks. In our proposal, we avoid making frequent changes in cluster architecture due to repeated election and re-election of cluster heads and gateways. Our primary objective has been to make Passive Clustering more practical by employing optimal number of gateways and reduce the number of rebroadcast packets

Multi-agent routing in shared guidepath networks

Motivated by a broad spectrum of applications ranging from automated zone-controlled, unit-load material handling systems to the movement of ions within a quantum computer, this thesis considers a class of multi-agent routing problems that seek to minimize the agents’ traveling time subject to certain congestion constraints. In more technical terms, the particular problem addressed in this work concerns the development of efficient, conflict-free, and deadlock-free schedules to route a set of non-interchangeable “agents” between their respective starting locations and destinations. Routes are specified as sequences of adjacent edges of the guidepath network, that are allocated sequentially and exclusively to the traveling agents by a traffic coordinator, according to an allocation protocol that seeks to ensure physical feasibility and other notions of “safety” for the agent motion. On the other hand, efficiency is measured by the schedule “makespan”—i.e., the time required for all agents to reach their respective destinations. In order to formally characterize the addressed scheduling problem and the corresponding notion of optimality for the sought schedules, this thesis first formulates the problem as a mixed-integer program (MIP). In this formulation, the system state at a given time is defined by the allocated edges and the directions of travel for the various agents, and the system is assumed to evolve this state at discrete time intervals that are defined by the required edge-traversal times. The presented MIP is derived according to a resource allocation system (RAS) perspective, and it is based on a set of binary decision variables that characterize the evolution of the system state over a sufficiently long time horizon. An additional auxiliary variable allows the computation of the schedule "makespan"—i.e., the number of discrete time periods required for the last agent to reach its designated destination.  An important feature of the developed MIP formulation is its ability to accommodate a broad range of variations of the considered traffic-scheduling problem that result from the variation of certain structural elements of the underlying traffic system and of the adopted edge-allocation protocol. From a computational standpoint, the optimal solution of all these problems is very complex. In many cases, even the identification of a feasible solution for a given problem instance can be a challenging problem. In view of all this complexity, the second part of the thesis formulates a Lagrangian dual problem for the generation of lower bounds for the original scheduling problem, and then describes two distinct methods to optimize this dual problem: (i) a customized dual-ascent algorithm, and (ii) a reformulation of the dual problem as a single, large linear program (LP). The first approach is proven to find an exact solution in a finite number of iterations, but the availability of very efficient LP solvers renders the second method more robust for larger problem instances. The two approaches provide consistent lower bounds for the optimal makespans of various problem instances, as well as Lagrange multipliers that optimize the Lagrangian dual and may be useful in the guidance of other heuristic algorithms for an optimized schedule. The third part of the thesis presents and analyzes a heuristic, "local-search" type of algorithm for minimizing the makespans of multi-agent routes on a shared guidepath network. For the context of conflict-free ion routing within a quantum computer, the thesis describes a complete algorithm for finding an initial feasible solution, and for optimizing that schedule by iterative reduction of the makespan, using dynamic programming (DP) to revise agent routes while eliminating conflicts between agents. Various methods for strengthening the makespan-reduction procedure (e.g., multi-agent simultaneous route revision, or controlled excursions into the infeasible region) are described and analyzed. Finally, the dissertation provides a set of experimental results that are obtained from the implementation of the developed methods for a carefully selected set of problem instances. For each instance, we find lower bounds (obtained either by hand, or by solving the Lagrangian dual problem) on the optimal objective values, as well as actual makespans for feasible schedules discovered by the heuristic scheduler. The considered problem instances include: (i) a small but difficult problem used to motivate our early research; (ii) a more complex "challenge" problem designed to maximize congestion; and (iii) a series of 150 randomized trials formulated on a grid-based configuration of the guidepath network that is typical of the corresponding structures that are encountered in many practical applications. The third set of experiments is further designed to evaluate the performance of the heuristic scheduler under increasing levels of congestion. The obtained results reveal that our heuristic algorithm can provide very efficient solutions for the targeted variations of the guidepath-based traffic-scheduling problem, in a way that is computationally efficient and complete. The thesis concludes with suggestions for future research that are aimed at (a) the further enhancement of the heuristic algorithm, (b) the extension of this algorithm and of the corresponding methodology to other variations of the considered traffic-scheduling problems, and (c) the embedding of all these results in a broader “rolling-horizon” framework that will address the dynamic nature of the operational (i.e., the transport) requirements of the considered traffic systems.Ph.D

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Sixth Goddard Conference on Mass Storage Systems and Technologies Held in Cooperation with the Fifteenth IEEE Symposium on Mass Storage Systems

This document contains copies of those technical papers received in time for publication prior to the Sixth Goddard Conference on Mass Storage Systems and Technologies which is being held in cooperation with the Fifteenth IEEE Symposium on Mass Storage Systems at the University of Maryland-University College Inn and Conference Center March 23-26, 1998. As one of an ongoing series, this Conference continues to provide a forum for discussion of issues relevant to the management of large volumes of data. The Conference encourages all interested organizations to discuss long term mass storage requirements and experiences in fielding solutions. Emphasis is on current and future practical solutions addressing issues in data management, storage systems and media, data acquisition, long term retention of data, and data distribution. This year's discussion topics include architecture, tape optimization, new technology, performance, standards, site reports, vendor solutions. Tutorials will be available on shared file systems, file system backups, data mining, and the dynamics of obsolescence

Fourth NASA Goddard Conference on Mass Storage Systems and Technologies

This report contains copies of all those technical papers received in time for publication just prior to the Fourth Goddard Conference on Mass Storage and Technologies, held March 28-30, 1995, at the University of Maryland, University College Conference Center, in College Park, Maryland. This series of conferences continues to serve as a unique medium for the exchange of information on topics relating to the ingestion and management of substantial amounts of data and the attendant problems involved. This year's discussion topics include new storage technology, stability of recorded media, performance studies, storage system solutions, the National Information infrastructure (Infobahn), the future for storage technology, and lessons learned from various projects. There also will be an update on the IEEE Mass Storage System Reference Model Version 5, on which the final vote was taken in July 1994

Technology 2001: The Second National Technology Transfer Conference and Exposition, volume 2

Proceedings of the workshop are presented. The mission of the conference was to transfer advanced technologies developed by the Federal government, its contractors, and other high-tech organizations to U.S. industries for their use in developing new or improved products and processes. Volume two presents papers on the following topics: materials science, robotics, test and measurement, advanced manufacturing, artificial intelligence, biotechnology, electronics, and software engineering