94 research outputs found
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A survey of behavioral-level partitioning systems
Many approaches have been developed to partition a system's behavioral description before a structural implementation is synthesized. We highlight the foundations and motivations for behavioral partitioning. We survey behavioral partitioning approaches, discussing abstraction levels, goals, major steps, and key assumptions in each
Self-resilient production systems : framework for design synthesis of multi-station assembly systems
Product design changes are inevitable in the current trend of time-based competition where
product models such as automotive bodies and aircraft fuselages are frequently upgraded and cause
assembly process design changes. In recent years, several studies in engineering change
management and reconfigurable systems have been conducted to address the challenges of frequent
product and process design changes. However, the results of these studies are limited in their
applications due to shortcomings in three aspects which are: (i) They rely heavily on past records
which might only be a few relevant cases and insufficient to perform a reliable analysis; (ii) They
focus mainly on managing design changes in product architecture instead of both product and
process architecture; and (iii) They consider design changes at a station-level instead of a multistation
level.
To address the aforementioned challenges, this thesis proposes three interrelated research
areas to simulate the design adjustments of the existing process architecture. These research areas
involve: (i) the methodologies to model the existing process architecture design in order to use the
developed models as assembly response functions for assessing Key Performance Indices (KPIs);
(ii) the KPIs to assess quality, cost, and design complexity of the existing process architecture
design which are used when making decisions to change the existing process architecture design;
and (iii) the methodology to change the process architecture design to new optimal design solutions
at a multi-station level.
In the first research area, the methodology in modeling the functional dependence of
process variables within the process architecture design are presented as well as the relations from
process variables and product architecture design. To understand the engineering change
propagation chain among process variables within the process architecture design, a functional
dependence model is introduced to represent the design dependency among process variables by
cascading relationships from customer requirements, product architecture, process architecture, and
design tasks to optimise process variable design. This model is used to estimate the level of process
variable design change propagation in the existing process architecture design
Next, process yield, cost, and complexity indices are introduced and used as KPIs in this
thesis to measure product quality, cost in changing the current process design, and dependency of
process variables (i.e, change propagation), respectively. The process yield and complexity indices
are obtained by using the Stream-of-Variation (SOVA) model and functional dependence model,
respectively. The costing KPI is obtained by determining the cost in optimizing tolerances of
process variables. The implication of the costing KPI on the overall cost in changing process
architecture design is also discussed. These three comprehensive indices are used to support
decision-making when redesigning the existing process architecture.
Finally, the framework driven by functional optimisation is proposed to adjust the existing
process architecture to meet the engineering change requirements. The framework provides a
platform to integrate and analyze several individual design synthesis tasks which are necessary to
optimise the multi-stage assembly processes such as tolerance of process variables, fixture layouts,
or part-to-part joints. The developed framework based on transversal of hypergraph and task
connectivity matrix which lead to the optimal sequence of these design tasks. In order to enhance
visibility on the dependencies and hierarchy of design tasks, Design Structure Matrix and Task
Flow Chain are also adopted. Three scenarios of engineering changes in industrial automotive
design are used to illustrate the application of the proposed redesign methodology. The thesis
concludes that it is not necessary to optimise all functional designs of process variables to
accommodate the engineering changes. The selection of only relevant functional designs is
sufficient, but the design optimisation of the process variables has to be conducted at the system
level with consideration of dependency between selected functional designs
Contagion dynamics on higher-order networks
Understanding the dissemination of diseases, information, and behavior stands
as a paramount research challenge in contemporary network and complex systems
science. The COVID-19 pandemic and the proliferation of misinformation are
relevant examples of the importance of these dynamic processes, which have
recently gained more attention due to the potential of higher-order networks to
unlock new avenues for their investigation. Despite being in its early stages,
the examination of social contagion in higher-order networks has witnessed a
surge of novel research and concepts, revealing different functional forms for
the spreading dynamics and offering novel insights. This review presents a
focused overview of this body of literature and proposes a unified formalism
that covers most of these forms. The goal is to underscore the similarities and
distinctions among various models, to motivate further research on the general
and universal properties of such models. We also highlight that while the path
for additional theoretical exploration appears clear, the empirical validation
of these models through data or experiments remains scant, with an unsettled
roadmap as of today. We therefore conclude with some perspectives aimed at
providing possible research directions that could contribute to a better
understanding of this class of dynamical processes, both from a theoretical and
a data-oriented point of view.Comment: Review article. 17 pages and 5 figures. Submitted for publicatio
New Constructions for Competitive and Minimal-Adaptive Group Testing
Group testing (GT) was originally proposed during the World War II in an attempt to minimize the \emph{cost} and \emph{waiting time} in performing identical blood tests of the soldiers for a low-prevalence disease.
Formally, the GT problem asks to find \emph{defective} elements out of elements by querying subsets (pools) for the presence of defectives.
By the information-theoretic lower bound, essentially queries are needed in the worst-case.
An \emph{adaptive} strategy proceeds sequentially by performing one query at a time, and it can achieve the lower bound. In various applications, nothing is known about beforehand and a strategy for this scenario is called \emph{competitive}. Such strategies are usually adaptive and achieve query optimality within a constant factor called the \emph{competitive ratio}.
In many applications, queries are time-consuming. Therefore, \emph{minimal-adaptive} strategies which run in a small number of stages of parallel
queries are favorable.
This work is mainly devoted to the design of minimal-adaptive strategies combined with other demands of both theoretical and practical interest. First we target unknown and show that actually competitive GT is possible in as few as stages only.
The main ingredient is our randomized estimate of a previously unknown using nonadaptive queries.
In addition, we have developed a systematic approach to obtain optimal competitive ratios for our strategies.
When is a known upper bound,
we propose randomized GT strategies which asymptotically achieve query optimality in just , or stages depending upon the growth of versus .
Inspired by application settings, such as at American Red Cross, where in most cases GT is applied to small instances, \textit{e.g.}, . We extended our study of query-optimal GT strategies to solve a given problem instance with fixed values , and . We also considered the situation when
elements to test cannot be divided physically (electronic devices), thus the pools must be disjoint. For GT with \emph{disjoint} simultaneous pools, we show that tests are sufficient, and also necessary for certain ranges of the parameters
Exact Algorithms for Mixed-Integer Multilevel Programming Problems
We examine multistage optimization problems, in which one or more decision makers solve a sequence of interdependent optimization problems. In each stage the corresponding decision maker determines values for a set of variables, which in turn parameterizes the subsequent problem by modifying its constraints and objective function. The optimization literature has covered multistage optimization problems in the form of bilevel programs, interdiction problems, robust optimization, and two-stage stochastic programming. One of the main differences among these research areas lies in the relationship between the decision makers. We analyze the case in which the decision makers are self-interested agents seeking to optimize their own objective function (bilevel programming), the case in which the decision makers are opponents working against each other, playing a zero-sum game (interdiction), and the case in which the decision makers are cooperative agents working towards a common goal (two-stage stochastic programming). Traditional exact approaches for solving multistage optimization problems often rely on strong duality either for the purpose of achieving single-level reformulations of the original multistage problems, or for the development of cutting-plane approaches similar to Benders\u27 decomposition. As a result, existing solution approaches usually assume that the last-stage problems are linear or convex, and fail to solve problems for which the last-stage is nonconvex (e.g., because of the presence of discrete variables). We contribute exact finite algorithms for bilevel mixed-integer programs, three-stage defender-attacker-defender problems, and two-stage stochastic programs. Moreover, we do not assume linearity or convexity for the last-stage problem and allow the existence of discrete variables. We demonstrate how our proposed algorithms significantly outperform existing state-of-the-art algorithms. Additionally, we solve for the first time a class of interdiction and fortification problems in which the third-stage problem is NP-hard, opening a venue for new research and applications in the field of (network) interdiction
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