Estimating performance indexes of a baggage handling system using metamodels

In this study, we develop some deterministic metamodels to quickly and precisely predict the future of a technically complex system. The underlying system is essentially a stochastic, discrete event simulation model of a big baggage handling system. The highly detailed simulation model of this is used for conducting some experiments and logging data which are then used for training artificial neural network metamodels. Demonstrated results show that the developed metamodels are well able to predict different performance measures related to the travel time of bags within this system. In contrast to the simulation models which are computationally expensive and expertise extensive to be developed, run, and maintained, the artificial neural network metamodels could serve as real time decision aiding tools which are considerably fast, precise, simple to use, and reliable.<br /

Transparency in Global Production Networks: Improving Disruption Management by Increased Information Exchange

Modern companies operate in global production networks. The operational performance of production networks is hampered by disrupting events. Digitalization and the horizontal interlinkage of production networks may increase information exchange and lead to more transparency. It is propagated as being an enabler for a faster identification and reaction to disruptions. This paper presents a metamodeling approach that maps disruptions as systematic parameter variations and analyzes their impact on the performance of production networks under different level of information exchange. The method aims for the determination of cause-effect relationships and contributes to the determination of the appropriate level of information exchange in production networks

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

MODEL-BASED PREDICTIVE ANALYTICS FOR ADDITIVE AND SMART MANUFACTURING

Qualification and certification for additive and smart manufacturing systems can be uncertain and very costly. Using available historical data can mitigate some costs of producing and testing sample parts. However, use of such data lacks the flexibility to represent specific new problems which decreases predictive accuracy and efficiency. To address these compelling needs, in this dissertation modeling techniques are introduced that can proactively estimate results expected from additive and smart manufacturing processes swiftly and with practical levels of accuracy and reliability. More specifically, this research addresses the current challenges and limitations posed by use of available data and the high costs of new data by tailoring statistics-based metamodeling techniques to enable affordable prediction of these systems. The result is an integrated approach to customize and build predictive metamodels for the unique features of additive and smart manufacturing systems. This integrated approach is composed of five main parts that cover the broad spectrum of requirements. A domain-driven metamodeling approach uses physics-based knowledge to optimally select the most appropriate metamodeling algorithm without reliance upon statistical data. A maximum predictive error updating method iteratively improves predictability from a given dataset. A grey-box metamodeling approach combines statistics-based black-box and physics-based white-box models to significantly increase predictive accuracy with less expensive data overall. To improve computational efficiency for large datasets, a dynamic metamodeling method modifies the traditional Kriging technique to improve its efficiency and predictability for smart manufacturing systems. Finally, a super-metamodeling method optimizes results regardless of problem conditions by avoiding the challenge with selecting the most appropriate metamodeling algorithm. To realize the benefits of all five approaches, an integrated metamodeling process was developed and implemented into a tool package to systematically select the suitable algorithm, sampling method, and combination of models. All the functions of this tool package were validated and demonstrated by the use of two empirical datasets from additive manufacturing processes

Design and computational optimization of a flexure-based XY nano-positioning stage

This thesis presents the design and computational optimization of a two-axis nano-positioning stage. The devised stage relies on double parallelogram flexure bearings with under-constraint eliminating linkages to enable motion in the primary degrees-of-freedom. The structural parameters of the underlying flexures were optimized to provide a large-range and high bandwidth with sub-micron resolution while maintaining a compact size. A finite element model was created to establish a functional relationship between the geometry of the flexure elements and the stiffness behavior. Then, a neural network was trained from the simulation results to explore the design space with a low computational expense. The neural net was integrated with a genetic algorithm to optimize the design of the flexures for compactness and dynamic performance. The optimal solutions resulted in a reduction of stage footprint by 14% and an increase in the first natural frequency by 75% relative to a baseline design, all while preserving the same 50mm range in each axis with a factor of safety of 2. This confirms the efficacy of the proposed approach in improving stage performance through an optimization of its constituent flexures.Mechanical Engineerin

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Front underride protection devices: design methodology for heavy vehicle crashworthiness

North American Heavy Vehicles contribute to a third of all road fatalities in Canada. Head on collisions are one of the most severe, as the mismatch of vehicle weight and sizing intensifies when a passenger vehicle is impacted. To improve crash safety, Front Underride Protection Devices (FUPDs) are a proposed solution to establishing a compatible collision between a passenger vehicle and a heavy vehicle. The European Union is among numerous administrations to regulate FUPDs, yet FUPDs are nonexistent in North America. Current regulations conform to European Cab-over Engine Tractors designs. Implementation of current regulations in North American conflicts with the widely driven Conventional Style Tractor due to the different design space for a FUPDs. This study builds on developing regulations for North America, and establishes a design methodology to developing and optimizing FUPDs for the Conventional Style Tractor enlightening the crashworthy importance of front underride protection devices to improving road safety. Advanced two stage optimization methodology was outlined to ensure industry targets are embedded with in the design to develop lightweight and cost effective devices. Recommendations for the modifications of the ECE R93 for Conventional Style Tractor are outlined; P1 load magnitudes requirements for FUPD stiffness should be increased from the regulated 80 kN to 160 kN to improve small overlap collisions. Regulated geometric parameters were recommended to have a minimal frontal contact height of 240mm, with ground clearance set between 350mm to 400mm. Geometric configurations were outlined and restricted to conform to the aerodynamic curvatures of the tractors bumper. After validation of the National Crash Analysis Center (NCAC) Toyota Yaris finite element analysis (FEA) model for side impact, the addition of a FUPD enhanced the survivability of passenger vehicle. The work achieved in enhancing the design methodology for industrial implementation and outlining regulations for North America