Advances in Modeling and Management of Urban Water Networks

The Special Issue on Advances in Modeling and Management of Urban Water Networks (UWNs) explores four important topics of research in the context of UWNs: asset management, modeling of demand and hydraulics, energy recovery, and pipe burst identification and leakage reduction. In the first topic, the multi-objective optimization of interventions on the network is presented to find trade-off solutions between costs and efficiency. In the second topic, methodologies are presented to simulate and predict demand and to simulate network behavior in emergency scenarios. In the third topic, a methodology is presented for the multi-objective optimization of pump-as-turbine (PAT) installation sites in transmission mains. In the fourth topic, methodologies for pipe burst identification and leakage reduction are presented. As for the urban drainage systems (UDSs), the two explored topics are asset management, with a system upgrade to reduce flooding, and modeling of flow and water quality, with analyses on the transition from surface to pressurized flow, impact of water use reduction on the operation of UDSs, and sediment transport in pressurized pipes. The Special Issue also includes one paper dealing with the hydraulic modeling of an urban river with a complex cross-section

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

In vivo research scheduling and coordination in the pharmaceutical industry

Includes bibliographical references (p. 89-90).Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2008.(cont.) A multi-criteria objective function uses the researcher's preference to optimize both room assignments and procedure start time. A Tabu search meta-heuristic has been developed to generate a near-optimal solution. The solution approach uses four neighborhood move strategies based on insert and interval exchange algorithms to optimize procedural room assignments. Although a functioning model was not developed, a recommended implementation plan is discussed.The pharmaceutical industry is experiencing significant competitive pressures. Innovation productivity continues to decline, while the costs for drug R&D steadily rise. This project, sponsored by Novartis Institutes for BioMedical Research (NIBR), is intended to lower drug R&D costs and increase R&D process efficiency through improved research operations. This analysis focuses on improving the scheduling and coordination of early stage, in vivo drug discovery research projects within NIBR's animal facilities. Many of the communication processes used to coordinate research activities in these facilities use ad hoc methods for relaying critical information between research teams and the operations staff. Greater efficiencies can be achieved with the application of risk pooling concepts where dispersed research activities are brought together under a consolidated management structure. These efficiencies cannot be realized until the communication processes are improved. Integral to this improvement effort is the development of a fair and robust method for allocating in vivo resources to research projects using a centralized scheduling system. This thesis provides the framework for developing a centralized scheduling system. The architecture of this tool requires a web-based interface in order to provide seamless access to the research community. Based on research workflows, the proposed tool coordinates input from scientists and uses this information to schedule the required resources. The complex constraints found in a research animal facility dictate the need for a unique scheduling approach. Adapted from existing airline gate scheduling research, this problem is formulated as a mixed integer linear program.by Brent A. Hill.S.M.M.B.A

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Efficient Multi-Objective NeuroEvolution in Computer Vision and Applications for Threat Identification

Concealed threat detection is at the heart of critical security systems designed to en- sure public safety. Currently, methods for threat identification and detection are primarily manual, but there is a recent vision to automate the process. Problematically, developing computer vision models capable of operating in a wide range of settings, such as the ones arising in threat detection, is a challenging task involving multiple (and often conflicting) objectives. Automated machine learning (AutoML) is a flourishing field which endeavours to dis- cover and optimise models and hyperparameters autonomously, providing an alternative to classic, effort-intensive hyperparameter search. However, existing approaches typ- ically show significant downsides, like their (1) high computational cost/greediness in resources, (2) limited (or absent) scalability to custom datasets, (3) inability to provide competitive alternatives to expert-designed and heuristic approaches and (4) common consideration of a single objective. Moreover, most existing studies focus on standard classification tasks and thus cannot address a plethora of problems in threat detection and, more broadly, in a wide variety of compelling computer vision scenarios. This thesis leverages state-of-the-art convolutional autoencoders and semantic seg- mentation (Chapter 2) to develop effective multi-objective AutoML strategies for neural architecture search. These strategies are designed for threat detection and provide in- sights into some quintessential computer vision problems. To this end, the thesis first introduces two new models, a practical Multi-Objective Neuroevolutionary approach for Convolutional Autoencoders (MONCAE, Chapter 3) and a Resource-Aware model for Multi-Objective Semantic Segmentation (RAMOSS, Chapter 4). Interestingly, these ap- proaches reached state-of-the-art results using a fraction of computational resources re- quired by competing systems (0.33 GPU days compared to 3150), yet allowing for mul- tiple objectives (e.g., performance and number of parameters) to be simultaneously op- timised. This drastic speed-up was possible through the coalescence of neuroevolution algorithms with a new heuristic technique termed Progressive Stratified Sampling. The presented methods are evaluated on a range of benchmark datasets and then applied to several threat detection problems, outperforming previous attempts in balancing multiple objectives. The final chapter of the thesis focuses on thread detection, exploiting these two mod- els and novel components. It presents first a new modification of specialised proxy scores to be embedded in RAMOSS, enabling us to further accelerate the AutoML process even more drastically while maintaining avant-garde performance (above 85% precision for SIXray). This approach rendered a new automatic evolutionary Multi-objEctive method for cOncealed Weapon detection (MEOW), which outperforms state-of-the-art models for threat detection in key datasets: a gold standard benchmark (SixRay) and a security- critical, proprietary dataset. Finally, the thesis shifts the focus from neural architecture search to identifying the most representative data samples. Specifically, the Multi-objectIve Core-set Discovery through evolutionAry algorithMs in computEr vision approach (MIRA-ME) showcases how the new neural architecture search techniques developed in previous chapters can be adapted to operate on data space. MIRA-ME offers supervised and unsupervised ways to select maximally informative, compact sets of images via dataset compression. This operation can offset the computational cost further (above 90% compression), with a minimal sacrifice in performance (less than 5% for MNIST and less than 13% for SIXray). Overall, this thesis proposes novel model- and data-centred approaches towards a more widespread use of AutoML as an optimal tool for architecture and coreset discov- ery. With the presented and future developments, the work suggests that AutoML can effectively operate in real-time and performance-critical settings such as in threat de- tection, even fostering interpretability by uncovering more parsimonious optimal models. More widely, these approaches have the potential to provide effective solutions to chal- lenging computer vision problems that nowadays are typically considered unfeasible for AutoML settings

Smart Manufacturing

This book is a collection of 11 articles that are published in the corresponding Machines Special Issue “Smart Manufacturing”. It represents the quality, breadth and depth of the most updated study in smart manufacturing (SM); in particular, digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the Internet to expand system capabilities, (3) supporting data-driven decision-making activities at various domains and levels of businesses, or (4) reconfiguring systems to adapt to changes and uncertainties. System smartness can be evaluated by one or a combination of performance metrics such as degree of automation, cost-effectiveness, leanness, robustness, flexibility, adaptability, sustainability, and resilience. This book features, firstly, the concepts digital triad (DT-II) and Internet of digital triad things (IoDTT), proposed to deal with the complexity, dynamics, and scalability of complex systems simultaneously. This book also features a comprehensive survey of the applications of digital technologies in space instruments; a systematic literature search method is used to investigate the impact of product design and innovation on the development of space instruments. In addition, the survey provides important information and critical considerations for using cutting edge digital technologies in designing and manufacturing space instruments