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Efficient transportation of resources is critical for network functionality at all scales. However, while natural systems adapt over time to achieve optimal structures for transportation, man-made networks are not built with a comparable evolutionary mechanism. Consequently, these structures frequently fall short of meeting their intended design criteria. This thesis presents adaptation rules rooted in biological systems that enable the design of plausible man-made infrastructures. Specifically, we extrapolate mathematical models classically used to study, for instance, the transport of nutrients in plants or the human body and extend them to model different problems with a paradigm shift: Use such equations to get instrumental insight on how to build artificial networks. We connect adaptation rules and optimality with Optimal Transport (OT) theory. Initially, we formulate adaptation equations tailored to the problem at hand. Then, we aim to find a well-defined Lyapunov functional for these equations, which is interpretable as the cost to transport mass along the edges of a network. This is the cost minimized in OT. This link allows us to leverage optimization insights and methods to enhance performance and validate our adaptation schemes. While this mechanism is established for greedy routing problems, we extend it to more complex scenarios. First, we consider a multicommodity problem where different immiscible mass types move in a shared network. By interacting in one infrastructure, the mass types contribute to minimizing a unique cost. We observe that thoughtfully devising the coupling of mass types is pivotal to producing optimal networks. We also explore traffic congestion regimes controlled through a critical exponent entering the adaptation rules and its corresponding optimization formulation. The multicommodity adaptation equations are used to study the routing of passengers in the Paris Métro and the streets of Bordeaux. These applications showcase which stations are crucial to alleviating traffic under targeted node failures and that trams are a valuable alternative to reduce bus congestion. Furthermore, we employ this method for ameliorating supervised image classification with OT. Here, mass types are RGB color distributions of images, and the OT cost is used as a proxy to assess their similarity. Second, we study optimal designs of transportation networks with time-dependent input mass loads. Our fundamental assumption is to model the slow evolution of the network infrastructure, which is governed by periodic and fast-fluctuating mass entering its nodes. By postulating the existence of these two different time scales, we derive closed-form adaptation rules that reduce the transport cost upon convergence. Additionally, they enable connecting analytical properties of the mass loads--their Fourier coefficients--with the topology of optimal networks. We use this method to study the robustness of Bordeaux's bus network. Third, we frame the competition of a network manager and greedy passengers competing in a bilevel optimization problem. The first aims to minimize traffic by tolling roads, while the second move to reduce their travel costs. To solve the problem, we devise a scheme where adaptation rules for greedy routing are alternated with closed-form Projected Stochastic Gradient Descent for tuning edge weights. Our study on the international E-road network demonstrates that an informed tolling of roads effectively trades off travel time against congestion and can help reduce the carbon footprint of roads. To make our results reproducible, we complement our methods with open-source codes. In summary, our models provide a systematic approach to designing optimal transportation networks for different tasks. These tools are valuable for practitioners interested in these problems, for example, policymakers aiming to assess whether a transport infrastructure effectively meets user demand.
This book explores the methodological and application developments of network design in transportation and logistics. It identifies trends, challenges and research perspectives in network design for these areas. Network design is a major class of problems in operations research where network flow, combinatorial and mixed integer optimization meet. The analysis and planning of transportation and logistics systems continues to be one of the most important application areas of operations research. Networks provide the natural way of depicting such systems, so the optimal design and operation of networks is the main methodological area of operations research that is used for the analysis and planning of these systems. This book defines the current state of the art in the general area of network design, and then turns to its applications to transportation and logistics. New research challenges are addressed. Network Design with Applications to Transportation and Logistics is divided into three parts. Part I examines basic design problems including fixed-cost network design and parallel algorithms. After addressing the basics, Part II focuses on more advanced models. Chapters cover topics such as multi-facility network design, flow-constrained network design, and robust network design. Finally Part III is dedicated entirely to the potential application areas for network design. These areas range from rail networks, to city logistics, to energy transport. All of the chapters are written by leading researchers in the field, which should appeal to analysts and planners.
This book explores the methodological and application developments of network design in transportation and logistics. It identifies trends, challenges and research perspectives in network design for these areas. Network design is a major class of problems in operations research where network flow, combinatorial and mixed integer optimization meet. The analysis and planning of transportation and logistics systems continues to be one of the most important application areas of operations research. Networks provide the natural way of depicting such systems, so the optimal design and operation of networks is the main methodological area of operations research that is used for the analysis and planning of these systems. This book defines the current state of the art in the general area of network design, and then turns to its applications to transportation and logistics. New research challenges are addressed. Network Design with Applications to Transportation and Logistics is divided into three parts. Part I examines basic design problems including fixed-cost network design and parallel algorithms. After addressing the basics, Part II focuses on more advanced models. Chapters cover topics such as multi-facility network design, flow-constrained network design, and robust network design. Finally Part III is dedicated entirely to the potential application areas for network design. These areas range from rail networks, to city logistics, to energy transport. All of the chapters are written by leading researchers in the field, which should appeal to analysts and planners.
This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and learning problems from streaming data through localized interactions among agents. The results derived in this work are useful in comparing network topologies against each other, and in comparing networked solutions against centralized or batch implementations. There are many good reasons for the peaked interest in distributed implementations, especially in this day and age when the word "network" has become commonplace whether one is referring to social networks, power networks, transportation networks, biological networks, or other types of networks. Some of these reasons have to do with the benefits of cooperation in terms of improved performance and improved resilience to failure. Other reasons deal with privacy and secrecy considerations where agents may not be comfortable sharing their data with remote fusion centers. In other situations, the data may already be available in dispersed locations, as happens with cloud computing. One may also be interested in learning through data mining from big data sets. Motivated by these considerations, this work examines the limits of performance of distributed stochastic-gradient solutions and discusses procedures that help bring forth their potential more fully. The presentation adopts a useful statistical framework and derives performance results that elucidate the mean-square stability, convergence, and steady-state behavior of the learning networks. The work also illustrates how distributed processing over graphs gives rise to some revealing phenomena due to the coupling effect among the agents. These phenomena are discussed in the context of adaptive networks, along with examples from a variety of areas including distributed sensing, intrusion detection, distributed estimation, online adaptation, network system theory, and machine learning.
Biological networks, such as vascular networks and neural circuits, are ubiquitous in nature. An understanding of these networks can help us understand their response to damages, which could lead to novel treatments. They can also inspire the design of man-made networks, as evolution has millions of years to figure out optimal designs. The advancement in imaging techniques has created high-dimensional data streams, which is difficult to analyze by conventional approaches. On the other hand, quantitative tools are naturally suited for processing large data sets, and they become more and more important in improving our knowledge on biological networks. Among existing tools ranging from network science to stochastic analysis, here we focus on optimization and dynamical system approach. Optimization links biological functions to corresponding network structures, which can give predictions to be compared with the data. The dynamical system approach is suited for analyzing time series data and complex interaction between the vertices, which is often exploited in biological systems for intricate signalings and regulations. This thesis is devoted to the study of biological networks with optimization and dynamical system, focused on two specific biological systems: microvascular network and bipolar disorder. For microvascular networks, we first study a specific example of embryonic zebrafish trunk network, and reveal the significance of flow uniformity in this network. Then we derive analytical structures of networks with optimal transport efficiency, which is widely regarded as the organizing principle of vascular networks, especially for large vessels such as aorta. To compare the morphologies of transport efficient and uniform flow networks, we develop algorithm that is capable of finding optimal networks with general target functions and constraints, and show that the principle of uniform flow creates more realistic microvascular networks under many different topologies. Finally, we propose an vessel adaptation mechanism based on stress sensing dynamic to explain how microvascular networks stay resilient to noise, and how they grow into uniform flow networks. For bipolar disorder, we mathematically analyze a dynamical model based on the interaction of mood and expectation. We show that bipolar disorder can be viewed as a bifurcation in the direction from normal to cyclic personality. We also consider the case where positive and negative events are sensed differently, and describe the bifurcation in this case. Finally we apply commonly used medicine on the model, and recover clinically observed phenomena on bipolar disorder patients.
The third edition of this highly regarded book provides a concise and accessible introduction to the principles and elements of policy design in contemporary governance. It examines in detail the range of substantive and procedural policy instruments that together comprise the toolbox from which governments choose tools to resolve policy problems and the principles and practices that lead to their use. Guiding readers through the study of the many different kinds of instruments used by governments in carrying out their tasks, adapting to, and altering, their environments, this book: • Considers the principles and practices behind the selection and use of specific types of Instruments in contemporary government and arrangements of policy tools esp. procedural tools and policy portfolios. • Evaluates in detail the merits, demerits, and rationales for the use of specific organization, regulatory, financial and information-based tools and the trends visible in their use. • Examines key issues such as policy success and failure and the role of design in it; policy volatility and risk management through policy design; how behavioural research can contribute to better policy designs; and the 'micro' calibrations of policies and their importance in designs and outcomes. • Addresses the issues not only surrounding individual tools but also concerning the evolution and development of instrument mixes, their relationship to policy styles and the challenges involved in their (re)design as well as the distinction between design and "non-design'. Providing a comprehensive overview of this essential component of modern governance and featuring helpful definitions of key concepts and further reading, this book is essential reading for all students of public policy, administration, and management.
This book explains the theory and methods of system optimization design for railway intelligent transportation systems (RITS), which optimizes RITS total performance by decreasing the difficulty and cost of system development and increasing the system efficiency. Readers will understand key concepts of RITS and the latest research relevant to China and other countries where RITSs have been developed. The book is suitable for university scholars in the field of railway transportation.
One aspect of the new economy is a transition to a networked society, and the emergence of a highly interconnected, interdependent and complex system of networks to move people, goods and information. An example of this is the in creasing reliance of networked systems (e. g. , air transportation networks, electric power grid, maritime transport, etc. ) on telecommunications and information in frastructure. Many of the networks that evolved today have an added complexity in that they have both a spatial structure – i. e. , they are located in physical space but also an a spatial dimension brought on largely by their dependence on infor mation technology. They are also often just one component of a larger system of geographically integrated and overlapping networks operating at different spatial levels. An understanding of these complexities is imperative for the design of plans and policies that can be used to optimize the efficiency, performance and safety of transportation, telecommunications and other networked systems. In one sense, technological advances along with economic forces that encourage the clustering of activities in space to reduce transaction costs have led to more efficient network structures. At the same time the very properties that make these networks more ef ficient have also put them at a greater risk for becoming disconnected or signifi cantly disruptedwh en super connected nodes are removed either intentionally or through a targeted attack.
This book constitutes the refereed proceedings of the 12th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2011, held in Sao Paulo, Brazil, in October 2011. The 61 revised papers presented were carefully selected from numerous submissions. They provide a comprehensive overview of recent advances in various collaborative network (CN) domains and their applications with a particular focus on adaptation of the networks and their value creation, specifically emphasizing topics related to evolution from social networking to collaborative networks; social capital; value chains; co-creation of complex products; performance management; behavioral aspects in collaborative networks; collaborative networks planning and modeling; benefit analysis and sustainability issues, as well as including important technical and scientific challenges in applying CNs to areas such as advanced logistics networks, business process modeling, service orientation, and other emerging application domains such as ageing, tourism, crisis, and emergency scenarios.