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"The research presented here, opening up space for synergies and innovation. It is directly applicable for relevant practical problems and has been carried out in cooperation with utility and dumping companies, infrastructure providers and planning offices. A close and direct connection to the practice of water management has been established by involving application-oriented know-how from the field of civil engineering. On the mathematical side all necessary disciplines were involved, including mixed-integer optimization, multi-objective and facility location optimization, numerics for cross-linked dynamic transportation systems and optimization as well as control of hybrid systems. Most of the presented research has been supported by the joint project „Discret-continuous optimization of dynamic water systems“ of the federal ministry of education and research (BMBF)."

"Mathematical Optimization and Economic Theory provides a self-contained introduction to and survey of mathematical programming and control techniques and their applications to static and dynamic problems in economics, respectively. It is distinctive in showing the unity of the various approaches to solving problems of constrained optimization that all stem back directly or indirectly to the method of Lagrange multipliers. In the 30 years since its initial publication, there have been many more applications of these mathematical techniques in economics, as well as some advances in the mathematics of programming and control. Nevertheless, the basic techniques remain the same today as when the book was originally published. Thus, it continues to be useful not only to its original audience of advanced undergraduate and graduate students in economics, but also to mathematicians and other researchers who are interested in learning about the applications of the mathematics of optimization to economics.The book is distinctive in that it covers in some depth both static programming problems and dynamic control problems of optimization and the techniques of their solution. It also clearly presents many applications of these techniques to economics, and it shows why optimization is important for economics. Many cchallenging problems for both students and researchers are included."

"This book provides the basic theory of complex networks with several new mathematical approaches and optimization techniques to design and analyze dynamic complex networks. A wide range of applications and optimization problems derived from research areas such as cellular and molecular chemistry, operations research, brain physiology, epidemiology, and ecology."

"This SpringerBrief introduces the basics of wireless powered communication networks (WPCNs). In particular, the background and concept of WPCNs are briefly discussed. Moreover, this brief provides an extensive study of the recent developments in this area from an optimization perspective. Wireless powered communication network (WPCN) is a new network paradigm for IoT, where wireless devices (WDs) are powered by radio frequency (RF) based wireless power transfer (WPT) to eliminate the need for recharging or replacing the batteries manually and to prolong their lifetime.In this context, the brief also discusses some optimization problems for state-of-the-art scenarios of wireless powered communication networks.The target audiences for this SpringerBrief are researchers, engineers, and undergraduate and graduate-level students, who are studying or working in wireless powered communication networks and its performance optimization."

"Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes.Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization problems (e.g. the task durations) are typically unknown at the time the decision problem arises.This monograph investigates solution techniques for optimization problems in temporal networks that explicitly account for this parameter uncertainty. We study several formulations, each of which requires different information about the uncertain problem parameters."

"This compact book, through the simplifying perspective it presents, will take a reader who knows little of interior-point methods to within sight of the research frontier, developing key ideas that were over a decade in the making by numerous interior-point method researchers. It aims at developing a thorough understanding of the most general theory for interior-point methods, a class of algorithms for convex optimization problems. The study of these algorithms has dominated the continuous optimization literature for nearly 15 years. In that time, the theory has matured tremendously, but much of the literature is difficult to understand, even for specialists. By focusing only on essential elements of the theory and emphasizing the underlying geometry, A Mathematical View of Interior-Point Methods in Convex Optimization makes the theory accessible to a wide audience, allowing them to quickly develop a fundamental understanding of the material.The author begins with a general presentation of material pertinent to continuous optimization theory, phrased so as to be readily applicable in developing interior-point method theory. This presentation is written in such a way that even motivated Ph.D. students who have never had a course on continuous optimization can gain sufficient intuition to fully understand the deeper theory that follows. Renegar continues by developing the basic interior-point method theory, with emphasis on motivation and intuition. In the final chapter, he focuses on the relations between interior-point methods and duality theory, including a self-contained introduction to classical duality theory for conic programming; an exploration of symmetric cones; and the development of the general theory of primal-dual algorithms for solving conic programming optimization problems.Rather than attempting to be encyclopedic, A Mathematical View of Interior-Point Methods in Convex Optimization gives the reader a solid understanding of the core concepts and relations, the kind of understanding that stays with a reader long after the book is finished."

"This book provides the basic theory of complex networks with several new mathematical approaches and optimization techniques to design and analyze dynamic complex networks. A wide range of applications and optimization problems derived from research areas such as cellular and molecular chemistry, operations research, brain physiology, epidemiology, and ecology."

"ABSTRACT: In this paper, the Riemann solution of an extended Riemann problem in channelnetworks is presented. The Riemann problem at a junction network is well defined in theliterature. However, it is limited to symmetric networks. Here, we extend the Riemann problemto non-symmetric networks such that neither the channel width equality nor the dischargeequality are assumed. The Riemann solution is given under subcritical flow conditions to ensurethe existence and uniqueness of the solution at the junction. Taking into account the massand energy conservation laws, the necessary conditions for the Riemann solution are drawn.The results are summarized in a theorem. The theorem is illustrated with a set of numericalexamples.In order to perform a one-dimensional simulation in channel networks, the inner boundaryconditions at the junction (i.e., the channel intersection point) are required. It has turned outthat the classical models (i.e., the Equality, Gurram, Hsu models) that have been used to supplysuch a boundary suffer from many drawbacks.Thus, here we propose to use the Riemann solution at the junction networks to provideproper boundary conditions. Then, we compare all the junction models together. The junctionmodels are validated against experimental results found in the literature for steady state flows.Generally, the Riemann model shows good results in matching the experimental data. Inparticular, the Riemann model shows the best results when the bottom discontinues at thejunction. For the unsteady state flows, we perform prototype case studies to test the junctionmodels in the channel networks, and the numerical solutions are compared with the analyticalsolutions. The Riemann model continues to show the best results that agree with the analyticalsolutions. However, the validation of the junction models in the unsteady state flows remainsfor future work due to the limited amount of real data."