A survey of variants and extensions of the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts. --project scheduling,modeling,resource constraints,temporal constraints,networks

Real-time Digital Twins

We live in a world of exploding complexity driven by technical evolution as well as highly volatile socio-economic environments. Managing complexity is a key issue in everyday decision making such as providing safe, sustainable, and efficient industrial control solutions as well as solving today's global grand challenges such as the climate change. However, the level of complexity has well reached our cognitive capability to take informed decisions. Digital Twins, tightly integrating the real and the digital world, are a key enabler to support decision making for complex systems. They allow informing operational as well as strategic decisions upfront through accepted virtual predictions and optimizations of their real-world counter parts. Here we focus on real-time Digital Twins for online prediction and optimization of highly dynamic industrial assets and processes. They offer significant opportunities in the context of the industrial Internet of Things for novel and more effective control and optimization concepts. Thereby, they meet the Internet of Things needs for novel technologies to overcome today's limitations in terms of data availability in industrial contexts. Integrating today's seemingly complementary technologies of model-based and data-based, as well as edge-based and cloud-based approaches has the potential to re-imagine industrial process performance optimization solutions

The Changing Fortunes of Central Banking. Bruegel Special Report

Understanding the changing role of central banks and the novel policies they have pursued recently is absolutely essential for analysing many economic, financial and political issues, ranging from financial regulation and crisis, to exchange rate dynamics and regime changes, and QE and prolonged low interest rates. This book features contributions by many of the world’s leading experts on central banking, providing in accessible essays a fascinating review of today’s key policy and research issues for central banks. Luminaries including Stephen Cecchetti, Takatoshi Ito, Anil Kashyap, Mervyn King, Donald Kohn, Otmar Issing, Hyun Shin and William White are joined by Charles Goodhart of the London School of Economics, whose many achievements in the field of central banking are honoured as the inspiration for this book. The Changing Fortunes of Central Banking discusses the developing role of central banks and the policies they pursue in seeking monetary and financial stabilisation, while also giving suggestions for model strategies. This comprehensive review will appeal to central bankers, financial supervisors, academics and economists working in think tanks

A continuous mechanobiological model of lateral inhomogeneous biological surfaces

Thin elastic surfaces containing molecules infuencing the mechanical prop- erties of the surface itself are wide spreaded structures of different scales in biological systems. Prominent examples are bilayer membranes and cell tis- sues. In this paper we present a continuous dynamical model of deforming lateral inhomogeneous surfaces, using the example of biological membranes. In agreement with experimental observations the membrane consists of dif- ferent molecule species undergoing lateral phase separation and influencing the mechanical properties of the membrane. The presented model is based on the minimization of a free energy leading to a coupled nonlinear PDE system of fourth order, related to the Willmore flow and the Cahn-Hilliard equation. First simulations show the development of budding structures from stochas- tic initial conditions as a result of the gradient flow, which is comparable to experimentally observed structures. In our model mechanical properties are described via macroscopic mechanical moduli. However, the qualitative and quantitative relationships of mechanical moduli and the local composi- tion of the membrane are unkown. Since the exact relationship significantly influences the emerging structures, this study motivates the development of techniques allowing for upscaling from the molecular scale

Multiscale Modelling, Analysis, and Simulation in Mechanobiology

The main object of this thesis is the rigorous derivation of continuum models in mechanobiology via multiscale analysis. On the microscopic level, models in terms of energy functionals defined on networks / lattices are considered. Using concepts of Gamma-convergence rigorous convergence results as well as explicit homogenisation formulae can be derived. Based on a characterisation via energy functionals, appropriate macroscopic stress-strain relationships (constitutive equations) are determined. Mechanics of the membrane-bound cytoskeleton of red blood cells, and accordingly mechanics of red blood cells, are considered as one test case. The rigorous derivation of a macroscopic continuum model is based on a realistic discrete microscopic model. Simulations of optical tweezer experiments confirm the model qualitatively as well as quantitatively. For these simulations an appropriate computational framework for single cell mechanics is developed using finite element methods. It accounts explicitly for membrane mechanics and its coupling with bulk mechanics. The approach is highly flexible and can be generalised to many other cell models, also including biochemical control. As a test case considering the interactions between biological processes and mechanics, growing cell cultures are investigated. From a discrete cellular-automaton-like description macroscopic continuum models are derived. Furthermore, it is shown that the models can account for branching morphogenesis - a typical phenomenon observed in growing cell cultures, where growth is promoted by a diffusing substance

CSAR: The Cross-Sectional Autoregression Model

The forecasting of time series data is an integral component for management, planning, and decision making. Following the Big Data trend, large amounts of time series data are available in many application domains. The highly dynamic and often noisy character of these domains in combination with the logistic problems of collecting data from a large number of data sources, imposes new requirements on the forecasting process. A constantly increasing number of time series has to be forecasted, preferably with low latency AND high accuracy. This is almost impossible, when keeping the traditional focus on creating one forecast model for each individual time series. In addition, often used forecasting approaches like ARIMA need complete historical data to train forecast models and fail if time series are intermittent. A method that addresses all these new requirements is the cross-sectional forecasting approach. It utilizes available data from many time series of the same domain in one single model, thus, missing values can be compensated and accurate forecast results can be calculated quickly. However, this approach is limited by a rigid training data selection and existing forecasting methods show that adaptability of the model to the data increases the forecast accuracy. Therefore, in this paper we present CSAR a model that extends the cross-sectional paradigm by adding more flexibility and allowing fine grained adaptations to the analyzed data. In this way, we achieve an increased forecast accuracy and thus a wider applicability