Modeling electricity storage to address challenges and opportunities of its applications for smart grids requires inter-temporal equalities to keep track of energy content over time. Prevalently, these constraints present crucial modeling elements as to what extent energy storage applications can enhance future electric power systems' sustainability, reliability, and efficiency. This paper presents a novel and improved mixed-integer linear problem (MILP) formulation for energy storage of plug-in (hybrid) electric vehicles (PEVs) for reserves in power system models. It is based on insights from the field of System Dynamics, in which complex interactions between different elements are studied by means of feedback loops as well as stocks, flows and co-flows. Generalized to a multi-bus system, this formulation includes improvements in the energy balance and surpasses shortcomings in the way existing literature deals with reserve constraints. Tested on the IEEE 14-bus system with realistic PEV mobility patterns, the deterministic results show changes in the scheduling of the units, often referred to as unit commitment (UC).

This paper provides an analysis of the long- and short-run determinants of domestic bank lending to the private sector in eleven Central, Eastern and Southeastern European (CESEE) countries. We identify regime shifts for the observation period of 1997 to 2009, and the resulting subperiods are characterized by a different impact of the credit growth determinants. Estimating a credit demand equation as the long-term relation, we find – for most countries – a cointegration relationship with economic activity. We then examine the shortrun dynamics by applying both a linear and a nonlinear (Markov-switching) error correction model. While there is a significant correlation between credit growth and supply factors, namely bank deposits and banks’ equity, its impact differs across the subperiods. Identified regime switches in the short-run relation are driven primarily by differences in the credit supply factors rather than by the adjustment toward the credit equilibrium as the error correction coefficients show only slight cross-regime differences. In terms of regime switching, we distinguish between two groups of countries: those with one dominant regime, which is only briefly interrupted by a second one, and those with two equally pronounced regimes. In the latter group, a marked switch occurred just before or when the global crisis hit the CESEE region in the latter part of 2008. This regime shift is associated with a decreased correlation between deposit and credit growth.

In this paper, an overview is presented of the existing metaheuristic solution procedures to solve the multi-mode resource-constrained-project scheduling problem, in which multiple execution modes are available for each of the activities of the project. A fair comparison is made between the different metaheuristic algorithms on the existing benchmark datasets and on a newly generated dataset. Computational results are provided and recommendations for future research are formulated.

Support Vector Machines are methods that stem from Artificial Intelligence and attempt to learn the relation between data inputs and one or multiple output values. However, the application of these methods has barely been explored in a project control context. In this paper, a forecasting analysis is presented that compares the proposed Support Vector Regression model with the best performing Earned Value and Earned Schedule methods. The parameters of the SVM are tuned using a cross-validation and grid search procedure, after which a large computational experiment is conducted. The results show that the Support Vector Machine Regression outperforms the currently available forecasting methods. Additionally, a robustness experiment has been set up to investigate the performance of the proposed method when the discrepancy between training and test set becomes larger.

This paper studies the effect of home–host country distance on the choice of governance mode in service offshoring. Using a Transaction Cost Economics approach, we explore the comparative costs of the hierarchical and contractual models to show that different dimensions of distance (geographic, cultural and institutional), because they generate different types of uncertainties, impact offshore governance choices in different ways. Empirical results confirm that, on the one hand, firms are more likely to respond to internal uncertainties resulting from geographic and cultural distance by leveraging the internal controls and collaboration mechanisms of a captive offshore service center. On the other hand, they tend to respond to external uncertainties resulting from institutional distance by limiting their foreign commitment and leveraging the resources and local experience of third party service providers. Finally, we find that the temporal distance component (time zone difference) of geographical dispersion between onshore and offshore countries plays a dominant role over the spatial distance component.