Strukturelle Hemmnisse für den Ausbau der Kindertagesbetreuung in Deutschland

Der derzeitige Stand des quantitativen und qualitativen Ausbaus der Kindertagesbetreuung in Deutschland wird von uns als Anlass genommen, die gegenwärtige Finanzierungsstruktur und deren Anreize für die Akteure im Bereich Kindertagesbetreuung zu hinterfragen. Dazu wird die politische Fokussierung auf die Aus- und Weiterbildung von Fachkräften analysiert. Zudem werden die Strukturen untersucht, die den finanziellen Mitteleinsatz bei der Angebotserstellung im Hinblick auf den Angebotsausbau beeinflussen. Wir argumentieren, dass die gegenwärtige Finanzierungsstruktur und die Struktur der Fachkraftarbeitsmärkte für die Anbieter von Kindertagesbetreuung ungenügende Anreize setzen, um die von den politischen Entscheidungsträgern angestrebte Kombination aus Angebotsquantität und Angebotsqualität zu erreichen. Wir zeigen auch auf, dass das vom Bundesministerium für Familie, Senioren, Frauen und Jugend vorgelegte Zehn-Punkte-Programm zum Ausbau der Kindertagesbetreuung diese Hemmnisse unzureichend aufgreift.   Structural Impediments to the Expansion and Improvement of Early Childhood Education and Care Services in Germany In recent years, the quantitative expansion and qualitative improvement of early childhood education and care (ECEC) services have had a rather sluggish progress in Germany. We investigate the structure of the ECEC financing system and its incentives for the market participants. In particular, we address the recent political issue of the improvement of initial training and further development of staff. We show that the present structure of financing ECEC together with the structures of the labour markets for qualified staff lack incentives to improve the quantity and the quality of ECEC services. The program for the enhancement of ECEC services recently published by the Federal Ministry of Family Affairs, Senior Citizens, Women and Youth also tackles these problems insufficiently

The SCIP optimization suite 5.0

This article describes new features and enhanced algorithms made available in version 5.0 of the SCIP Optimization Suite. In its central component, the constraint integer programming solver SCIP, remarkable performance improvements have been achieved for solving mixed-integer linear and nonlinear programs. On MIPs, SCIP 5.0 is about 41 % faster than SCIP 4.0 and over twice as fast on instances that take at least 100 seconds to solve. For MINLP, SCIP 5.0 is about 17 % faster overall and 23 % faster on instances that take at least 100 seconds to solve. This boost is due to algorithmic advances in several parts of the solver such as cutting plane generation and management, a new adaptive coordination of large neighborhood search heuristics, symmetry handling, and strengthened McCormick relaxations for bilinear terms in MINLPs. Besides discussing the theoretical background and the implementational aspects of these developments, the report describes recent additions for the other software packages connected to SCIP, in particular for the LP solver SoPlex, the Steiner tree solver SCIP-Jack, the MISDP solver SCIP-SDP, and the parallelization framework UG

The SCIP Optimization Suite 6.0

The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. This paper discusses enhancements and extensions contained in version 6.0 of the SCIP Optimization Suite. Besides performance improvements of the MIP and MINLP core achieved by new primal heuristics and a new selection criterion for cutting planes, one focus of this release are decomposition algorithms. Both SCIP and the automatic decomposition solver GCG now include advanced functionality for performing Benders’ decomposition in a generic framework. GCG’s detection loop for structured matrices and the coordination of pricing routines for Dantzig-Wolfe decomposition has been significantly revised for greater flexibility. Two SCIP extensions have been added to solve the recursive circle packing problem by a problem-specific column generation scheme and to demonstrate the use of the new Benders’ framework for stochastic capacitated facility location. Last, not least, the report presents updates and additions to the other components and extensions of the SCIP Optimization Suite: the LP solver SoPlex, the modeling language Zimpl, the parallelization framework UG, the Steiner tree solver SCIP-Jack, and the mixed-integer semidefinite programming solver SCIP-SDP