Analysing orchestrations with risk profiles and angel-daemon games

In this paper game theory is used to analyse the effect of a number of service failures during the execution of a grid orchestration. A service failure may be catastrophic in that it causes an entire orchestration to fail. Alternatively, a grid manager may utilise alternative services in the case of failure, allowing an orchestration to recover, A risk profile provides a means of modelling situations in a way that is neither overly optimistic nor overly pessimistic. Risk profiles are analysed using angel and daemon games. A risk profile can be assigned a valuation through an analysis of the structure of its associated Nash equilibria. Some structural properties of valuation functions, that show their validity as a measure for risk, are given. Two main cases are considered, the assessment of Orc expressions and the arrangement of a meeting using reputations.

On the evolutionary status of short-period cataclysmic variables

We present high-speed, three-colour photometry of seven short-period (Porb

A Hybrid Evolutionary Approach to the Nurse Rostering Problem

Nurse rostering is a difficult search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimisation benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better in finding feasible solutions but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridise it with a recently proposed simulated annealing hyper-heuristic within a local search and genetic algorithm framework. The hybrid algorithm shows significant improvement over both the genetic algorithm with stochastic ranking and the simulated annealing hyper-heuristic alone. The hybrid algorithm also considerably outperforms the methods in the literature which have the previously best known results.

Evolutionary agglomeration theory: increasing returns, diminishing returns, and the industry life cycle

According to Marshall’s agglomeration theory, Krugman’s New Economic Geography models, and Porter’s cluster policies, firms should receive increasing returns from a trinity of agglomeration economies: a local pool of skilled labour, local supplier linkages, and local knowledge spillovers. Recent evolutionary theories suggest that whether agglomeration economies generate increasing returns or diminishing returns depends on time, and especially the evolution of the industry life cycle. At the start of the twenty-first century, we re-examine Marshall’s trinity of agglomeration economies in the city-region where he discovered them. The econometric results from our multivariate regression models are the polar opposite of Marshall’s. During the later stages of the industry life cycle, Marshall’s agglomeration economies decrease the economic performance of firms and create widespread diminishing returns for the economic development of the city-region, which has evolved to become one of the poorest city-regions in Europe.