In today's economic turmoil, the pay-per-use pricing model of cloud computing, its flexibility and scalability and the potential for better security and availability levels are alluring to both SMEs and large enterprises. However, cloud computing is fraught with security risks which need to be carefully evaluated before any engagement in this area. This article elaborates on the most important risks inherent to the cloud such as information security, regulatory compliance, data location, investigative support, provider lock-in and disaster recovery. We focus on risk and control analysis in relation to a sample of Swiss companies with regard to their prospective adoption of public cloud services. We observe a sufficient degree of risk awareness with a focus on those risks that are relevant to the IT function to be migrated to the cloud. Moreover, the recommendations as to the adoption of cloud services depend on the company's size with larger and more technologically advanced companies being better prepared for the cloud. As an exploratory first step, the results of this study would allow us to design and implement broader research into cloud computing risk management in Switzerland.

We consider a complex recyclable waste collection problem that extends the class of vehicle routing problems with intermediate facilities by integrating a heterogeneous fixed fleet and a flexible assignment of destination depots. Several additional side constraints, such as a mandated break period contingent on tour start time, multiple vehicle capacities and site dependencies are also included. This specific problem was inspired by a real-world application and does not appear in the literature. It is modeled as an MILP which is enhanced with several valid inequalities. Due to the rich nature of the problem, state-of-the-art commercial solvers are only able to tackle instances of small to medium size. To solve realistic instances, we propose a local search heuristic capable of systematically treating all problem features and general enough to respond to the varying characteristics of the case study regions for which it is intended. The results show that the heuristic achieves optimality on small random instances, exhibits competitive performance in comparison to state-of-the-art solution methods for special cases of our problem, and leads to important savings in the state of practice. Moreover, it highlights and quantifies the savings from allowing a flexible assignment of destination depots. The data from the state of practice comes from a recyclable waste collection company in Geneva, Switzerland.

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