The COVID-19 emergency suddenly obliged schools and universities around the world to deliver on-line lectures and services. While the urgency of response resulted in a fast and massive adoption of standard, public on-line platforms, generally owned by big players in the digital services market, this does not sufficiently take into account privacy-related and security-related issues and potential legal problems about the legitimate exploitation of the intellectual rights about contents. However, the experience brought to attention a vast set of issues, which have been addressed by implementing these services by means of private platforms. This work presents a modeling and evaluation framework, defined on a set of high-level, management-oriented parameters and based on a Vectorial Auto Regressive Fractional (Integrated) Moving Average based approach, to support the design of distance learning architectures. The purpose of this framework is to help decision makers to evaluate the requirements and the costs of hybrid cloud technology solutions. Furthermore, it aims at providing a coarse grain reference organization integrating low-cost, long-term storage management services to implement a viable and accessible history feature for all materials. The proposed solution has been designed bearing in mind the ecosystem of Italian universities. A realistic case study has been shaped on the needs of an important, generalist, polycentric Italian university, where some of the authors of this paper work.

The design of supply chain networks (SCNs) aims at determining the number, location, and capacity of production facilities, as well as the allocation of markets (customers) and suppliers to one or more of these facilities. This paper reviews the existing literature on the use of simulation-optimization methods in the design of resilient SCNs. From this review, we classify some of the many works in the topic according to factors such as their methodology, the approach they use to deal with uncertainty and risk, etc. The paper also identifies several research opportunities, such as the inclusion of multiple criteria (e.g., monetary, environmental, and social dimensions) during the design-optimization process and the convenience of considering hybrid approaches combining metaheuristic algorithms, simulation, and machine learning methods to account for uncertainty and dynamic conditions, respectively.

The complexity of systems now under consideration (be they biological, physical, chemical, social, etc), together with the technicalities of experimentation in the real-world and the non-linear nature of system dynamics, means that computational modelling is indispensible in the pursuit of furthering our understanding of complex systems. Agent-based modelling and simulation is rapidly increasing in its popularity, in part due to the increased appreciation of the paradigm by the non-computer science community, but also due to the increase in the usability, sophistication and number of modelling frameworks that use the approach. The Flexible Large-scale Agent-based Modelling Environment (FLAME) is a relatively recent addition to the list. FLAME was designed and developed from the outset to deal with massive simulations, and to ensure that the simulation code is portable across different scales of computing and across different operating systems. In this study, we report our experiences when using FLAME to model the development and propagation of conflict within large multi-partner enterprise system implementations, which acts as an example of a complex dynamical social system. We believe FLAME is an excellent choice for experienced modellers, who will be able to fully harness the capabilities that it has to offer, and also be competent in diagnosing and solving any limitations that are encountered. Conversely, because FLAME requires considerable development of instrumentation tools, along with development of statistical analysis scripts, we believe that it is not suitable for the novice modeller, who may be better suited to using a graphical user interface driven framework until their experience with modelling and competence in programming increases.

The exceptional outbreaks of a number of epidemic diseases such as Ebola, SARS, Zika and H1N1 and their wide distribution over multiple regions calls for a reliable global health awareness system. This system is needed to achieve early detection of such emergencies. Furthermore, such health awareness system should be capable of predicting the outbreaks patterns to facilitate future countermeasure planning. This health awareness system should cover large scale regions that can be extended to multiple countries, continents and ultimately the globe. Many advanced and industrial countries are still struggling in building such system effectively even with the availability of resources and domain experts. The realization of a reliable health awareness system is accompanied with multiple challenges such as the availability of resources and experts, the global agreements about the system from the legislative and control point of view and the availability of the infrastructure that will support the system functionality with a reasonable cost. This paper presents a novel global health awareness system that overcomes the aforementioned challenges. The system is exploiting the emerging cloud computing services availability over the globe. To handle the large scale requirements, we introduce a multi-tier based cloud system that spans over four tiers starting from the monitored subjects to a centralized global cloud system. Also, we present a mixed integer optimization formulation to tackle the issues related to the latency of detecting outbreaks. Our results show that processing the data in multi-tier health awareness system will reduce the overall delay significantly and enable efficient health data sharing.

The frequency, social, and economic impacts of natural disasters show exponential increases in recent decades. Cities and countries around the world have begun to realize that these events are no longer "hundred year" storms, but repeat within a few years. As urbanisation continues throughout this century, more and more people and more economic activity will be concentrated in at-risk areas; especially as new arrivals in cities throughout Asia and Africa are likely to be concentrated in the highest risk districts, much as they often are in North America and Europe today. This article reviews recent growth of natural disasters and considers how a systems approach can improve approaches to mitigation and adaptation of these risks and to recovery from such events.

Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM's applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models' system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example.