A visual planning solution to streamline the processes of hybrid cross-dockings

Cross-docking is a logistic strategy widely adopted both in manufacturing and distribution contexts. Despite the huge number of researches on this topic, most of them remain rather theoretical as they do not pay enough attention to operational aspects and daily functioning patterns of cross-dockings. This article aims at introducing a new focus for cross-docking research by proposing a visual planning solution to manage daily operation of a manufacturing cross-docking. By avoiding complex optimization algorithms and by adopting concepts belonging to the lean management theory, the article shows the potential of visual planning techniques to improve performance of cross-dockings. Starting from a specific industrial context, the shipping warehouse of an Italian company of the oil andand gas sector, a visual management methodology has been proposed to support the operation of cross-dockings. Although developed in a particular context, the proposed methodology and its underlying principles are to be considered general and potentially applicable to a wide range of cross-dockings. The methodology fully exploits an analytical tool, namely, the Safety Margin coefficientthat could be used for capacity planning purposes and to evaluate quantitatively the system performance at a given time instant.     

Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities

In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.     

Underground testing: Name-altering practices as probes in electronic music

Name-altering practices are common in many creative fields—pen names in literature, stage names in the performing arts, and aliases in music. More than just reflecting artistic habits or responding to the need for distinctive brands, these practices can also serve as test devices to probe, validate, and guide the artists’ active participation in a cultural movement. At the same time, they constitute a powerful probe to negotiate the boundaries of a subculture, especially when its features are threatened by appropriation from the mass-oriented culture. Drawing evidence from electronic music, a field where name-altering practices proliferate, we outline dynamics of pseudonymity, polyonymy, and anonymity that surround the use of aliases. We argue that name-altering practices are both a tool that artists use to probe the creative environment and a device to recursively put one’s creative participation to the test. In the context of creative subcultures, name-altering practices constitute a subtle but effective form of underground testing.     

Abnormalising minorities. The state and expert knowledge addressing the Roma in Italy

This article expands the research on abnormalisation and the construction of social deviance of minorities. It focuses on the relationships between state practices, policies and expert knowledge addressing the Roma in Italy; it does so by first contextualising recent ethnographic findings on Turin authorities’ social inclusion practices addressing Roma within the history of national and regional policies for Roma; it then contextualises those policies within the history of expert knowledge about Roma. Unlike what other studies on abnormalisation suggest, we argue that the abnormalisation of Roma in Italy is not primarily predicated upon the idea that they are at present unfit to follow the norms of the majority; rather, it stays upon a historically rooted representation of Roma oscillating between the poles of potential re-educability and potential dangerousness. In the conclusion we encourage further comparative research on abnormalisation, especially including practices and knowledge addressing other European minorities such as the Jews.     

Model selection in high-dimensional noisy data: a simulation study

In many practical applications, high-dimensional regression analyses have to take into account measurement error in the covariates. It is thus necessary to extend regularization methods, that can handle the situation where the number of covariates p largely exceed the sample size n, to the case in which covariates are also mismeasured. A variety of methods are available in this context, but many of them rely on knowledge about the measurement error and the structure of its covariance matrix. In this paper, we set the goal to compare some of these methods, focusing on situations relevant for practical applications. In particular, we will evaluate these methods in setups in which the measurement error distribution and dependence structure are not known and have to be estimated from data. Our focus is on variable selection, and the evaluation is based on extensive simulations.     

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.     

LEANING AGAINST WINDY BANK LENDING

Using an estimated dynamic stochastic general equilibrium model with banking, this paper first provides evidence that monetary policy reacted to bank loan growth in the United States during the Great Moderation. It then shows that the optimized simple interest‐rate rule features no response to the growth of bank credit. However, the welfare loss associated to the empirical responsiveness is small. The sources of business cycle fluctuations are crucial in determining whether a “leaning‐against‐the‐wind” policy is optimal or not. In fact, the predominant role of supply shocks in the model gives rise to a trade‐off between inflation and financial stabilization. (JEL E32, E44, E52)     

Earthquakes occurrences estimation through a parametric semi-Markov approach

The estimation of earthquakes’ occurrences prediction in seismic areas is a challenging problem in seismology and earthquake engineering. Indeed, the prevention and the quantification of possible damage provoked by destructive earthquakes are directly linked to this kind of prevision. In our paper, we adopt a parametric semi-Markov approach. This model assumes that a sequence of earthquakes is seen as a Markov process and besides it permits to take into consideration the more realistic assumption of events’ dependence in space and time. The elapsed time between two consecutive events is modeled as a general Weibull distribution. We determine then the transition probabilities and the so-called crossing states probabilities. We conclude then with a Monte Carlo simulation and the model is validated through a large database containing real data.     

SIMPCA: a framework for rotating and sparsifying principal components

We propose an algorithmic framework for computing sparse components from rotated principal components. This methodology, called SIMPCA, is useful to replace the unreliable practice of ignoring small coefficients of rotated components when interpreting them. The algorithm computes genuinely sparse components by projecting rotated principal components onto subsets of variables. The so simplified components are highly correlated with the corresponding components. By choosing different simplification strategies different sparse solutions can be obtained which can be used to compare alternative interpretations of the principal components. We give some examples of how effective simplified solutions can be achieved with SIMPCA using some publicly available data sets.