Abstract
Electrification of society and economy is crucial to fight against climate changes assuming simultaneously a large-scale integration of electricity generation exploiting Renewable Energy Sources (RES). The increasing presence of RES and Electric Vehicles (EV) in Low Voltage (LV) networks, and the emergence of the Smart Grid paradigm will require relevant changes in the operational management of both LV and Medium Voltage (MV) networks. In this paper, two different strategies (separated and coordinated management) for the operational management of MV and LV networks are compared regarding their ability to integrate large amounts of RES and to accept increased electrification of consumption, including EV. Each management strategy is modeled through optimization problems, being then applied to an electrical distribution system consisting of MV and LV networks. Results show that a coordinated operational management outperforms the separated strategy, by allowing the integration of much higher volumes of RES and EV.

Abstract
In sectorization problems (SPs), a large area is divided into smaller regions for administrative purposes. SPs have applications in many fields. Since real-life problems are often dynamic, in this study, a new model for dynamic SP is proposed. In the problem, points are assigned to service centres and in this way sectors are formed. The sectors must be balanced in terms of distance and demand, which is defined in the objective function and constraints of the model. In the problem, in a certain time period, the coordinates and demands of some points change according to certain statistical distributions. A two-stage solution method is suggested for this problem. In the first stage, the expected values of coordinates and demands of the points are estimated by a Monte Carlo simulation, and in the second stage, the problem is solved like a deterministic optimization problem. The model is nonlinear, but after linearization, it is solved in Python’s Pulp library for benchmarks of different sizes and the results are discussed.

Abstract
Omnichannel retailing and digitalization result in considerable challenges for the management and optimization of retail operations. The continued demand of quantitative insights, their practical need, and the growing availability of data motivates an increasing number of scientists and practitioners to intensify research on demand and supply-related issues in retailing. This featured cluster provides the state-of-the art literature on forecasting and digitalization technologies, channel structures and delivery concepts as well as logistics in omnichannel and online retailing. The featured cluster contains 17 articles that deal with such topics. © 2021 Elsevier B.V.

Abstract
Big data offers great potential to improve organizational performance and generate competitive advantages. In this sense, knowing this phenomenon is relevant and this study aims to explore the role of big data on firm performance through a process of synthesis of several studies that have been published in recent years in different organizational contexts. This study adopts a mixed-methods approach. Initially, a systematic literature review is performed to characterize the studies that adopt structural equation modeling to determine the relationship between big data and firm performance. Additionally, a meta-analysis method is used to quantify the association between these two phenomena. The findings reveal a moderate positive relationship between the adoption of big data in the firm performance. This ratio is estimated at 0.38 with a confidence interval between 0.32 and 0.44 for a significance level of 0.05. The results of this study also allow us to conclude that the performance of organizations is also determined by other factors such as human capital, the data-driven organizational culture, or the learning capacity of the organization. This study offers mainly implications for companies that intend to invest in big data to know the potential value of this technology in organizational performance.

Abstract
The resilient smart distribution system is intended to cope with low probability, high-risk extreme events, including extreme natural disasters. In this regard, the flexible partitioning distribution system into supply-sufficient microgrids can be considered an interesting subject. This paper introduces a novel technique for partitioning a smart distribution system into supply-sufficient microgrids. In the presented model, the impact of demand response programs and energy storage has been considered in the proposed framework. Furthermore, besides the electricity aspects, the effects of gas network infrastructure on flexible portioning are also investigated to improve system resiliency facing natural disasters. The proposed model aims to maximize served load, which has been structured as a mixed integer linear programming (MILP) problem. The IEEE 34 bus standard test system is used to investigate the effectiveness of the proposed structure. The results of the study show that the proposed structure can increase the served load and the supply-sufficiently of the smart grid power system.

Abstract
The Real-Time Monitoring and Performance Management suite tool, known as UIL (User Interface Layer), was developed in the FASTEN project, a R&D initiative financed by the innovation and research program H2020 within a bilateral Europe-Brazil call. UIL was conceived and deployed in the IIoT architecture of the project. The goal was to provide a usercentered assistance to the human operator for both decision-responsibility and control loop, in a continuously updating information fashion, related to system's state. In order to have experimental results, a qualitative assessment was conducted in an industrial environment. The architecture proposed was based on the adoption of a Knowledge Engineering User Interface to support Operator 4.0. Our empirical experiments point out to a successful set of results. Copyright (C) 2021 The Authors.