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Nowadays, end customers require personalized products to match their specific needs. Thus, production systems must be extremely flexible. Companies typically exploit assembly lines to manufacture produces in great volumes. The development of assembly lines distinguished by mixed or multi models increases their flexibility concerning the number of product variants able to be manufactured. However, few scientific contributions deal with customizable products, i.e., produces which can be designed and ordered requiring or not a large set of available accessories.This manuscript proposes an original two-step procedure to deal with the multi-manned assembly lines for customized product manufacturing. The first step of the procedure groups the accessories together in clusters according to a specific similarity index. The accessories belonging to a cluster are typically requested together by customers and necessitate a significant mounting time. Thus, this procedure aims to split accessories belonging to the same cluster to different assembly operators avoiding their overloads.The second procedure step consists of an innovative optimization model which defines tasks and accessory assignment to operators. Furthermore, the developed model defines the activity time schedule in compliance with the task precedencies maximizing the operator workload balance. An industrial case study is adopted to test and validate the proposed procedure. The obtained results suggest superior balancing of such assembly lines, with an average worker utilization rate greater than 90%. Furthermore, in the worst case scenario in terms of customer accessories requirement, just 4 line operators out of 16 are distinguished by a maximum workload greater than the cycle time.

Assembly line balancing problems aim to an efficient and effective assignment of all the required tasks to workstations in a flow oriented production system. Nowadays, assembly lines have to face the manufacturing of extremely personalized products (e.g. cars) as requested by an increasingly higher portion of the market demand. Several literature contributions focus on different balancing problems affected by the wide variety of the final product, e.g. mixed and multi model assembly lines. However, no contribution seems to tackle the personalized production of goods. Such products require to assemble a certain number of tasks whatever the final product personalization is, and a variable number of optional of different type determined by the specifications of every single costumer. This paper faces the generalized assembly of personalized goods proposing an innovative two step methodology to optimize the workload balancing between the assembly line stations, considering traditional tasks and the optional required by the product personalization, which could occur with different frequencies and pairings. The first phase of the developed methodology executes a clustering of product options required by the costumers based on a similarity index. This phase leads to the definition of several sets of optional typically requested together by the customer and with similar mounting time. The methodology second phase leverages the defined clusters of optional. Indeed, optional of the same cluster shouldn’t be assigned to the same workstation to reduce the overload or underload of the assembly operators. An integer programming model is proposed to assign both traditional tasks and optional to stations, to maximize the assembly line balancing considering the order frequency and assembly time of the clusterized optional. An industrial case study is adopted to test and validate the proposed two steps methodology. The obtained results highlight a consistent time balancing between assembly line workstations and a significant limitation of the operator overloads.

Aims: The present investigation aimed at evaluating differences in psychiatric hospitalizations in Italy during and after the lockdown due to the novel coronavirus disease 2019 (COVID-19), compared to the same periods in 2018 and 2019. Methods: We obtained and analyzed anonymized data on psychiatric admissions (n = 4550) from 12 general hospital psychiatric wards (GHPWs) in different Italian regions (catchment area = 3.71 millions of inhabitants). Using a mixed-effects Poisson regression model, we compared admission characteristics across three periods: (a) March 1–June 30, 2018 and 2019; (b) March 1–April 30, 2020 (i.e., lockdown); and (c) May 1–June 30, 2020 (i.e., post-lockdown). Results: During the COVID-19 lockdown, there was a 41% reduction (IRR = 0.59; p < 0.001, CI: 0.45–0.79) in psychiatric admissions in the enrolled GHPWs with respect to the 2018 and 2019 control period. Conversely, admission rates in the post-lockdown period were similar to those observed in the control period. Notably, a consistent and significant reduction in psychiatric hospitalizations of older patients (aged >65 years) was observed in the lockdown (40%; IRR = 0.60; 95% CI: 0.44–0.82) and post-lockdown (28%; IRR = 0.72; 95% CI: 0.54–0.96) periods. Long-stay admissions (>14 days) increased (63%; IRR = 1.63; 95% CI: 1.32–2.02) during the lockdown and decreased by 39% thereafter (IRR = 0.61; 95% CI: 0.49–0.75). A significant 35% increase in patients reporting suicidal ideation was observed in the post-lockdown period, compared to the rate observed in the 2018 and 2019 control period (IRR = 1.35; 95% CI: 1.01–1.79). Conclusion: The COVID-19 lockdown was associated with changes in the number of psychiatric admissions, particularly for older patients and long-stay hospitalizations. Increased admission of patients reporting suicidal ideation in the post-lockdown period merits special attention. Further studies are required to gain insight into the observed phenomena.

The great diffusion of renewable energy sources in the latest decades led to a pervasive diffusion of hybrid energy systems (HESs), in which several sources of energy are combined to fulfil the user demand. As the number of HES sources and related hardware components raise, the number of interconnections between these latter increases more than proportionately. Such condition determines high HES complexity, that can be exploited to achieve greater overall efficiency. This research deals with the HES designed to meet the electricity load required by a single user. Furthermore, the considered HESs leverage single or multiple components in charge of electricity production (e.g. PV module, wind turbine, etc.) a battery energy storage system (BES) and a connection with the national grid that allows the electricity purchase and sale processes. A two-step approach is proposed to deal firstly with the HES sizing, by exploiting an iterative method to identify the optimal size of each energy component. The second step consists in the energy flows management, i.e. the hourly dispatching between the HES components. Indeed, two different strategies are proposed to intelligently manage the hourly electricity flows. On the one hand, the proposed heuristic algorithm (HA) aims to minimize the purchased energy from grid imposing a constant dispatching of the electricity flows according to the amount of produced energy, the state of charge of the BES and the user request. On the other hand, a mixed integer linear programming (MILP) model leverages the short-time forecasts of environmental parameters, energy tariffs, and load request to optimise in real-time the electricity trade process from an economic point of view. The overall approach, including the comparison between the HA and the MILP, has been tested and validated on several case studies, concerning specific HES architectures, a range of European countries, both domestic and non-domestic users as well as multiple values of energy request. The results justify the HES installation in all the considered case studies, leading to an economic saving greater than 35% compared to electricity purchase from grid uniquely. However, the MILP adoption for the management of energy flows is recommended only for HESs fuelled by multiple energy sources.

The complexity of systems for energy production through renewable energy sources (RESs) is constantly increasing considering the integration of multiple modules, e.g. different RES sources, energy storage in batteries and connection to national grid for energy trade purpose. This significant complexity could represent a threat but also an opportunity if adequately managed. Aim of this paper is to propose two different approaches to manage the hourly electricity flows between the different components of a hybrid energy system (HES) fueled by PV modules and a wind turbine, equipped with a battery storage system (BES) to satisfy the demand of a user load with the opportunity to sell and purchase the electricity to/from the national grid. The first approach is a heuristic algorithm (HA) which defines robust but constant dispatching criteria of the energy flows between the HES components considering just the current value of energy production and demand with the aim of minimizing the electricity purchased by the grid. On the contrary, the second approach is a mixed integer linear programing (MILP) model which defines the optimal value of the energy flows to maximize the net profit of the HES operations determined by the electricity sales revenues minus the energy purchase costs. The developed MILP leverages the short-term forecast of the atmospheric conditions and user demand as well it considers variable energy sale and purchase pricing in the different daily hours. Both these approaches have been tested and validated through a case study of a residential building in which multiple households live located in the suburban area of Munich (Germany). The obtained results highlight how the MILP outperforms HA considering the net profit achievable weekly due to electricity trade with the grid. In particular, the MILP improve the HA economic performance of the HES operation management of 18% on average over the different months of the year.

Evidence from a few studies indicates the existence of several issues related to psychiatric patients' decisional capacity to give informed consent to clinical research. Clinicians often face difficulties in acquiring valid informed consent in clinical practice and even more so in drug trials. Participants often fail to fully understand or retain information regarding the actual implications of research protocols. The Brief Assessment for Consent to Clinical Research (BACO) was developed to investigate capacity to consent to clinical trials and further compare patients with schizophrenia and healthy comparisons' decisional capacity. A method to avoid possible confounding effects of choosing a treatment regarding a current disease was applied. The study groups were administered the BACO and the MacArthur Competence Assessment Tool for Clinical Research. Psychiatric patients performed poorer in comprehending, appreciating, and reasoning abilities, than their healthy counterparts. Impaired cognitive functioning and psychiatric symptoms severity were associated with reduced capacity to consent.