196,284 research outputs found
Architettura digitale
No full textUn approccio innovativo ai cent'anni più densi e avvicenti della storia dell'architettura mondiale, un punto di vista contemporaneo lontano da ogni compilazione enciclopedica che tende invece a «costruire - e non semplicemente ricostruire - un possibile scenario con quanto ritenuto memorabile tra tutto ciò che è stato progettato, realizzato, teorizzato nel corso del XX secolo».
I curatori Marco Biraghi, docente di Storia dell'architettura contemporanea del Politecnico di Milano, e Alberto Ferlenga, docente di Progettazione architettonica presso l'Istituto universitario di architettura di Venezia, inaugurano con questo primo volume, focalizzato su tutto ciò che riguarda l'architettura senza tuttavia «esserlo» in senso proprio, la nuova Grande Opera Einaudi dedicata all'architettura del Novecento. Al primo seguirà nel 2013 un secondo volume, suddiviso in due tomi, e dedicato a edifici, luoghi fisici di progettazione, città e territori.
Teorie, scuole, eventi esplora il secolo appena trascorso allo scopo di individuare i temi, le correnti di pensiero e le radici della ricerca architettonica contemporanea. Un'indagine a tutto campo che, evitando le convenzionali elencazioni dei grandi nomi, sposta l'asse interpretativo dal piano biografico a quello del concreto lavoro architettonico - nella cultura novecentesca e nella produzione ideativa che si è andata depositando libri e riviste, si è diffusa e sintetizzata mediante l'insegnamento o è stata valorizzata grazie all'attività dei luoghi espositivi e l'assegnazione di premi. A queste macro-categorie interpretative i curatori affiancano una serie di voci tematiche di carattere generale, al fine di illustrare di volta in volta un aspetto o un problema cruciale per l'architettura del Novecento o la sua relazione con discipline all'apparenza distanti, come la fotografia o il cinema
A stepped approach to support preassembly tasks assignment in bus production
No full textThis paper proposes a structured approach to assign operations related to material and subassembly preparation before line assembly, to a group of multiskilled operators. It first characterizes the preparation tasks and their compatibility with the skill level of operators. Then, it formulates and solves the corresponding assignment problem. In case of infeasibility or not optimal solutions, the approach permits also to identify appropriate training measures in order to minimize the number of involved operators or maximize the number of assigned preparation tasks. This approach is the outcome of an action research case study which dealt with a bus assembly line. The results showed that the approach was relatively easy to implement and effectively led some crucial decisions which were mostly experience-based and not made in a structured manner
Nueva publicación: Pintando al converso.
No full textRecietemente ha visto la luz una nueva publicación llevada a cabo por miembros de nuestro proyecto. Se trata del volumen titulado: Pintando al converso: la imagen del morisco en la península ibérica (1492-1614). (Madrid, Cátedra). Los autores son Borja Franco (UNED) y Francisco Javier Moreno (Universidad de Castilla La Mancha). El objetivo de esta publicación es, por una parte, desmitificar las estereotípicas conclusiones que ciertas tendencias de pensamiento habían creado para definir al m..
Modelling a (MTO) Flow Shop in M-2-M production logic by System Dynamic matrix approach
No full textThe following paper proposes a framework based on a System Dynamics matrix approach with the aim to support the reengineering decision process of a plant department, operating in Make-To-Order context, and characterized by a bottleneck process. The approach develops a simulation model able to reproduces production process, plant resources, items orders and, most of all, the stochastic behaviour of the entire system, considering furthermore the variables retroactions through the evolution of a matrix flow. The purpose consists in optimizing the mix of possible solutions between the strengthening of plant workstation (hardware reengineering) or the purchase of semi-finished parts (process reengineering). The framework has been tested in an Italian factory operating in sheet metal industry and has been completed by an economic evaluation
La natura di organo costituzionale del Csm tra argomenti di ieri ed effettività dell’ordinamento
No full textA simulation model for dichotomous approach between governance and operations management: Case study of financial sector
No full textAssessing maintenance planning and scheduling using Deep Reinforcement Learning
No full textMaintenance scheduling is critical in many industries, and recent advances in Deep Reinforcement Learning (DRL) have shown that it can optimise scheduling decisions in complex and dynamic contexts. Traditional methods of maintenance scheduling frequently confront obstacles, making DRL an appealing alternative. This study presents a novel approach for autonomously determining optimal maintenance scheduling decisions in production systems that blends a simulation-based model with a DRL agent. The learning agent makes intelligent judgements based on the chance of failure and machine availability through trial and error. The setup of the DRL setting, particularly the reward function, has a considerable impact on the approach's performance. The proposed hybrid simulation-based and DRL methodology outperforms existing heuristic methods in rigorous evaluation, demonstrating its promise for efficient and effective maintenance planning and scheduling. This work sets the way for better system reliability and productivity in companies that rely on complex systems
Sometimes it drains, sometimes it sustains: The dual role of the relationship with students for university professors
No full textUniversity organizational contexts have been changing significantly in recent years, and academic staff are expected to manage larger workloads at an increased pace. This can threaten their well-being and exacerbate work-related stress—possibly creating negative impacts on their mental and physical states. Surprisingly, academic occupational psychological health is still rarely studied. By referring to the Job Demands-Resources (JD-R) conceptual model, this study aimed to analyze the relationship between university teachers’ well-being and job demands and resources, with a particular focus on the role of the relationship with students. Specifically, 550 associate and full professors were studied to determine the impact of job characteristics, quality of relationships in the work environment, and negative and positive relations with students regarding emotional exhaustion and work engagement. Hierarchical multiple regression models allowed us to highlight the fact that emotional exhaustion was positively and significantly associated with workload, conflicts with colleagues, and requests from students, and it was negatively associated with work meaning. Work engagement was positively and significantly associated with work meaning and social support from students. Our study points out that the flexible and renowned JD-R model can successfully be used to analyze the occupational psychological health of academics. Further, our study underscores the fact that, among job demands and resources, the often-neglected relations with external users (the students) can play an important role in university teachers’ perceptions of exhaustion and engagement
Dynamic scheduling of a due date constrained flow shop with Deep Reinforcement Learning
No full textManufacturers are increasingly under pressure to develop dynamic production systems and supply networks that can adjust to the climate, political, and social changes anywhere in the world at any time. Adoption of the Industry 4.0 paradigm aids in the completion of these objectives. Modern production systems necessitate a high level of manufacturing flexibility. At the same time, to keep up with the competition, manufacturers must make pledges to meet specified deadlines. In recent years, there has been a rise in interest in employing machine learning, particularly reinforcement learning, to solve production scheduling challenges of varying complexity. The general technique is to decompose the scheduling problem into a Markov Decision Process (MDP), after which an RL agent is trained using a simulation that implements the MDP. In this setting, this paper presents, in an application environment, a dispatching rule based on a deep reinforcement learning (DRL) algorithm. A DRL approach uses the DQN as the learning agent's training algorithm. The network's task is to identify the position of the job that will be executed. The objective is to present an algorithm that takes both the due date and the state of the production line into consideration to schedule jobs to meet the due dates and, at the same time, boost productivity. A flow shop configuration is considered and the performances of the proposed method are compared with the ones of dispatching rules already proposed in the scientific literature. To do so, the settings of the DRL algorithm must be specified, such as the state space, the reward function, and the hyperparameters, whereas the action is the choice of which job to be introduced in the production line. The overall objective of this research is to provide a general scheduling tool that may be used in a variety of situations, including unexpected ones
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