This paper presents work aimed at improved organization and performance of production in housing renovation projects. The purpose is to explore and demonstrate the potential of lean work organization and industrialized product technology to improve workflow and productive time. The research included selected case studies that have been found to implement lean work organization and industrialized product technology in an experimental setting. Adjustments to the work organization and construction technology have been implemented on site. The effects of the adjustments have been measured and were reviewed with operatives and managers. The data have been collected and analyzed, in comparison to traditional settings. Two projects were studied. The first case implied am application of lean work organization in which labor was reorganized redistributing and balancing operations among operatives of different trades. In the second case industrialized solution for prefabricated installation of prefabricated roofs. In both cases the labor productivity increased substantially compared to traditional situations. Although the limited number of cases, both situations appeared to be representative for other housing projects. This has led to conclusions extrapolated from both cases applicable to other projects, and contribution to the knowledge to improve production in construction. Vrijhoef, R. (2016). “Effects of Lean Work Organization and Industrialization on Workflow and Productive Time in Housing Renovation Projects.” In: Proc. 24 th Ann. Conf. of the Int’l. Group for Lean Construction, Boston, MA, USA, sect.2 pp. 63–72. Available at: .
MULTIFILE
Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
Industry 4.0 has placed an emphasis on real-time decision making in the execution of systems, such as semiconductor manufacturing. This article will evaluate a scheduling methodology called Evolutionary Learning Based Simulation Optimization (ELBSO) using data generated by a Manufacturing Execution System (MES) for scheduling a Stochastic Job Shop Scheduling Problem (SJSSP). ELBSO is embedded within Ordinal Optimization (OO), where in the first phase it uses a meta model, which previously was trained by a Discrete Event Simulation model of a SJSSP. The meta model used within ELBSO uses Genetic Programming (GP)-based Machine Learning (ML). Therefore, instead of using the DES model to train and test the meta model, this article uses historical data from a front-end fab to train and test. The results were statistically evaluated for the quality of the fit generated by the meta-model.
