Training the next generation of industrial engineers and managers is a constant challenge for academia, given the fast changes of industrial technology. The current and predicted development trends in applied technologies affecting industry worldwide as formulated in the Industry 4.0 initiative have clearly emphasized the needs for constantly adapting curricula. The sensible socioeconomic changes generated by the COVID-19 pandemic have induced significant challenges to society in general and industry. Higher education, specifically when dealing with Industry 4.0, must take these new challenges rapidly into account. Modernization of the industrial engineering curriculum combined with its migration to a blended teaching landscape must be updated in real-time with real-world cases. The COVID-19 crisis provides, paradoxically, an opportunity for dealing with the challenges of training industrial engineers to confront a virtual dematerialized work model which has accelerated during and will remain for the foreseeable future after the pandemic. The paper describes the methodology used for adapting, enhancing, and evaluating the learning and teaching experience under the urgent and unexpected challenges to move from face-to-face university courses distant and online teaching. The methodology we describe is built on a process that started before the onset of the pandemic, hence in the paper we start by describing the pre-COVID-19 status in comparison to published initiatives followed by the real time modifications we introduced in the faculty to adapt to the post-COVID-19 teaching/learning era. The focus presented is on Industry 4.0. subjects at the leading edge of the technology changes affecting the industrial engineering and technology management field. The manuscript addresses the flow from system design subjects to implementation areas of the curriculum, including practical examples and the rapid decisions and changes made to encompass the effects of the COVID-19 pandemic on content and teaching methods including feedback received from participants.
This paper is based upon a software Process Improvement Experiment (PIE) under the auspices of the European Systems and Software Initiative (ESSr) being conducted at IsraelAircraft Industries. The objective of the PIE is the application of advanced requirements management and software testing methods and tools in order to improve software productivity and quality in a measurable way. This controlled experiment was initiated in April 1997 and is being performed on a large ongoing avionics upgrade project. The paper describes the methods and tools selected for the experiment. the systematic process for their selection, the process of their deployment within the context of the existing organizational culture, and the data selected for collection to demonstrate the process improvement quantitatively. It presents lessons learned that can be applied by organizations that are investing in software process improvement.
The problem of controlling the spatial flux distribution in a reactor is formulated as an optimization problem.A nodal representation of the distributed parameter system that results in a set of non-linear ordinary differential equations is used. This non-linear optimal control problem is solved directly by Differential Dynamic Programming. Several numerical examples are presented to show the efficiency of the method, the short computational time needed for convergence, and the wide range of sudden demands that can be handled.
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