The Asus Zephyrus g14 is a 14 inch thin and light laptop which has the 4900HS, so, not quite a Macbook Air, bit still very compact for an 8 core laptop.
I recently presented my master's thesis exactly on a process involving kanbans. I implemented a discrete event simulator that compares different dynamic scheduling agent policies of which one is a MIP optimization model. All very cool, but the company still uses paper kanbans and the whole process is far from the so called "industry 4.0". All in all, change costs and the change needs to give enough fruits to justify it.
There is perspective, so it's 3D. Unless you are talking about the fact that your display can show only 2D images then you are technically correct since most displays are not 3D displays.
Having developed a discrete event simulator myself for my master's thesis I can guarantee that the usefulness of tools like this one depends heavily on the veracity and quality of the time parameters and the random distributions.
This one is a very cool exercise, but applicability is dubious.
Anyway, does anybody have any suggestion about job positions where the job requires developing this kind of stuff (simulators, process optimization, heuristics)? Being a fresh graduate in this time period is pretty bad, but doesn't hurt to do some research on interesting job positions. I only see web related job positions and "machine learning" job positions lately.
The point of Anki is to learn while making the cards, because making them takes mental effort and requires enough comprehension of the concepts. It would be harder and less motivating to use something premade, that maybe gives priority to concepts that don't really interest you.
I'm currently working on a linear algebra heavy linear programming model for an optimization thesis and the general trick is, as with writing code, to improve iteration by iteration of the model. Starting with a complex mathematical model is always a bad idea, so start small and iteratively improve it. This means no use of advanced concepts unless needed. Also, nobody uses every bit of math in the everyday work. As with programming, you just need to understand the general concepts and the rest can be figured out step by step.
I'm all for linear optimization and other optimization techniques. It's refreshing to see other people talk about Gurobi, CPLEX, etc... Having done research in the field of scheduling and now getting contacted by companies, it's demoralizing to see that everybody usually speaks about machine learning while many problems can be solved in a more precise way with other techniques.
