Chevron Left
返回到 Basic Modeling for Discrete Optimization

學生對 墨尔本大学 提供的 Basic Modeling for Discrete Optimization 的評價和反饋

395 個評分


Optimization is a common form of decision making, and is ubiquitous in our society. Its applications range from solving Sudoku puzzles to arranging seating in a wedding banquet. The same technology can schedule planes and their crews, coordinate the production of steel, and organize the transportation of iron ore from the mines to the ports. Good decisions in manpower and material resources management also allow corporations to improve profit by millions of dollars. Similar problems also underpin much of our daily lives and are part of determining daily delivery routes for packages, making school timetables, and delivering power to our homes. Despite their fundamental importance, all of these problems are a nightmare to solve using traditional undergraduate computer science methods. This course is intended for students interested in tackling all facets of optimization applications. You will learn an entirely new way to think about solving these challenging problems by stating the problem in a state-of-the-art high level modeling language, and letting library constraint solving software do the rest. This will allow you to unlock the power of industrial solving technologies, which have been perfected over decades by hundreds of PhD researchers. With access to this advanced technology, problems that are considered inconceivable to solve before will suddenly become easy. Watch the course promotional video here:




Excellent course! I learned a lot. Although the assignments were sometimes hard to fathom, and its online nature mean getting hints was difficult, there's enough there to help get through.



Thank you so much for the course. I had to fight my coding habits, programming CP models is quite different from the traditional programming I'm, and it could even be more fun!


76 - Basic Modeling for Discrete Optimization 的 97 個評論(共 97 個)

創建者 Dominik T


Terrific course

創建者 Viet H N


Useful course

創建者 Martin J


so very good

創建者 Johan F M G


I loved

創建者 Olivier M



創建者 breezef



創建者 Kenny T



創建者 Paul T


MiniZinc is an extraordinarily useful tool, and I definitely feel I learned a great deal here that will be hugely useful in countless situations in the future. I'm also definitely moving on to the next course in Advanced Modeling ASAP. And while I found most of the general concepts fairly straightforward (I've solved most of these sorts of problems before in Excel Solver in some form or another), I was also frustrated by large gaps between the content taught in each section and the workshops at the end of that section (things I learned in each set of videos were often totally irrelevant to the later workshops and assignments), unnecessarily vague variable names (such as lower-case 'l' which looks just like the number '1' and is used in several examples), lack of downloadable example files to support the various videos, a fairly obtuse help system in MiniZinc itself that makes looking up keywords fairly difficult, sudden jumps in difficulty between the sections, too much time spent on topics irrelevant to the course (such as formatting of "output" statements, which takes the whole second half of the video for the week 4 workshop), and lots of little discrepancies between the PDF files for the various workshops and assignments and the actual workshops and assignments. This is a good course but would be a stellar one with a bit more polish to the MiniZinc help system, more polished examples, a better learning curve, and downloadable MiniZinc samples to accompany each video (really, just providing the MiniZinc files that are shown in the videos, along with a bit of reasonable sample data, would help a lot here).

創建者 Ollie D


Pretty good overall. The teaching is a bit boring at times but it's not too bad. I was also hoping that using constraint modeling means you don't have to worry about efficiency. Sadly this course spent a lot of time focusing on efficiency issues. I guess solvers are not clever enough to be able to reframe a set of constraints in a more efficient way, so the user apparently needs to remember things like to convert a "not(and)" into an "or" and other tricks.

創建者 Kittiphon P


The stories and examples used in the course are great and creative. I would like to thank both professors and all TAs for creating wonderful online course and for their hard work. However, some programming assignments are too tedious with too much constraints, like PA 3.



Super interesting. Would be nice to have corrections of the assignment too but overall, super fun.

The pdf description are sometimes unclea (constraints are sometimes put in the data description.)

創建者 Shambo R C


great course for beginners and those who are looking for applications of optimisation. the only problem is it forces you to work with minizinc and may consume extra time than expected

創建者 Andrew G


Great course. Is it "basic" though? It probably helps to have a good background in mathematical modeling , coding and optimization, this moves pretty fast. Heading to Course #2

創建者 Michael M


Challenging programming assignments and helpful workshops. Good video material. Hoped to have some more real world examples, rather than just fable-based examples.

創建者 Bhavik J


A classic combination of the story mode pitched in learning. I wish that the topics covered in the course were more elaborated.

創建者 Esteban C d V


Very interesting, but I miss some theory explaining how the optimizations work behind.

創建者 J.S. P


Great course. Would absolutely recommend.

創建者 Leo


Novel, creative and interesting

創建者 Krunal P


Good Assignments.

創建者 Lucas P M


The problem-based approach is very interesting and the course is very practical. However, it has no theoretical content, even superficial, to assess the best way to model. Some assignments have bugs or incomplete information that make it difficult to get good grades.

創建者 Can K


Should be called "Minizinc tutorial". The exercises are easy to pass, but the feedback on them are unhelpful, "You can have a better objective function" does not mean anything if you don't say which part of the model should the person improve.