17 votes
Accepted

Are there any COVID-19 (coronavirus) related optimization problems with input datasets that we can "crowd solve"?

Actually, yes, and Folding@Home is doing this right now. https://foldingathome.org/2020/02/27/foldinghome-takes-up-the-fight-against-covid-19-2019-ncov/ We need your help! Folding@home is joining ...
user avatar
10 votes

Are there any COVID-19 (coronavirus) related optimization problems with input datasets that we can "crowd solve"?

There is the following data set on the number of infections in China provided by Isaac Lin from the University of Hong Kong. https://github.com/BlankerL/DXY-COVID-19-Data/blob/master/README.en.md I ...
user avatar
  • 424
10 votes

Are there any COVID-19 (coronavirus) related optimization problems with input datasets that we can "crowd solve"?

I was thinking of ( no data but): determining which airline routes to block that will minimize the economic consequences given a very low risk of getting to the virus (like a chance constraint or ...
user avatar
8 votes

Are there any COVID-19 (coronavirus) related optimization problems with input datasets that we can "crowd solve"?

We (operations research community in Italy) have just offered our availability to contribute as we can. On our web site https://webgol.dinfo.unifi.it we just starting collecting ideas on how to help. ...
user avatar
7 votes

Are there any COVID-19 (coronavirus) related optimization problems with input datasets that we can "crowd solve"?

Another optimization related problem (no data) is raised in the following recent article: How America Can Avoid Italy’s Ventilator Crisis. The formulation of this ventilator distribution problem is ...
user avatar
  • 171
7 votes

How would you characterize "optimization data?"

I would just call it "planning data". I think it might be easier to convince an administrator that "planning data" needs to be recorded/captured than to sell them on "<...
user avatar
  • 29.7k
7 votes

How would you characterize "optimization data?"

I would call them decision-relevant data, because most optimization problems in practice help people do decisions better, which they already do in a heuristic fashion. This puts the focus on the ...
user avatar
  • 3,477
6 votes

How would you characterize "optimization data?"

Adhering to the rules of encapsulation, I would simply call it "parameters". If we're thinking of an optimisation model and, as you said, what changes is the number of things (number of ...
user avatar
6 votes
Accepted

Sources to learn about Sample Average Approximation for practitioners

SAA is a very widely used technique for stochastic optimization problems and as far as I can see there are two frequently used approaches for the implementation of SAA. Please check Homem-de-Mello's ...
user avatar
6 votes

What are best practices to make optimization user interfaces intuitive for the user?

I think it's a bit of general question. AFAIK, this subject can be surveyed in many aspects. Designing of an optimization software depends on its specific and related field. For example, architecting ...
user avatar
  • 5,833
5 votes
Accepted

How to reduce the risk of wrong modelling in OR industry projects?

One of my favorite things about working in operations research is diving into a new problem to understand all the complexities. Getting a non-OR person to list all the requirements for an optimization ...
user avatar
5 votes

How to reduce the risk of wrong modelling in OR industry projects?

In my experience the best way to manage risk in these kinds of situations is to correctly manage client expectation and avoid 'big design up front'. You should expect the unexpected - i.e. new ...
user avatar
5 votes

How to reduce the risk of wrong modelling in OR industry projects?

Simulation experts have dealt with this question for years. If you do a search for "simulation validation" and/or "simulation face validation", you will find lots of hits. As I ...
user avatar
  • 29.7k
5 votes

How to reduce the risk of wrong modelling in OR industry projects?

One way is to always start by modeling a baseline (a model which pictures the current situation). Basically, if you are working with linear programming (for example), you write your model and add ...
user avatar
  • 10.2k
5 votes

What are best practices to make optimization user interfaces intuitive for the user?

You mentioned: "Often, a planner will not have an operations research background and the software needs to be easy and intuitive so the user (and organization)...". So, I assume by optimization ...
user avatar
  • 5,661
5 votes

Are there any COVID-19 (coronavirus) related optimization problems with input datasets that we can "crowd solve"?

I'm not defining an optimization problem challenge, but providing a dataset I came across when visiting our world in data website. There is this data repository by Johns Hopkins Center for Systems ...
user avatar
  • 5,661
4 votes

Are there any COVID-19 (coronavirus) related optimization problems with input datasets that we can "crowd solve"?

I cannot write regular comments, but the BOINC platform and their Rosetta team are fighting too: https://www.ipd.uw.edu/2020/02/rosettas-role-in-fighting-coronavirus/ and https://boinc.bakerlab.org/...
user avatar
  • 41
4 votes

What are best practices to make optimization user interfaces intuitive for the user?

As well as the visual design, discussed in other answers, it's worth thinking about how back-end choices in the optimisation model can make for a more intuitive system. One thing that can sometimes ...
user avatar
4 votes

Are there any COVID-19 (coronavirus) related optimization problems with input datasets that we can "crowd solve"?

Data about the COVID-19 cases in Italy is available on Github from the government: https://github.com/pcm-dpc/COVID-19
user avatar
3 votes

How would you characterize "optimization data?"

I think, answers provided so far are great. When talking to professionals in the field, I second Nikos and call them "parameters" and cross my fingers they know the difference between a ...
user avatar
  • 1,229
3 votes

Are there any COVID-19 (coronavirus) related optimization problems with input datasets that we can "crowd solve"?

My research group at MIT has collated a dataset of 130+ papers on COVID-19 clinical outcomes, where each paper contains aggregate demographic information/comorbidities/lab data/treatment/clinical ...
user avatar
1 vote

Are there any COVID-19 (coronavirus) related optimization problems with input datasets that we can "crowd solve"?

We've recently been working on hospital staff rostering with covid-19 specific constraints, such as these: There's a working demo on this branch on github. It's open source in java.
user avatar

Only top scored, non community-wiki answers of a minimum length are eligible