Academics using AIMMS

StackExchange got an Operations Research corner now. https://or.stackexchange.com/

Hi, I have a non convex MIQCP problem, where the non convexity is illustrated in bilinear constrains like : x+yz=u where x,y,z,u are continuous variables, a general form of this topic is modeling or approaching the constraint : "xy=z" neither gurobi or cplex can handle it. I m wondering, should i use a non linear solver (like Baron)or is there a way to linearize the problem or to approach differently with gurobi or cplex ? Thnaks for your answers.

Dear Staff, I am experiencing a weird behaviour within one of my models implemented in AIMMS. I have a simple flow model. Among others, there are the following variables: F, continous variable X, continuous variable The variables do not have lower or upper bounds, so they are free. Then there are some flow balance constraints in the network (i.e. flow in equal to flow out). The model works very fine when only variable F is used. But when I add the following constraint: F = X then I get an infeasible solution. While if I add the constraint F

Dear Staff, I would like your tips for an if-then constraint. In particular, I have the following variables: R, binary variable P, continuous variable Y, continuous variable I have the following parameters: A B The constraints I would like to write are the following: 1) if R=1 then P = A*Y 2) if R=0 then P = B*Y I would like to know if it is possible to have a linear formulation without using indicator constraints. Thank you,

Hi! We were able to execute the model already but we're having warnings that the result for this column is similar with the other column. Take for example the constraints 22 and 26? How do we code the "summation"? Sorry I'm just a beginner and I have no one to teach me and I only have a month to study everything because the deadline is just for 2 months. Our aimms model can be downloaded here: https://drive.google.com/file/d/1zcGWpj_FOGOrUKZiCdutLQ_gxANGrPI6/view?usp=sharingMODEL 2

Hi! I have a constraint (shown at the photo below) wherein two sets need to be subtracted. I tried introducing another set Kw with a definition of k-Ks but it says subtraction of element valued expressions require the set range to be equal, but the ranges domain set "Subblock" and set "Candidate_Subblocks" differ. Identifier: Subblock Index: k Identifier: Candidate_Subblock Index: Ks Identifier: Subblock_Difference Subset of: Subblock, Candidate_Subblocks Index: Kw Definition: k-Ks above is the current code I have. Do you have any Idea on how I can code my constraint?

Dear AIMMS communians😉, We started some years ago at AIMMS an investigation on how machine learning and optimization can work together, or how machine learning algorithms could help optimization modeling. During the AIMMS Summit, in addition to the very interesting presentation from Ger Koole, we presented a 45 min workshop demoing an interesting use case on this matter - Optimizing a power production plan with renewable resources. This example showcases a combination between machine learning

Hi, the "objective function" of my program is read as a variable in AIMMS. All the parameters in the objective function already have a data read from excel. My problem is, AIMMS keeps on saying "the scope of index i has not been specified" when I try to specify the index, the program won't run because it will say that the objective function should be a free scalar variable. What should I do?

Hi all, For our program, we have a 3*36 parameter input that we want to vary using a normal distribution for 1000 times in order to save the objective values to, eventually, create confidence intervals. We have the 1000 variation matrices, but we do not know manage to load these into AIMMS and let the program save the 1000 objective values. On the other hand, it is impossible to copy-paste these manually. Could you perhaps give us some advise on how to implement such a for-loop in AIMMS? Kind regards, Marten

This interview, originally published in 2014, comes from our Tech Blog archives. Strategic Forest Management Model (SFMM) was among the first applications of AIMMS and is still going strong. Please note, some cited data is a bit dated, but we think that the story is still worth sharing. Ontario’s Ministry of Natural Resources takes us through decades of effective forest management Forest ecosystems are highly complex and influenced by a diversity of factors. Sustainable forest management is therefore an ongoing and constantly evolving process which requires an integrated approach. Government bodies, such as The

This article, originally published in 2013, comes from our Tech Blog archives. As such, some cited data is a bit dated, but we think that the story's value illustrating practical application of OR still holds up. The mathematical story behind North Star Alliance’s POLARIS More than 35 million people worldwide are infected with HIV or are living with AIDS, and approximately 70% live in Sub-Saharan Africa. Mobile populations, such as long distance truck drivers, are particularly at risk of contracting and transmitting the virus. In 2007, TNT Express and the United Nations World Food Programme joined forces to form North Star Alliance (North Star) - a public-private partnership that is working to increase access to health services along major transport corridors in sub-Saharan Africa. ORTEC, a longstanding AIMMS partner, joined North Star in 2008 to design their award-winning Corridor Medical Transfer System (COMETS), which enabl

When the going gets tough ... we at AIMMS use this paper a lot! Even with state-of-the-art hardware and software, mixed integer programs can require hours, or even days, of run time and are not guaranteed to yield an optimal (or near-optimal, or any!) solution. In this paper, we present suggestions for appropriate use of state-of-the-art optimizers and guidelines for careful formulation, both of which can vastly improve performance. http://inside.mines.edu/~anewman/MIP_practice120212.pdf Are you using this as well? You can add 'your bible' as a reply.

A nice Ted Talk, by Marco Lubbecke, about Optimization and Machine Learning. See here.