Decision Optimization

I'm very new to multi-objective optimization, so my questions could be pretty silly..

Until now I used CPLEX to solve single-objective optimization problems only, but I now I need to solve a two-objective optimization problem..

I've just discovered that CPLEX 12.6.9 is able (unlike its previous versions) to solve even multi-objective problems.

So, I'm wondering about two questions:

1. Which algorithm does CPLEX use to solve two-objective problems?
2. Which are the main differences (efficiency, computational time, etc) between, solving a two-objective optimization problem:

• by passing it directly as a two-objective problem to CPLEX, or
  • by solving it, in CPLEX, by adopting the "Augmented Epsilon-Constraint Method"?

I'm wondering if these two methods may generate different solutions..

You can find some general discussion of multi-objective optimization in CPLEX at https://www.ibm.com/support/knowledgecenter/en/SSSA5P_20.1.0/ilog.odms.cplex.help/CPLEX/UsrMan/topics/multiobj/multiobj_intro.html

If you need further discussion, please open a case if you are entitled. Otherwise, please discuss your question in Data Science Community https://community.ibm.com/community/user/datascience/communities/community-home/digestviewer?communitykey=ab7de0fd-6f43-47a9-8261-33578a231bb7&tab=digestviewer

Ok, thank you!