Many engineering, operations, and scientific applications
involve both discrete decisions and nonlinear relationships
that significantly affect the feasibility and optimality of
solutions. Mixed-integer nonlinear programming (MINLP) problems
combine the difficulty of optimizing over discrete variable
sets with the challenges of handling nonlinear functions.
MINLP is one of the most flexible modeling paradigms available:
An expanding body of researchers and practitioners, including
chemical engineers, operations researchers, industrial
engineers, mechanical engineers, economists, statisticians,
computer scientists, operations managers, and mathematical
programmers are interested in solving large-scale MINLPs.
Unfortunately, the wealth of applications that can be
by using MINLP is not yet matched by the capability of
optimization solvers. Yet, the two components of MINLP, namely
mixed-integer linear programming (MIP), and nonlinear
have witnessed tremendous progress over the past 15 years. By
incorporating many theoretical advances in MIP research,
academic, open source, and commercial solvers paved the way for
emerge as a viable, widely used decision-making tool.
paradigms and a better theoretical understanding have created
more reliable NLP solvers that work well even under adverse
such as failures of constraint qualifications.
The time is right to synthesize these advances and inspire new
ideas in order to transform MINLP into an area in which
researchers and practitioners can access robust tools and
methods capable of solving a wide range of important, commonly
occurring decision support problems. This workshop brings
together experts from relevant optimization areas to exchange
recent results on MINLP, chart the future of MINLP, explore new
and innovative applications, and outline the challenges facing
this area. The workshop will discuss novel solution approaches
and the impact of new powerful computational resources to solve