Invited Session Fri.2.MA 041

Friday, 13:15 - 14:45 h, Room: MA 041

Cluster 14: Mixed-integer nonlinear programming [...]

Applications of MINLP II

 

Chair: RĂ¼diger Schultz

 

 

Friday, 13:15 - 13:40 h, Room: MA 041, Talk 1

Wei Huang
Primal heuristics for nonconvex MINLPs modelling optimal operation of water distribution networks

Coauthors: Harald Held, Raymond Hemmecke

 

Abstract:
A water distribution network is a system containing engineered hydraulic
components to provide water supply to consumers. The main task in
operating a water distribution network is to choose between different
sources of water and determine a configuration of pumps and valves to satisfy
reliable customer demands. The cost of water, energy, and
the number of pump switches should be minimized.
We present a detailed mixed integer nonlinear programming (MINLP)
model involving nonconvex constraints and objective.
Due to the difficulty of solving these problems directly,
most approaches in the literature focus on approximations or relaxations.
In our research we aim at solving them to global optimality.
The solver used in the computational test implements a spatial branch and
bound algorithm to find global optimality for factorable MINLPs.
Concerning nonconvexities and integralities as well as the size of networks
and time horizon, the general-purpose solver cannot find a primal solution
within 24 hours. In this talk, we present several primal heuristics creating
fully feasible solutions.

 

 

Friday, 13:45 - 14:10 h, Room: MA 041, Talk 2

Harald Held
Challenges and requirements for MINLPs in industrial applications

 

Abstract:
Practitioners often face the question how to operate, e.g., a plant, a network, or a manufacturing process.
In many cases, modeling this as a mathematical optimization problem supports the operator to find a good, ideally best, operating decision. Since there are many well developed mixed-integer
linear programming (MILP) solvers, this is what has so far typically been used to provide an operator with good decisions in reasonable time.
However, in many industrial applications, describing a system's physical behavior involves non-linear functions. In these cases, simplification to an MILP could mean a significant loss of accuracy, and
a mixed-integer non-linear programming (MINLP) model would be more appropriate. Thanks to recent algorithmic advances and software implementations, the integration of MINLP models into industrial
applications has become more viable, yet some challenges remain.
In this talk, we give a few examples of industrial applications where MINLPs can be employed, and demonstrate some challenges and requirements to gain an operator's acceptance.

 

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