Invited Session Mon.3.H 1058

Monday, 15:15 - 16:45 h, Room: H 1058

Cluster 10: Implementations & software [...]

MILP software I


Chair: Thorsten Koch



Monday, 15:15 - 15:40 h, Room: H 1058, Talk 1

Dieter Weninger
SCIP preprocessing for MIPs arising in supply chain management

Coauthors: Gerald Gamrath, Thorsten Koch, Alexander Martin, Matthias Miltenberger


Supply Chain Management (SCM) deals with the combination of procurement, production, storage, transport and delivery of commodities. Problems of this kind occur in different industry branches. Since the integrated planning of these processes contain a high potential for optimization,
it is of great importance for the efficiency of a related company. The method of choice to find optimal solutions for SCM problems is mixed integer programming. However, there are big challenges to overcome due to the very detailed and therefore large models. One way to reduce the large models is to perform an extensive preprocessing. We show preprocessing algorithms which decisively help reducing and solving the
problems. The implementations of the preprocesing algorithms are done within the non-commercial mixed integer programming solver SCIP.



Monday, 15:45 - 16:10 h, Room: H 1058, Talk 2

Philipp Christophel
Research topics of the SAS MILP solver development team

Coauthors: Amar Narisetty, Yan Xu


This talk will give an overview of current research interests of the SAS MILP solver development team. The focus will be on the use and customization of simplex algorithms inside MILP solvers. Other topics will be branching, cutting planes and primal heuristics.



Monday, 16:15 - 16:40 h, Room: H 1058, Talk 3

Gerald Gamrath
The SCIP Optimization Suite 3.0 - It's all in the bag!


We present the latest release of the SCIP Optimization Suite, a tool for modeling and solving optimization problems. It consists of the modeling language ZIMPL, the LP solver SoPlex, and the constraint integer programming framework SCIP.
Besides being one of the fastest MIP solvers available in source code, SCIP can also be used as a branch-cut-and-price framework. Furthermore, SCIP is able to solve a much wider range of optimization problems including pseudo-boolean optimization, scheduling, and non-convex MINLP. Its plugin-based design allows to extend the framework to solve even more different kinds of problems and to customize the optimization process.
We report on current developments and new features of the SCIP
Optimization Suite 3.0 release, including enhanced MINLP support, a framework to parallelize SCIP and the new exact solving capabilities for MIPs.


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