Contributed Session Wed.2.MA 042

Wednesday, 13:15 - 14:45 h, Room: MA 042

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

Topics in mixed-integer nonlinear programming II


Chair: Michael Engelhart



Wednesday, 13:15 - 13:40 h, Room: MA 042, Talk 1

Melania Calinescu
Optimal resource allocation in survey designs

Coauthors: Sandjai Bhulai, Barry Schouten


Resource allocation is a relatively new research area in survey designs and has not been fully addressed in the literature. Survey organizations across the world are considering the development of new mathematical models in order to improve the quality of survey results while taking into account optimal resource planning.
The resource allocation problem for survey designs has specific features that lead to a formulation as a nonconvex integer nonlinear problem, which prohibits the application of many algorithms that are found in the literature. Current global optimization tools that address general nonconvex integer problems suffer from long computational times and limitations in the problem size. Moreover, implementing solutions from convex approximations of the problem may result in major errors in survey results.
We present an algorithm that solves the problem to optimality using Markov decision theory. Additionally to optimal resource planning, the algorithm can handle various practical constraints that aim at improving the quality of survey results. The algorithm is implemented in C++, it achieves short computational times and it can handle large-scaled problems.



Wednesday, 13:45 - 14:10 h, Room: MA 042, Talk 2

Michael Engelhart
A new test-scenario for analysis and training of human decision making with a tailored decomposition approach

Coauthors: Joachim Funke, Sebastian Sager


In the research domain complex problem solving in psychology, where the aim is to analyze complex human decision making and problem solving, computer-based test-scenarios play a major role. The approach is to evaluate the performance of participants within microworlds and correlate it to certain attributes, e.g., the participant's capacity to regulate emotions. In the past, however, these test-scenarios have usually been defined on a trial-and-error basis to realize specific requirements for the testee. The more complex models become, the more likely it is that unforeseen and unwanted characteristics emerge in studies. To overcome this important problem, we propose to use mathematical optimization methodology on three levels: first, in the design stage of the complex problem scenario, second, as an analysis tool, and third, to provide feedback in real time for learning purposes. We present a novel test scenario, the IWR Tailorshop, with functional relations and model parameters that have been formulated based on optimization results, as well as a tailored decomposition approach to address the resulting nonconvex nonlinear mixed-integer programs.


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