The solution of nonconvex parameter estimation problems with deterministic global optimization methods is desirable but challenging, especially if large measurement datasets are considered. We propose to exploit the s...
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The solution of nonconvex parameter estimation problems with deterministic global optimization methods is desirable but challenging, especially if large measurement datasets are considered. We propose to exploit the structure of this class of optimization problems to enable their solution with the spatialbranch -and -boundalgorithm. In detail, we start with a reduced dataset in the root node and progressively augment it, converging to the full dataset. We show for nonlinear programs (NLPs) that our algorithm converges to the global solution of the original problem considering the full dataset. The implementation of the algorithm extends our opensource solver MAiNGO. A numerical case study with a mixed -integer nonlinear program (MINLP) from chemical engineering and a dynamic optimization problem from biochemistry both using noise -free measurement data emphasizes the potential for savings of computational effort with our proposed approach.
This paper studies the optimization of an urban single-line metro timetable for total passenger travel time adapted to dynamic passenger demand, which arises in an urban metro service and is a common problem in major ...
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This paper studies the optimization of an urban single-line metro timetable for total passenger travel time adapted to dynamic passenger demand, which arises in an urban metro service and is a common problem in major cities. After analyzing the components of the total passenger travel time, a model is presented with the aim of minimizing total passenger travel time. An S-pattern function is proposed to represent the cumulative demand function for each pair of origin and destination in an urban single-line metro. Furthermore, a spatial branch and bound algorithm that is applicable to the model is presented. The advantages of designing a timetable that optimizes the total passenger travel time adapted to dynamic passenger demand are depicted through extensive computational experiments on several cases derived from a real urban single-line metro. An extensive computational comparison of a regular timetable, a timetable optimizing average waiting time, and a timetable optimizing total passenger travel time timetable are performed. Crown Copyright (C) 2016 Published by Elsevier Ltd.
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