## 90C35 Programming involving graphs or networks [See also 90C27]

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#### Document Type

- Master's Thesis (2)
- Doctoral Thesis (1)

#### Keywords

- Aggregation (1)
- Dynamic Network Flow Problem (1)
- Evacuation Planning (1)
- Hub Location Problem (1)
- Lagrange (1)
- Large-Scale Problems (1)
- Lucena (1)
- Minimum Cost Network Flow Problem (1)
- Mixed integer programming (1)
- Multiperiod planning (1)

- Das dynamische Travelling-Salesman Problem (1996)
- Das TSP wird auf zeitabhängige Kosten und Wegelängen verallgemeinert, der Komplexitätstatus untersucht, verschiedene Formulierungen verglichen, Spezialfälle untersucht und ein auf Lagrange-Relaxation und Branch&Bound beruhendes exaktes Lösungsverfahren von Lucena erweitert, implementiert und getestet. Für das TDTSP wird die Dimension des ganzzahligen Polyeders bestimmt.

- Aggregation of Large-Scale Network Flow Problems with Application to Evacuation Planning at SAP (2005)
- Our initial situation is as follows: The blueprint of the ground floor of SAP’s main building the EVZ is given and the open question on how mathematic can support the evacuation’s planning process ? To model evacuation processes in advance as well as for existing buildings two models can be considered: macro- and microscopic models. Microscopic models emphasize the individual movement of evacuees. These models consider individual parameters such as walking speed, reaction time or physical abilities as well as the interaction of evacuees during the evacuation process. Because of the fact that the microscopic model requires lots of data, simulations are taken for implementation. Most of the current approaches concerning simulation are based on cellular automats. In contrast to microscopic models, macroscopic models do not consider individual parameters such as the physical abilities of the evacuees. This means that the evacuees are treated as a homogenous group for which only common characteristics are considered; an average human being is assumed. We do not have that much data as in the case of the microscopic models. Therefore, the macroscopic models are mainly based on optimization approaches. In most cases, a building or any other evacuation object is represented through a static network. A time horizon T is added, in order to be able to describe the evolution of the evacuation process over time. Connecting these two components we finally get a dynamic network. Based on this network, dynamic network flow problems are formulated, which can map evacuation processes. We focused on the macroscopic model in our thesis. Our main focus concerning the transfer from the real world problem (e.g. supporting the evacuation planning) will be the modeling of the blueprint as a dynamic network. After modeling the blueprint as a dynamic network, it will be no problem to give a formulation of a dynamic network flow problem, the so-called evacuation problem, which seeks for an optimal evacuation time. However, we have to solve a static large-scale network flow problem to derive a solution for this formulation. In order to reduce the network size, we will examine the possibility of applying aggregation to the evacuation problem. Aggregation (lat. aggregare = piling, affiliate; lat. aggregatio = accumulation, union; the act of gathering something together) was basically used to reduce the size of general large-scale linear or integer programs. The results gained for the general problem definitions were then applied to the transportation problem and the minimum cost network flow problem. We review this theory in detail and look on how results derived there can be used for the evacuation problem, too.

- Hub Location Models in Public Transport Planning (2008)
- The dissertation deals with the application of Hub Location models in public transport planning. The author proposes new mathematical models along with different solution approaches to solve the instances. Moreover, a novel multi-period formulation is proposed as an extension to the general model. Due to its high complexity heuristic approaches are formulated to find a good solution within a reasonable amount of time.