Refine
Year of publication
Document Type
- In Proceedings (38)
- Article (31)
- ZIB-Report (21)
- Book chapter (5)
- Other (4)
- Book (2)
- Habilitation (1)
- Master's Thesis (1)
Language
- English (103)
Is part of the Bibliography
- no (103)
Keywords
Institute
This book promotes the use of mathematical optimization and operations research methods in rail transportation. The editors assembled thirteen contributions from leading scholars to present a unified voice, standardize terminology, and assess the state-of-the-art.
There are three main clusters of articles, corresponding to the classical stages of the planning process: strategic, tactical, and operational. These three clusters are further subdivided into five parts which correspond to the main phases of the railway network planning process: network assessment, capacity planning, timetabling, resource planning, and operational planning. Individual chapters cover:
Simulation
Capacity Assessment
Network Design
Train Routing
Robust Timetabling
Event Scheduling
Track Allocation
Blocking
Shunting
Rolling Stock
Crew Scheduling
Dispatching
Delay Propagation
We present a new strategy for the direct optimization of the values of policy functions. This approach is particularly well suited to model actors with a global perspective on the system and relies heavily on modern mathematical white-box optimization methods. We demonstrate our strategy on two classical models: market growth and World2. Each model is first transformed into an optimization problem by defining how the actor can influence the models' dynamics and by choosing objective functions to measure improvements. To improve comparability between different runs, we also introduce a comparison measure for possible interventions. We solve the optimization problems, discuss the resulting policies and compare them to the existing results from the literature. In particular, we present a run of the World2 model which significantly improves the published “towards a global equilibrium” run with equal cost of intervention.
A model for the optimisation of the location and configuration of base stations in a UMTS network is described. The focus is primarily on modelling the configuration problem sufficiently accurate using mixed-integer variables and (essentially) linear constraints. These constraints reflect the limited downlink code capacity in each cell, the interference limitations for successful up- and downlink transmissions, the need for sufficiently strong (cell) pilot signals, and the potential gain for mobiles from being in soft(er) hand-over. It is also explained how to use the model as a basis for rating network configurations.
The UMTS radio network planning problem poses the challenge of designing a cost-effective network that provides users with sufficient coverage and capacity. We describe an optimization model for this problem that is based on comprehensive planning data of the EU project MOMENTUM. We present heuristic mathematical methods for this realistic model, including computational results.
Gas distribution networks are complex structures that consist of
passive pipes, and active, controllable elements such as valves and
compressors. Controlling such network means to find a suitable setting
for all active components such that a nominated amount of gas can be
transmitted from entries to exits through the network, without
violating physical or operational constraints. The control of a
large-scale gas network is a challenging task from a practical point
of view. In most companies the actual controlling process is supported
by means of computer software that is able to simulate the flow of the
gas. However, the active settings have to be set manually within such
simulation software. The solution quality thus depends on the
experience of a human planner.
When the gas network is insufficient for the transport then topology
extensions come into play. Here a set of new pipes or active elements
is determined such that the extended network admits a feasible control
again. The question again is how to select these extensions and where
to place them such that the total extension costs are
minimal. Industrial practice is again to use the same simulation
software, determine extensions by experience, add them to the virtual
network, and then try to find a feasible control of the active
elements. The validity of this approach now depends even more on the
human planner.
Another weakness of this manual simulation-based approach is that it
cannot establish infeasibility of a certain gas nomination, unless all
settings of the active elements are tried. Moreover, it is impossible
to find a cost-optimal network extension in this way.
In order to overcome these shortcomings of the manual planning
approach we present a new approach, rigorously based on mathematical
optimization. Hereto we describe a model for finding feasible
controls and then extend this model such that topology extensions can
additionally and simultaneously be covered. Numerical results for real-world instances are presented and
discussed.