Chapter 6: Integer Optimization Techniques for Train Dispatching in Mass Transit and Main Line

Author(s):  
Leonardo Lamorgese ◽  
Carlo Mannino ◽  
Mauro Piacentini
Author(s):  
Xiaohong Fang ◽  
Leishan Zhou ◽  
Ming Xia

On urban mass transit network, some connections inherently exist between trains on different lines through transfer stations. Schedule coordination for different lines, especially optimizing the arriving and departure times at transfer stations, may significantly reduce transfer waiting times at stations where various routes are interconnected, so as to improve the passenger service level. Based on the passengers flow characters of urban mass transit (UMT), both the convenience and rationality of connection between different lines were considered, and then an optimization model, with the aims of the minimal total waiting time of transfer passengers and inboard passengers, was set up. Combining the inner coordination of arriving and departure time sequence of trains in transfer nodes with the exterior coordination of transfer nodes on whole urban mass transit network, a multi-layers coordination policy was proposed, and the integrated optimization of the urban mass transit system was realized through taking some small time shifts of the proposed singleline timetables. In order to verify the validity and feasibility of the model and algorithm, we conducted an experimental study. The result turns out that improvement on UMT network can be determined by such optimization techniques.


Author(s):  
Cameron J. Turner ◽  
Richard H. Crawford ◽  
Matthew I. Campbell

The challenge of determining the best design in a multimodal design space with multiple local optimal solutions often challenges the best available optimization techniques. By casting the objective function of the optimization problem in the form of a Non-Uniform Rational B-spline (NURBs) metamodel, known as a HyPerModel, significant optimization advantages can be achieved, including the ability to efficiently find the global metamodel optimum solution with less computational expense than traditional approaches. This optimization strategy, defined by the HyPerOp algorithm, uses the underlying structure of a HyPerModel to intelligently select starting points for optimization runs and to identify regions of the design space that do not contain locations for the global metamodel optimum location. This paper describes the application of the HyPerOp algorithm to mixed integer programming problems and demonstrates its use with two example applications. The algorithm works with design spaces composed of continuous and integer design variables and provides a complementary approach for improved optimization capabilities.


1998 ◽  
Vol 6 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Partha Chakroborty

Scheduling of a bus transit system must be formulated as an optimization problem, if the level of service to passengers is to be maximized within the available resources. In this paper, we present a formulation of a transit system scheduling problem with the objective of minimizing the overall waiting time of transferring and nontransferring passengers while satisfying a number of resource- and service-related constraints. It is observed that the number of variables and constraints for even a simple transit system (a single bus station with three routes) is too large to tackle using classical mixed-integer optimization techniques. The paper shows that genetic algorithms (GAs) are ideal for these problems, mainly because they (i) naturally handle binary variables, thereby taking care of transfer decision variables, which constitute the majority of the decision variables in the transit scheduling problem; and (ii) allow procedure-based declarations, thereby allowing complex algorithmic approaches (involving if then-else conditions) to be handled easily. The paper also shows how easily the same GA procedure with minimal modifications can handle a number of other more pragmatic extensions to the simple transit scheduling problem: buses with limited capacity, buses that do not arrive exactly as per scheduled times, and a multiple-station transit system having common routes among bus stations. Simulation results show the success of GAs in all these problems and suggest the application of GAs in more complex scheduling problems.


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