Conferencia Latino Ibero Americana de Investigación de Operaciones

Presentation's date: 2016-10-03

Abstract:

Rapid transit network design is highly dependent on the future system usage. These spatially distributed systems are vulnerable to disruptions: during daily operations dierent incidents may occur. Despite the unpredictable nature of them, efective mitigation methods from an engineering perspective should be designed. In this paper, we present several risk averse measures for risk reduction in the rapid transit network design problem based on a set of finite scenarios to represent the disruptions’ uncertainty. As a counter-parts of the typical risk neutral strategy, some measures that are presented are aiming to minimizing the impacts of the worst scenario in the network operation, and another additionally, takes into account different risk reduction profiles. Computational experience is presented.]]>

EURO Working Group on Locational Analysis (EWGLA) and 7th International Workshop on Locational Analysis and Related Problems

p. 200-201

Presentation's date: 2016-09-15

Abstract:

Rapid transit network design is highly dependent on the future system usage. These spatially distributed systems are vulnerable to disruptions: during daily operations different incidents may occur. Despite the unpredictable nature of them, effective mitigation methods from an engineering perspective should be designed. In this paper, we present several risk averse measures for risk reduction in the rapid transit network design problem based on a set of finite scenarios to represent the disruptions’ uncertainty. As a counter-parts of the typical risk neutral strategy, some measures that are presented are aiming to minimizing the impacts of the worst scenario in the network operation, and another additionally, takes into account different risk reduction profiles. Some computational experience is presented.]]>

Meeting of the EURO Working Group on Transportation

p. 1-2

Presentation's date: 2016-09-05

Abstract:

This paper is focused on a mathematical programming based model for setting the schedule of a given number of services on a public transportation network. The structure of the model is that of a time expanded or diachronic network where origin to destination flows vary dynamically over a short time horizon (several hours) and are assumed to be known deterministically. The model aims basically to minimize the total travel time of the passengers during the time horizon by setting properly the lag times between services of the lines, accommodating conveniently the schedules of the services of dierent lines in order to enhance transfers. Two dierent types of diachronic networks are considered on which three basic algorithmic alternatives have been tested: an heuristic method based on the classical projected gradient, Benders decomposition and a combination of both. The model has a potential application, amongst others, in operational aspects for recovery of disruptions in Rail-Rapid Transit and Suburban Railway systems using bus shuttles or auxiliary bus lines. The computational viability of the model, in terms of an adequate response time, is shown using test networks of auxiliary bus lines and its computational performance is also evaluated in readjusting schedules for larger systems of suburban rails.]]>

EURO Working Group on Transportation

p. 1-2

Presentation's date: 2016-09-05

Abstract:

Rapid transit network design is highly dependent on the future system usage. These spatially distributed systems are vulnerable to disruptions: during daily operations dierent incidents may occur. Despite the unpredictable nature of them, eective mitigation methods from an engineering perspective should be designed. In this paper, we present several risk averse measures for risk reduction in the rapid transit network design problem based on a set of finite scenarios to represent the disruptions’ uncertainty. As a counter-parts of the typical risk neutral strategy, some measures that are presented are aiming to minimizing the impacts of the worst scenario in the network operation, and another additionally, takes into account dierent risk reduction profiles. Some computational experience is presented]]>

EURO Working Group on Transportation

p. 32-33

Presentation's date: 2014-07

Abstract:

Incidents such as special social events, infrastructure malfunction, rolling stock breakdowns and bad weather conditions are commonplace in rapid transit rail systems and may disrupt the system performance imposing with deviations from planned operations. A robust network design may be so expensive to be operated on a regular daily basis that, in practice, only the likely disruptions need to be taken into account, thus minimizing the under-utilization of the network during normal operation. When considering the possible sources for system disruptions, the corresponding scenarios may have associated failure probabilities that are constant and are not a function of the level of operation of the system. Other scenarios of disruption, however, may be associated to failures of the rolling stock which may block the system at given points of the network. In this case the failure probabilities are considered as a function of both, the amount of services and the routing of the rolling stock on the designed network and they cannot be calculated a priori but are a result of the design process itself. We propose a recoverable robust network design model as an alternative to robust design for reducing the effect of disruptions less likely to occur. The objective is to minimize either a) cost of recovery or b) deviations from undisrupted scenarios. The proposed model can be considered as a two recourse stochastic programming model where the probabilities of failure are an implicit function of the number of services and routing of the transit lines that make up the transport system. Therefore, the model has a multilevel structure and a heuristic solution method is examined for small to medium networks demonstrating the computational viability of the approach.]]>

European Conference on Operational Research

p. 202

Presentation's date: 2013-07-02