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  • 1.
    Josyula, Sai Prashanth
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Törnquist Krasemann, Johanna
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Lundberg, Lars
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    A parallel algorithm for train rescheduling2018Inngår i: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 95, s. 545-569Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    One of the crucial factors in achieving a high punctuality in railway traffic systems, is the ability to effectively reschedule the trains when disturbances occur. The railway traffic rescheduling problem is a complex task to solve both from a practical and a computational perspective. Problems of practically relevant sizes have typically a very large search space, making them time-consuming to solve even for state-of-the-art optimization solvers. Though competitive algorithmic approaches are a widespread topic of research, not much research has been done to explore the opportunities and challenges in parallelizing them. This paper presents a parallel algorithm to efficiently solve the real-time railway rescheduling problem on a multi-core parallel architecture. We devised (1) an effective way to represent the solution space as a binary tree and (2) a novel sequential heuristic algorithm based on a depth-first search (DFS) strategy that quickly traverses the tree. Based on that, we designed a parallel algorithm for a multi-core architecture, which proved to be 10.5 times faster than the sequential algorithm even when run on a single processing core. When executed on a parallel machine with 8 cores, the speed further increased by a factor of 4.68 and every disturbance scenario in the considered case study was solved within 6 s. We conclude that for the problem under consideration, though a sequential DFS approach is fast in several disturbance scenarios, it is notably slower in many other disturbance scenarios. The parallel DFS approach that combines a DFS with simultaneous breadth-wise tree exploration, while being much faster on an average, is also consistently fast across all scenarios.

  • 2.
    Krasemann, Johanna Törnquist
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Design of an effective algorithm for fast response to the re-scheduling of railway traffic during disturbances2012Inngår i: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 20, nr 1, s. 62-78Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    An attractive and sustainable railway traffic system is characterized by having a high security, high accessibility, high energy performance and offering reliable services with sufficient punctuality. At the same time, the network is to be utilized to a large extent in a cost-effective way. This requires a continuous balance between maintaining a high utilization and sufficiently high robustness to minimize the sensitivity to disturbances. The occurrence of some disturbances can be prevented to some extent but the occurrence of unpredictable events are unavoidable and their consequences then need to be analyzed, minimized and communicated to the affected users. Valuable information necessary to perform a complete consequence analysis of a disturbance and the re-scheduling is however not always available for the traffic managers. With current conditions, it is also not always possible for the traffic managers to take this information into account since he or she needs to act fast without any decision-support assisting in computing an effective re-scheduling solution. In previous research we have designed an optimization-based approach for re-scheduling which seems promising. However, for certain scenarios it is difficult to find good solutions within seconds. Therefore, we have developed a greedy algorithm which effectively delivers good solutions within the permitted time as a complement to the previous approach. To quickly retrieve a feasible solution the algorithm performs a depth-first search using an evaluation function to prioritise when conflicts arise and then branches according to a set of criteria.

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