Heuristic shortest path algorithms for transportation applications: State of the art

Fu, Liping; Sun, Dongyang; Rilett, Laurence R.

doi:10.1016/j.cor.2005.03.027

Cited by 256 publications

(148 citation statements)

References 54 publications

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“…CLOSE list have the nodes which have been already checked, further these nodes are kept in this list to ensure no duplications. It implies that the OPEN list has the nodes which need to be considered for further processing and the entries in CLOSE list indicate the nodes which may not be re-required in further steps [6,7].…”

Section: Best First Methods For Shortest Path Findingmentioning

confidence: 99%

Comparison of Various Heuristic Search Techniques for Finding Shortest Path

Potdar¹,

Thool²

2014

IJAIA

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Section: Best First Methods For Shortest Path Findingmentioning

confidence: 99%

Comparison of Various Heuristic Search Techniques for Finding Shortest Path

Potdar¹,

Thool²

2014

IJAIA

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“…This graph has at most nK nodes and at most Many algorithms can compute optimal solutions to the shortest path problem. 23 The results in this paper were generated with one of these algorithms (a dynamic programming value iteration algorithm), but other algorithms like the A * algorithm could also be used to find a minimum-cost configuration schedule. The computational complexity of the dynamic programming value iteration algorithm is O(n 2 K).…”

Section: Algorithm Solution Methodsmentioning

confidence: 99%

Advisory Algorithm for Scheduling Open Sectors, Operating Positions, and Workstations

Bloem

Drew

Lai

et al. 2012

12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis And

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Air traffic controller supervisors configure available sector, operating position, and workstation resources to safely and efficiently control air traffic in a region of airspace. In this paper, an algorithm for assisting supervisors with this task is described and demonstrated on two sample problem instances. The algorithm produces configuration schedule advisories that minimize a cost. The cost is a weighted sum of two competing costs: one penalizing mismatches between configurations and predicted air traffic demand and another penalizing the effort associated with changing configurations. The problem considered by the algorithm is a shortest path problem that is solved with a dynamic programming value iteration algorithm. The cost function contains numerous parameters. Default values for most of these are suggested based on descriptions of air traffic control procedures and subject-matter expert feedback. The parameter determining the relative importance of the two competing costs is tuned by comparing historical configurations with corresponding algorithm advisories. Two sample problem instances for which appropriate configuration advisories are obvious were designed to illustrate characteristics of the algorithm. Results demonstrate how the algorithm suggests advisories that appropriately utilize changes in airspace configurations and changes in the number of operating positions allocated to each open sector. The results also demonstrate how the advisories suggest appropriate times for configuration changes.

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“…Multiple possibilities exist to speed up shortest path queries [15], like goal-directed search [19], bidirectional search [31], hierarchical search [18], etc. Some of them have already been applied to the multi-objective shortest path problem, such as the goal-directed (A*) algorithm of [42].…”

Section: The Algorithmmentioning

confidence: 99%

Speeding up Martins’ algorithm for multiple objective shortest path problems

et al. 2013

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The latest transportation systems require the best routes in a large network with respect to multiple objectives simultaneously to be calculated in a very short time. The label setting algorithm of Martins efficiently finds this set of Pareto optimal paths, but sometimes tends to be slow, especially for large networks such as transportation networks. In this article we investigate a number of speedup measures, resulting in new algorithms. It is shown that the calculation time to find the Pareto optimal set can be reduced considerably. Moreover, it is mathematically proven that these algorithms still produce the Pareto optimal set of paths.

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Heuristic shortest path algorithms for transportation applications: State of the art

Cited by 256 publications

References 54 publications

Comparison of Various Heuristic Search Techniques for Finding Shortest Path

Comparison of Various Heuristic Search Techniques for Finding Shortest Path

Advisory Algorithm for Scheduling Open Sectors, Operating Positions, and Workstations

Speeding up Martins’ algorithm for multiple objective shortest path problems

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