Xavier-MaYiMing / The-ripple-spreading-algorithm-for-the-many-to-many-shortest-path-tour-problem

The ripple-spreading algorithm for the many-to-many shortest path tour problem

The Ripple-Spreading Algorithm for the Many-to-Many Shortest Path Tour Problem

The shortest path tour problem aims to find the shortest path that traverses multiple disjoint node subsets in a given order. The many-to-many shortest path tour problem has multiple sources and destinations. It aims to determine the shortest path tour for every source node to any one of the destination nodes.

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Variables	Meaning
network	Dictionary, {node1: {node2: length, node3: length, ...}, ...}
node_subset	List, [[subset1], [subset2], ...]
source	List, the source nodes of this subproblem of SPTP
destination	List, the destination nodes of this subproblem of SPTP
init_time	List, the initial time that should generate initial ripples at source nodes
init_radius	List, the initial radius of initial ripples at source nodes
nn	The number of nodes
neighbor	Dictionary, {node1: [the neighbor nodes of node1], ...}
v	The ripple-spreading speed (i.e., the minimum length of arcs)
t	The simulated time index
nr	The number of ripples - 1
epicenter_set	List, the epicenter node of the ith ripple is epicenter_set[i]
path_set	List, the path of the ith ripple from the source node to node i is path_set[i]
radius_set	List, the radius of the ith ripple is radius_set[i]
active_set	List, active_set contains all active ripples
Omega	Dictionary, Omega[n] = i denotes that ripple i is generated at node n

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Example

if __name__ == '__main__':
    test_network = {
        0: {1: 2, 2: 3, 3: 3},
        1: {0: 2, 3: 2},
        2: {0: 3, 3: 3},
        3: {0: 3, 1: 2, 2: 3, 4: 2, 5: 3, 6: 3},
        4: {3: 2, 6: 2},
        5: {3: 3, 6: 3},
        6: {3: 3, 4: 2, 5: 3},
    }
    subset = [[0, 2], [1, 3], [5, 6]]
    print(main(test_network, subset))

Output

{
    0: {'path': [0, 3, 5], 'length': 6}, 
    2: {'path': [2, 3, 5], 'length': 6},
}