By C.H. Ashdown
Оружейная история Британии и других стран. Книга проиллюстрирована 450 гравюрами и forty two тоновыми иллюстрациями. Охватывает период с древнейших времен до 17 века
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1 Introduction 31 Fig. 3 Approaches to minimizing the sum of the shortest distances between nodes and their allotted service centers Constrained Network Voronoi Diagrams (CCNVD) create Network Voronoi Diagram (NVD) that honors both capacity and contiguity constraints (Fig. 3). 4 illustrates examples of related work. 4a shows the input with a transportation network (15 graph-nodes (A, B, . . , O) and three service centers (X, Y , and Z)). , travel time), as indicated by the number displayed above it.
The PE algorithm tries to satisfy capacity constraints for every service center, maintain service area contiguity constraints, and reduce the sum of the shortest distances from graph-nodes to their allotted service centers (PE-nodes). At each step, the algorithm re-allots a graph-node from an excess PE-node to fulfill the capacity constraint. 1) n∈SA(s2) where sd(n, s) is the length of the SA shortest distance from n to s in SA(s) . A key idea behind PE is to first choose the best boundary graph-node that minimizes the cost of re-allotment and then re-allot this graph-node to fulfill capacity constraints.
Within distance dmax ) (Line 5). If this sub-network can be added to an existing sub-network under the distance constraint, two individuals are combined (Line 6). If it cannot be combined with an existing sub-network, it becomes a new sub-network (Line 6). 2 Algorithm for Distance-Constrained k Spatial Sub-networks (a) Input (3rd iteration) (b) NND 53 (c) Input (4th iteration) (d) NND Fig. 5 RSG-NND (k = 3, dmax = 2) (3rd to 4th iteration produces a DCSSN) (a) Input (5th iteration) (b) NND (c) Input (6th iteration) (d) NND Fig.