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[Jozefowiez et al. (2009]) developed a multi-objective vehicle routing problem by minimizing the route length and route balance by using a meta-heuristic method with traditional operators.
Of course, routing should use the straight line to the sink for minimizing the route length.
Considerable research efforts have been devoted to minimizing the route recovery delay or to anticipating link breaks by using preemptive mechanisms.
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By introducing the transmission cost function (mathcal {C} (x_{v},y_{v})) (i.e., the cost paid by the node v at (x v,y v ) to transmit one unit amount of information), a routing problem may be formulated as a geodesic problem which minimizes the route cost (sum limits _{v^{prime } in llbracket {P}rrbracket }mathcal {C} (x_{v^{prime }},y_{v^{prime }})).
It allows to minimize the route length, the traveling time, the number of maneuvers, or other objectives.
The first one primarily aims at minimizing the number of routes and secondarily minimizing the routing cost, whereas the second one only aims at minimizing the cost of the generated route set.
The objective aims to minimize the total route service cost, including the asset decay cost.
The model's objective aims to minimize the overall route service cost.
Then we establish an expected value model and design a modified particle swarm optimization algorithm to minimize the en route risks and site risks.
They show that it is is possible therefore to minimize the impact route validation has on convergence, by being careful with signatures, rather than consumptive of memory.
Lee et al. [21] proposes a mechanism for on-demand routing, named split multipath routing (SMR), which uses multiple routes to minimize the effect of route failures during the routing process.
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