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We consider stochastic, time-varying transportation networks, where the arc weights (arc travel times) are random variables with probability distribution functions that vary with time. Efficient procedures are widely available for determining least time paths in deterministic networks. In stochastic but time-invariant networks, least expected time paths can(More)
In congested transportation and data networks, travel (or transmission) times are time-varying quantities that are at best known a priori with uncertainty. In such stochastic, time-varying (or STV) networks, one can choose to use the a priori least-expected time (LET) path or one can make improved routing decisions en route as traversal times on traveled(More)
Travel times in congested transportation networks are time-varying quantities that can at best be known a priori probabilistically. In such networks, the arc weights (travel times) are represented by random variables whose probability distribution functions vary with time. These networks are referred to herein as stochastic, time-varying, or STV, networks.(More)
The selection of routes in a network along which to transport hazardous materials is explored, taking into consideration several key factors pertaining to the length of time of the transport and the risk of population exposure in the event of an incident. That travel time and risk measures are not constant over time and at best can be known with uncertainty(More)
ÐIn this paper, two computationally ecient algorithms are presented for determining the least possible time paths for all origins to a single destination in networks where the arc weights are discrete random variables whose probability distribution functions vary with time. The ®rst algorithm determines the least possible time path from each node for each(More)
A note on " Multicriteria adaptive paths in stochastic, time-varying networks " Logistics/SCM Research Group Abstract In a recent paper, Opasanon and Miller-Hooks study multicriteria adaptive paths in stochastic time-varying networks. They propose a label correcting algorithm for finding the full set of efficient strategies. In this note we show that their(More)