000196324 001__ 196324
000196324 005__ 20190316235835.0
000196324 020__ $$a978-1-4673-2065-8
000196324 0247_ $$2doi$$a10.1109/CDC.2012.6426188
000196324 037__ $$aCONF
000196324 245__ $$aRisk-averse shortest path problems
000196324 269__ $$a2012
000196324 260__ $$bIEEE$$c2012
000196324 336__ $$aConference Papers
000196324 520__ $$aWe investigate routing policies for shortest path problems with uncertain arc lengths. The objective is to minimize a risk measure of the total travel time. We use the conditional value-at-risk (CVaR) for when the arc lengths (durations) have known distributions and the worst-case CVaR for when these distributions are only partially described. Policies which minimize the expected travel time (average-optimal policies) are desirable for experiments that are repeated several times, but the fact that they take no account of risk makes them unsuitable for decisions that need to be taken only once. In these circumstances, policies that minimize a risk measure provide protection against rare events with high cost.
000196324 6531_ $$aDynamic programming
000196324 6531_ $$aHeuristic algorithms
000196324 6531_ $$aOptimization
000196324 6531_ $$aRandom variables
000196324 6531_ $$aRouting
000196324 6531_ $$aShortest path problem
000196324 6531_ $$aUncertainty
000196324 700__ $$aGavriel, Christos
000196324 700__ $$aHanasusanto, Grani A.
000196324 700__ $$0247589$$aKuhn, Daniel$$g239987
000196324 7112_ $$a2012 IEEE 51st Annual Conference on Decision and Control (CDC)$$cMaui, HI, USA$$dDecember 10-13, 2012
000196324 773__ $$q2533-2538$$t2012 IEEE 51st IEEE Conference on Decision and Control (CDC)
000196324 8564_ $$uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6426188$$zURL
000196324 909C0 $$0252496$$pRAO$$xU12788
000196324 909CO $$ooai:infoscience.tind.io:196324$$pconf$$pCDM$$qGLOBAL_SET
000196324 917Z8 $$x112541
000196324 937__ $$aEPFL-CONF-196324
000196324 973__ $$aEPFL$$rNON-REVIEWED$$sPUBLISHED
000196324 980__ $$aCONF