Nikolova 2010a - Revision history

Scipediacontent at 15:13, 21 January 2021

2021-01-21T15:13:05Z

Scipediacontent: Scipediacontent moved page Draft Content 611324352 to Nikolova 2010a

2020-10-14T13:50:07Z

Scipediacontent moved page Draft Content 611324352 to Nikolova 2010a

Scipediacontent: Created page with " == Abstract == We consider a stochastic routing model in which the goal is to find the optimal route that incorporates a measure of risk The problem arises in traffic engine..."

2020-10-14T13:50:05Z

Created page with " == Abstract == We consider a stochastic routing model in which the goal is to find the optimal route that incorporates a measure of risk The problem arises in traffic engine..."

New page

== Abstract ==

We consider a stochastic routing model in which the goal is to find the optimal route that incorporates a measure of risk The problem arises in traffic engineering, transportation and even more abstract settings such as task planning (where the time to execute tasks is uncertain), etc The stochasticity is specified in terms of arbitrary edge length distributions with given mean and variance values in a graph The objective function is a positive linear combination of the mean and standard deviation of the route Both the nonconvex objective and exponentially sized feasible set of available routes present a challenging optimization problem for which no efficient algorithms are known In this paper we evaluate the practical performance of algorithms and heuristic approaches which show very promising results in terms of both running time and solution accuracy.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_611324352-beopen875-4821-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://people.csail.mit.edu/enikolova/papers/ENikolova-LSSC-cameraready.pdf http://people.csail.mit.edu/enikolova/papers/ENikolova-LSSC-cameraready.pdf]

← Older revision		Revision as of 15:13, 21 January 2021
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	We consider a stochastic routing model in which the goal is to find the optimal route that incorporates a measure of risk The problem arises in traffic engineering, transportation and even more abstract settings such as task planning (where the time to execute tasks is uncertain), etc The stochasticity is specified in terms of arbitrary edge length distributions with given mean and variance values in a graph The objective function is a positive linear combination of the mean and standard deviation of the route Both the nonconvex objective and exponentially sized feasible set of available routes present a challenging optimization problem for which no efficient algorithms are known In this paper we evaluate the practical performance of algorithms and heuristic approaches which show very promising results in terms of both running time and solution accuracy.		We consider a stochastic routing model in which the goal is to find the optimal route that incorporates a measure of risk The problem arises in traffic engineering, transportation and even more abstract settings such as task planning (where the time to execute tasks is uncertain), etc The stochasticity is specified in terms of arbitrary edge length distributions with given mean and variance values in a graph The objective function is a positive linear combination of the mean and standard deviation of the route Both the nonconvex objective and exponentially sized feasible set of available routes present a challenging optimization problem for which no efficient algorithms are known In this paper we evaluate the practical performance of algorithms and heuristic approaches which show very promising results in terms of both running time and solution accuracy.
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−	~~Document type: Part of book or chapter of book~~
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−	~~== Full document ==~~
−	~~<pdf>Media:Draft_Content_611324352-beopen875-4821-document.pdf</pdf>~~


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	* [http://people.csail.mit.edu/enikolova/papers/ENikolova-LSSC-cameraready.pdf http://people.csail.mit.edu/enikolova/papers/ENikolova-LSSC-cameraready.pdf]		* [http://people.csail.mit.edu/enikolova/papers/ENikolova-LSSC-cameraready.pdf http://people.csail.mit.edu/enikolova/papers/ENikolova-LSSC-cameraready.pdf]
		+
		+	* [http://link.springer.com/content/pdf/10.1007/978-3-642-12535-5_41.pdf http://link.springer.com/content/pdf/10.1007/978-3-642-12535-5_41.pdf],
		+	: [http://dx.doi.org/10.1007/978-3-642-12535-5_41 http://dx.doi.org/10.1007/978-3-642-12535-5_41]
		+
		+	* [https://dblp.uni-trier.de/db/conf/lssc/lssc2009.html#Nikolova09 https://dblp.uni-trier.de/db/conf/lssc/lssc2009.html#Nikolova09],
		+	: [http://users.ece.utexas.edu/~nikolova/papers/ENikolova-LSSC-cameraready.pdf http://users.ece.utexas.edu/~nikolova/papers/ENikolova-LSSC-cameraready.pdf],
		+	: [https://link.springer.com/chapter/10.1007/978-3-642-12535-5_41 https://link.springer.com/chapter/10.1007/978-3-642-12535-5_41],
		+	: [https://www.scipedia.com/public/Nikolova_2010a https://www.scipedia.com/public/Nikolova_2010a],
		+	: [https://doi.org/10.1007/978-3-642-12535-5_41 https://doi.org/10.1007/978-3-642-12535-5_41],
		+	: [https://rd.springer.com/chapter/10.1007/978-3-642-12535-5_41 https://rd.springer.com/chapter/10.1007/978-3-642-12535-5_41],
		+	: [https://academic.microsoft.com/#/detail/1498519796 https://academic.microsoft.com/#/detail/1498519796]

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