Bayesian ranking and selection model for the Second-best Network Pricing Problem

Tan Zhen, H. Oliver Gao

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

We adopt a Bayesian ranking and selection (R&S) model to solve the Second-best Network Pricing Problem (SNPP) in transportation. The objective of SNPP is to find an optimal subset of links and toll levels so as to minimize the total travel time on the network. It is an NP-hard problem with a large number of candidate solutions. We consider every combination of tollable link(s) and toll levels as an 'alternative', and the problem's objective function value is regarded as a 'reward', with uncertainties modeled by normal perturbations to the travel demand. We use a linear belief based Knowledge Gradient sampling policy to maximize the expected reward, with Monte Carlo sampling of the hyperparameters used to reduce the choice set size. Simulation experiments for a benchmark network show the effectiveness of the proposed method and its superior performance to a Sample Average Approximation based Genetic Algorithm.

Original languageEnglish
Title of host publication2016 Winter Simulation Conference
Subtitle of host publicationSimulating Complex Service Systems, WSC 2016
EditorsTheresa M. Roeder, Peter I. Frazier, Robert Szechtman, Enlu Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2487-2498
Number of pages12
ISBN (Electronic)9781509044863
DOIs
Publication statusPublished - 2 Jul 2016
Externally publishedYes
Event2016 Winter Simulation Conference, WSC 2016 - Arlington, United States
Duration: 11 Dec 201614 Dec 2016

Publication series

NameProceedings - Winter Simulation Conference
Volume0
ISSN (Print)0891-7736

Conference

Conference2016 Winter Simulation Conference, WSC 2016
Country/TerritoryUnited States
CityArlington
Period11/12/1614/12/16

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Computer Science Applications

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