Home > Research > Publications & Outputs > Extended Hypercube Models for Location Problems...

Research

Associated organisational units

Electronic data

View graph of relations

Extended Hypercube Models for Location Problems with Stochastic Demand

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Published

Standard

Extended Hypercube Models for Location Problems with Stochastic Demand. / Boyacı, Burak; Geroliminis, Nikolas.
2013. Paper presented at hEART 2013 - 2nd Symposium of the European Association for Research in Transportation, Stockholm, Sweden.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Harvard

Boyacı, B & Geroliminis, N 2013, 'Extended Hypercube Models for Location Problems with Stochastic Demand', Paper presented at hEART 2013 - 2nd Symposium of the European Association for Research in Transportation, Stockholm, Sweden, 4/09/13 - 6/09/13.

APA

Boyacı, B., & Geroliminis, N. (2013). Extended Hypercube Models for Location Problems with Stochastic Demand. Paper presented at hEART 2013 - 2nd Symposium of the European Association for Research in Transportation, Stockholm, Sweden.

Vancouver

Boyacı B, Geroliminis N. Extended Hypercube Models for Location Problems with Stochastic Demand. 2013. Paper presented at hEART 2013 - 2nd Symposium of the European Association for Research in Transportation, Stockholm, Sweden.

Author

Boyacı, Burak ; Geroliminis, Nikolas. / Extended Hypercube Models for Location Problems with Stochastic Demand. Paper presented at hEART 2013 - 2nd Symposium of the European Association for Research in Transportation, Stockholm, Sweden.4 p.

Bibtex

@conference{b026af18e91b416b82a048f3cf9d7ace,
title = "Extended Hypercube Models for Location Problems with Stochastic Demand",
abstract = "In spatial queues, servers travel to the customers and provide service on the scene. This property makes them applicable to emergency response (e.g. ambulances, police) and on-demand transportation systems (e.g. paratransit, taxis) location problems. However, in spatial queues, there exist a different service rate for each customer-server pairs which creates Markovian models with enormous number of states and makes these approaches difficult to apply on even medium sized problems. Because of demand uncertainty, the nearest servers to a customer might not be available to intervene and this can significantly increase the service times. In this paper, we propose two new aggregate models and an approximate solution method with a dynamic programming heuristic. Results are compared with existing location models on hypothetical and real cases.",
author = "Burak Boyacı and Nikolas Geroliminis",
year = "2013",
month = sep,
day = "4",
language = "English",
note = "hEART 2013 - 2nd Symposium of the European Association for Research in Transportation, hEART 2013 ; Conference date: 04-09-2013 Through 06-09-2013",
url = "http://transp-or.epfl.ch/heart/2013.php",

}

RIS

TY - CONF

T1 - Extended Hypercube Models for Location Problems with Stochastic Demand

AU - Boyacı, Burak

AU - Geroliminis, Nikolas

PY - 2013/9/4

Y1 - 2013/9/4

N2 - In spatial queues, servers travel to the customers and provide service on the scene. This property makes them applicable to emergency response (e.g. ambulances, police) and on-demand transportation systems (e.g. paratransit, taxis) location problems. However, in spatial queues, there exist a different service rate for each customer-server pairs which creates Markovian models with enormous number of states and makes these approaches difficult to apply on even medium sized problems. Because of demand uncertainty, the nearest servers to a customer might not be available to intervene and this can significantly increase the service times. In this paper, we propose two new aggregate models and an approximate solution method with a dynamic programming heuristic. Results are compared with existing location models on hypothetical and real cases.

AB - In spatial queues, servers travel to the customers and provide service on the scene. This property makes them applicable to emergency response (e.g. ambulances, police) and on-demand transportation systems (e.g. paratransit, taxis) location problems. However, in spatial queues, there exist a different service rate for each customer-server pairs which creates Markovian models with enormous number of states and makes these approaches difficult to apply on even medium sized problems. Because of demand uncertainty, the nearest servers to a customer might not be available to intervene and this can significantly increase the service times. In this paper, we propose two new aggregate models and an approximate solution method with a dynamic programming heuristic. Results are compared with existing location models on hypothetical and real cases.

UR - http://transp-or.epfl.ch/heart/2013.php

M3 - Conference paper

T2 - hEART 2013 - 2nd Symposium of the European Association for Research in Transportation

Y2 - 4 September 2013 through 6 September 2013

ER -