TY - JOUR
T1 - A scalable non-myopic dynamic dial-a-ride and pricing problem
AU - Sayarshad, Hamid R.
AU - Chow, Joseph Y.J.
N1 - Funding Information:
This research was undertaken, in part, thanks to funding from the Canada Research Chairs program. Helpful feedback on the manuscript and experiment results were provided by Shadi Djavadian from Ryerson University and Han Zou from University of Southern California, which are gratefully acknowledged. Comments from two anonymous referees helped improve the quality of this paper. Any errors found are solely the authors’.
Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/11/1
Y1 - 2015/11/1
N2 - Non-myopic dial-a-ride problem and other related dynamic vehicle routing problems often ignore the need for non-myopic pricing under the assumption of elastic demand, which leads to an overestimation of the benefits in level of service and resulting inefficiencies. To correct this problem, a new dynamic dial-a-ride policy is introduced, one that features non-myopic pricing based on optimal tolling of queues to fit with the multi-server queueing approximation method proposed by Hyttiä et al. (2012) for large-scale systems. By including social optimal pricing, the social welfare of the resulting system outperforms the marginal pricing assumed for previous approaches over a range of test instances. In the examples tested, improvements in social welfare of the non-myopic pricing over the myopic pricing were in the 20-31% range. For a given demand function, we can derive the optimal fleet size to maximize social welfare. Sensitivity tests to the optimal price confirm that it leads to an optimal social welfare while the marginal pricing policy does not. A comparison of single passenger taxis to shared-taxis shows that system cost may reduce at the expense of decreased social welfare, which agrees with the results of Jung et al. (2013).
AB - Non-myopic dial-a-ride problem and other related dynamic vehicle routing problems often ignore the need for non-myopic pricing under the assumption of elastic demand, which leads to an overestimation of the benefits in level of service and resulting inefficiencies. To correct this problem, a new dynamic dial-a-ride policy is introduced, one that features non-myopic pricing based on optimal tolling of queues to fit with the multi-server queueing approximation method proposed by Hyttiä et al. (2012) for large-scale systems. By including social optimal pricing, the social welfare of the resulting system outperforms the marginal pricing assumed for previous approaches over a range of test instances. In the examples tested, improvements in social welfare of the non-myopic pricing over the myopic pricing were in the 20-31% range. For a given demand function, we can derive the optimal fleet size to maximize social welfare. Sensitivity tests to the optimal price confirm that it leads to an optimal social welfare while the marginal pricing policy does not. A comparison of single passenger taxis to shared-taxis shows that system cost may reduce at the expense of decreased social welfare, which agrees with the results of Jung et al. (2013).
KW - Approximate dynamic programming
KW - Dynamic dial a ride problem
KW - Dynamic pricing
KW - Flexible transport services
KW - Last mile problem
KW - Multiserver queue
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U2 - 10.1016/j.trb.2015.06.008
DO - 10.1016/j.trb.2015.06.008
M3 - Article
AN - SCOPUS:84947490752
VL - 81
SP - 539
EP - 554
JO - Transportation Research, Series B: Methodological
JF - Transportation Research, Series B: Methodological
SN - 0191-2615
ER -