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Choice-based demand management and vehicle routing in e-fulfillment

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Choice-based demand management and vehicle routing in e-fulfillment. / Yang, Xinan; Strauss, Arne Karsten; Currie, Christine et al.
In: Transportation Science, Vol. 50, No. 2, 01.05.2016, p. 473-488.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Yang, X, Strauss, AK, Currie, C & Eglese, RW 2016, 'Choice-based demand management and vehicle routing in e-fulfillment', Transportation Science, vol. 50, no. 2, pp. 473-488. https://doi.org/10.1287/trsc.2014.0549

APA

Yang, X., Strauss, A. K., Currie, C., & Eglese, R. W. (2016). Choice-based demand management and vehicle routing in e-fulfillment. Transportation Science, 50(2), 473-488. https://doi.org/10.1287/trsc.2014.0549

Vancouver

Yang X, Strauss AK, Currie C, Eglese RW. Choice-based demand management and vehicle routing in e-fulfillment. Transportation Science. 2016 May 1;50(2):473-488. Epub 2014 Aug 7. doi: 10.1287/trsc.2014.0549

Author

Yang, Xinan ; Strauss, Arne Karsten ; Currie, Christine et al. / Choice-based demand management and vehicle routing in e-fulfillment. In: Transportation Science. 2016 ; Vol. 50, No. 2. pp. 473-488.

Bibtex

@article{f3c2c1073a244a428b0763644e43b1d6,
title = "Choice-based demand management and vehicle routing in e-fulfillment",
abstract = "Attended home delivery services face the challenge of providing narrow delivery time slots to ensure customer satisfaction, while keeping the significant delivery costs under control. To that end, a firm can try to influence customers when they are booking their delivery time slot so as to steer them toward choosing slots that are expected to result in cost-effective schedules. We estimate a multinomial logit customer choice model from historic booking data and demonstrate that this can be calibrated well on a genuine e-grocer data set. We propose dynamic pricing policies based on this choice model to determine which and how much incentive (discount or charge) to offer for each time slot at the time a customer intends to make a booking. A crucial role in these dynamic pricing problems is played by the delivery cost, which is also estimated dynamically. We show in a simulation study based on real data that anticipating the likely future delivery cost of an additional order in a given location canlead to significantly increased profit as compared with current industry practice.",
keywords = "home delivery services, vehicle routing, yield management",
author = "Xinan Yang and Strauss, {Arne Karsten} and Christine Currie and Eglese, {Richard William}",
year = "2016",
month = may,
day = "1",
doi = "10.1287/trsc.2014.0549",
language = "English",
volume = "50",
pages = "473--488",
journal = "Transportation Science",
issn = "0041-1655",
publisher = "INFORMS",
number = "2",

}

RIS

TY - JOUR

T1 - Choice-based demand management and vehicle routing in e-fulfillment

AU - Yang, Xinan

AU - Strauss, Arne Karsten

AU - Currie, Christine

AU - Eglese, Richard William

PY - 2016/5/1

Y1 - 2016/5/1

N2 - Attended home delivery services face the challenge of providing narrow delivery time slots to ensure customer satisfaction, while keeping the significant delivery costs under control. To that end, a firm can try to influence customers when they are booking their delivery time slot so as to steer them toward choosing slots that are expected to result in cost-effective schedules. We estimate a multinomial logit customer choice model from historic booking data and demonstrate that this can be calibrated well on a genuine e-grocer data set. We propose dynamic pricing policies based on this choice model to determine which and how much incentive (discount or charge) to offer for each time slot at the time a customer intends to make a booking. A crucial role in these dynamic pricing problems is played by the delivery cost, which is also estimated dynamically. We show in a simulation study based on real data that anticipating the likely future delivery cost of an additional order in a given location canlead to significantly increased profit as compared with current industry practice.

AB - Attended home delivery services face the challenge of providing narrow delivery time slots to ensure customer satisfaction, while keeping the significant delivery costs under control. To that end, a firm can try to influence customers when they are booking their delivery time slot so as to steer them toward choosing slots that are expected to result in cost-effective schedules. We estimate a multinomial logit customer choice model from historic booking data and demonstrate that this can be calibrated well on a genuine e-grocer data set. We propose dynamic pricing policies based on this choice model to determine which and how much incentive (discount or charge) to offer for each time slot at the time a customer intends to make a booking. A crucial role in these dynamic pricing problems is played by the delivery cost, which is also estimated dynamically. We show in a simulation study based on real data that anticipating the likely future delivery cost of an additional order in a given location canlead to significantly increased profit as compared with current industry practice.

KW - home delivery services

KW - vehicle routing

KW - yield management

U2 - 10.1287/trsc.2014.0549

DO - 10.1287/trsc.2014.0549

M3 - Journal article

VL - 50

SP - 473

EP - 488

JO - Transportation Science

JF - Transportation Science

SN - 0041-1655

IS - 2

ER -