Stochastic and dynamic routing with flexible deliveries for an e-grocer

Conference Paper (2021)
Author(s)

Pieter S. Bouwstra (Student TU Delft)

Gonçalo Correia (Transport and Planning)

Peter Bijl (Picnic Technologies BV)

Rudy R. Negenborn (TU Delft - Transport Engineering and Logistics)

Bilge Atasoy (TU Delft - Transport Engineering and Logistics)

Research Group
Transport Engineering and Logistics
DOI related publication
https://doi.org/10.1109/ITSC48978.2021.9564672 Final published version
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Publication Year
2021
Language
English
Research Group
Transport Engineering and Logistics
Pages (from-to)
3354-3359
ISBN (print)
978-1-7281-9143-0
ISBN (electronic)
978-1-7281-9142-3
Event
ITSC 2021 (2021-09-19 - 2021-09-22), Virtual at Indianapolis, United States
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Abstract

The quality of the delivery service is a crucial asset for an e-grocer to create and maintain a loyal customer-base. With the rapid market growth of e-grocers over the last decade, there is an urgent need for e-grocer specific routing systems. Although stochastic and dynamic routing models are studied for a wide range of applications, e-grocer specific models are missing in the literature. This paper investigates the concept of flexible deliveries, which introduces differentiated time window sizes. This creates the possibility for real-time re-optimization of the sequence of customers in a trip in order to improve the on-time delivery performance. The potential of flexible deliveries is investigated by means of computational experiments in which historic trip instances from the Dutch e-grocer Picnic are used. It is shown that, when re-optimization is activated, on-time delivery performance is improved and this benefit is significant when flexible deliveries represent at least 10% of the deliveries. When 10% of the deliveries are flexible, the number of late deliveries can be reduced by up to 18% and the number of extreme late deliveries (≥15 min late) up to 27%. This improved on-time delivery performance comes at the cost of a maximum of 2% increase in the average time spent per delivery.

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