From 41d5271a9ba4428033f75a0f31feccf1442dad39 Mon Sep 17 00:00:00 2001 From: Alexander Hess Date: Sun, 4 Oct 2020 22:24:38 +0200 Subject: [PATCH] Add Introduction section --- tex/1_intro.tex | 102 ++++++++++++++++++++++++++++++++++++++++++++- tex/glossary.tex | 11 +++++ tex/preamble.tex | 9 +++- tex/references.bib | 85 +++++++++++++++++++++++++++++++++++++ 4 files changed, 205 insertions(+), 2 deletions(-) diff --git a/tex/1_intro.tex b/tex/1_intro.tex index 3c74279..87d0171 100644 --- a/tex/1_intro.tex +++ b/tex/1_intro.tex @@ -1,2 +1,102 @@ \section{Introduction} -\label{intro} \ No newline at end of file +\label{intro} + +In recent years, many meal delivery platform providers (e.g., Uber Eats, + GrubHub, DoorDash, Deliveroo) with different kinds of business models have + entered the markets in cities around the world. +A study by \cite{hirschberg2016} estimates the global market size to surpass + 20 billion Dollars by 2025. +A common feature of these platforms is that they do not operate kitchens but + focus on marketing their partner restaurants' meals, unifying all order + related processes in simple smartphone apps, and managing the delivery via + a fleet of either employees or crowd-sourced sub-contractors. + +Various kind of urban delivery platforms (UDP) have received attention in + recent scholarly publications. +\cite{hou2018} look into heuristics to simultaneously optimize courier + scheduling and routing in general, while \cite{masmoudi2018} do so + for the popular dial-a-ride problem and \cite{wang2018} investigate + the effect of different fulfillment strategies in the context of urban + meal delivery. +\cite{ehmke2018} and \cite{alcaraz2019} focus their research on the routing + aspect, which is commonly modeled as a so-called vehicle routing problem + (\gls{vrp}). + +Not covered in the recent literature is research focusing on the demand + forecasting problem a UDP faces. +Due to the customers' fragmented locations and the majority of the orders + occurring ad-hoc for immediate delivery in the case of a meal delivery + platform, forecasting demand for the near future (i.e., several hours) + and distinct locations of the city in real-time is an essential factor + in achieving timely fulfillment. +In general, demand forecasting is a well-researched discipline with a + decades-long history in scholarly journals as summarized, for example, by + \cite{de2006}. +Even some meal delivery platforms themselves publish their practices: For + example, \cite{bell2018} provide a general overview of supply and demand + forecasting at Uber and benchmarks of the methods used while + \cite{laptev2017} investigate how extreme events can be incorporated. + +The conditions such platforms face are not limited to meal delivery: + Any entity that performs ad-hoc requested point-to-point transportation at + scale in an urban area benefits from a robust forecasting system. +Examples include ride-hailing, such as the original Uber offering, or bicycle + courier services. +The common characteristics are: +\begin{itemize} +\item \textbf{Geospatial Slicing}: + Forecasts for distinct parts of a city in parallel +\item \textbf{Temporal Slicing}: + Forecasts on a sub-daily basis (e.g., 60-minute windows) +\item \textbf{Order Sparsity}: + The historical order time series exhibit an intermittent pattern +\item \textbf{Double Seasonality}: + Demand varies with the day of the week and the time of day +\end{itemize} +Whereas the first two points can be assumed to vary with the concrete + application's requirements, it is the last two that pose challenges for + forecasting a platform's demand: +Intermittent demand (i.e., many observations in the historic order time series + exhibit no demand at all) renders most of the commonly applied error + metrics useless. +Moreover, many of the established forecasting methods can only handle a single + and often low seasonality (i.e., repeated regular pattern), if at all. + +In this paper, we develop a rigorous methodology as to how to build and + evaluate a robust forecasting system for an urban delivery platform + (\gls{udp}) that offers ad-hoc point-to-point transportation of any kind. +We implement such a system with a broad set of commonly used forecasting + methods. +We not only apply established (i.e., "classical") time series methods but also + machine learning (\gls{ml}) models that have gained traction in recent + years due to advancements in computing power and availability of larger + amounts of data. +In that regard, the classical methods serve as benchmarks for the ML methods. +Our system is trained on and evaluated with a dataset obtained from an + undisclosed industry partner that, during the timeframe of our study, was + active in several European countries and, in particular, in France. +Its primary business strategy is the delivery of meals from upper-class + restaurants to customers in their home or work places via bicycles. +In this empirical study, we identify the best-performing methods. +Thus, we answer the following research questions: +\begin{enumerate} +\item[\textbf{Q1}:] + Which forecasting methods work best under what circumstances? +\item[\textbf{Q2}:] + How do classical forecasting methods compare with ML models? +\item[\textbf{Q3}:] + How does the forecast accuracy change with more historic data available? +\item[\textbf{Q4}:] + Can real-time information on demand be exploited? +\item[\textbf{Q5}:] + Can external data (e.g., weather data) improve the forecast accuracy? +\end{enumerate} +To the best of our knowledge, no such study has yet been published in a + scholarly journal. + +The subsequent Section \ref{lit} reviews the literature on the forecasting + methods included in the system. +Section \ref{mod} introduces our forecasting system, and Section \ref{stu} + discusses the results obtained in the empirical study. +Lastly, Section \ref{con} summarizes our findings and concludes + with an outlook on further research opportunities. \ No newline at end of file diff --git a/tex/glossary.tex b/tex/glossary.tex index 5a69948..b34e1b9 100644 --- a/tex/glossary.tex +++ b/tex/glossary.tex @@ -1 +1,12 @@ +% Abbreviations for technical terms. +\newglossaryentry{ml}{ + name=ML, description={Machine Learning} +} +\newglossaryentry{udp}{ + name=UDP, description={Urban Delivery Platform} +} +\newglossaryentry{vrp}{ + name=VRP, description={Vehicle Routing Problem} +} + \printglossaries \ No newline at end of file diff --git a/tex/preamble.tex b/tex/preamble.tex index 7fcd468..bc34c42 100644 --- a/tex/preamble.tex +++ b/tex/preamble.tex @@ -1,2 +1,9 @@ +% Use the document width more effectively. +\usepackage[margin=2.5cm]{geometry} + \usepackage[acronym]{glossaries} -\makeglossaries \ No newline at end of file +\makeglossaries + +% Make opening quotes look different than closing quotes. +\usepackage[english=american]{csquotes} +\MakeOuterQuote{"} \ No newline at end of file diff --git a/tex/references.bib b/tex/references.bib index e69de29..be2c815 100644 --- a/tex/references.bib +++ b/tex/references.bib @@ -0,0 +1,85 @@ +@article{alcaraz2019, +title={Rich vehicle routing problem with last-mile outsourcing decisions}, +author={Alcaraz, Juan J and Caballero-Arnaldos, Luis and Vales-Alonso, Javier}, +year={2019}, +journal={Transportation Research Part E: Logistics and Transportation Review}, +volume={129}, +pages={263--286} +} + +@misc{bell2018, +title = {Forecasting at Uber: An Introduction}, +author={Bell, Franziska and Smyl, Slawek}, +year={2018}, +howpublished = {\url{https://eng.uber.com/forecasting-introduction/}}, +note = {Accessed: 2020-10-01} +} + +@article{de2006, +title={25 Years of Time Series Forecasting}, +author={De Gooijer, Jan and Hyndman, Rob}, +year={2006}, +journal={International Journal of Forecasting}, +volume={22}, +number={3}, +pages={443--473} +} + +@article{ehmke2018, +title={Optimizing for total costs in vehicle routing in urban areas}, +author={Ehmke, Jan Fabian and Campbell, Ann M and Thomas, Barrett W}, +year={2018}, +journal={Transportation Research Part E: Logistics and Transportation Review}, +volume={116}, +pages={242--265} +} + +@misc{hirschberg2016, +title = {McKinsey: The changing market for food delivery}, +author={Hirschberg, Carsten and Rajko, Alexander and Schumacher, Thomas + and Wrulich, Martin}, +year={2016}, +howpublished = "\url{https://www.mckinsey.com/industries/high-tech/ + our-insights/the-changing-market-for-food-delivery}", +note = {Accessed: 2020-10-01} +} + +@article{hou2018, +title={Ride-matching and routing optimisation: Models and a large + neighbourhood search heuristic}, +author={Hou, Liwen and Li, Dong and Zhang, Dali}, +year={2018}, +journal={Transportation Research Part E: Logistics and Transportation Review}, +volume={118}, +pages={143--162} +} + +@misc{laptev2017, +title = {Engineering Extreme Event Forecasting + at Uber with Recurrent Neural Networks}, +author={Laptev, Nikolay and Smyl, Slawek and Shanmugam, Santhosh}, +year={2017}, +howpublished = {\url{https://eng.uber.com/neural-networks/}}, +note = {Accessed: 2020-10-01} +} + +@article{masmoudi2018, +title={The dial-a-ride problem with electric vehicles and battery + swapping stations}, +author={Masmoudi, Mohamed Amine and Hosny, Manar and Demir, Emrah + and Genikomsakis, Konstantinos N and Cheikhrouhou, Naoufel}, +year={2018}, +journal={Transportation research part E: logistics and transportation review}, +volume={118}, +pages={392--420} +} + +@article{wang2018, +title={Delivering meals for multiple suppliers: Exclusive or sharing + logistics service}, +author={Wang, Zheng}, +year={2018}, +journal={Transportation Research Part E: Logistics and Transportation Review}, +volume={118}, +pages={496--512} +} \ No newline at end of file