From b76ddd0e2f55a757381302375ddee69df6337cb7 Mon Sep 17 00:00:00 2001 From: Alexander Hess Date: Fri, 9 Oct 2020 14:48:10 +0200 Subject: [PATCH 01/11] Streamline text --- tex/5_con/3_implications.tex | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/tex/5_con/3_implications.tex b/tex/5_con/3_implications.tex index 6fe91a4..ce9c194 100644 --- a/tex/5_con/3_implications.tex +++ b/tex/5_con/3_implications.tex @@ -10,8 +10,9 @@ Somewhat surprisingly, despite ML-based methods` popularity in both business and academia in recent years, we must conclude that classical forecasting methods suffice to reach the best accuracy in our study. There is one case where ML-based methods are competitive in our case study: - In a high demand pixel, if only about four to six weeks of past data is - available, the \textit{vrfr} model outperformed the classical ones. + In a high demand pixel (defined as more than 25 orders per day on average), + if only about four to six weeks of past data is available, + the \textit{vrfr} model outperformed the classical ones. So, we recommend trying out ML-based methods in such scenarios. In addition, with the \textit{hsma} and \textit{hets} models being the overall winners, incorporating real-time data is not beneficial, in particular, @@ -53,6 +54,8 @@ We emphasize that for the most part, our proposed forecasting system is calibrated automatically and no manual work by a data scientist is required. The only two parameters where assumptions need to be made are the pixel size and the time step. -While they can only be optimized by the data scientist over time, the results in our - empirical study suggest that a pixel size of $1~\text{km}^2$ and a time step of - one hour are ideal. \ No newline at end of file +The results in our empirical study suggest + that a pixel size of $1~\text{km}^2$ and a time step of one hour are ideal, + which results in the optimal trade-off + between signal strength and spatial-temporal resolution. +Future research may explore adaptive grid-sizing depending on, for instance, demand density. \ No newline at end of file From a440752c4e4f7bd40d906d5d8ae30f060ddbe4ba Mon Sep 17 00:00:00 2001 From: Alexander Hess Date: Fri, 9 Oct 2020 14:48:29 +0200 Subject: [PATCH 02/11] Rephrase abstract - streamline first half - add "on a grid" - add note on predictive routing applications - mention managerial findings and winning forecasting method in second half --- tex/meta.tex | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/tex/meta.tex b/tex/meta.tex index d944b33..8296540 100644 --- a/tex/meta.tex +++ b/tex/meta.tex @@ -23,18 +23,18 @@ Emails: \begin{abstract} Meal delivery platforms like Uber Eats shape the landscape in cities around the world. -This paper addresses forecasting demand into the short-term future. +This paper addresses forecasting demand on a grid into the short-term future, + enabling, for example, predictive routing applications. We propose an approach incorporating - both classical forecasting - and machine learning methods. -Model evaluation and selection is adapted to demand typical for such a platform - (i.e., intermittent with a double-seasonal pattern). -The results of an empirical study with a European meal delivery service show - that machine learning models become competitive - once the average daily demand passes a threshold. -As a main contribution, the paper explains - how a forecasting system must be set up - to enable predictive routing. + both classical forecasting and machine learning methods + and adapt model evaluation and selection to typical demand: + intermittent with a double-seasonal pattern. +An empirical study shows that + an exponential smoothing based method trained on past demand data alone + achieves optimal accuracy, + if at least two months are on record. +With only a shorter demand history, + machine learning methods and real-time data may improve prediction. \end{abstract} 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znNPvZrF}tkfdl@(RxG*LLEn)P$$=dD@XV@K-e#n1TpaMsnxwj{r0k@u|BNaQ4zB-< ztfVZYD)7uQ_T~=%G`9b%iIeIINV0QraIvw9h=_}`OLFi^uySxqvhnb8v$1fAvGR(r z36cK)hv*~yKMQ-@>>U5I!wIBHlCxD5!3{gIivu2hDJkiGuabMn_nt{zY>Kk3z$w>k zNu4dPM=cu+3yr8nC1Z@29xN9X4bpBh_(*B7>7PDGl63Kn^2z zURxYFs2M*P@W#1tfCg-X1B8=ES47AzLsF>dzd*-4q9Ygft&4a6%DDFKls0A_2f0WU5=1;W&RCpK8&l|Ax>%vq;Z=*Du9fOW7_>&|#Gc&y4QjN8 zDmB^i*CAC)mH?xmBm7%my-!H%Agd1|T_cv^meA(>1-gjE8WHSGaz&o^nyHtYgdzb) zF9Cp_7u-ILyI%nI7pOj=TRO)NvsKa*z|^yA(8Vr0tb0_hXswJdc3<`NT*QeZB=0H! zdVR#LDBUscivFP2?^rn_wDk=*t2fxeM0Y^eeU+jjWVB0RkphqU#GGtS@p+f5LWZ8* zj`Ra2QY>9oLRv3^0gJ-P;%6CDb)5o{8WenLVkK3^@&amv2PIpm3nL7mk1PT7jM yzph8t7HLc5+U}VtpJ3;S0h+E(@)m From cff8e480be088886b20b0c4e5353d89bcf9b7bd1 Mon Sep 17 00:00:00 2001 From: Alexander Hess Date: Wed, 14 Oct 2020 11:46:44 +0200 Subject: [PATCH 04/11] Rephrase abstract --- tex/meta.tex | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tex/meta.tex b/tex/meta.tex index 8296540..b5d0aa0 100644 --- a/tex/meta.tex +++ b/tex/meta.tex @@ -33,8 +33,9 @@ An empirical study shows that an exponential smoothing based method trained on past demand data alone achieves optimal accuracy, if at least two months are on record. -With only a shorter demand history, - machine learning methods and real-time data may improve prediction. +With a more limited demand history, + machine learning is shown + to yield more accurate prediction results than classical methods. \end{abstract} \begin{keyword} From e356af7c778eaef9a0710dbb7d048c293c61676f Mon Sep 17 00:00:00 2001 From: Alexander Hess Date: Wed, 14 Oct 2020 11:46:53 +0200 Subject: [PATCH 05/11] Update rendered PDF version --- paper.pdf | Bin 791062 -> 791333 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/paper.pdf b/paper.pdf index ebca2c7a28ec6dd400533d2c4b1014968245ef85..6eeca4486f967b872e002af1379c0eee49816fee 100644 GIT binary patch delta 26001 zcmV(Rv9C!h0Cx(B9t7`=OEEHtl 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z#cAc^M}S4US^hg_pz)x5EFQl~jRQ6!HcVZe9M#>X3(MI1SSTNPJJ3d*?eoTFi4AVw z(zE0B0%CVjXiUL&J7PClcS@z$qiwf{PkrAjY&A_2^8ePlQ&F)|F_6I=+{o0_5=xTD F{|`C+A Date: Sun, 25 Oct 2020 17:41:16 +0100 Subject: [PATCH 06/11] Mention corresponding author --- tex/meta.tex | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/tex/meta.tex b/tex/meta.tex index b5d0aa0..941fc1f 100644 --- a/tex/meta.tex +++ b/tex/meta.tex @@ -3,7 +3,7 @@ \journal{Transportation Research Part E} \title{Real-time Demand Forecasting for an Urban Delivery Platform} -\author[WHU]{Alexander Hess\fnref{emails}} +\author[WHU]{Alexander Hess\fnref{emails}\fnref{corresponding}} \author[WHU]{Stefan Spinler\fnref{emails}} \author[MIT]{Matthias Winkenbach\fnref{emails}} \address[WHU]{ @@ -21,6 +21,11 @@ Emails: mwinkenb@mit.edu } +\fntext[corresponding]{ +The corresponding author is Alexander Hess. +Use the provided email. +} + \begin{abstract} Meal delivery platforms like Uber Eats shape the landscape in cities around the world. This paper addresses forecasting demand on a grid into the short-term future, From 7797cad0582c061c6054c83763ab32a12102e6d3 Mon Sep 17 00:00:00 2001 From: Alexander Hess Date: Sun, 25 Oct 2020 17:48:25 +0100 Subject: [PATCH 07/11] Use Author (Year) citation style --- paper.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper.tex b/paper.tex index 333f2f1..2e5a040 100644 --- a/paper.tex +++ b/paper.tex @@ -1,4 +1,4 @@ -\documentclass[preprint,review,12pt]{static/elsarticle} +\documentclass[preprint,review,12pt,authoryear]{static/elsarticle} \input{tex/preamble} From f8321bf8354b296bb26d069db2f74a0a18bfd2d7 Mon Sep 17 00:00:00 2001 From: Alexander Hess Date: Sun, 25 Oct 2020 18:25:53 +0100 Subject: [PATCH 08/11] Remove dots in sub-sub-sections --- tex/2_lit/2_class/2_ets.tex | 2 +- tex/2_lit/2_class/3_arima.tex | 2 +- tex/2_lit/2_class/4_stl.tex | 2 +- tex/2_lit/3_ml/2_learning.tex | 2 +- tex/2_lit/3_ml/3_cv.tex | 2 +- tex/2_lit/3_ml/4_rf.tex | 2 +- tex/2_lit/3_ml/5_svm.tex | 2 +- tex/3_mod/7_models/2_hori.tex | 2 +- tex/3_mod/7_models/3_vert.tex | 2 +- tex/3_mod/7_models/4_rt.tex | 2 +- tex/3_mod/7_models/5_ml.tex | 2 +- 11 files changed, 11 insertions(+), 11 deletions(-) diff --git a/tex/2_lit/2_class/2_ets.tex b/tex/2_lit/2_class/2_ets.tex index 6db9781..53537da 100644 --- a/tex/2_lit/2_class/2_ets.tex +++ b/tex/2_lit/2_class/2_ets.tex @@ -1,4 +1,4 @@ -\subsubsection{Na\"{i}ve Methods, Moving Averages, and Exponential Smoothing.} +\subsubsection{Na\"{i}ve Methods, Moving Averages, and Exponential Smoothing} \label{ets} Simple forecasting methods are often employed as a benchmark for more diff --git a/tex/2_lit/2_class/3_arima.tex b/tex/2_lit/2_class/3_arima.tex index d55ffd8..3432b7e 100644 --- a/tex/2_lit/2_class/3_arima.tex +++ b/tex/2_lit/2_class/3_arima.tex @@ -1,4 +1,4 @@ -\subsubsection{Autoregressive Integrated Moving Averages.} +\subsubsection{Autoregressive Integrated Moving Averages} \label{arima} \cite{box1962}, \cite{box1968}, and more papers by the same authors in the diff --git a/tex/2_lit/2_class/4_stl.tex b/tex/2_lit/2_class/4_stl.tex index 127123c..39c987a 100644 --- a/tex/2_lit/2_class/4_stl.tex +++ b/tex/2_lit/2_class/4_stl.tex @@ -1,4 +1,4 @@ -\subsubsection{Seasonal and Trend Decomposition using Loess.} +\subsubsection{Seasonal and Trend Decomposition using Loess} \label{stl} A time series $y_t$ may exhibit different types of patterns; to fully capture diff --git a/tex/2_lit/3_ml/2_learning.tex b/tex/2_lit/3_ml/2_learning.tex index 86d157a..5e7c2bc 100644 --- a/tex/2_lit/3_ml/2_learning.tex +++ b/tex/2_lit/3_ml/2_learning.tex @@ -1,4 +1,4 @@ -\subsubsection{Supervised Learning.} +\subsubsection{Supervised Learning} \label{learning} A conceptual difference between classical and ML methods is the format diff --git a/tex/2_lit/3_ml/3_cv.tex b/tex/2_lit/3_ml/3_cv.tex index 1d5186b..abfdebc 100644 --- a/tex/2_lit/3_ml/3_cv.tex +++ b/tex/2_lit/3_ml/3_cv.tex @@ -1,4 +1,4 @@ -\subsubsection{Cross-Validation.} +\subsubsection{Cross-Validation} \label{cv} Because ML models are trained by minimizing a loss function $L$, the diff --git a/tex/2_lit/3_ml/4_rf.tex b/tex/2_lit/3_ml/4_rf.tex index 784d2a7..eeb7161 100644 --- a/tex/2_lit/3_ml/4_rf.tex +++ b/tex/2_lit/3_ml/4_rf.tex @@ -1,4 +1,4 @@ -\subsubsection{Random Forest Regression.} +\subsubsection{Random Forest Regression} \label{rf} \cite{breiman1984} introduce the classification and regression tree diff --git a/tex/2_lit/3_ml/5_svm.tex b/tex/2_lit/3_ml/5_svm.tex index 1c12af5..528fe9e 100644 --- a/tex/2_lit/3_ml/5_svm.tex +++ b/tex/2_lit/3_ml/5_svm.tex @@ -1,4 +1,4 @@ -\subsubsection{Support Vector Regression.} +\subsubsection{Support Vector Regression} \label{svm} \cite{vapnik1963} and \cite{vapnik1964} introduce the so-called support vector diff --git a/tex/3_mod/7_models/2_hori.tex b/tex/3_mod/7_models/2_hori.tex index 5d5959a..21cc627 100644 --- a/tex/3_mod/7_models/2_hori.tex +++ b/tex/3_mod/7_models/2_hori.tex @@ -1,4 +1,4 @@ -\subsubsection{Horizontal and Whole-day-ahead Forecasts.} +\subsubsection{Horizontal and Whole-day-ahead Forecasts} \label{hori} The upper-left in Figure \ref{f:inputs} illustrates the simplest way to diff --git a/tex/3_mod/7_models/3_vert.tex b/tex/3_mod/7_models/3_vert.tex index 5459b81..43aaaf1 100644 --- a/tex/3_mod/7_models/3_vert.tex +++ b/tex/3_mod/7_models/3_vert.tex @@ -1,4 +1,4 @@ -\subsubsection{Vertical and Whole-day-ahead Forecasts without Retraining.} +\subsubsection{Vertical and Whole-day-ahead Forecasts without Retraining} \label{vert} The upper-right in Figure \ref{f:inputs} shows an alternative way to diff --git a/tex/3_mod/7_models/4_rt.tex b/tex/3_mod/7_models/4_rt.tex index 75122cc..6fa038d 100644 --- a/tex/3_mod/7_models/4_rt.tex +++ b/tex/3_mod/7_models/4_rt.tex @@ -1,4 +1,4 @@ -\subsubsection{Vertical and Real-time Forecasts with Retraining.} +\subsubsection{Vertical and Real-time Forecasts with Retraining} \label{rt} The lower-left in Figure \ref{f:inputs} shows how models trained on vertical diff --git a/tex/3_mod/7_models/5_ml.tex b/tex/3_mod/7_models/5_ml.tex index 26f22a6..7ca00c4 100644 --- a/tex/3_mod/7_models/5_ml.tex +++ b/tex/3_mod/7_models/5_ml.tex @@ -1,4 +1,4 @@ -\subsubsection{Vertical and Real-time Forecasts without Retraining.} +\subsubsection{Vertical and Real-time Forecasts without Retraining} \label{ml_models} The lower-right in Figure \ref{f:inputs} shows how ML models take From ec7b18822a0854a83e691770cb7994b53327d023 Mon Sep 17 00:00:00 2001 From: Alexander Hess Date: Sun, 25 Oct 2020 18:43:47 +0100 Subject: [PATCH 09/11] Move glossary into an appendix --- paper.tex | 5 ++--- tex/1_intro.tex | 9 +++++---- tex/{ => apx}/glossary.tex | 5 ++++- 3 files changed, 11 insertions(+), 8 deletions(-) rename tex/{ => apx}/glossary.tex (98%) diff --git a/paper.tex b/paper.tex index 2e5a040..4659df3 100644 --- a/paper.tex +++ b/paper.tex @@ -42,9 +42,6 @@ \input{tex/5_con/4_further_research} \newpage -\input{tex/glossary} -\newpage - \appendix \newpage \input{tex/apx/tabular_ml_models} @@ -55,6 +52,8 @@ \newpage \input{tex/apx/peak_results} \newpage +\input{tex/apx/glossary} +\newpage \bibliographystyle{static/elsarticle-harv} \bibliography{tex/references} diff --git a/tex/1_intro.tex b/tex/1_intro.tex index 87d0171..c35cf91 100644 --- a/tex/1_intro.tex +++ b/tex/1_intro.tex @@ -11,8 +11,9 @@ A common feature of these platforms is that they do not operate kitchens but 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. +Various kinds of urban delivery platforms + (\gls{udp}; \ref{glossary} provides a glossary with all abbreviations) + 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 @@ -63,8 +64,8 @@ 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. + evaluate a robust forecasting system for an 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 diff --git a/tex/glossary.tex b/tex/apx/glossary.tex similarity index 98% rename from tex/glossary.tex rename to tex/apx/glossary.tex index 63cb402..77eb8b3 100644 --- a/tex/glossary.tex +++ b/tex/apx/glossary.tex @@ -1,3 +1,6 @@ +\section{Glossary} +\label{glossary} + % Abbreviations for technical terms. \newglossaryentry{add}{ name=ADD, description={Average Daily Demand} @@ -138,4 +141,4 @@ trained on vertical time series} } -\printglossaries \ No newline at end of file +\printglossary[title=] \ No newline at end of file From 3706dd44b727c9abacb09bfb64b4662fa6dc4ec5 Mon Sep 17 00:00:00 2001 From: Alexander Hess Date: Sun, 25 Oct 2020 19:18:54 +0100 Subject: [PATCH 10/11] Amend literature --- tex/3_mod/3_grid.tex | 8 +++++--- tex/references.bib | 42 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 47 insertions(+), 3 deletions(-) diff --git a/tex/3_mod/3_grid.tex b/tex/3_mod/3_grid.tex index baa0828..222b1ec 100644 --- a/tex/3_mod/3_grid.tex +++ b/tex/3_mod/3_grid.tex @@ -9,9 +9,11 @@ Figure \ref{f:grid} shows how the orders' delivery locations are each covering the entire service area within a city. This gridification step is also applied to the pickup locations separately. The lower-left corner is chosen at random. -\cite{winkenbach2015} apply the same gridification idea and slice an urban - area to model a location-routing problem, and \cite{singleton2017} portray - it as a standard method in the field of urban analytics. +Applications of this gridification idea to model location-routing problems + can be viewed, for example, in \cite{winkenbach2015}, \cite{bergmann2020}, + \cite{janjevic2019}, \cite{snoeck2020}, and \cite{janjevic2020} + while \cite{singleton2017} portray it as a standard method in the field of + urban analytics. With increasing pixel sizes, the time series exhibit more order aggregation with a possibly stronger demand pattern. On the other hand, the larger the pixels, the less valuable become the diff --git a/tex/references.bib b/tex/references.bib index 8926f3f..3dca6ea 100644 --- a/tex/references.bib +++ b/tex/references.bib @@ -56,6 +56,17 @@ pages={8--15}, publisher={Elsevier} } +@article{bergmann2020, +title={Integrating first-mile pickup and last-mile delivery + on shared vehicle routes for efficient urban e-commerce distribution}, +author={Bergmann, Felix M and Wagner, Stephan M and Winkenbach, Matthias}, +year={2020}, +journal={Transportation Research Part B: Methodological}, +volume={131}, +pages={26--62}, +publisher={Elsevier} +} + @article{box1962, title={Some statistical Aspects of adaptive Optimization and Control}, author={Box, George and Jenkins, Gwilym}, @@ -376,6 +387,26 @@ pages={211--225}, publisher={INFORMS} } +@article{janjevic2019, +title={Integrating collection-and-delivery points + in the strategic design of urban last-mile e-commerce distribution networks}, +author={Janjevic, Milena and Winkenbach, Matthias and Merch{\'a}n, Daniel}, +year={2019}, +journal={Transportation Research Part E: Logistics and Transportation Review}, +volume={131}, +pages={37--67}, +publisher={Elsevier} +} + +@article{janjevic2020, +title={Designing Multi-tier, Multi-service-level, and Multi-modal + Last-Mile Distribution Networks for Omni-Channel Operations}, +author={Janjevic, Milena and Merchan, Daniel and Winkenbach, Matthias}, +year={2020}, +journal={European Journal of Operational Research}, +publisher={Elsevier} +} + @article{kim2016, title={A new Metric of Absolute Percentage Error for Intermittent Demand Forecasts}, @@ -559,6 +590,17 @@ number={3}, pages={199--222} } +@article{snoeck2020, +title={The value of physical distribution flexibility + in serving dense and uncertain urban markets}, +author={Snoeck, Andr{\'e} and Winkenbach, Matthias}, +year={2020}, +journal={Transportation Research Part A: Policy and Practice}, +volume={136}, +pages={151--177}, +publisher={Elsevier} +} + @article{stitson1999, title={Support Vector Regression with ANOVA Decomposition Kernels}, author={Stitson, Mark and Gammerman, Alex and Vapnik, Vladimir From ea0f5edc628d9e97e08bd96fd437720b8caa7c46 Mon Sep 17 00:00:00 2001 From: Alexander Hess Date: Sun, 25 Oct 2020 19:31:17 +0100 Subject: [PATCH 11/11] Update rendered version - move tables to avoid half blank pages --- paper.pdf | Bin 791333 -> 795190 bytes tex/4_stu/4_overall.tex | 62 +++++++++++++++++++++------------------- 2 files changed, 32 insertions(+), 30 deletions(-) diff --git a/paper.pdf b/paper.pdf index 6eeca4486f967b872e002af1379c0eee49816fee..4bdce7d835acbcb952ada2255ef8a1f08ae1ac0f 100644 GIT binary patch delta 149236 zcmV)LK)JuArZBd|FpwhyH8ePr@fRq6l^4m5+cxy>uh3BfMv6pjR8E;`CQVxml4d;3 zA;^K2Xp4xplGHfv*Y}Z+s_jH6TKFK6Z{MHqwwo7k(#lK9h$MODZTB86qmrgx8COwK zR^GPpKKUOw>q0Gh9)xL3{MWo^LxblxL0S2u zDSR$Z`0~DE8YueU^Y+&lZ%FPXNmS)IG1?R)iVNz6DT&fWo7;|m1^L{_06uTIQb?M` zUKv$ooEXXy^iNAKi_+B5u^X}`0*+}?dEo`-4RU_ed~ekzrT;jIp#zbtg5spO7L@!@ 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