Move glossary into an appendix
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3 changed files with 11 additions and 8 deletions
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@ -42,9 +42,6 @@
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\input{tex/5_con/4_further_research}
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\newpage
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\input{tex/glossary}
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\newpage
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\appendix
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\newpage
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\input{tex/apx/tabular_ml_models}
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@ -55,6 +52,8 @@
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\newpage
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\input{tex/apx/peak_results}
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\newpage
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\input{tex/apx/glossary}
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\newpage
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\bibliographystyle{static/elsarticle-harv}
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\bibliography{tex/references}
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@ -11,8 +11,9 @@ A common feature of these platforms is that they do not operate kitchens but
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related processes in simple smartphone apps, and managing the delivery via
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a fleet of either employees or crowd-sourced sub-contractors.
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Various kind of urban delivery platforms (UDP) have received attention in
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recent scholarly publications.
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Various kinds of urban delivery platforms
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(\gls{udp}; \ref{glossary} provides a glossary with all abbreviations)
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have received attention in recent scholarly publications.
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\cite{hou2018} look into heuristics to simultaneously optimize courier
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scheduling and routing in general, while \cite{masmoudi2018} do so
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for the popular dial-a-ride problem and \cite{wang2018} investigate
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@ -63,8 +64,8 @@ Moreover, many of the established forecasting methods can only handle a single
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and often low seasonality (i.e., repeated regular pattern), if at all.
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In this paper, we develop a rigorous methodology as to how to build and
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evaluate a robust forecasting system for an urban delivery platform
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(\gls{udp}) that offers ad-hoc point-to-point transportation of any kind.
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evaluate a robust forecasting system for an UDP
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that offers ad-hoc point-to-point transportation of any kind.
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We implement such a system with a broad set of commonly used forecasting
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methods.
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We not only apply established (i.e., "classical") time series methods but also
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@ -1,3 +1,6 @@
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\section{Glossary}
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\label{glossary}
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% Abbreviations for technical terms.
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\newglossaryentry{add}{
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name=ADD, description={Average Daily Demand}
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@ -138,4 +141,4 @@
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trained on vertical time series}
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}
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\printglossaries
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\printglossary[title=]
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