Amend literature
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@ -9,9 +9,11 @@ Figure \ref{f:grid} shows how the orders' delivery locations are each
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covering the entire service area within a city.
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This gridification step is also applied to the pickup locations separately.
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The lower-left corner is chosen at random.
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\cite{winkenbach2015} apply the same gridification idea and slice an urban
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area to model a location-routing problem, and \cite{singleton2017} portray
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it as a standard method in the field of urban analytics.
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Applications of this gridification idea to model location-routing problems
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can be viewed, for example, in \cite{winkenbach2015}, \cite{bergmann2020},
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\cite{janjevic2019}, \cite{snoeck2020}, and \cite{janjevic2020}
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while \cite{singleton2017} portray it as a standard method in the field of
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urban analytics.
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With increasing pixel sizes, the time series exhibit more order aggregation
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with a possibly stronger demand pattern.
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On the other hand, the larger the pixels, the less valuable become the
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