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Greedy and Forecasting were known from the liter-
ature. They were perfectly background and com-
petitors for a new breaking through Cellular Process-
ing Algorithm. Experimental data model was pre-
sented in previous research, however, for the first
time an instance generator was created to addition-
ally contribute the paper. It will be available online as
open access. First computational experimentsdemon-
strated results obtained by all the algorithms. Results
were carefully analyzed and widely commented.
Our current work is focused on computational ex-
periment run. It is planed to solve a vast number of
instances using all the algorithms. Moreover, the idea
is to provide scalability analysis - with a set of exper-
iments for heuristic algorithms. This type of exper-
iment will be performed for much bigger instances
of the problem (with a significantly bigger number
of shops and products) to compare all heuristic algo-
rithms. Important part of a discussion will be disper-
sion analysis. In the full version of this position pa-
per a very detailed description of the computational
experiment will follow a vast number of exhaustive
tests that will be performed. Moreover, it is planed
to describe each algorithm in a very detailed way (in-
cluding algorithm pseudo-code) to enable repetition
and evaluation of these algorithms. Furthermore, an
instance generator described within this paper will be
available online for open usage. An interesting ex-
tension can be made when the model (with instance
generator) will be enhanced to complete to analysis
several kinds of products and compare the different
scenarios.
To alleviate the problem, and also deal with scalabil-
ity we consider investigating a parallel version of the
algorithm on a GPU infrastructure. Moreover, very
interesting topic could be to combine a method of
selecting column widths for a given set of advertise-
ment on a web page (Marszałkowskiand Drozdowski,
2013) with ISOP when preparing online working ap-
plication.
ACKNOWLEDGEMENTS
This study was partially supported by the FNR
(Luxembourg) and NCBiR (Poland), through IShOP
project, INTER/POLLUX/13/6466384.
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