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In this instance, size does matter.
Extensive experimental trials on benchmark instances demonstrate a significant decrease in instance size for certain classes of problems.
We examine the advantages and disadvantages of using such user-optimal solutions, and show that our algorithms sufficiently scale with instance size to be used for practical purposes.
The use of the SMD concept, for encoding three commonly applied quadratic local search operators, results into a VRP local search method which exhibits an almost linearithmic complexity in respect to the instance size.
A thorough experimental plan has been designed and implemented to test the effectiveness and the robustness of the algorithm: first, drawing inspiration from the response surface methodology, we calibrate the algorithm by identifying the optimal parameters value for any given instance size.
The mean represents the instance size that is most likely to be demanded by user i.
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Current commercial sorters based on machine vision only solve the problems that require less computing time, as for instance, sizing or classification in colours.
They cite AWS lack of flexibility with its instance sizes.
AWS has specific instance sizes that come with preset cores and RAM allotments.
Moreover, the solution times increase significantly as the instance sizes get larger.
The standard deviation represents the degree of variation in the distribution of instance sizes demanded by user i.
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