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In the previous section (Table 6), we solved instance 5 by considering from the outset the difference among surgeons in terms of skill: the resulting cost was €3,650, which is significantly lower than €6,048.
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When applied to the standard set of instances this approach improves heuristic values on initial states, detects considerable more deadlocks in random states, and doubles the number of optimally solved instances compared to previous methods.
For two of the three optimally solved instances, the heuristic finds the optimal solution within one minute.
The proposed exact methods are able to optimally solve instances with up to 60 nodes.
We then propose a primal decomposition method to solve instances of the problem to optimality.
We are also able to solve instances with up to 1000 customers.
The hybrid method presented herein allowed to solve instances with up to six AGVs.
We propose an efficient heuristic approach for solving instances of the Single String Planning Problem (SSPP) arising in the liner shipping industry.
The PCA maps give a valuable indication of the combination of features characterizing easy and hard to solve instances.
Two architectures for optical processors designed to solve instances of NP-Complete problems, trading space for time, are suggested.
To solve instances involving a large number of aircraft spread over several flight levels, we introduce two decomposition algorithms.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com