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Finally, we design two sets of instances.
The computational results, on two sets of instances taken from the literature, show the effectiveness of the proposed algorithm.
To investigate the effectiveness of the solution approach, two sets of instances are tested, including real-world instances and randomly generated instances.
We test and compare solution approaches on two sets of instances with different geography scenarios, size, information dynamism, and order timing variability.
Let (boldsymbol {X} = [!boldsymbol {x}_{1},ldots,boldsymbol {x}_{N}]in mathbb {R}^{d_{x}times N}) and (boldsymbol {Y} = [boldsymbol {!y}_{1},ldots,boldsymbol {y}_{N}]in mathbb {R}^{d_{y}times N}) be the two sets of instances of x and y, respectively.
(All new best results are available at http://prolog.univie.ac.at/research/DARP/.) In a next step, we apply the proposed hybrid LNS to two sets of instances for which optimal solutions are known.
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Its performance is tested over three sets of instances for the inventory location routing, location-routing and inventory-routing problems.
The computational evaluation on three sets of instances (34 instances in total), with 5 10 potential depots and 20 88 customers, shows that 26 instances with five depots are solved to optimality, including all instances with up to 40 customers and three with 50 customers.
Their effectiveness is tested on two sets of realistic instances.
There are two sets of feedback instances in this section.
Two sets of benchmark instances were generated to evaluate the proposed algorithms.
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