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The main contribution of this paper is a design method for allocation rules that return solutions in the core or ε-core of the game under delayed information on the coalitions' values, and therefore the resulting allocation rule is called robust.
However, this formulation does not impose any fairness consideration and can return solutions that favor some users substantially.
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Return solution(s).
Thus, the system is hard to immediately return solution sets to users.
Based on the parameters, the methods return solution mappings that represent a result of the triple pattern.
This combined approach has been shown to improve the quality of the returned solutions by 37% on average and reducing the overall search time by 50% with respect to state-of-the-art heuristics based on tiers utilization thresholds.
Regarding the HV metric, IBEA returns solutions that are more concentrated and closer to PFref.
Therefore, although DPCPE is not guaranteed to return the optimal solution, it usually returns solutions that work better in practice for the BFs algorithm than directly using the database with no preprocessing (Section 6.6).
In this case, genetic algorithm run under different scenarios and ended up returning solutions equal to or better than GAMS results, revealing good performance of the algorithm.
Among the evaluated algorithms, MOCell returns solutions that are nearer to the extreme points from the PFref and thus has good diversity.
These quantities are being processed by Algorithm 2. The first is |M|, the number of candidates of a source edge, and the second is |T|, the number of returned solutions for the tail.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com