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It shows that we are succeeding in finding and improving solutions to poverty and injustice.
The paper presents an agent-supported framework for improving solutions for enterprise interoperability and enterprise collaboration.
A mixed integer programming solver and its built-in feasibility heuristics is used to search a neighborhood for improving solutions.
Adding usability improving solutions during late stage development is to some extent restricted by the software architecture.
This discussion supports the algorithm proposed here which, thanks to a particular change of variables, leads to sequences of improving solutions.
In addition, we analyze the extent to which each method can focus on applying the knowledge that improves the search performance, handle conflicting information that suggests improving solutions with opposing modifications, and balance the exploitation of the available knowledge with the exploration of the tradespace to prevent premature convergence on local optima.
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Providing additional speed-up, the parallel algorithm further expands on its sequential counterpart, matching or even improving solution quality.
Open-ended problems are identified, and suggestions for improving solution models are given.
Careful analysis of literature on FFSP reveals that meta-heuristic approaches are widely used for improving solution quality.
Finally, bringing diverse voices into the process is also known to improve solutions.
Introducing a bias in the MU selection improves solutions compared to the conventional method (Method 1).
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