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In many cases, GA2 improves the solution found by the BS method.
This greatly improves the solution time, but at the expense of solution quality.
In this case, the solution provided by the method proposed in this paper improves the solution implemented by the company.
The results indicate that HAOM improves the solution quality and outperforms other techniques in most of the instances.
This paper will demonstrate how the software tool Sequence Planner (SP) not only supports this understanding by sequence visualization, but also improves the solution using optimization and verification.
It is shown that the adoption of derivative-based optimization methods within hybrid optimization strategies significantly improves the solution quality of continuous-time simulation optimization problems.
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The experimental results show that the proposed meta-heuristic algorithm improves the solutions.
The results demonstrated that in most problem cases, the local search improves the solutions of GA in term of system workload.
The method presented in this paper uses simple heuristics to find possible optimal operation points for the ESS and improves the solutions found using genetic algorithm optimisation.
An iterative improvement procedure is introduced to improve the solution.
Furthermore, we applied tabu search to improve the solution quality.
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