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These objective functions are of- ten in conflict, making them highly challenging in terms of determining optimal solutions and analysing solutions obtained.
We present a binary integer program to formulate the studied problem and then develop an O(n22n) dynamic programming algorithm for determining optimal solutions.
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Mixed integer optimization (MIP) determines optimal solutions of such complex problems; the development of new algorithms, software and hardware allow the solution of larger problems in acceptable times.
Samples found to be non-aberrant by ABSOLUTE (SCNAs were absent and thus could not be used to determine optimal solutions during the manual curation step of ABSOLUTE) were excluded from analysis.
Independent optimisation operations successfully identified solutions that were very similar, though not identical, thereby providing confidence in the approach to determine optimal solutions to complex problems.
This framework embodies the main concepts of a local energy management system and adopts a multicriteria perspective to determine optimal solutions for providing energy services through distributed generation.
An experimental study establishes up to what parameter values the dynamic programs can determine optimal solutions in a couple of minutes.
Such a concept can be advantageous in deriving design solutions and determining optimal process parameters for tube hydroforming processes.
Simulations of varying sizes for problems and sensitivity analyses were conducted, and the results indicate that LRD is more effective than previous methods in determining global optimal solutions for the integrated decision problem.
In the proposed method, the tolerance membership functions for the fuzzily described numerator and denominator part of the objective functions of all levels as well as the control vectors of the higher level decision makers are respectively defined by determining individual optimal solutions of each of the level decision makers.
This paper has demonstrated that the integration of uniform design and quantum-behaved particle swarm optimization can effectively determine the optimal solutions for suspension parameters.
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