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All our solution representations contain activity lists as a substantial element.
After formulation of stabilization problem in Section 1, we discuss formulas of solution representations in Section 2.
The approach utilizes two types of solution representations, a combination of multiple neighborhoods, and objective value-based cut inequalities for improving the evaluation of candidate solutions.
The algorithm uses on-line learning to balance computational loads between a construction and an improvement method, both working on high level solution representations.
For this reason, we developed several extended solution representations and modified decoding mechanisms that can all be seen as generalizations of the established SSGS approach for the RCPSP.
For, we can see from (3.9) that it is useful to find other solution representations which are equivalent to (3.10) and (3.11), but use instead of, if.
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It follows from the solution representation formula (2.3) that (4.5).
By the solution representation formula (2.3), we get (4.27).
Thus, we will only prove the solution representation formula (2.16).
A simple solution representation is provided in this paper to encode the solutions.
The solution representation for a higher-order Riemann fractional difference equation is obtained as well.
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