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The main contributions of this paper are two folds: a mathematical model for formulating the AEOS scheduling problem, and an anytime branch and bound algorithm for problem solution.
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They showed that B&P has better performance than the Branch and bound algorithm for the problem.
Optimal utility LT is the output of Figure 5. Figure 5 Branch and bound algorithm for solving the problem TUMP.
"Branch and bound algorithms for the maximum clique problem under a unified framework," by R. Carmo and A. Zuge.
Jiao et al. [17, 18] present the branch and bound algorithms for globally solving sum of linear and generalized polynomial ratios problems, by solving a sequence of linear relaxation programming problems.
We also proposed a branch and bound algorithm to solve the problem, and the results demonstrated efficiency of the algorithm.
We develop a simple branch and bound algorithm to solve the scheduling problem optimally.
The optimal method to the CARP problem uses the branch and bound algorithm to solve a binary integer programming problem.
We propose a branch and bound algorithm to find an optimal solution for this problem.
For solving this problem, a parallelized, bi-directional branch and bound algorithm with dynamic search strategies has been developed to solve the problem on large computer clusters.
Second, we implement a branch and bound algorithm that terminates exploration early; this algorithm makes orders of magnitude improvement in run times for difficult problems.
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