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Prediction-based relaxation solution approach for the fixed charge network flow problem
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DOI：10.1016/j.cie.2016.07.014.png)
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A new heuristic procedure for the fixed charge network flow problem is proposed. The new method leverages a probabilistic model to create an informed reformulation and relaxation of the FCNF problem. The technique relies on probability estimates that an edge in a graph should be included in an optimal flow solution. These probability estimates, derived from a statistical learning technique, are used to reformulate the problem as a linear program which can be solved efficiently. This method can be used as an independent heuristic for the fixed charge network flow problem or as a primal heuristic. In rigorous testing, the solution quality of the new technique is evaluated and compared to results obtained from a commercial solver software. Testing demonstrates that the novel prediction-based relaxation outperforms linear programming relaxation in solution quality and that as a primal heuristic the method significantly improves the solutions found for large problem instances within a given time limit. (C) 2016 Elsevier Ltd. All rights reserved.
Keyword:
Network optimization
Fixed charge network flow
Heuristics
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IF:
6.5
论文数:
1.0W
被引数:
3.8W
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