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Benders' Decomposition Based Heuristics for Large-Scale Dynamic Quadratic Assignment Problems | Science Publications

机译:大规模动态二次分配问题的基于Benders分解的启发式算法科学出版物

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> Problem statement: Dynamic Quadratic Assignment Problem (DQAP) is NP hard problem. Benders decomposition based heuristics method is applied to the equivalent mixed-integer linear programming problem of the original DQAP. Approach: Approximate Benders Decomposition (ABD) generates the ensemble of a subset of feasible layout for Approximate Dynamic Programming (ADP) to determine the sub-optimal optimal solution. A Trust-Region Constraint (TRC) for the master problem in ABD and a Successive Adaptation Procedure (SAP) were implemented to accelerate the convergence rate of the method. Results: The sub-optimal solutions of large-scales DQAPs from the method and its variants were compared well with other metaheuristic methods. Conclusion: Overall performance of the method is comparable to other metaheuristic methods for large-scale DQAPs.
机译: > 问题陈述:动态二次分配问题(DQAP)是NP难题。将基于Benders分解的启发式方法应用于原始DQAP的等效混合整数线性规划问题。 方法:近似折角分解(ABD)生成了可行布局的子集,用于近似动态规划(ADP),以确定次优最优解。针对ABD中主要问题的信任区域约束(TRC)和连续适应过程(SAP)实施,以加快该方法的收敛速度。 结果:该方法及其变体对大型DQAP的次优解与其他元启发式方法进行了比较。 结论:对于大规模DQAP,该方法的整体性能可与其他元启发式方法相媲美。

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