@article {10.3844/jcssp.2009.64.70,
article_type = {journal},
title = {Benders' Decomposition Based Heuristics for Large-Scale Dynamic Quadratic Assignment Problems },
author = {Muenvanichakul, Sirirat and Charnsethikul, Peerayuth},
volume = {5},
number = {1},
year = {2009},
month = {Jan},
pages = {64-70},
doi = {10.3844/jcssp.2009.64.70},
url = {https://thescipub.com/abstract/jcssp.2009.64.70},
abstract = {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.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}