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Showing 2 results for Fakharzadeh
A Fakharzadeh, S Mahmoodi,
Volume 5, Issue 2 (10-2014)
Abstract
The traffic assignment problem is one of the most important problems for analyzing and optimizing the transportation network to find optimal flows. This study presented a new formulation based on a generalized Benders' decomposition approach to solve its important part, i.e. user equilibrium problems, in deterministic and stochastic cases. The new approach decomposed the problem into a master problem and a sub problem. The first one was a nonlinear and the latter a linear programming problem. Iteratively, the master problem was solved and its outputs were used to solve the sub-problem in which to form appropriate cuts and add them to the master problem for solving it in the next iteration. Based on the convergence of Benders' decomposition, the iterative process was terminated in a finite number of steps. In this manner, some numerical examples were explained and compared with other methods.
Dr Hassan Rostamzadeh, Dr Ali Reza Fakharzadeh,
Volume 17, Issue 1 (5-2026)
Abstract
Existing merger approaches in data envelopment analysis integrate decision-making units in a single stage, but in practice, merging units may not be possible or affordable at once. We propose a finite multi-stage framework for the gradual merger of decision-making units that incorporates practical constraints. The model determines input and output contributions of the merged units at each stage and constructs a strictly increasing efficiency sequence that converges to a Pareto-efficient state. Each new unit is optimized using both input and output orientation while preserving a uniform return to scale type across stages. The framework is validated through a multi-stage merger application on a subset of Iranian banks.
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