Mohadeseh Rahbar
University of Queensland
13 Papers
44 Citations
Mohadeseh Rahbar is an academic researcher from University of Queensland. The author has contributed to research in topics: Metaheuristic & Markov chain Monte Carlo. The author has an hindex of 7, co-authored 13 publications. Previous affiliations of Mohadeseh Rahbar include Iran University of Science and Technology.
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Papers
A hybrid simulated annealing and column generation approach for capacitated multicommodity network design
TL;DR: A hybrid simulated annealing (SA) and column generation (CG) algorithm for the path-based formulation of the capacitated multicommodity network design (PCMND) problem and is compared with the solutions of CPLEX solver and the best-known method in the literature under different time limits.
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A set covering approach for multi-depot train driver scheduling
TL;DR: This paper presents a matheuristic by combining a tabu search metaheuristic and the proposed neighbourhood structure for solving the set covering problem (SCP), and the algorithm is successfully applied to solve the TDS in Iranian railways.
23
Calibrating a Bayesian Transit Assignment Model Using Smart Card Data
TL;DR: A Bayesian hierarchical model is proposed to estimate attributes of travel time components and to calibrate a transit assignment model, and in order to consider travel time variability, it is assumed that travel time on links follows a gamma distribution.
18
A Cutting-Plane Neighborhood Structure for Fixed-Charge Capacitated Multicommodity Network Design Problem
TL;DR: A cutting-plane neighborhood structure for the fixed-charge capacitated multicommodity network design (CMND) problem is proposed and the efficiency and effectiveness of the tabu search algorithm compared to the best methods found in the literature are shown.
16
A Hybrid Simulated Annealing and Simplex Method for Fixed-Cost Capacitated Multicommodity Network Design
TL;DR: This paper presents a matheuristic algorithm combining Simulated Annealing (SA) metaheuristic and Simplex method for CMND problem, where a binary array is considered as solution representation and the SA algorithm manages open and closed arcs.
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