M.Sc. (fall 2009 - winter 2012) 

Industrial Engineering, Isfahan University of Technology, Iran 

GPA 4.00 of 4.00, Average score 19.15 out of 20 (1st rank), Thesis grade: 19.9 out of 20 (highest grade among all 26 peers)

Thesis title: Minimizing the total completion time for the blocking flow shop scheduling

Advisor: Professor Ghasem Moslehi, Department of Industrial and Systems Engineering, Isfahan University of Technology

Journal publications: 2 COR

In the M.Sc. thesis, I investigated the blocking flow scheduling problem for minimizing the total completion time. The blocking flow shop is a flow shop without any buffer between stages. I developed different exact and heuristic methods for solving this problem.

First, I proposed an iterated greedy algorithm. This algorithm found new best solutions for 88 out of 120 benchmark instances.

Second, I developed a branch and bound algorithm. Some lower bound procedures based on the graph for a specific sequence, and also some dominance rules were applied in this B&B algorithm.  This algorithm found the optimal solutions of 17 instances of Taillard instances in less than 20 minutes.

After defending my thesis and before starting my PhD, I also investigated the limited-buffer flow shop scheduling problem with makespan criterion. The blocking flow shop is a special case of the limited-buffer flow shop.  For this problem, I developed a speed-up method based on the graph of a sequence for increasing the efficiency of local search approaches. I presented a hybrid VNS algorithm that was superior than state-of-the-art algorithms.

Publications

1. Ghasem Moslehi, Danial Khorasanian: Optimizing blocking flow shop scheduling problem with total completion time criterion. Computers & Operations Research, Vol. 40 (7):1874–1883, 2013.

2. Ghasem Moslehi, Danial Khorasanian: A hybrid variable neighborhood search algorithm for solving the limited-buffer permutation flow shop scheduling problem with the makespan criterion. Computers & Operations Research, Vol. 52 (Part B): 260-268, December 2014.