Md Shahriar Mahbub

Work place: Ahsanullah Institute of Science and Technology, 141-142 Love road, Dhaka 1208, Bangladesh



Research Interests: Theoretical Computer Science, Computational Science and Engineering, Applied computer science, Computer Science & Information Technology


Md. Shahriar Mahbub obtained his Ph.D. from the University of Trento, Italy. His Ph.D. research work focused on the development of tools for optimizations of energy systems. In his research, he developed a framework for optimizing an energy system for minimizing more than one objective. Currently, he is working as an associate professor in the department of Computer Science and Engineering at Ahsanullah University of Science and Technology, Bangladesh. His current research focuses on applications of multiobjective optimization algorithms and the development of different techniques for the improvements of multi-objective optimization algorithms.

Author Articles
A Multi-Objective Optimization Approach for Solving AUST Classtimetable Problem Considering Hard and Soft Constraints

By Md Shahriar Mahbub Shihab Shahriar Ahmed Kazi Irtiza Ali Md. Taief Imam

DOI:, Pub. Date: 8 Oct. 2020

Preparing a class timetable or routine is a difficult task because it requires an iterative trial and error method to handle all the constraints. Moreover, it has to be beneficial both for the students and teachers. Therefore, the problem becomes a multi-objective optimization problem with a good number of constraints. There are two types of constraints: hard and soft constraint. As the problem is an NP-hard problem, population based multi-objective optimization algorithms (multi-objective evolutionary algorithm) is a good choice for solving the problem. There are well established hard constraints handling techniques for multi-objective evolutionary algorithms, however, the technique is not enough to solve the problem efficiently. In the paper, a smart initialization technique is proposed to generate fewer constraints violated solutions in the initial phase of the algorithm so that it can find feasible solutions quickly. An experimental analysis supports the assumption. Moreover, there are no well-known techniques available for handling soft constraints. A new soft constraints handing technique is proposed. Experimental results show a significant improvement can be achieved. Finally, proposed combined approach integrates smart initialization and soft constraints handling techniques. Better results are reported when comparing with a standard algorithm.

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