Shihab Shahriar Ahmed

Work place: Enosis Solutions, Banani, Dhaka 1212, Bangladesh



Research Interests:


Shihab Shahriar has been working as a software developer for almost three years. He has completed his graduation in CSE from Ahsanullah University of Science and Technology in late 2018. He has worked as an adjunct lecturer in the same university. He wants to see himself as a successful entrepreneur and so he is working accordingly. is one of the developing websites that he initiates. His favourite pastime includes playing football, gaming etc.

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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