Academic mail ID: [email protected]
||Master of Science (Research)
||Universiti Teknologi PETRONAS, Malaysia
||Bachelor of Science
||Bangladesh University of Textiles
|Graduate Teaching Assistant
||Universiti Teknologi Petronas (UTP)
||Apr 2019 – Apr 2022
||Crystal Industrial Bangladesh Ltd.
||Jun 2017-Dec 2017
- Nature-Inspired Metaheuristic Techniques for Combinatorial Optimization Problems: Overview and Recent Advances
Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been implemented in a wide area of combinatorial optimization problems. Metaheuristic algorithms have been evolved and modified with respect to the problem nature since it was recommended for the first time. As there is a growing interest in incorporating necessary methods to develop metaheuristics, there is a need to rediscover the recent advancement of metaheuristics in combinatorial optimization. From the authors’ point of view, there is still a lack of comprehensive surveys on current research directions. Therefore, a substantial part of this paper is devoted to analyzing and discussing the modern age metaheuristic algorithms that gained popular use in mostly cited combinatorial optimization problems such as vehicle routing problems, traveling salesman problems, and supply chain network design problems. A survey of seven different metaheuristic algorithms (which are proposed after 2000) for combinatorial optimization problems is carried out in this study, apart from conventional metaheuristics like simulated annealing, particle swarm optimization, and tabu search. These metaheuristics have been filtered through some key factors like easy parameter handling, the scope of hybridization as well as performance efficiency. In this study, a concise description of the framework of the selected algorithm is included. Finally, a technical analysis of the recent trends of implementation is discussed, along with the impacts of algorithm modification on performance, constraint handling strategy, the handling of multi-objective situations using hybridization, and future research opportunities.
- Genetic Algorithm and Particle Swarm Optimization Techniques in Supply Chain Design Problems: A Survey
Metaheuristics has become a top research area. Numerous optimization problems have been solved by metaheuristics as they showed comprehensive improvements to solve these intractable optimization problems. Complex problems like supply chain design problems need strategic decisions, and metaheuristics can intensify the decisions while designing supply chain network. In this chapter, the authors have introduced how nature memetic algorithms (e.g., genetic algorithm and particle swarm algorithms) are implemented to solve supply chain network design problem. A discussion about the recent research in this field shows an important direction to the future research.
- A hybrid multi-objective cellular spotted hyena optimizer for wellbore trajectory optimization
Cost and safety are critical factors in the oil and gas industry for optimizing wellbore trajectory, which is a constrained and nonlinear optimization problem. In this work, the wellbore trajectory is optimized using the true measured depth, well profile energy, and torque. Numerous metaheuristic algorithms were employed to optimize these objectives by tuning 17 constrained variables, with notable drawbacks including decreased exploitation/exploration capability, local optima trapping, non-uniform distribution of non-dominated solutions, and inability to track isolated minima. The purpose of this work is to propose a modified multi-objective cellular spotted hyena algorithm (MOCSHOPSO) for optimizing true measured depth, well profile energy, and torque. To overcome the aforementioned difficulties, the modification incorporates cellular automata (CA) and particle swarm optimization (PSO). By adding CA, the SHO’s exploration phase is enhanced, and the SHO’s hunting mechanisms are modified with PSO’s velocity update property. Several geophysical and operational constraints have been utilized during trajectory optimization and data has been collected from the Gulf of Suez oil field. The proposed algorithm was compared with the standard methods (MOCPSO, MOSHO, MOCGWO) and observed significant improvements in terms of better distribution of non-dominated solutions, better-searching capability, a minimum number of isolated minima, and better Pareto optimal front. These significant improvements were validated by analysing the algorithms in terms of some statistical analysis, such as IGD, MS, SP, and ER. The proposed algorithm has obtained the lowest values in IGD, SP and ER, on the other side highest values in MS. Finally, an adaptive neighbourhood mechanism has been proposed which showed better performance than the fixed neighbourhood topology such as L5, L9, C9, C13, C21, and C25. Hopefully, this newly proposed modified algorithm will pave the way for better wellbore trajectory optimization.
- Metaheuristic Approaches To Optimize Supply Chain Design Problems
Every manufacturing process in this industry booming world requires cost effective and precise design of supply chain. Eventually, the population demand as well as the sustainability issues have impacted on overall supply chain network system. As like as other optimization fields like energy, system, the supply chain network optimization has become an indispensable part of every industry. In this research authors will try to concentrate on an efficient optimization method in a supply chain planning (SCP). At first, authors would demonstrate a supply chain model consisting of suppliers, assemblers, distribution centers and retailers. This model would be expressed as MIXED INTEGER NON- LINEAR PROGRAMMING MODEL firstly and two widely used metaheuristic technique e.g. Particle Swarm Optimization (PSO) and Teaching Learning based Optimization (TLBO) have been implemented to optimize that model. Core objective of this optimization model concludes cost minimization with respect to the service level in every echelon of supply chain. In the comparison between PSO and TLBO, TLBO shows better optimality in this minimization problem.
- As a co-author accomplished a research grant worth 90K RM from the Yayasan Universiti Teknologi PETRONAS Research grant scheme. Project title “Enhanced Deep Learning Framework for the High-Dimensional Sparse Data”
- Awarded Graduate Assistantship (GA) from Universiti Teknologi PETRONAS (UTP) 2019-2021
- Received GA merit awards for publishing high-impact research papers from Universiti Teknologi PETRONAS (UTP) in 2021
- Awarded honorary scholarship from Education board, Comilla, Bangladesh for obtaining extraordinary results in Secondary School Certificate exam -2009 and Higher Secondary Certificate exam- 2011
- Ranked Runner up at “Dutch Bangla Bangladesh Mathematical Olympiad – 2010” in the higher secondary category.