Susmita Bandyopadhyay
I am a Ph.D., M.Tech., MBA, MCA and B.Tech.-IT equivalent diploma holder. I have a total of 13 years of teaching, industrial and research experience. I have several publications in International Journals, International Conferences, in International book series and one book. I am a Reviewer of 6 reputed International Journals. Besides, there are some other achievements.
Subjects: Engineering - Industrial & Manufacturing
Biography
Main research area: Multi-Objective Optimization. Other research areas of Interest: Operations and Supply Chain Management and Operations Research. I have some research publications in these areas which have received appreciation. Research is my passion.Education
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PhD, MTech, MBA, MCA
Areas of Research / Professional Expertise
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Operations Management, Operations Research and Supply Chan Management
Personal Interests
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Conducting Research Studies along with one other extra curricular activity
Books
Articles
On Some Aspects of Nature-Based Algorithms to Solve Multi-Objective Problems
Published: Aug 03, 2014 by Artificial Intelligence, Evolutionary Computation and Metaheuristics, Xin-She Yang (Ed.), Studies in Computational Inte
Authors: Susmita Bandyopadhyay and Ranjan Bhattacharya
Subjects:
Computer Science & Engineering
This chapter presents an overview of various nature-based algorithms to solve multi-objective problems with the particular emphasis on Multi-Objective Evolutionary Algorithms based on Genetic Algorithm. Some of the significant hybridization and the modification of the benchmark algorithms have also been discussed as well. The complexity issues have been outlined and various test problems to show the effectiveness of such algorithms have also been summarized.
Solving a Tri-Objective Supply Chain Problem with modified NSGA-II Algorithm
Published: Jan 01, 2014 by Journal of Manufacturing Systems
Authors: Susmita Bandyopadhyay and Ranjan Bhattacharya
Subjects:
Engineering - Industrial & Manufacturing
We have also proposed a modification of Non-dominated Sorting Genetic Algorithm-II (NSGA-II). We have proposed a mutation algorithm which has been embedded into the modified NSGA-II. Experimental results show that the proposed algorithm performs better than the original NSGA-II and SPEA2 (Strength Pareto Evolutionary Algorithm 2).
A Generalized Measure of Bullwhip Effect in Supply Chain with ARMA Demand Pro
Published: Sep 01, 2013 by International Journal of Advanced Manufacturing Technology
Authors: Susmita Bandyopadhyay and Ranjan Bhattacharya
Subjects:
Engineering - Industrial & Manufacturing
We have derived generalized expressions of Bullwhip Effect (BWE) based on the generalized ARMA (p,q) demand process under the various replenishment policies. The expressions have been compared both algebraically and numerically in order to find out the appropriate replenishment policy that leads to minimum valued expression for BWE.
Applying Modified NSGA-II for Bi-Objective Supply Chain Problem
Published: Aug 01, 2013 by Journal of Intelligent Manufacturing Systems
Authors: Susmita Bandyopadhyay and Ranjan Bhattacharya
Subjects:
Engineering - Industrial & Manufacturing
This paper minimizes the value of total cost and bullwhip effect in a supply chain. The objectives have been achieved through developing a new multi-objective formulation for minimizing the total cost and minimizing the bullwhip effect of a two-echelon serial supply chain. A new crossover algorithm for a fuzzy variable and a new mutation algorithm have also been proposed while applying Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to the proposed problem.
Solving Multi-Objective Parallel Machine Scheduling Problem by a Modified NSG
Published: Jun 01, 2013 by Applied Mathematical Modelling
Authors: Susmita Bandyopadhyay and Ranjan Bhattacharya
Subjects:
Engineering - Industrial & Manufacturing
In this paper, we modify a Multi-Objective Evolutionary Algorithm, known as Nondominated sorting Genetic Algorithm-II (NSGA-II) for a parallel machine scheduling problem with three objectives. The formulated problem has been solved by three Multi-Objective Evolutionary Algorithms. A new mutation algorithm has also been proposed depending on the type of problem and embedded in the modified NSGA-II.
Finding Optimum Neighbor for Routing Based on Multi-Criteria, Multi-Agent and
Published: Mar 01, 2013 by Journal of Intelligent Manufacturing
Authors: Susmita Bandyopadhyay and Ranjan Bhattacharya
Subjects:
Engineering - Industrial & Manufacturing
In this paper, a hierarchical multi-agent based routing strategy based on Tarantula mating behavior has been introduced. The approach in this paper routes a job to the next optimum neighboring node from the current position, instead of deciding over the entire path before the journey begins.
NSGA-II Based Multi-Objective Evolutionary Algorithm for a Multi-Objective S
Published: Mar 01, 2012 by IEEE-International Conference on Advances in Engineering, Science and Management 2012
Authors: Susmita Bandyopadhyay and Ranjan Bhattacharya
Subjects:
Engineering - Industrial & Manufacturing
This paper proposes a NSGA-II based multi-objective evolutionary algorithm and applies the proposed algorithm on a novel multi-objective problem for a two echelon serial supply chain.
A review of the causes of bullwhip effect in a supply chain
Published: Jun 01, 2011 by International Journal of Advanced Manufacturing Technology
Authors: Ranjan Bhattacharya and Susmita Bandyopadhyay
Subjects:
Engineering - Industrial & Manufacturing
A review of the past research studies on the causes of bullwhip effect is presented in this paper. This paper is an effective study from the point of view that it presents a detailed classified study of the overall research studies on the effect of both the operational and the behavioral factors on bullwhip effect. A total of 19 causes of bullwhip effect have been shown here. We have also identified the various gaps of research in the past research studies.
Solving conflicting bi-objective facility location problem by NSGA II evoluti
Published: Nov 01, 2010 by International Journal of Advanced Manufacturing Technology
Authors: Ranjan Bhattacharya and Susmita Bandyopadhyay
Subjects:
Engineering - Industrial & Manufacturing
This paper focuses on the facility location problem with two conflicting objectives. We observe that minimization of the total cost of a particular echelon may lead to the increase in the total cost of a supply chain as a whole. We have solved the problem formulated in mixed nonlinear programming by a multi-objective evolutionary algorithm (MOEA) known as non-dominated sorting algorithm.
An improved strategy to solve shop deadlock problem based on the study on existi
Published: Mar 01, 2010 by International Journal of Advanced Manufacturing Technology
Authors: Ranjan Bhattacharya and Susmita Bandyopadhyay
Subjects:
Engineering - Industrial & Manufacturing
We study the effect of various benchmark deadlock recovery strategies, and we find that each of these recovery strategies either results into another kind of deadlock or results into very high handling cost and long handling time. We also propose a new improved strategy in order to solve the shop deadlock problem through the use of both automated guided vehicle and a central buffer.