Ph.D, M.Tech, BE

Dr. T. Srinivas Rao received his B. E. from College of Engineering, Sewagram, Wardha and M. Tech. in Industrial Engineering from Visvesraya National Institute of Technology, Nagpur (Formerly REC) in 1988 and 1997. He obtained his Ph. D. from Visvesvaraya Technological University, Belgaum for his thesis titled ”Design of Aggergate Material Planning in a Supply Chain Network. Currently he is guiding one student in the area of Reverse Logistics. He has also served in various Mechanical industries from the period 1988 to 1994. At present, he is working as an Associate Professor in the department of Mechanical Engineering, Amrita School of Engineering, Bengaluru.


  • PhD : Mechanical Engineering Sciences from Visvesvaraya Technological University, Belgaum for the thesis titled Design of Aggregate Material Planning in a Supply Chain Network, September 2012.
  • MTech : Industrial Engineering Visvesvariah National Institute of Technology, (REC) Nagpur, June 1997.                                                                                                   
  • BE : Production Engineering, College of Engineering Sewagram, Wardha, Nagpur University. October 1988.


Publication Type: Conference Paper

Year of Publication Title


Dr. T. Srinivas Rao and Gopi, S., “Solving a Reverse Supply Chain TSP by Genetic Algorithm”, in ICMME 2015 conference organized by SCSVM university Kanchipuram , 2015.[Abstract]

Stringent legislative, social concerns & clean carbon emissions are constraining companies to take a fresh look at the impact of supply chain operations on environment, society and individuals when designing reverse supply chain networks. A challenging task in today’s globalised environment where companies mandatorily have to collect back goods after its reliable life is making companies integrate supply chain decisions objective here is to minimize transportation cost and distance. In this paper problem of designing a TSP (Travelling Sales man problem) is addressed which is one of the NP-hard problem in combinatorial optimization. Computational experiments conducted with GA (Genetic algorithm) on large and small size TSP cases where compared with NNA (Nearest neighboring algorithm) and have proved that the GA provides optimal tour every time in reasonable time by outperforming the NNA solution when number of cities are increased.

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Dr. T. Srinivas Rao, R., N. N. V., and Mallikharjuna, B., “Design of Web Based Aggregation of Materials in a supply chain network using genetic Algorithm”, in 29 th International Conference on Statistics, Probability, Operations research organized by ISPS( Indian Society for Probability & Statistics ), Andhra University, Vishakhapatnam, 2010.


Dr. T. Srinivas Rao, R., N. N. V., and Mallikharjuna, B., “Design of Web Based Aggregation of Materials in a supply chain network”, in International Conference on Operations research for Growing Nation &41st Annual Convention of Operational Research Society of India, 2008.


N. N.V.R., Dr. T. Srinivas Rao, and Mallikharjuna, B., “Multi Attribute Combinatorial Algorithms To Aggregate Raw Materials In A Supply Chain Network ”, in 26th Convention of International Conference of Indian Society for Statistics & Probability , Organized by Dravidian University, Held at Tirupati , 2007.

Publication Type: Journal Article

Year of Publication Title


Dr. T. Srinivas Rao, K. Babu, M., and N.V.R., N., “An extensive analysis of optimal suppliers with better purchasing period for customers in web based material demand aggregation”, IJCEE( International Journal for Computer Science Engineering & Electrical Engineering, vol. 2, no. 1, pp. 1793-8163 , 2010.[Abstract]

In today’s modern trading world, web based material demand aggregation has emerged as a significant and lively research front in the supply chain management. In common, the Demand Aggregation technique synchronizes and unites the requirements of the buyers with the active participation of a web based agent to offer a lucid depiction of purchasing requests throughout the enterprise. The objective of utilizing material demand aggregation is to offer services to all the buyers (manufacturers) in a cost effective manner. The web based agent is responsible for fulfilling this ultimate goal of web based material demand aggregation. It discovers the optimal suppliers who can supply the raw materials in a cost effective manner as manufacturers may require raw materials in different quantities and different suppliers are likely to offer materials at different slab rates. Moreover, the dilemma of web based agent increases as each manufacturer may require the raw materials at different instants of time. If the web based agent works only with the intention of enjoying more slab rates, it drives all the manufacturers to purchase at a single instant of time, on account of which some of the manufacturers will be affected because of high inventory costs incurred. Equally, if the manufacturers are suggested to purchase the materials just in time to avoid the inventory costs, they will miss the offer of slab rates leading to high purchasing cost. Thereby, we propose an analytical model which will pin down the optimal period of purchasing for all the manufacturers so that they can avoid the high inventory cost incurred and can also enjoy a good slab rate. With the aid of the proposed analytical model designed to achieve optimal purchasing period, the web based agent can guide all the manufacturers to purchase the required raw materials in a cost effective manner. More »»


Dr. T. Srinivas Rao, Naidu, N. V. R., and K. Babu, M., “A Novel and Efficient Approach for Material Demand Aggregation using Genetic Algorithm”, IJCSNS (International Journal of Computer Science and Network Security), vol. 9, pp. 203 – 212 , 2009.