Chandrasekar.K
@psncet.ac.in
Professor, Aeronautical Engineering
PSN College of Engineering and Technology
RESEARCH, TEACHING, or OTHER INTERESTS
Mechanical Engineering, Industrial and Manufacturing Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Jinka Rupesh Kumar, K. Mayandi, S. Joe Patrick Gnanaraj, K. Chandrasekar, and P. Sethu Ramalingam
Elsevier BV
R. Nafeena, M. Ettappan, M. Monica, K. Chandrasekar, M. Rathi, and A. Abdul Munaf
IOP Publishing
Abstract For the optimal replacement of phasor measuring units (PMUs) in the presence of conventional measurements, the integrated linear programming (ILP) and genetic algorithms are proposed. In fact, the power system remains fully measurable for the lines and measuring instruments during all conceivable single contingencies. In doing so, a process of equations entirely utilizes the capability of circuit equations related to standard measurements and PMUs, and the system topology, to attain the minimum possible amount of required PMUs.
S. Joe Patrick Gnanaraj, V. Balasubramanian, A. Abdul Munaf, V. Mago Stalany, K. Chandrasekar, and M. Saravana Kumar
Elsevier BV
P. Sethu Ramalingam, K. Mayandi, V. Balasubramanian, K. Chandrasekar, V. Mago Stalany, and A. Abdul Munaf
Elsevier BV
Dr. Nafeena R.
Institute of Advanced Scientific Research
V. Rajagopal, K. Chandrasekar, and M. Victor Raj
Inderscience Publishers
A large number of non–traditional search algorithms are available for function optimisation. The cell formation problem is the important step in the design of a cellular manufacturing system. The objective is to identify part families and machine groups and consequently to form manufacturing cells with respect to minimising the number of exceptional elements. An efficient tabu search (TS) algorithm is proposed to solve cell formation problem because it perform considerable search before terminating to provide a good solution to the problem. In this work the implementation of tabu search for the design of cell formation problem and minimise the number of exceptional elements has been done by this method and it is compared with other existing methods.
P. Sethu Ramalingam, K. Chandrasekar, and M. Victor Raj
Inderscience Publishers
Flexible jobshop scheduling problem (FJSP) is an extended traditional jobshop scheduling problem, which more approximates to practical scheduling problems. This paper presents a genetic algorithm–based (GA) algorithm to solve the multi objective FJSP. Flexible jobshop manufacturing system (FJMS) is a complex network of processing, inspecting, and buffering nodes connected by system of transportation mechanisms. For an FJMS, it is desirable to be capable to increase or decrease the output with the rise and fall of demand. Such specifications show the complexity of decision making in the field of FJMSs and the need for concise and accurate modelling methods. Therefore, in this paper, an AGV–based flexible jobshop automated manufacturing system is considered to optimise the material flow and makespan. The flexibility is on the multishops of the same type and also multiple products that can be produced. An automated guided vehicle is applied for material handling. The objective is to optimise the material flow regarding the demand fluctuations and machine specifications and the makespan. An illustrative example is adopted from the literature to test the validity of the proposed algorithm.
Karuppasamy Chandrasekar and Ponnusamy Venkumar
ACTA Press
K. Chandrasekar and P. Venkumar
Inderscience Publishers
Cellular manufacturing system (CMS) is based on the principle of similar things should be done similarly. Cell formation (CF), within cell machine layout design and cell layout design are important steps in design of CMS. The existing models for solving CMS problems are focused mainly on CF. The design of machine layout and cell layout are considered in few research papers. The most of existing research papers have used binary data for CFs. They do not focus on production volume, operational sequences, production cost, inventory and other production data. In this research work, hierarchical genetic algorithm (HGA) approach is used to solve the CF, within cell machine layout design and cell layout design. The input data for this design of CMS is machine-part incidence matrix with operational sequence. The grouping efficiency and the grouping efficacy are used to measure the effectiveness of the CMS design. The results and consistency of the HGA are compared with earlier approaches and the results are presented. The proposed algorithm gives best results when compared to the earlier approaches.