Pandian Vasant

Innovation in Power, Control, and Optimization: Emerging Energy Technologies

Pandian Vasant was born in Sungai Petani, Malaysia in 1961. Currently, he is a Senior Lecturer of Engineering Mathematics for Electrical & Electronics Engineering Program and Fundamental & Applied Sciences Department at University Teknologi Petornas in Tronoh, Perak, Malaysia. He has graduated in 1986 from University of Malaya (MY) in Kuala Lumpur, obtaining his BSc Degree with Honors (II Class Upper) in Mathematics, and in 1988 also obtained a Diploma in English for Business from Cambridge Tutorial College, Cambridge, England. In the year 2002 he has obtained his MSc (by research) in Engineering Mathematics from the School of Engineering & Information Technology of University of Malaysia Sabah, Malaysia, and has earned a Doctoral Degree (2008) from University Putra Malaysia in Malaysia. After graduation, during 1987-88 he was Tutor in operational research at University Science Malaysia in Alor Setar, Kedah and during from 1989-95 he was teacher of Engineering Mathematics at the same university but with Engineering Campus at Tronoh, Perak. Thereafter during from 1996-2003 he became a lecturer in Advanced Calculus and Engineering Mathematics at Mara University of Technology, in Kota Kinabalu. He became Senior Lecturer of Engineering Mathematics in American Degree Program at Nilai International College, Nilai (MY), during 2003-2004 before taking his present position at University Teknologi Petronas in Tronoh. His main research interests are in the areas of optimization methods and applications to decision making and industrial engineering, fuzzy optimization, computational intelligence, and hybrid soft computing. Vasant has published seven articles in national journals and another fifty in international journals and book chapters, and more than eighty in international and national conference proceedings. He has been serving on TC-9.3 (Developing Countries) as a group initiator and Vice Chair for Asia from September 2004 July 2011. Currently he’s a reviewer for some reputed international journals and conference proceedings. Pandian Vasant (University Technology Petronas, Malaysia), Nadar Barsoum (Curtin University, Malaysia) and Jeffrey Webb (Swinburne University of Technology, Malaysia)

A multi-objective evolutionary approach for fuzzy optimization in production planning

This paper outlines, first, a real-world industrial problem for product-mix selection involving 8 variables and 21 constraints with fuzzy coefficients and thereafter, a multi-objective optimization approach to solve the problem. This problem occurs in production planning in which a decisionmaker plays a pivotal role in making decision under fuzzy environment. Decision-maker should be aware of his/her level-of-satisfaction as well as degree of fuzziness while making the product-mix decision. Thus, the authors have analyzed using a modified S-curve membership function the fuzziness patterns and fuzzy sensitivity of the solution found from the multi-objective optimization methodology. An ad hoc Pareto-based multi-objective evolutionary algorithm is proposed to capture multiple non dominated solutions in a single run of the algorithm. Results obtained have been compared with the well-known multi-objective evolutionary algorithm NSGA-II.

A Multi-Objective Evolutionary Approach for Fuzzy Optimization in Production Planning

This paper outlines, first, a real-world industrial problem for product-mix selection involving 8 variables and 21 constraints with fuzzy coefficients and thereafter, a multi-objective optimization approach to solve the problem. This problem occurs in production planning in which a decision-maker plays a pivotal role in making decision under fuzzy environment. Decision-maker should be aware of his/her level-of-satisfaction as well as degree of fuzziness while making the product-mix decision. Thus, the authors have analyzed using a modified S-curve membership function the fuzziness patterns and fuzzy sensitivity of the solution found from the multi-objective optimization methodology. An ad hoc Pareto-based multi-objective evolutionary algorithm is proposed to capture multiple non dominated solutions in a single run of the algorithm. Results obtained have been compared with the well-known multi-objective evolutionary algorithm NSGA-II.

HYBRID SIMULATED ANNEALING AND GENETIC ALGORITHMS FOR INDUSTRIAL PRODUCTION MANAGEMENT PROBLEMS

This paper describes the origin and significant contribution on the development of the Hybrid Simulated Annealing and Genetic Algorithms (HSAGA) approach for finding global optimization. HSAGA provide an insight approach to handle in solving complex optimization problems. The method is, the combination of meta‐heuristic approaches of Simulated Annealing and novel Genetic Algorithms for solving a non‐linear objective function with uncertain technical coefficients in an industrial production management problems. The proposed novel hybrid method is designed to search for global optimal for the non‐linear objective function and search for the best feasible solutions of the decision variables. Simulated experiments were carried out rigorously to reflect the advantages of the proposed method. A description of the well developed method and the advanced computational experiment with MATLAB technical tool is presented. An industrial production management optimization problem is solved using HSAGA technique. The re...

Meta-Learning evolutionary artificial neural network for selecting flexible manufacturing systems

This paper proposes the application of Meta-Learning Evolutionary Artificial Neural Network (MLEANN) in selecting flexible manufacturing systems (FMS) from a group of candidate FMS’s. First, multi-criteria decisionmaking (MCDM) methodology using an improved S-shaped membership function has been developed for finding out the ‘best candidate FMS alternative’ from a set of candidate-FMSs. The MCDM model trade-offs among various parameters, namely, design parameters, economic considerations, etc., affecting the FMS selection process in multi-criteria decision-making environment. Genetic algorithm is used to evolve the architecture and weights of the proposed neural network method. Further, a back-propagation (BP) algorithm is used as the local search algorithm. The selection of FMS is made according to the error output of the results found from the MCDM model.

HYBRID GENETIC AGORITHMS AND LINE SEARCH METHOD FOR INDUSTRIAL PRODUCTION PLANNING WITH NON‐LINEAR FITNESS FUNCTION

Many engineering, science, information technology and management optimization problems can be considered as non linear programming real world problems where the all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research paper is to solve non linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers which was represented by logistic membership functions by using hybrid evolutionary optimization approach. To explore the applicability of the present study a numerical example is considered to determine the production planning for the decision variables and profit of the company.

Using ILOG OPL‐CPLEX and ILOG Optimization Decision Manager (ODM) to Develop Better Models

This session will provide an in‐depth overview on building state‐of‐the‐art decision support applications and models. You will learn how to harness the full power of the ILOG OPL‐CPLEX‐ODM Development System (ODMS) to develop optimization models and decision support applications that solve complex problems ranging from near real‐time scheduling to long‐term strategic planning. We will demonstrate how to use ILOG’s Open Programming Language (OPL) to quickly model problems solved by ILOG CPLEX, and how to use ILOG ODM to gain further insight about the model. By the end of the session, attendees will understand how to take advantage of the powerful combination of ILOG OPL (to describe an optimization model) and ILOG ODM (to understand the relationships between data, decision variables and constraints).