TY  - JOUR
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953189139&doi=10.1142%2fS0219876210002209&partnerID=40&md5=010fefe0f764eacaab00e743bbf759de
SP  - 279
A1  - Vasant, P.
SN  - 02198762
N1  - cited By 60
IS  - 2
JF  - International Journal of Computational Methods
TI  - Hybrid simulated annealing and genetic algorithms for industrial production management problems
EP  - 297
N2  - This paper describes the origin and the significant contribution of the development of the hybrid simulated annealing and genetic algorithms (HSAGA) approach to obtaining global optimization. HSAGA provides an insightful way to solve complex optimization problems. It is a combination of the metaheuristic approaches of simulated annealing and novel genetic algorithms to solving a nonlinear objective function with uncertain technical coefficients in industrial production management problems. The proposed novel hybrid method is designed to search for global optimization for the nonlinear objective function and to search for the best feasible solutions to the decision variables. Simulated experiments were carried out rigorously to reflect the advantages of the method. A description of the well-developed method and the advanced computational experiment with the Matlab® technical tool is presented. An industrial production management optimization problem is solved using the HSAGA technique. The results are very promising. © 2010 World Scientific Publishing Company.
Y1  - 2010///
ID  - scholars1242
VL  - 7
KW  - Complex optimization; Computational experiment; Decision variables; Feasible solution; Hybrid method; Industrial production; Level of satisfaction; Meta-heuristic approach; Nonlinear objective functions; Nonliner objective function; Novel genetic algorithm; Objective functions; Optimal profit; Optimization problems; Simulated experiments; Technical tools
KW  -  Annealing; Decision making; Genetic algorithms; Global optimization; Industrial management; Industry; Profitability
KW  -  Simulated annealing
AV  - none
ER  -