@article{scholars1242,
            year = {2010},
         journal = {International Journal of Computational Methods},
           pages = {279--297},
          number = {2},
          volume = {7},
            note = {cited By 60},
             doi = {10.1142/S0219876210002209},
           title = {Hybrid simulated annealing and genetic algorithms for industrial production management problems},
            issn = {02198762},
          author = {Vasant, P.},
        abstract = {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{\^A}(R) technical tool is presented. An industrial production management optimization problem is solved using the HSAGA technique. The results are very promising. {\^A}{\copyright} 2010 World Scientific Publishing Company.},
        keywords = {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, Annealing; Decision making; Genetic algorithms; Global optimization; Industrial management; Industry; Profitability, Simulated annealing},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953189139&doi=10.1142\%2fS0219876210002209&partnerID=40&md5=010fefe0f764eacaab00e743bbf759de}
}