Hybrid Line Search and Simulated Annealing For Production Planning System in Industrial Engineering

Hybrid Line Search and Simulated Annealing For Production Planning System in Industrial Engineering

P. Vasant (University Technology Petronas, Tronoh, Perak, Malaysia)
DOI: 10.4018/ijmmme.2014040101
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In this paper, the details on the optimization methods such as line search and simulated annealing for the local and global optimization have been highlighted. The hybrid line search and simulated annealing technique has been extensively explored in this research work. In this research the hybridization of line search and simulated annealing method provides satisfactory outcomes for the production planning problem in an uncertain environment. The major advantages and disadvantages of line search and simulated annealing also provided in this paper. The results for industrial production problem have been obtained successfully in the form of 2D and 3D plots.
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Line Search (Ls)

In this article, the focus is on line search method in solving industrial production planning problems. The main advantage of this method is its ability to locate the near global optimal solutions for the fitness function with his strong criteria of global convergence. The line search method used fmincon approach from MATLAB computational toolbox. FMINCON is a gradient-based method that designed to work on problems where the objective and constraint functions are both continuous and have continuous first derivatives. Function with continuous first and second derivatives are suitable for the optimization process because the algorithm uses gradient-based methods. FMINCON uses a sequential quadratic programming (SQP) method. In this method, the function solves a quadratic programming (QP) of sub-problem at each iteration. A line search performed using a merit function similar to that proposed by Powel (1983). The QP sub-problem solved using an active set strategy similar to that described in Powel (1983). A full description of this algorithm found in Constrained Optimization in Standard Algorithms (Powel, 1983).

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