Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
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Supply planning optimization is one of the most important issues for manufacturers and distributors. Supply is planned to meet the future demand. Under the uncertainty involved in demand forecasting, profit is maximized and risk is minimized. In order to simulate the uncertainty and evaluate the profit and risk, we introduced Monte Carlo simulation. The fitness function of GA used the statistics of the simulation. The supply planning problems are multi-objective, thus there are several Pareto optimal solutions from high-risk and high-profit to lowrisk and low-profit. Those solutions are very helpful as alternatives for decision-makers. For the purpose of providing such alternatives, a multi-objective genetic algorithm was employed. In practice, it is important to obtain good enough solutions in an acceptable time. So as to search the solutions in a short time, we propose Boundary Initialization which initializes population on the boundary of constrained space. The initialization makes the search efficient. The approach was tested on the supply planning data of an electric appliances manufacturer, and has achieved a remarkable result.