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For complex multi-source,multi-product,multi-stage Supply chain network(SCN)design problem,we propose an optimization supply chain network model.We consider cash conversion cycle as an objective to this model and utilize a modified genetic algorithm to solve the problem.To describe the structure of supply chain network,we propose a new encoding method and a genetic algorithm with modified genetic operators.We use the Pareto approach to obtain the set of Pareto-optimal solutions.In order to evaluate the performance of the modified genetic algorithm and validate the model,we conduct comparisons with standard genetic algorithm and the simulated annealing genetic algorithm.Experimental results show that the modified genetic algorithm achieved better CPU time and the accuracy of the Pareto-optimal solutions than the alternative algorithms and the model was effective.
For complex multi-source, multi-product, multi-stage Supply chain network (SCN) design problem, we propose an optimization supply chain network model. We consider cash conversion cycle as an objective to this model and utilize a modified genetic algorithm to solve the problem. To describe the structure of supply chain network, we propose a new encoding method and a genetic algorithm with modified genetic operators. We use the Pareto approach to obtain the set of Pareto-optimal solutions. Order to evaluate the performance of the modified genetic algorithm and validate the model, we conduct comparisons with standard genetic algorithm and the simulated annealing genetic algorithm. Experimental results show that the modified genetic algorithm achieved better CPU time and the accuracy of the Pareto-optimal solutions than the alternative algorithms and the model was effective.