Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
UKSIM '09 Proceedings of the UKSim 2009: 11th International Conference on Computer Modelling and Simulation
Advances in Engineering Software
Fuzzy multi-objective programming model for logistics service supplier selection
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Computers & Mathematics with Applications
Improved NSGA-II Algorithm for Optimization of Constrained Functions
MVHI '10 Proceedings of the 2010 International Conference on Machine Vision and Human-machine Interface
An efficient multi-objective HBMO algorithm for distribution feeder reconfiguration
Expert Systems with Applications: An International Journal
DCABES '10 Proceedings of the 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science
A weighted additive fuzzy programming approach for multi-criteria supplier selection
Expert Systems with Applications: An International Journal
Improved differential evolution approach for optimization of surface grinding process
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Journal of Intelligent Manufacturing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
Multi-objective optimization of facility planning for energy intensive companies
Journal of Intelligent Manufacturing
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This paper minimizes the value of total cost and bullwhip effect in a supply chain. The objectives have been achieved through developing a new multi-objective formulation for minimizing the total cost and minimizing the bullwhip effect of a two-echelon serial supply chain. A new crossover algorithm for a fuzzy variable and a new mutation algorithm have also been proposed while applying Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to the proposed problem. The formulated problem has been simulated by Matlab software and the results of the modified NSGA-II have been compared with those of original NSGA-II. It is found from the results that the modified NSGA-II algorithm performs better than the original NSGA-II algorithm since the minimum values for both total cost and the bullwhip effect are obtained in case of the modified NSGA-II. The formulated bi-objective problem is new to the research community. The minimization of bullwhip effect has never been considered in a multi-objective optimization before. Besides crossover operator applied to the fuzzy variable and the mutation operator are newly introduced operators.