Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Multicriteria optimization of simulation models
WSC '91 Proceedings of the 23rd conference on Winter simulation
Proceedings of the 32nd conference on Winter simulation
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Simulation-based optimization: practical introduction to simulation optimization
Proceedings of the 35th conference on Winter simulation: driving innovation
WSC '04 Proceedings of the 36th conference on Winter simulation
Trade-offs Between Customer Service and Cost in Integrated Supply Chain Design
Manufacturing & Service Operations Management
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Simulation-based multi-objective optimization of a real-world scheduling problem
Proceedings of the 38th conference on Winter simulation
A web-based simulation optimization system for industrial scheduling
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Proceedings of the 40th Conference on Winter Simulation
A simulation-based evolutionary multiobjective approach to manufacturing cell formation
Computers and Industrial Engineering
A Lexicographic Nelder-Mead simulation optimization method to solve multi-criteria problems
Computers and Industrial Engineering
Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine
ACM Transactions on Intelligent Systems and Technology (TIST)
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This paper presents an improved genetic algorithm approach, based on new ranking strategy, to conduct multi-objective optimization of simulation modeling problems. This approach integrates a simulation model with stochastic nondomination-based multiobjective optimization technique and genetic algorithms. New genetic operators are introduced to enhance the algorithm performance of finding Pareto optimal solutions and its efficiency in terms of computational effort. An elitism operator is employed to ensure the propagation of the Pareto optimal set, and a dynamic expansion operator to increase the population size. An importation operator is adapted to explore some new regions of the search space. Moreover, new concepts of stochastic and significant dominance are introduced to improve the definition of dominance in stochastic environments.