Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Supply-Chain Analysis at Volkswagen of America
Interfaces - supply-chain management
Proceedings of the 32nd conference on Winter simulation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Proceedings of the 33nd conference on Winter simulation
Proceedings of the 35th conference on Winter simulation: driving innovation
Proceedings of the 38th conference on Winter simulation
International Journal of Business Information Systems
Proceedings of the 40th Conference on Winter Simulation
International Journal of Intelligent Systems Technologies and Applications
Hybrid algorithm for discrete event simulation based supply chain optimization
Expert Systems with Applications: An International Journal
Evolutionary multiobjective optimization in noisy problem environments
Journal of Heuristics
A simulation-optimization approach for integrated sourcing and inventory decisions
Computers and Operations Research
An intelligent algorithm for modeling and optimizing dynamic supply chains complexity
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Modeling supply chain complexity using a distributed multi-objective genetic algorithm
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
Optimization of distribution of centers in the supply chain using genetic algorithms
ACMIN'12 Proceedings of the 14th international conference on Automatic Control, Modelling & Simulation, and Proceedings of the 11th international conference on Microelectronics, Nanoelectronics, Optoelectronics
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A critical decision companies are faced with on a regular basis is the ordering of products and/or raw materials. Poor decisions can lead to excess inventories that are costly or to insufficient inventory that cannot meet its customer demands. These decisions may be as simple as "How much to order" or "How often to order" to more complex decision forecasting models. This paper addresses optimizing these sourcing decisions within a supply chain to determine robust solutions. Utilizing an existing supply chain simulator, an optimization methodology that employs genetic algorithms is developed to optimize system parameters. The performance measure that is optimized plays a very important role in the quality of the results. The deficiencies in using traditionally used performance measures in optimization are discussed and a new multi-objective GA methodology is developed to overcome these limitations.