Proceedings of the 30th 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
WSC '04 Proceedings of the 36th conference on Winter simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Simulation based sales forecasting on retail small stores
Proceedings of the 40th Conference on Winter Simulation
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This paper describes an innovative framework, iFAO-Simo, which integrates optimization, simulation and GIS (geographic information system) techniques to handle complex spatial facility network optimization problems ever challenged from retailing, banking and logistics nowadays. At the top level of iFAO-Simo, an optimization engine serves to generate and test candidate solutions iteratively by use of optimization algorithms such as Tabu Search and Genetic Algorithms. For each scenario given by the candidate solutions, a discrete event simulation engine is triggered to simulate customer and facility behaviors based on a GIS platform to characterize and visualize the spatial, dynamic and indeterministic environments. As the result, the target measures can be easily calculated to evaluate the solution and feedback to the optimization engine. This paper studies a real case of banking branch network optimization problem, and the results show that iFAO-Simo provides a useful way to handle complex spatial optimization problems.