Empirical model-building and response surface
Empirical model-building and response surface
An experimental procedure for simulation response surface model identification
Communications of the ACM
Simulating computer systems: techniques and tools
Simulating computer systems: techniques and tools
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A tutorial on simulation optimization
WSC '92 Proceedings of the 24th conference on Winter simulation
Queueing networks with blocking
Queueing networks with blocking
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Simulation with visual SLAM and AweSim
Simulation with visual SLAM and AweSim
Simulation optimization: methods and applications
Proceedings of the 29th conference on Winter simulation
Multicriteria optimization of simulation models
WSC '91 Proceedings of the 23rd conference on Winter simulation
Optimization in simulation: a survey of recent results
WSC '87 Proceedings of the 19th conference on Winter simulation
SAS/ETS User's Guide, Version 6
SAS/ETS User's Guide, Version 6
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Simulation of Local Area Networks
Simulation of Local Area Networks
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Simulation of Computer Communication Systems
Simulation of Computer Communication Systems
Strategies for optimization of multiple-response simulation models
WSC '77 Proceedings of the 9th conference on Winter simulation - Volume 1
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Soft computing for softgoods supply chain analysis and decision support
Soft computing in textile sciences
EVALUATING PERFORMANCE OF FLOW LINE SYSTEMS WITH BLOCKING UNDER FUZZY ENVIRONMENTS
Cybernetics and Systems
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Simulation optimization deals with finding the values of input parameters of a complex simulated system which result in desired output, Traditional techniques may require an enormous amount of simulation runs to evaluate the system. To alleviate this problem, the proposed work provides the means of incorporating knowledge, expressed in natural language, that is often available among analysts and decision makers. Using convenient linguistic representations, the proposed mechanism can satisfy vaguely stated goals to a high degree (e.g. "high utilization" or "low inventory"). This mechanism is also able to generate an approximate Pareto optimal set in the presence of multiple goals. The optimization strategy used here depends on a fuzzy controller guided by a set of rules derived from statistical concepts, response surface models, and experts' knowledge. To illustrate this approach we present computational experiments on the design of a flow line manufacturing system (in terms of a tandem of queues with blocking) with one and two goals. The actions derived from the controller, using approximate reasoning, are able to generate a high quality solution in only a few iterations. The results are compared extensively with those obtained with a genetic-based approach.