Almost all problems in manufacturing are multi/many-objective by nature, because of the constraints of money and resources. Multi-objective optimization can help to answer the following questions: What is the minimum investment cost to achieve the target capacity? What is the minimum number of buffers and where to put them (optimal buffer allocations) to achieve the target capacity? What are the minimum changes and where in order to improve the overall capacity of the line (a new definition of bottleneck analysis)?
Unlike many discrete-event simulation packages whereas optimization is an add-on feature, simulation-based optimization (SBO) is an integral module of FACTS Analyzer that facilitates managers/engineers to run Artificial-Intelligence-based optimizations in order to seek the optimal combinations of design variables to support more confident decision making in the design and improvement of production systems. The integrated SBO module renders the possibility for any variables in a model to be optimized, instead of like the case in many commercial discrete-event simulation software in which optimization variables have to be added in form of programming code. FACTS Analyzer also inherently supports multi-objective optimization and post-optimality analyses of the generated Pareto-optimal solutions so that deeper knowledge can be gained, for instance, on the relationships of the key influencing variables on multiple optimization objectives.
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