An introduction to possibilistic and fuzzy logics
Readings in uncertain reasoning
VLSI cell placement techniques
ACM Computing Surveys (CSUR)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A genetic algorithm for macro cell placement
EURO-DAC '92 Proceedings of the conference on European design automation
Algorithmic aspects of three dimensional MCM routing
DAC '94 Proceedings of the 31st annual Design Automation Conference
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Complexity theory and design automation
DAC '80 Proceedings of the 17th Design Automation Conference
A quantitative approach to the plant layout problem using genetic algorithms
IEA/AIE'93 Proceedings of the 6th international conference on Industrial and engineering applications of artificial intelligence and expert systems
Hybrid genetic algorithms for constrained placement problems
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Robotics and Computer-Integrated Manufacturing
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Designing technical plants is a complex and demanding process. It has been shown that the optimization of the simple facility placement problem is already NP-hard. Optimization of plant designs must obey a number of criteria derived from several fields of process engineering. We discuss an expansion of the simple facility placement problem with non-regular floor spaces and more than one layer. Additionally, we allow forbidden zones and predefined ways. In contrast to other approaches our system can cope with competitive criteria. These can be defined by a plant designer in an intuitive way according to concepts from fuzzy logic. This leads to the multiobjective optimization of costs and fulfillment of weighted design rules. We describe an evolutionary algorithm to construct Pareto-optimal blueprints of chemical plants. The smart indexing of rules and assignment of conclusions to components allows an efficient calculation of the rule fulfillment as part of the fitness function. Optimized blueprints for a real existing chemical plant dominate the original design.