Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Applications of type-2 fuzzy logic systems to forecasting of time-series
Information Sciences—Informatics and Computer Science: An International Journal
Centroid of a type-2 fuzzy set
Information Sciences: an International Journal
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
A fuzzy genetic algorithm for real-world job shop scheduling
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management
Information Sciences: an International Journal
Type-2 Fuzzy Logic: A Historical View
IEEE Computational Intelligence Magazine
IEEE Transactions on Fuzzy Systems
A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots
IEEE Transactions on Fuzzy Systems
Interval Type-2 Fuzzy Logic Systems Made Simple
IEEE Transactions on Fuzzy Systems
Geometric Type-1 and Type-2 Fuzzy Logic Systems
IEEE Transactions on Fuzzy Systems
International Journal of Approximate Reasoning
Interval type-2 fuzzy modelling and simulated annealing for real-world inventory management
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
On interval type-2 rough fuzzy sets
Knowledge-Based Systems
Interval type-2 fuzzy logic for encoding clinical practice guidelines
Knowledge-Based Systems
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Planning resources for a supply chain is a major factor determining its success or failure. In this paper we build on previous work introducing an Interval Type-2 Fuzzy Logic model of a multiple echelon supply chain. It is believed that the additional degree of uncertainty provided by Interval Type-2 Fuzzy Logic will allow for better representation of the uncertainty and vagueness present in resource planning models. First, the subject of Supply Chain Management is introduced, then some background is given on related work using Type-1 Fuzzy Logic. A description of the Interval Type-2 Fuzzy model is given, and a test scenario detailed. A Genetic Algorithm uses the model to search for a near-optimal plan for the scenario. A discussion of the results follows, along with conclusions and details of intended further work.