Robust programming involves the optimization of a precise objective function subject to a fuzzy decision space delimited by constraints with fuzzy coefficients and fuzzy capacities (Inuiguchi and Sakawa, 1998).
In this paper, fuzzy wind capacity factor is proposed to analyze and propagate the uncertainty of wind resources based on the wind speed distribution.
Due to uncontrollable factors, the parameters in the queuing models may be uncertain and so in this paper we present the design of a fuzzy finite capacity queuing model in which the arrival pattern and the service pattern follow an exponential distribution under uncertain parameter.
It was able to correctly fix the positions of the centroids, (which was beyond the capacity of fuzzy-k means), and correctly estimated which of the data were genuine extragrades, outperforming fuzzy-k means with extragrades.
By extending to fuzzy environment, the finite-capacity queueing models would have wider applications.
Application of the model to a hypothetical case indicated that the model was capable of handling uncertainties expressed as fuzzy sets and taking capacity-expansion issues of energy facilities into consideration.
Lau et al. (2010) was the first one who presented FDFLPs with fuzzy parameters of demand, facility capacity and delivery cost in an e-commerce network and then two fuzzy parameters, demand and facility capacity were developed by Taghipourian et al. (2012) in dynamic Hub facility location problems.
More formally, a fuzzy measure (alternatively called a capacity in the literature) is defined as follows: (Fuzzy measure): Let N = { 1, 2, …, n } be the index set of n criteria.
In Section 2, we consider an urban system that understands the timing of expansions of system capacity, a dynamic fuzzy criterion model for reservoir operations and assigning priorities to welfare policies.
The simulation results show that the controller is not only very reliable as a PID controller, robust and requires only a short adjustment time of fuzzy control, but possesses also capacity of global optimization of the immune algorithm.
