This study considers a practical overhead crane control problem of designing a fuzzy control to ensure that the trolley arrives precisely at its destination; the load swing angle approaches to zero, and the energy of input and states is minimized. Furthermore, the constraints for the overhead crane operation are also considered and satisfied. Those constraints are that when the crane is moving,...

Many practical optimization problems are characterized by some flexibility in the problem constraints, where this flexibility can be exploited for additional trade-off between improving the objective function and satisfying the constraints. Especially in decision making, this type of flexibility could lead to workable solutions, where the goals and the constraints specified by different parties...

This paper presents an innovative Fuzzy-Stochastic Approach (FSA) to solve Binary Linear Programming (BLP) problems under uncertainties. An Interval-coefficient Fuzzy Binary Linear Programming (IFBLP) model is applied here to reflect two different types of uncertainty in a BLP problem. In the proposed IFBLP model the interval coefficient is used to reflect parameter uncertainty, and the fuzzy g...

This paper proposes a method for solving linear programming problems where all the coefficients are, in general, fuzzy numbers. We use a fuzzy ranking method to rank the fuzzy objective values and to deal with the inequality relation on constraints. It allows us to work with the concept of feasibility degree. The bigger the feasibility degree is, the worst the objective value will be. We offer ...

This paper considers a probabilistic inventory model with uniform leadtime demand and fuzzy cost components under probabilistic and imprecise constraints. Firstly we solve the model by general fuzzy non-linear programming technique. Then intuitionistic fuzzy optimization technique is applied and finally, regarding the optimization of the objective function a comparative study is presented among...

Fuzzy multiobjective programming for a deterministic case involves maximizing the minimum goal satisfaction level among conflicting goals of different stakeholders using Max-min approach. Uncertainty due to randomness in a fuzzy multiobjective programming may be addressed by modifying the constraints using probabilistic inequality (e.g., Chebyshev’s inequality) or by addition of new constraints...

Neuro-fuzzy classi cation systems allow to derive fuzzy classi ers by learning from data. The obtained fuzzy rule bases are sometimes hard to interpret, even if the learning method uses constraints to ensure an appropriate fuzzy partitioning of the input domains. This paper describes an approach to build more expressive rules by performing boolean transformations during and after the learning p...

The Fuzzy Constraint Networks model, a generalization of the Disjunctive Temporal Fuzzy Constraint Networks, is a framework that allows representing and reasoning with fuzzy qualitative and quantitative complex constraints. However, its general complexity is exponential, and we need to find tractable subclasses. In this paper we propose two algorithms to deal with a tractable subclass named Ser...

One of the critisisms of rule-based fuzzy controllers has been their use of only shallow knowledge bases. This shallowness is primarily attributable to the expressiveness of rule-based systems, which is that of the Horn clause subset of rst-order predicate calculus (FOPC). We would argue that the language in which declarative knowledge is represented must be at least as expressive as the full F...

Many business decisions can be modeled as multiple objective linear programming (MOLP) problems. When formulating a MOLP problem, objective functions and constraints involve many parameters which possible values are assigned by the experts who are often imprecisely or ambiguously known. So, it would be certainly more appropriate to interpret the experts’ understanding of the parameters as fuzzy...