A genetic algorithm for the generalised assignment problem
Computers and Operations Research
Swarm intelligence
Fuzzy programming with recourse
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Fuzzy Optimization Problems with Critical Value-at-Risk Criteria
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Addressing capacity uncertainty in resource-constrained assignment problems
Computers and Operations Research
The infinite dimensional product possibility space and its applications
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
Convergent results about the use of fuzzy simulation in fuzzy optimization problems
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
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This paper constructs a new class of two-stage fuzzy generalized assignment problems, in which the resource amounts consumed are uncertain and assumed to be characterized by fuzzy variables with known possibility distributions. Motivated by the definitions of the positive part and negative part, we can transform the second-stage programming to its equivalent one. To calculate the expected value in the objective function, an approximation approach (AA) is employed to turn the fuzzy GAP model into an approximating one. Since the approximating GAP model is neither linear nor convex, traditional optimization methods cannot be used to solve it. To overcome this difficulty, we design a hybrid algorithm integrating the approximation approach and particle swarm optimization (PSO) to solve the approximating two-stage GAP model. Finally, one numerical example with six tasks and three agents is presented to illustrate the effectiveness of the designed hybrid algorithm.