A novel binary differential evolution algorithm for a class of fuzzy-stochastic resource allocation problems

Abstract

This paper studies a class of fuzzy-stochastic resource-allocation (fSRA) problems which involve both subjective and objective uncertainty (i.e., fuzziness and randomness). In the FSRA, the capability of a resource to complete a task is characterized by a probability parameter which is uncertain and stochastic while the reward of a task is expressed as a fuzzy number. The FSRA problem is formulated under a robust optimization model and an expected-value model, respectively. Then, a binary differential evolution (BDE) algorithm with new operators is proposed to solve the formulated FSRA problems. A specific and efficient constraint handling technique is also proposed and incorporated into BDE to guarantee the generation of feasible solutions. Comparative computational experiments validate the effectiveness and advantages of the proposed BDE.