Models And Algorithms Of Project Scheduling Problem

Project scheduling problem is to determine the optimal schedule of allocating resources so as to balance the total cost and the completion time under some time or resource constraints. To meet the need of modelling uncertain project scheduling problem, the dissertation studies project scheduling problem under some uncertain environments, establishes several uncertain models according to different optimization requirements, and designs hybrid intelligent algorithms to solve the models.Though in the past forty years uncertain project scheduling problem has been developed quickly, one of the shortages of the studies in this field is the lack of mathematical models to meet different optimization requirements in practical problems. The dissertation studies a type of project scheduling problem, in which project cost is optimized under some time constraints. According to different decision-making rules, three stochastic models as stochastic expected cost minimization model, stochastic α-cost minimization model and probability maximization model are proposed. And to calculate several stochastic functions in the above three models, stochastic simulations are designed and are embedded into genetic algorithm to establish a hybrid intelligent algorithm, whose effectiveness is proved by several numerical examples. Then to compensate for the lack of mathematical models of fuzzy project scheduling problem, the dissertation builds three fuzzy models as fuzzy expected cost minimization model, fuzzy α-cost minimization model and credibility maximization model and integrates methods of fuzzy simulations and genetic algorithm to design a hybrid intelligent algorithm, which is proved to be effective to solve the models by some numerical experiments. Furthermore, the dissertation studies project scheduling problem with mixed uncertainty of randomness and fuzziness. The random fuzzy theory is introduced and three random fuzzy models as random fuzzy expected cost minimization model, random fuzzy (α,β)-cost minimization model and chance maximization model are built. The degeneration cases are studied. A hybrid intelligent algorithm is designed and some numerical examples are given to show the effectiveness of the algorithm.In conclusion, this dissertation promotes the development of project schedulingproblem with (1) several types of new mathematical models of project scheduling problem with stochastic, fuzzy and random fuzzy activity duration times, respectively, and the study of degeneration cases of random fuzzy models; (2) hybrid intelligent algorithms integrated by uncertain simulations and genetic algorithm, and numerical experiments to show the effectiveness of the hybrid intelligent algorithms.