A hybrid integer grey programming for an integrated problem of project selection and scheduling with interval data

Abstract Projects are inherently subject to uncertainty since in most cases, the information at hand is either approximate or partial. Grey numbers can handle the uncertainty in projects without requiring a predefined membership or probability function. The current paper proposes a hybrid solution technique for the integrated problem of project selection and scheduling with interval grey data. The presented method combines the concept of grey programming with branch and bound algorithm to achieve Pareto interval solutions. The algorithm is then applied to a grey bi-objective project selection and scheduling model. Additional experiments are carried out regarding the effects of grey weights and objective weights on the goal values. Moreover, the proposed algorithm is compared with fuzzy goal programming method. The results indicate that the developed approach has more precision than fuzzy programming and can achieve acceptable alternate solutions for small to medium scale problems.

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