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Scarce resources may cause delay in completion of a project on time. In this research, a multi-objective decision making model is developed for scheduling multi-mode resource constraint scheduling problem in the presence of uncertain resources. The objectives are profit, execution cost and completion time. To develop this idea, a multi-objective non-linear mixed integer programming model is developed where resource availability is not deterministic and expressed by triangular probability function. In continue a multi-objective weighting genetic algorithm is proposed (MOWGA) which is flexible enough to be used in real projects. To verify the performance of the proposed method, a number of experiments are solved and results are analyzed. The outcomes, indicated that while resource uncertainty increases, higher complexity in schedules is observed. It is also found that optimizing one objective function is not necessarily resulted in optimizing the others. The MOWGA is then successfully applied for a project with real data.

Scarce resources may cause delay in completion of a project. In this research, a multi-objective decision making model is developed for scheduling multi-mode resource constraint scheduling problem in the presence of uncertain resources. The objectives are profit, execution cost and completion time. To develop this idea, a multi-objective non-linear mixed integer programming model is developed where resource availability is not deterministic and expressed by triangular probability function. Then, a multi-objective weighting genetic algorithm is proposed (MOWGA) which is flexible enough to be used in real projects. To verify the performance of the proposed method, a number of experiments are solved and results are analyzed. The outcomes indicated that while resource uncertainty increases, higher complexity in schedules is observed. It is also found that optimizing one objective function does not necessarily resulted in optimizing the others. The MOWGA is then successfully applied for a project with real data. [ABSTRACT FROM AUTHOR]

Abstract: An ant colony optimization (ACO)-based methodology for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) considering both renewable and nonrenewable resources is presented. With regard to the MRCPSP solution consisting of activity sequencing and mode selection, two levels of pheromones are proposed to guide search in the ACO algorithm. Correspondingly, two types of heuristic information and probabilities as well as related calculation algorithms are introduced. Nonrenewable resource-constraint and elitist-rank strategy are taken into account in updating the pheromones. The flowchart of the proposed ACO algorithm is described, where a serial schedule generation scheme is incorporated to transform an ACO solution into a feasible schedule. The parameter-selection and the resultant performance of the proposed ACO methodology are investigated through a series of computational experiments. It is expected to provide an effective alternative methodology for solving the MRCPSP by utilizing the ACO theory. [Copyright &y& Elsevier]