An Dao Duc Cuong creator Voratas Kachitvichyanukul Chairperson Gong, Dah-Chuan Examination committee Huynh, Trung Luong Examination Committee PetroVietnam Scholarship donor text series technical report th Pathum Thani, Thailand Asian Institute of Technology 2009 continuing eng 92 p. : ill. This thesis focuses on the multi execution modes resource-constrained project scheduling problem with the objective of minimizing the project makespan. There might be more than one operating mode to perform an activity; each mode requires different amount of resources and related time duration. Besides, there are two types of resources: renewable and non-renewable. Adaptive particle swarm optimization and genetic algorithm are integrated in an algorithm called APSO-GA to solve the MRCPSP. The major motivation of APSO-GA is to use PSO to find the best priority of activities while GA is used to search for the combination mode of the activities. A potential solution of MRCPSP is represented as a pair of particle and chromosome and an active schedule can be achieved by transforming this representation by serial schedule method. In adaptive PSO algorithm, the two parameters evolved in velocity updating mechanism 12,cc can be self-adaptive depending on three factors, including their old values, the degree of acceleration and the swarm response. At the beginning of the searching procedure, the cognitive learning term plays a more important role than the social learning term in effect to particle{u2018}s velocity. The effect of cognitive learning term is decreased while that of social learning term is increased through the PSO iteration. Genetic algorithm is put inside the PSO algorithm, i.e. in each PSO iteration, a GA loop is used to search for a better mode combination of activities. The GA loop is performed until the exploring process cannot find a better combination of mode for current activities priority arrangement. Moreover, three difference types of GA crossover variants are applied to enhance the quality of searching procedure. The performance of the APSO-GA algorithm is investigated on standard instance sets. Experiment results show that the proposed adaptive PSO algorithm has proved the advantages over the non-adaptive one and the best GA crossover types is uniform crossover. The makespan results and computational time results of the proposed algorithm are also very competitive in comparing with others heuristics previously published. Submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Industrial and Manufacturing Engineering, School of Engineering Technology Thesis (M.Eng.) - Asian Institute of Technology, 2009 Production scheduling Computer algorithms Asian Institute of Technology. http://203.159.5.9/ait-thesis/detail.php?q=B03248 http://203.159.5.9/ait-thesis/detail.php?q=B03248 110107 20260817161525.0