Kharazmi University International Journal of Supply and Operations Management 2383-1359 2 3 2015 11 01 An Efficient Genetic Agorithm for Solving the Multi-Mode Resource-Constrained Project Scheduling Problem Based on Random Key Representation 905 924 2545 10.22034/2015.3.06 EN Mohammad Hassan Sebt Amirkabir University of Technology, Tehran, Iran Mohammad Reza Afshar Amirkabir University of Technology, Tehran, Iran Yagub Alipouri Amirkabir University of Technology, Tehran, Iran Journal Article 2015 10 26 In this paper, a new genetic algorithm (GA) is presented for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. A random key and the related mode list (ML) representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. In this paper, a simple, efficient fitness function is proposed which has better performance compared to the other fitness functions in the literature. Defining a new mutation operator for ML is the other contribution of the current study. Comparing the results of the proposed GA with other approaches using the well-known benchmark sets in PSPLIB validates the effectiveness of the proposed algorithm to solve the MRCPSP. http://www.ijsom.com/article_2545_9d28de8ff9bb6a3fa41fddfdc28f3bc1.pdf