خلاصة:
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.
ملخص الجهاز:
"Keywords: Combinatorial optimization; Multi-mode project scheduling; Resource constraints; Genetic algorithm; Random key representation.
The resource-constrained project scheduling problem (RCPSP) and its extended version, the Multi-mode RCPSP (MRCPSP), are important problems in this context, and the latter problem is more related to the real world (Hartmann and Briskorn 2009).
Peteghem and Vanhoucke (2014) evaluated different meta-heuristic algorithms for solving the MRCPSP and proposed new benchmark instances for this problem.
Accordingly, our results are compared with the existing state-of-the-art methods for solving the MRCPSP such as the GA and the AIS presented by Peteghem and Vanhoucke (2010, 2009) denoted respectively as VPVGA and VPVAIS, EDA and SFLA presented by Wang and Fang (2012, 2011) denoted respectively as LCEDA and LCSFLA, the hybrid genetic algorithm developed by Lova et al.
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