An efficient differential evolution algorithm for multi–mode resource–constrained project scheduling problems
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This paper considers a general resource–constrained project scheduling problem in which activities may be executed in more than one operating mode with both renewable and non–renewable resources. Each mode may have different durations and requires different amounts of renewable and non–renewable resources. To solve this NP–hard problem, an efficient differential evolution (eDE) algorithm is proposed with linear decreasing crossover factor and adaptive penalty cost. The purpose of these modifications is to enhance the ability of DE algorithm to quickly search for better solutions by encouraging solutions to escape from local solution and effectively evolve solutions in the search space. The performance of the proposed algorithm is compared with other algorithms in the literature and shows that the proposed algorithm is very efficient. It outperforms several heuristics in terms of lower average deviation from the optimal makespan. Moreover, it is capable of finding quality solutions for large–scale problems in reasonable computational time.