A DECOMPOSITION-BASED HEURISTIC FOR THE RESOURCE-CONSTRAINED PROJECT SCHEDULING PROBLEM
In the last few decades the resource-constrained project scheduling problem has become a popular problem type in operations research. However, due to its strongly NP-hard status, the effectiveness of exact optimisation procedures is restricted to relatively small instances. In this paper we present a new genetic algorithm (GA) for this problem, able to provide near-optimal heuristic solutions. This GA procedure has been extended by a so-called decomposition-based heuristic (DBH) which iteratively solves subparts of the project. We present computational experiments on two datasets. The first benchmark set is used to illustrate the contribution of both the GA and the DBH. The second set is used to compare the results with current state-of-the-art heuristics, and to show that the procedure is capable of producing consistently good results for challenging instances of the resource-constrained project scheduling problem. We illustrate that GA is currently the best performing RCPSP meta-heuristic, and that the DBH further improves the performance of the GA
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