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A Memetic Algorithm for the Solution of the Resource Leveling Problem
(Mdpi, 2023) Iranagh, Mehdi; Sonmez, Rifat; Atan, Tankut; Uysal, Furkan; Bettemir, Onder Halis
In this paper, we present a novel memetic algorithm (MA) for the solution of the resource leveling problem (RLP). The evolutionary framework of the MA is based on integration of a genetic algorithm and simulated annealing methods along with a resource leveling heuristic. The main objective of the proposed algorithm is to integrate complementary strengths of different optimization methods and incorporate the individual learning as a separate process for achieving a successful optimization method for the RLP. The performance of the MA is compared with the state-of-the-art leveling methods. For small instances up to 30 activities, mixed-integer linear models are presented for two leveling metrics to provide a basis for performance evaluation. The computational results indicate that the new integrated framework of the MA outperforms the state-of-the-art leveling heuristics and meta-heuristics and provides a successful method for the RLP. The limitations of popular commercial project management software are also illustrated along with the improvements achieved by the MA to reveal potential contributions of the proposed integrated framework in practice.