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A decomposition-based multi-objective genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem
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DOI：10.1016/j.knosys.2021.107099.png)
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Abstract
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In this paper, an efficient decomposition-based multi-objective genetic programming hyper-heuristic (MOGP-HH/D) approach is proposed for the multi-skill resource constrained project scheduling problem (MS-RCPSP) with the objectives of minimizing the makespan and the total cost simultaneously. First, the decomposition mechanism is presented to improve the diversity of solutions. Second, a single-list encoding scheme and an improved repair-based decoding scheme are designed to represent individuals and construct feasible schedules, respectively. Third, ten adaptive heuristics are developed elaborately to constitute a list of low-level heuristics (LLHs). Fourth, genetic programming is employed as the high-level heuristic (HLH) to generate a promising heuristics sequence from the LLHs set flexibly. Finally, the Taguchi method of design-of-experiment (DOE) is conducted to analyze the performance of parameter settings. The effectiveness of MOGP-HH/D is evaluated on a typical benchmark dataset and computational results exhibit the superiority of the proposed algorithm over the existing methods in solving multi-objective MS-RCPSP. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Decomposition
Multi-objective
Genetic programming
Hyper-heuristic
Resource constrained scheduling
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