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Offshore engineering construction projects are large and complex, having the characteristics of multiple execution modes and multiple resource constraints. Their complex internal scheduling processes can be regarded as resource-constrained project scheduling problems (RCPSPs). To solve RCPSP problems in offshore engineering construction more rapidly, a hybrid genetic algorithm was established. To solve the defects of genetic algorithms, which easily fall into the local optimal solution, a local search operation was added to a genetic algorithm to defend the offspring after crossover/mutation. Then, an elitist strategy and adaptive operators were adopted to protect the generated optimal solutions, reduce the computation time and avoid premature convergence. A calibrated function method was used to cater to the roulette rules, and appropriate rules for encoding, decoding and crossover/mutation were designed. Finally, a simple network was designed and validated using the case study of a real offshore project. The performance of the genetic algorithm and a simulated annealing algorithm was compared to validate the feasibility and effectiveness of the approach.

Optimization of Multi-Execution Modes and Multi-Resource-Constrained Offshore Equipment Project Scheduling Based on a Hybrid Genetic Algorithm