In real-life project management, resource leveling is an important technique to ensure the effective use of resources, in which activities (a) can often be executed in alternative modes and (b) are constrained by precedence relations with minimum and maximum time lags that can be modeled using generalized precedence relations (GPRs). In addition, the values of the time lags tend to depend on activity modes. The resource leveling problem with multiple modes and mode-dependent GPRs (MRLP-GPR) is a generalization of the classic NP-hard resource leveling problem. To our knowledge, no literature exists regarding the MRLP-GPR. We propose several heuristics for the MRLP-GPR built upon two solution approaches: (a) a steepest descent algorithm and a fast descent algorithm that are based on a decomposition approach and (b) a hybrid estimation of distribution algorithm (EDA), which is based on an integration approach. Extensive computational experiments on a large number of benchmark instances are conducted to evaluate the proposed heuristics. A comparison of the results shows that our EDA outperforms or is competitive with three baseline heuristics (a random search algorithm and two variants of a genetic algorithm that is the best-performing metaheuristic for the single-mode resource leveling problem with GPRs). Our results can serve as a benchmark for future research. Our model and solution algorithms provide an automatic tool for the project manager's multi-mode resource leveling decision-making.
Li, H., & Dong, X. (2018). Multi-mode resource leveling in projects with mode-dependent generalized precedence relations. Expert Systems with Applications, 97, 193–204. https://doi.org/10.1016/j.eswa.2017.12.030