A hybrid genetic algorithm application for a bi-objective, multi-project, multi-mode, resource-constrained project scheduling problem

In this study, we considered a bi-objective, multi-project, multi-mode, resource-constrained project-scheduling problem. We adopted different objectives pairs, combinations of time-based and financial performance measures. As a solution method, we used the non-dominated sorting genetic algorithm II (NSGA-II). To improve NSGA-II, a backward–forward pass (BFP) procedure was proposed for new population generation as well as for post-processing. Different alternatives for implementing BFP were tested with the results reported for different objective function combinations. To increase diversity, an injection procedure was introduced and implemented. Both the BFP and injection procedures led to improved objective function values. Moreover, the injection procedure generated a significantly higher number of non-dominated solutions resulting in more diversity. An extensive computational study was performed. The results were further assessed from the perspective of maximum cash balance. Managerial insights were presented