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

Here we consider a bi-objective, multi-project, multi-mode, resource-constrained project-scheduling problem. The objectives were to minimize the makespan, minimize the mean of the flow times for individual projects, minimize the mean completion times for individual projects and maximize the total net present value of all projects. 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 post-processing and for new population generation. 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 with more diversity. A detailed fine-tuning process was conducted by employing a response surface optimization method. An extensive computational study was performed. Managerial insights are presented