Population-based metaheuristics for R&D project scheduling problems under activity failure risk

In this paper, we study scheduling of R&D projects in which activities may to be failed due to the technological risks. We consider two introduced problems in the literature referred to as R&D Project Scheduling Problem (RDPSP) and Alternative Technologies Project Scheduling Problem (ATPSP). In both problems, the goal is maximization of the expected net present value of activities where activities are precedence related and each of them is accompanied with a cost, a duration, and a probability of technical success. In RDPSP, a project payo is obtained if all activities are succeeded, while in ATPSP, if one of activities is implemented successfully, the project payo is attained. We construct a solution representation for each of these problems and construct two population-based metaheuristics including scatter search algorithm and genetic algorithm as solution approaches. Computational experiments indicate scatter search outperforms genetic algorithm and also available exact solution algorithms.