A Hybridized Metaheuristic Algorithm to Solve the Robust Resource Constrained Multi-Project Scheduling Problem

In this paper, the multi-project scheduling problem is studied. The duration of the activities is subjected to the considerable uncertainty and the robust optimization approach is considered to deal with the uncertainty. The maximum total tardiness of the projects is defined as the objective function which should be minimized. In order to allocate the constrained resources to the multi-projects, two models are proposed. In the first model, the projects are scheduled separately while in the second model, the multi-project approach is applied and the resource sharing policy is used. It is demonstrated that how the tardiness of the projects will be decreased when the multi-project approach is applied. Also, the Adaptive Bee Genetic Algorithm (ABGA) is designed as a hybrid metaheuristic algorithm and proposed in this paper to solve the first stage model of the Robust Resource Constrained Multi-Project Scheduling Problem (RRCMPSP). The results of ABGA is compared with the results of scenario-relaxation algorithm as an exact algorithm for the small size problems. Also, the performance of ABGA is studied compared to the Genetic Algorithm (GA) and Artificial Bee Colony (ABC) as two basic algorithms for the large size problems. The results show the effectiveness of the proposed algorithm in solving the RRCMPSP.