A Multiobjective Resource-Constrained Project-Scheduling Problem

The planning and scheduling activities are viewed profoundly important to generate successful plans and to maximize the utilization of scarce resources. Moreover, real life planning problems often involve several objectives that should be simultaneously optimized and real world environment is usually characterized by uncertain and incontrollable information. Thus, finding feasible and efficient plans is a considerable challenge. In this respect, theMulti-Objective Resource-Constrained Project- Scheduling problem (RCPSP) tries to schedule activities and allocate resources in order to find an efficient course of actions to help the project manager and to optimize several optimization criteria. In this research, we are developing a new method based on Ant System meta-heuristic and multi-objective concepts to raise the issue of the environment uncertainty and to schedule activities. We implemented and ran it on various sizes of the problem. Experimental results show that the CPU time is relatively short. We have also developed a lower bound for each objective in order to measure the degree of correctness of the obtained set of potentially efficient solutions. We have noticed that our set of potentially efficient solutions is comparable with these lower bounds. Thus, the average gap of the generated solutions is not far from the lower bounds.