A Multi-objective Resource-Constrained Project-Scheduling Problem Using Mean Field Annealing Neural Networks

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, the Multi-Objective Resource-Constrained Project-Scheduling problem (MRCPSP) 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, A Potts mean field feedback artificial neural network algorithm is developed and explored for the multi-objective resource constrained project scheduling problem. A convenient encoding of inequality constraints is achieved by means of multi-linear penalty function. An approximate energy minimum is obtained by iterating a set of Potts means field equation, is combination with annealing. Priority rule-based heuristics are the most widely used scheduling methods though their performance depends on the characteristics of the projects. To overcome this deficiency, a Potts mean field feedback artificial neural network is designed and integrated into the scheduling scheme so as to automatically select the suitable activity for each stage of project scheduling. Testing on Paterson's classic test problems and comparison with other exact method how that the proposed Potts mean field annealing neural network based heuristic is able to improve the performance of project scheduling.