A Model for Resource Constraint Project Scheduling Problem Using Quantum Inspired PSO

Abstract

Resource Constrained Project Scheduling Problem (RCPSP) is a NP-hard project planning scheduling problem. It has been widely applied in real life industrial scenarios to optimize the project makespan with the limitation of the resources. In order to solve RCPSP, this paper suggests a Quantum inspired Particle Swarm Optimization (Q-PSO) probabilistic optimization technique. A classical PSO is very hard to map to the RCPSP because of its solution lies in continuous values position vector. To overcome this, Sequence Position Vector (SPV) rule is incorporated into PSO. Since, the activities of the project follows dependency constrains, due to updation in position vector, the dependency constrains are violated. To handle this situation, Valid Particle Generator (VPG) is used. With an assembling of these operators, a Q-PSO is introduced to solve RCPSP effectively. The effectiveness of the QPSO is verified on a standard dataset of PSPLIB for J30. Results show that Q-PSO has significant improvement in the performance over number of state of the arts. Since it uses probabilistic particle representation in terms of quantum bits (Q-bits) and thus replaces the inertia weight tuning and velocity update method in classical PSO.