Quantum Particle Swarm Optimization for batch scheduling problem of a single processor in chemical industries

In this paper we consider batch scheduling problem of a single processor in chemical industries with precedence constraints to minimize the total weighted tardiness of jobs. This problem is known to be strongly NP-hard. Solution methodologies based on model and Quantum Particle Swarm Optimization (QPSO) are developed to solve it. In this approach, some strategies are designed such as Schedule Generation Scheme (SGS). Computations show that model can obtain 100% optical solutions. QPSO is effective and better than standard PSO and quantum evolution algorithm.