Solving a Single Machine Scheduling Problem with Uncertain Demand Using QPSO Algorithms

By considering the imprecise or fuzzy nature of the data in real-world problems, a single machine scheduling problem with uncertainty demand is investigated. A triangular fuzzy number is used to represent the uncertainty demand, and a half-trapezoid one is employed to represent fuzzy duedate. On the basis of the agreement index of fuzzy duedate and fuzzy completion time, this problem is formulated with the objective to maximize the total weighting agreement indexes for all the customer orders. We presented a hybrid algorithm QPSO of particle swarm optimization (PSO) and quantum evolutionary algorithm (QEA) to solve this problem. In the proposed QPSO, some novel coding schemes are designed for transforming a particle into a feasible process sequence of customer orders. Moreover, a mutation mechanism is also introduced into the QPSO and improves the diversity of the swarm greatly. The feasibility and effectiveness of the proposed QPSO is demonstrated by some simulation experiments.