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.
Single machine scheduling Fuzzy demand Particle swarm optimization Quantum evolutionary algorithm
Ping Yan Ming-hai Jiao Xu Yao
School of Economics and Management, Shenyang Aerospace University, Shenyang, 110034 Computing Center, Northeastern University, Shenyang, 110819 Institute Scientific & Technical Information of Liaoning Province, Shenyang, 110181
国际会议
the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)
贵阳
英文
2741-2745
2013-05-01（万方平台首次上网日期，不代表论文的发表时间）