DocumentCode :
620179
Title :
Solving a single machine scheduling problem with uncertain demand using QPSO algorithms
Author :
Ping Yan ; Ming-hai Jiao ; Xu Yao
Author_Institution :
Sch. of Econ. & Manage., Shenyang Aerosp. Univ., Shenyang, China
fYear :
2013
fDate :
25-27 May 2013
Firstpage :
2741
Lastpage :
2745
Abstract :
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.
Keywords :
demand forecasting; evolutionary computation; fuzzy set theory; number theory; order processing; particle swarm optimisation; single machine scheduling; QEA; QPSO algorithms; agreement index; coding schemes; customer order process sequence; fuzzy completion time; fuzzy data; fuzzy duedate; hybrid algorithm; mutation mechanism; particle swarm optimization; quantum evolutionary algorithm; real-world problems; single machine scheduling problem; total weighting agreement indexes; triangular fuzzy number; uncertain demand; Decoding; Indexes; Iterative decoding; Job shop scheduling; Single machine scheduling; Sociology; Statistics; Fuzzy demand; Particle swarm optimization; Quantum evolutionary algorithm; Single machine scheduling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
Type :
conf
DOI :
10.1109/CCDC.2013.6561408
Filename :
6561408
Link To Document :
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