A united search particle swarm optimization algorithm for multiobjective scheduling problem

The performance of a scheduling system, in practice, is not evaluated to satisfy a single objective, but to obtain a trade-off schedule regarding multiple objectives. Therefore, in this research, I make use of multiple objective decision-making method, a global criterion approach, to develop a multi-objective scheduling problem model with different due-dates on parallel machines processes, in which consider three performance measures, namely minimum run time of every machine, earlierness time (no tardiness) and process time of every job, simultaneously. According to this special multi-objective scheduling problem, the method of reverse order drawing GATT will be proposed, at the same time, bring forward a united search particle swarm optimization algorithm (USPSOA) solves this multi-objective scheduling problem. The validity and adaptability of the USPSOA is investigated through experimental results.

[1]  Yeong-Dae Kim,et al.  Scheduling on parallel identical machines to minimize total tardiness , 2007, Eur. J. Oper. Res..

[2]  Pei-Chann Chang,et al.  Genetic algorithm integrated with artificial chromosomes for multi-objective flowshop scheduling problems , 2008, Appl. Math. Comput..

[3]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[4]  John W. Fowler,et al.  A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines , 2003, Comput. Oper. Res..

[5]  Vinícius Amaral Armentano,et al.  Genetic local search for multi-objective flowshop scheduling problems , 2005, Eur. J. Oper. Res..

[6]  William G. Ferrell,et al.  MULTIOBJECTIVE SINGLE MACHINE SCHEDULING WITH NONTRADITIONAL REQUIREMENTS , 2007 .

[7]  M. Zandieh,et al.  A multi-phase covering Pareto-optimal front method to multi-objective parallel machine scheduling , 2010 .

[8]  Zhiming Wu,et al.  An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems , 2005, Comput. Ind. Eng..

[9]  Yen-Ting Lin,et al.  Dynamic hard-real-time scheduling using genetic algorithm for multiprocessor task with resource and timing constraints , 2009, Expert Syst. Appl..

[10]  Ling Wang,et al.  An Effective PSO-Based Hybrid Algorithm for Multiobjective Permutation Flow Shop Scheduling , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[11]  Salah E. Elmaghraby,et al.  Polynomial time algorithms for two special classes of the proportionate multiprocessor open shop , 2010, Eur. J. Oper. Res..

[12]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[13]  Guoqing Wang,et al.  Parallel machine earliness and tardiness scheduling with proportional weights , 2003, Comput. Oper. Res..

[14]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[15]  Jun Wang,et al.  WBMOAIS: A novel artificial immune system for multiobjective optimization , 2010, Comput. Oper. Res..

[16]  Chinyao Low,et al.  Modelling and heuristics of FMS scheduling with multiple objectives , 2006, Comput. Oper. Res..

[17]  Bin Jiao,et al.  A similar particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan , 2006, Appl. Math. Comput..

[18]  Abdullah Al Mamun,et al.  An evolutionary artificial immune system for multi-objective optimization , 2008, Eur. J. Oper. Res..

[19]  Appa Iyer Sivakumar,et al.  Multi-objective scheduling of two-job families on a single machine , 2005 .