Hybrid PSO/DE solution for the earliness/tardiness case of hybrid flow-shop scheduling problem

In this paper, a hybrid method based on particle swarm optimization (PSO) and differential evolution (DE) algorithm is presented to solve the earliness tardiness (E/T) hybrid flow-shop scheduling problem (HFSP). PSO/DE is used to take global optimization. The local assignment rules are used to determine the job's starting time and processing sequence at each stage. The fitness function is constructed with considering the Earliness/Tardiness penalty in the optimal process of hybrid flow shop. Then the scheduling model of hybrid flow shop is obtained, the global assignment of production task can be realized, and each job's process route can be determined. Several scheme comparisons with experiment results show the better effectiveness of hybrid PSO/DE algorithm to solve E/T problem of HFSP.

[1]  L. Wang,et al.  A DE-based approach to no-wait flow-shop scheduling , 2009, Comput. Ind. Eng..

[2]  A. Vasan,et al.  Application of Differential Evolution for Irrigation Planning: An Indian Case Study , 2007 .

[3]  S. Khamsawang,et al.  Hybrid PSO-DE for solving the economic dispatch problem with generator constraints , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[4]  T. S. Chung,et al.  Hybrid PSO and DE approach for dynamic economic dispatch with non-smooth cost functions , 2009, Int. J. Model. Identif. Control..

[5]  Mehmet Fatih Tasgetiren,et al.  A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem , 2007, Eur. J. Oper. Res..

[6]  Hossein Nezamabadi-pour,et al.  Unit commitment scheduling using binary differential evolution algorithm , 2009 .

[7]  Wendong Xiao,et al.  Hybrid flow shop scheduling using genetic algorithms , 2000, Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393).

[8]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[9]  M. A. Abido,et al.  Optimal power flow using differential evolution algorithm , 2009 .

[10]  Feng Qiao,et al.  DE solution for the earliness/tardiness case of Hybrid Flow-shop Scheduling problem with priority strategy , 2011, Proceedings of 2011 International Conference on Modelling, Identification and Control.

[11]  S. R. Spea,et al.  Optimal power flow using differential evolution algorithm , 2010 .

[12]  Andreas C. Nearchou,et al.  A differential evolution approach for the common due date early/tardy job scheduling problem , 2008, Comput. Oper. Res..

[13]  Godfrey C. Onwubolu,et al.  Scheduling flow shops using differential evolution algorithm , 2006, Eur. J. Oper. Res..

[14]  Liu Chang,et al.  Differential Evolution Algorithm for the Earliness/Tardiness Hybrid Flow-shop Scheduling Problem , 2009, 2009 Third International Symposium on Intelligent Information Technology Application.

[15]  Saul I. Gass,et al.  Erratum to "Cycling in linear programming problems" [Computers and Operations Research 31 (2002) 303-311] , 2006, Comput. Oper. Res..

[16]  Éric D. Taillard,et al.  Benchmarks for basic scheduling problems , 1993 .

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

[18]  Yi Wu,et al.  A genetic algorithm for solving flow shop scheduling problems with parallel machine and special procedure constraints , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).