A Variable Iterated Greedy Algorithm with Differential Evolution for Solving No-Idle Flowshops

In this paper, we present a variable iterated greedy algorithm where its parameters (basically destruction size and probability of whether or not to apply the iterated greedy algorithm to an individual) are optimized by the differential evolution algorithm. A unique multi-chromosome solution representation is presented in such a way that the first chromosome represents the destruction size and the probability whereas the second chromosome is simply a job permutation assigned to each individual in the population randomly. The proposed algorithm is applied to the no-idle permutation flowshop scheduling problem with the makespan criterion. The performance of the proposed algorithm is tested on the Ruben Ruiz's benchmark suite and compared to their best known solutions available in http://soa.iti.es/rruiz as well as to a very recent discrete differential evolution algorithm from the literature. The computational results show its highly competitive performance and ultimately, 183 out of 250 instances are further improved. In comparison to the very recent hybrid discrete differential evolution algorithm, 114 out of 150 new best known solutions they provided are also further improved.

[1]  Alain Guinet,et al.  A travelling salesman approach to solve the F , 2005, Eur. J. Oper. Res..

[2]  Thomas Stützle,et al.  A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem , 2007, Eur. J. Oper. Res..

[3]  Quan-Ke Pan,et al.  A novel differential evolution algorithm for no-idle permutation flow-shop scheduling problems , 2008 .

[4]  Jerzy Kamburowski,et al.  More on three-machine no-idle flow shops , 2004, Comput. Ind. Eng..

[5]  V. S. Tanaev,et al.  Scheduling Theory: Multi-Stage Systems , 1994 .

[6]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[7]  Rubén Ruiz,et al.  Scheduling in Flowshops with No-Idle Machines , 2009 .

[8]  Inyong Ham,et al.  A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem , 1983 .

[9]  Ling Wang,et al.  No-idle permutation flow shop scheduling based on a hybrid discrete particle swarm optimization algorithm , 2008 .

[10]  Porpan Vachajitpan,et al.  Job sequencing with continuous machine operation , 1982 .

[11]  C. R. Woollam.  Flowshop with no idle machine time allowed , 1986 .

[12]  P. C. Bagga,et al.  Flowshop/No-idle Scheduling to Minimize Total Elapsed Time , 2005, J. Glob. Optim..

[13]  Xingsheng Gu,et al.  A hybrid discrete differential evolution algorithm for the no-idle permutation flow shop scheduling problem with makespan criterion , 2012, Comput. Oper. Res..

[14]  Uday K. Chakraborty.  Computational Intelligence in Flow Shop and Job Shop Scheduling , 2009 .

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

[16]  P. C. Bagga,et al.  Flowshop/no-idle scheduling to minimise the mean flowtime , 2005, The ANZIAM Journal.

[17]  Milan Vlach,et al.  Note: On the two-machine no-idle flowshop problem , 2000 .

[18]  Alain Guinet,et al.  Three stage no-idle flow-shops , 2003 .

[19]  Jerzy Kamburowski,et al.  On no-wait and no-idle flow shops with makespan criterion , 2007, Eur. J. Oper. Res..

[20]  D. Pohoryles,et al.  Flowshop/no-idle or no-wait scheduling to minimize the sum of completion times , 1982 .

[21]  V. Tanaev,et al.  Scheduling theory single-stage systems , 1994 .

[22]  M. F. Tasgetiren,et al.  A differential evolution algorithm for the no-idle flowshop scheduling problem with total tardiness criterion , 2011 .

[23]  Gur Mosheiov,et al.  A note on a greedy heuristic for flow-shop makespan minimization with no machine idle-time , 2008, Eur. J. Oper. Res..