An effective heuristic for flow shop problems with total flow time as criterion
Proceedings of the 15th annual conference on Computers and industrial engineering
A heuristic algorithm for mean flowtime objective in flowshop scheduling
Computers and Operations Research
Parallel partitioning method (PPM): A new exact method to solve bi-objective problems
Computers and Operations Research
Minimizing the number of late jobs for the permutation flowshop problem with secondary resources
Computers and Operations Research
A discrete differential evolution algorithm for the permutation flowshop scheduling problem
Computers and Industrial Engineering
Computers and Operations Research
Computers and Operations Research
Hybrid Heuristic for m-Machine No-Wait Flowshops to Minimize Total Completion Time
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Computers and Operations Research
Minimizing total flow time in permutation flow shop scheduling with improved simulated annealing
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary clustering search for flowtime minimization in permutation flow shop
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
International Journal of Bio-Inspired Computation
Parallel Simulated Annealing with Genetic Enhancement for flowshop problem with Csum
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Estimation of distribution algorithm for permutation flow shops with total flowtime minimization
Computers and Industrial Engineering
Information Sciences: an International Journal
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
A more effective constructive algorithm for permutation flowshop problem
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Computers and Operations Research
Computers and Operations Research
Flowshop scheduling with a general exponential learning effect
Computers and Operations Research
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In this paper, we address the problem of sequencing jobs in a permutation flow shop with the objective of minimising the sum of completion times or flowtime. This objective is considered to be relevant and meaningful for today's dynamic production environment, and therefore it has attracted the attention of researchers during the last years. As a result, a number of different types of heuristics have recently been developed, each one claiming to be the best for the problem. However, some of these heuristics have been independently developed and only partial comparisons among them exist. Consequently, there are no conclusive results on their relative performance. Besides, some of these types of heuristics are of a different nature and could be combined in order to obtain composite heuristics. In this paper, we first classify and conduct an extensive comparison among the existing heuristics. Secondly, based on the results of the experiments, we suggest two new composite heuristics for the problem. The subsequent computational experience shows these two heuristics to be efficient for the problem under consideration.