Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Total flowtime in no-wait flowshops with separated setup times
Computers and Operations Research
An introduction to differential evolution
New ideas in optimization
Swarm intelligence
An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
Journal of Global Optimization
Comparison of heuristics for flowtime minimisation in permutation flowshops
Computers and Operations Research
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Two-machine flow shop problems with a single server
Journal of Scheduling
Differential evolution for sequencing and scheduling optimization
Journal of Heuristics
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
An efficient stochastic hybrid heuristic for flowshop scheduling
Engineering Applications of Artificial Intelligence
An improved heuristic for permutation flowshop scheduling
International Journal of Information and Communication Technology
Advances in Differential Evolution
Advances in Differential Evolution
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
A simulated annealing approach to a bi-criteria sequencing problem in a two-stage supply chain
Computers and Industrial Engineering
Computational Intelligence in Flow Shop and Job Shop Scheduling
Computational Intelligence in Flow Shop and Job Shop Scheduling
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Effective PSO-Based Hybrid Algorithm for Multiobjective Permutation Flow Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Approximative procedures for no-wait job shop scheduling
Operations Research Letters
Swarm-based neighbourhood search for fuzzy job shop scheduling
International Journal of Innovative Computing and Applications
Population-based dynamic scheduling optimisation for complex production process
International Journal of Computer Applications in Technology
International Journal of Bio-Inspired Computation
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For over 50 years now, the famous problem of permutation flow shop scheduling has been attracting the attention of researchers in operations research, engineering and computer science. Over the past several years, there has been a spurt of interest in computational intelligence heuristics and metaheuristics for solving this problem – ranging from genetic algorithms to tabu search to complex hybrid techniques. Most recently, differential evolution, one of the newest members of the evolutionary algorithm family, has emerged as a popular technique for application to this problem. The main problem in applying differential evolution to the permutation flow shop is that differential evolution works on continuous, or real-valued, parameters (it is a continuous optimisation method), whereas the flow shop problem involves finding sequences or schedules of n jobs, expressed as permutations of n distinct objects (integers). A mapping, or encoding, of floating-point numbers to integer permutations is therefore necessary for differential evolution to be applied to this problem. This paper provides a review and evaluation of the best-known encoding schemes in the literature.