Computers and Operations Research
A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
Scheduling flowshops with finite buffers and sequence-dependent setup times
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Improved genetic algorithm for the permutation flowshop scheduling problem
Computers and Operations Research
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Mixed integer programming for scheduling flexible flow lines with limited intermediate buffers
Mathematical and Computer Modelling: An International Journal
An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers
Computers and Operations Research
A hybrid genetic algorithm for the multi-depot vehicle routing problem
Engineering Applications of Artificial Intelligence
An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers
Computers and Operations Research
A tabu search heuristic for the hybrid flowshop scheduling with finite intermediate buffers
Computers and Operations Research
Computers and Operations Research
Scheduling Mixed-Model Assembly Lines with Cost Objectives by a Hybrid Algorithm
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part II
Semi-dynamic Demand in a Non-permutation Flowshop with Constrained Resequencing Buffers
Large-Scale Scientific Computing
Computers and Industrial Engineering
A Multi-swarm Approach to Multi-objective Flexible Job-shop Scheduling Problems
Fundamenta Informaticae - Swarm Intelligence
Information Sciences: an International Journal
Non-identical parallel machine scheduling using genetic algorithm
Expert Systems with Applications: An International Journal
A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem
Computers and Industrial Engineering
A chaotic harmony search algorithm for the flow shop scheduling problem with limited buffers
Applied Soft Computing
Integration of particle swarm optimization and genetic algorithm for dynamic clustering
Information Sciences: an International Journal
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Heuristics and metaheuristics for mixed blocking constraints flowshop scheduling problems
Computers and Operations Research
Solving the Fm\block\Cmax problem using Bounded Dynamic Programming
Engineering Applications of Artificial Intelligence
A Multi-swarm Approach to Multi-objective Flexible Job-shop Scheduling Problems
Fundamenta Informaticae - Swarm Intelligence
Enhancing the performance of hybrid genetic algorithms by differential improvement
Computers and Operations Research
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As a typical manufacturing and scheduling problem with strong industrial background, flow shop scheduling with limited buffers has gained wide attention both in academic and engineering fields. With the objective to minimize the total completion time (or makespan), such an issue is very hard to solve effectively due to the NP-hardness and the constraint on the intermediate buffer. In this paper, an effective hybrid genetic algorithm (HGA) is proposed for permutation flow shop scheduling with limited buffers. In the HGA, not only multiple genetic operators based on evolutionary mechanism are used simultaneously in hybrid sense, but also a neighborhood structure based on graph model is employed to enhance the local search, so that the exploration and exploitation abilities can be well balanced. Moreover, a decision probability is used to control the utilization of genetic mutation operation and local search based on problem-specific information so as to prevent the premature convergence and concentrate computing effort on promising neighbour solutions. Simulation results and comparisons based on benchmarks demonstrate the effectiveness of the HGA. Meanwhile, the effects of buffer size and decision probability on optimization performances are discussed.