Genetic algorithm integrated with artificial chromosomes for multi-objective flowshop scheduling problems

DOI10.1016/j.amc.2008.05.027zbMath1152.90432OpenAlexW2077666004MaRDI QIDQ2518432

Shih-Hsin Chen, Chien-Lung Chan, Pei-Chann Chang, Chin-Yuan Fan

Publication date: 16 January 2009

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2008.05.027

zbMATH Keywords

makespangenetic algorithmflowshop schedulingmaximum tardinessNSGA II

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Learning and adaptive systems in artificial intelligence (68T05) Deterministic scheduling theory in operations research (90B35)

Related Items (5)

A united search particle swarm optimization algorithm for multiobjective scheduling problem ⋮ A hybrid genetic-immune algorithm with improved lifespan and elite antigen for flow-shop scheduling problems ⋮ A new vision of approximate methods for the permutation flowshop to minimise makespan: state-of-the-art and computational evaluation ⋮ A self-guided genetic algorithm for permutation flowshop scheduling problems ⋮ Multiple parents crossover operators: a new approach removes the overlapping solutions for sequencing problems

Uses Software

• MOTGA

Cites Work

• Towards a new evolutionary computation. Advances on estimation of distribution algorithms.
• MOTGA: a multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem
• A genetic algorithm for flowshop sequencing
• An evolutionary artificial immune system for multi-objective optimization
• An effective hybrid genetic algorithm for flow shop scheduling with limited buffers
• An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering
• The Complexity of Flowshop and Jobshop Scheduling
• Edge histogram based sampling with local search for solving permutation problems
• Evolutionary Multi-Criterion Optimization
• Multi-objective meta-heuristics: An overview of the current state-of-the-art
• Genetic local search for multi-objective combinatorial optimization
• A survey of optimization by building and using probabilistic models
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