A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem

DOI10.1016/j.cor.2016.10.003zbMath1391.90327OpenAlexW2527918309MaRDI QIDQ1652147

Lixin Tang, Xianpeng Wang

Publication date: 11 July 2018

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cor.2016.10.003

zbMATH Keywords

data analysismemetic algorithmmulti-objective permutation flowshop scheduling

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) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (6)

An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem ⋮ Pattern mining‐based pruning strategies in stochastic local searches for scheduling problems ⋮ Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: a state-of-the-art ⋮ A Pareto-based adaptive variable neighborhood search for biobjective hybrid flow shop scheduling problem with sequence-dependent setup time ⋮ An improved discrete artificial bee colony algorithm for flexible flowshop scheduling with step deteriorating jobs and sequence-dependent setup times ⋮ Learning to select operators in meta-heuristics: an integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem

Uses Software

• JADE

Cites Work

• Self-adaptive learning based particle swarm optimization
• A multi-objective particle swarm for a flow shop scheduling problem
• A multi-objective iterated greedy search for flowshop scheduling with makespan and flowtime criteria
• Ant-colony algorithms for permutation flowshop scheduling to minimize makespan/total flowtime of jobs.
• Benchmarks for basic scheduling problems
• A dynamic clustering based differential evolution algorithm for global optimization
• Adaptive evolutionary programming based on reinforcement learning
• Genetic local search for multi-objective flowshop scheduling problems
• A multi-objective simulated-annealing algorithm for scheduling in flowshops to minimize the makespan and total flowtime of jobs
• A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem
• A data mining approach to evolutionary optimisation of noisy multi-objective problems

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