Multipopulation genetic algorithms with different interaction structures to solve flexible job-shop scheduling problems: a network science perspective

DOI10.1155/2020/8503454zbMath1459.90091OpenAlexW3110187255MaRDI QIDQ2217021

Yanyan Li

Publication date: 18 December 2020

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2020/8503454

Mathematics Subject Classification ID

Deterministic scheduling theory in operations research (90B35) Approximation methods and heuristics in mathematical programming (90C59)

Uses Software

• MOEA/D

Cites Work

• Evaluation of mathematical models for flexible job-shop scheduling problems
• Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions
• Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in small and medium enterprises
• Island models for cluster geometry optimization: how design options impact effectiveness and diversity
• Job-shop scheduling with multi-purpose machines
• Renormalization group analysis of the small-world network model
• A hybrid multiobjective evolutionary approach for flexible job-shop scheduling problems
• The networked evolutionary algorithm: a network science perspective
• A MILP model for an extended version of the flexible job shop problem
• Multi-population genetic algorithm to solve the synchronized and integrated two-level lot sizing and scheduling problem
• Performance analysis of active schedules in identical parallel machine
• A PARTICLE SWARM OPTIMIZATION ALGORITHM ON JOB-SHOP SCHEDULING PROBLEMS WITH MULTI-PURPOSE MACHINES
• Collective dynamics of ‘small-world’ networks

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