A new adaptive neural network and heuristics hybrid approach for job-shop scheduling

DOI10.1016/S0305-0548(00)00018-6zbMath0985.90043OpenAlexW1987433326WikidataQ127956869 ScholiaQ127956869MaRDI QIDQ5945329

Dingwei Wang, Shengxiang Yang

Publication date: 3 June 2002

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

Full work available at URL: https://doi.org/10.1016/s0305-0548(00)00018-6

zbMATH Keywords

heuristicsadaptive neural networkjob-shop scheduling

Mathematics Subject Classification ID

Deterministic scheduling theory in operations research (90B35) Approximation methods and heuristics in mathematical programming (90C59) Neural networks for/in biological studies, artificial life and related topics (92B20)

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A PARTICLE SWARM OPTIMIZATION-BASED ALGORITHM FOR JOB-SHOP SCHEDULING PROBLEMS ⋮ An improved constraint satisfaction adaptive neural network for job-shop scheduling ⋮ Using MLP networks to design a production scheduling system

Cites Work

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• ``Neural computation of decisions in optimization problems
• Neural network methods in combinatorial optimization
• A Review of Production Scheduling
• Technical Note—Finding Some Essential Characteristics of the Feasible Solutions for a Scheduling Problem
• The Complexity of Flowshop and Jobshop Scheduling

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