Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms

DOI10.1016/j.ejor.2006.03.040zbMath1114.90400OpenAlexW2024390183MaRDI QIDQ869164

Jim Ward, Herbert Moskowitz, Hoi-Ming Chi, Okan K. Ersoy

Publication date: 26 February 2007

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2006.03.040

zbMATH Keywords

decision support systemsgenetic algorithmssupply chain managementsupport vector machinesmachine learning

Mathematics Subject Classification ID

Management decision making, including multiple objectives (90B50) Approximation methods and heuristics in mathematical programming (90C59) Inventory, storage, reservoirs (90B05)

Related Items (2)

Genetic algorithms in supply chain management: a critical analysis of the literature ⋮ Machine learning and design of experiments with an application to product innovation in the chemical industry

Uses Software

• SVMlight
• GAToolBox
• ElemStatLearn

Cites Work

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• Genetic algorithms for supply-chain scheduling: a case study in the distribution of ready-mixed concrete
• Development and validation of genetic algorithm-based facility layout a case study in the pharmaceutical industry
• A competitive neural network approach to multi-objective FMS scheduling
• Integration of inductive learning and neural networks for multi-objective FMS scheduling
• A learning-based methodology for dynamic scheduling in distributed manufacturing systems
• A simulation-based genetic algorithm for inventory optimization in a serial supply chain
• 10.1162/153244303322753733
• Learning-based adaptive controller for dynamic manufacturing cells
• The elements of statistical learning. Data mining, inference, and prediction

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