Learning approaches for developing successful seller strategies in dynamic supply chain management
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- @Article{Fasli2011,
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author = "Maria Fasli and Yevgeniya Kovalchuk",
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title = "Learning approaches for developing successful seller
strategies in dynamic supply chain management",
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journal = "Information Sciences",
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year = "2011",
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volume = "181",
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number = "16",
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pages = "3411--3426",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "0020-0255",
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DOI = "doi:10.1016/j.ins.2011.04.014",
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URL = "http://www.sciencedirect.com/science/article/B6V0C-52M4V3W-4/2/e88e5f17659c1d3f021a4e6052e7b965",
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abstract = "Variable, dynamic pricing is a key characteristic of
the modern electronic trading environments, allowing
for prices that change or fluctuate due to uncertainty
and different conditions and context. Being able to
manage dynamic pricing strategies is vital for
companies wishing to succeed in the world of modern
business. The ability to accurately predict selling
prices at a given time can help organisations to
maximise their profit. This paper addresses the problem
of predicting customer order prices and choosing the
selling strategy which can lead to a greater profit in
the context of supply chain management (SCM). The
potential of the Neural Networks (NN) and Genetic
Programming (GP) learning techniques is explored for
making price forecasts. In particular, different
parameter settings and methods for preprocessing input
data are investigated in the paper. Although, both
techniques showed the potential for dealing with the
problem of dynamic pricing in SCM, NN models outperform
GP models in the context under consideration in terms
of accuracy of prediction, complexity of
implementation, and execution time.",
- }
Genetic Programming entries for
Maria Fasli
Yevgeniya Kovalchuk
Citations