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Policy gradient reinforcement learning for solving supply-chain management problems
Dhanvin Mehta, Devesh Yamparala
Published in Association for Computing Machinery
2014
Volume: 9-11-October-2014
   
Abstract
Supply-chain management problems are quite common in various industries and it is becoming increasingly necessary to tackle uncertainties while making decisions due to the rapid rise in production and consumption levels, and shortening of product life cycles. In our work, we tackle this problem of general stochastic supply-chain management problem by formulating it as a multi-arm non-contextual bandit problem and then taking a policy gradient descent approach (a Reinforcement Learning approach) to find a robust policy. The gradient descent is guided by cost from a simulator which models the demand, lead times and other uncertainties. Our experiments demonstrate that it finds better solutions than naive worst-case linear programming solutions to such problems. Copyright 2014 ACM.
About the journal
JournalData powered by TypesetACM International Conference Proceeding Series
PublisherData powered by TypesetAssociation for Computing Machinery
Open AccessNo
Concepts (14)
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    Decision making
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    Life cycle
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    Linear programming
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    Reinforcement learning
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    Stochastic systems
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    CONTEXTUAL BANDITS
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    Gradient descent
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    MAKING DECISION
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    POLICY GRADIENT
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    POLICY GRADIENT REINFORCEMENT LEARNING
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    Product life cycles
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    PRODUCTION AND CONSUMPTION
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    Reinforcement learning approach
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    Supply chain management