Bin packing restricted to the AI instance class is solvable in polynomial time and to the ANI class in pseudopolynomial time, with algorithms that solve all known hard benchmarks far faster than prior exact methods.
International Journal of Production Research 61, 2895–2916
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PF-CD3Q uses online particle filtering to estimate fatigue parameters and constrains a deep Q-learning agent to solve fatigue-aware human-robot task planning as a CMDP.
citing papers explorer
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Polynomial and Pseudopolynomial Algorithms for Two Classes of Bin Packing Instances
Bin packing restricted to the AI instance class is solvable in polynomial time and to the ANI class in pseudopolynomial time, with algorithms that solve all known hard benchmarks far faster than prior exact methods.
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Safe reinforcement learning with online filtering for fatigue-predictive human-robot task planning and allocation in production
PF-CD3Q uses online particle filtering to estimate fatigue parameters and constrains a deep Q-learning agent to solve fatigue-aware human-robot task planning as a CMDP.