The scheduling problem of social workers is a class of combinatorialoptimization problems that can be solved in exponential time at best. Because is belongs to class of problems known as NP-Hard, which have huge impa...
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ISBN:
(纸本)9783030617059;9783030617042
The scheduling problem of social workers is a class of combinatorialoptimization problems that can be solved in exponential time at best. Because is belongs to class of problems known as NP-Hard, which have huge impact huge impact on our society. Nowadays, the focus on the quantum computer should no longer be just for its enormous computing capacity but also for the use of its imperfection, (Noisy Intermediate-Scale Quantum (NISQ) era) to create a powerful machine learning device that uses the variational principle to solve the optimization problem by reducing their complexity's class. We propose a formulation of the Vehicle Rooting Problem (VRP) with time windows to solve efficiently the social workers schedule problem using Variational Quantum Eigensolver (VQE). The quantum feasibility of the algorithm will be modelled with docplex and tested on IBMQ computers.
The problem of social workers visiting their patients at home is a class of combinatorialoptimization problems and belongs to the class of problems known as NP-Hard. These problems require heuristic techniques to pro...
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The problem of social workers visiting their patients at home is a class of combinatorialoptimization problems and belongs to the class of problems known as NP-Hard. These problems require heuristic techniques to provide an efficient solution in the best of cases. In this article, in addition to providing a detailed resolution of the social workers’ problem using the Quadratic Unconstrained Binary optimization Problems (QUBO) formulation, an approach to mapping the inequality constraints in the QUBO form is given. Finally, we map it in the Hamiltonian of the Ising model to solve it with the Quantum Exact Solver and Variational Quantum Eigensolvers (VQE). The quantum feasibility of the algorithm will be tested on IBMQ computers.
Recently, there is growing attention on applying deep reinforcement learning (DRL) to solve the 3D bin packing problem (3D BPP), given its favorable generalization and independence of ground-truth label. However, due ...
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ISBN:
(纸本)9781713832621
Recently, there is growing attention on applying deep reinforcement learning (DRL) to solve the 3D bin packing problem (3D BPP), given its favorable generalization and independence of ground-truth label. However, due to the relatively less informative yet computationally heavy encoder, and considerably large action space inherent to the 3D BPP, existing methods are only able to handle up to 50 boxes. In this paper, we propose to alleviate this issue via an end-to-end multimodal DRL agent, which sequentially addresses three sub-tasks of sequence, orientation and position, respectively. The resulting architecture enables the agent to solve large-scale instances of 100 boxes or more. Experiments show that the agent could learn highly efficient policies that deliver superior performance against all the baselines on instances of various scales.
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