A numerical method for interval multi-objective mixed-integer optimal control problems based on quantum heuristic algorithm

Liu, Zhe; Li, Shurong

doi:10.1007/s10479-021-03998-1

Cited by 7 publications

(2 citation statements)

References 89 publications

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“…In order to make QA usable for such real-world applications, multi-criteria objectives have to be investigated. For multi-objective optimization problems, quantum-based algorithms have also been developed and shown to outperform classical algorithms [39]. However, the full potential of quantum algorithms for multi-objective optimization cannot be fully exploited if there are not implemented on the corresponding quantum hardware.…”

Section: Research Gapmentioning

confidence: 99%

Solving flexible job shop scheduling problems in manufacturing with Quantum Annealing

Schworm

Glatt

et al. 2022

Prod. Eng. Res. Devel.

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Quantum Annealing (QA) is a metaheuristic for solving optimization problems in a time-efficient manner. Therefore, quantum mechanical effects are used to compute and evaluate many possible solutions of an optimization problem simultaneously. Recent studies have shown the potential of QA for solving such complex assignment problems within milliseconds. This also applies for the field of job shop scheduling, where the existing approaches however focus on small problem sizes. To assess the full potential of QA in this area for industry-scale problem formulations, it is necessary to consider larger problem instances and to evaluate the potentials of computing these job shop scheduling problems while finding a near-optimal solution in a time-efficient manner. Consequently, this paper presents a QA-based job shop scheduling. In particular, flexible job shop scheduling problems in various sizes are computed with QA, demonstrating the efficiency of the approach regarding scalability, solutions quality, and computing time. For the evaluation of the proposed approach, the solutions are compared in a scientific benchmark with state-of-the-art algorithms for solving flexible job shop scheduling problems. The results indicate that QA has the potential for solving flexible job shop scheduling problems in a time efficient manner. Even large problem instances can be computed within seconds, which offers the possibility for application in industry.

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Section: Research Gapmentioning

confidence: 99%

Solving flexible job shop scheduling problems in manufacturing with Quantum Annealing

Schworm

Glatt

et al. 2022

Prod. Eng. Res. Devel.

View full text Add to dashboard Cite

show abstract

“…Gu et al 22 used Legendre polynomials to solve the optimal control problem involving a duffing oscillator. Liu and Li 23 used an interval multi‐objective mixed integer based on a quantum heuristic algorithm to solve the optimal control problem. Liao et al 24 proposed a numerical method for the optimal control problem consisting of offline and online parts.…”

Section: Introductionmentioning

confidence: 99%

Gauss pseudospectral‐based velocity optimization for rail transit trains with running and computation delays

Liu

Zhou

Chen

et al. 2021

Optim Control Appl Methods

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With the progress of urban intelligent construction, urban rail transit trains have become one of the most important ways for urban traffic. Optimization operation is a key to further improve the performance of urban rail transit. In this work, an energy saving velocity planning algorithm for rail transit train with running and computation delays is proposed. First, an optimization model with considering both running and computation delays is established for velocity planning. Then, the velocity planning algorithm frame is proposed based on Gauss pseudospectral distribution, where the running delay and computation delay are tackled separately. Meanwhile, the gradient information of transform nonlinear programming (NLP) problem is derived in detail so that NLP solver can be employed to obtain accuracy results. Furthermore, the implementation steps of the proposed algorithm are given. Finally, simulation tests are carried out on a two-station running rail transit train model with three different delay situations and two different slopes railway. Numerical results and comparison show the effectiveness and correctness of the proposed method for rail transit train velocity planning with delays.

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An Integrated Mathematical Model and an Enhanced Quantum-Computing Algorithm for the Berth and Quay Crane Optimization Problem

Li,

2024

Lecture Notes in Electrical Engineering

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A numerical method for interval multi-objective mixed-integer optimal control problems based on quantum heuristic algorithm

Cited by 7 publications

References 89 publications

Solving flexible job shop scheduling problems in manufacturing with Quantum Annealing

Solving flexible job shop scheduling problems in manufacturing with Quantum Annealing

Gauss pseudospectral‐based velocity optimization for rail transit trains with running and computation delays

An Integrated Mathematical Model and an Enhanced Quantum-Computing Algorithm for the Berth and Quay Crane Optimization Problem

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